The immediate early gene arc regulates the development of binocular vision

In the developing brain, predetermined patterning shapes early neuronal circuits while a later period of heightened experience-dependent synaptic plasticity refines them. In the visual cortex, there is a brief developmental window known as the ocular dominance critical period when neuronal circuits are sensitive to binocular experience. If one eye has disrupted vision during the critical period, the input from that eye weakens and cannot recover later in life. It is unclear both why experience can drive such plasticity during the critical period but not in adulthood, and what aspects of binocular development require this heightened experience-dependent plasticity. Experience drives activity-dependent gene expression, and one of the first transcribed is Arc, a gene required for many types of neuronal plasticity. Indeed, mice lacking Arc do not have an ocular dominance critical period. Arc seems well-positioned to link experience to plasticity, and we, therefore, hypothesized that Arc protein expression gates binocular developmental plasticity in the visual cortex. We first assessed whether Arc expression changes from development to adulthood. We found that visual experience drives Arc expression during the critical period but not in adult animals. We reasoned that lack of Arc could explain why critical period plasticity is absent in the adult. Using electrophysiology, we measured critical period plasticity following unilateral eye closure in mice overexpressing Arc. We found that these adult mice displayed critical period plasticity similar to juvenile animals. iv Indeed, acute viral overexpression of Arc was sufficient to reopen the critical period. However, it was still unclear if Arc mediated normal experience-dependent binocular development. To examine the development of binocular neurons, we utilized two-photon calcium imaging. We found that few neurons were binocular before the critical period and that early visual experience was required for binocular neurons to emerge and mature. Surprisingly, in the absence of Arc, more binocular neurons emerged. In the adult brain, acutely eliminating Arc expression still led to the appearance of more binocular neurons. Thus, visual experience serves to promote the development of binocular neurons while the experience-dependent transcription of Arc limits their number, potentially to conserve the capacity of the brain for later experience-dependent change

THE IMMEDIATE EARLY GENE ARC REGULATES THE DEVELOPMENT OF BINOCULAR VISION by Kyle Robert Jenks A dissertation submitted to the faculty of The University of Utah in partial fulfillment of the requirements for the degree of Doctor of Philosophy Interdepartmental Program in Neuroscience The University of Utah December 2019 Copyright © Kyle Robert Jenks 2019 All Rights Reserved The University of Utah Graduate School STATEMENT OF DISSERTATION APPROVAL Kyle Robert Jenks The dissertation of has been approved by the following supervisory committee members: Jason Dennis Shepherd , Chair 8/15/2019 Kevin C. Brennan , Member 8/19/2019 Adam Douglass , Member 8/19/2019 Dale Matthew Wachowiak , Member 8/19/2019 Karen S. Wilcox , Member 8/19/2019 and by David Krizaj the Department/College/School of and by David B. Kieda, Dean of The Graduate School. Date Approved Date Approved Date Approved Date Approved Date Approved , Chair/Dean of Neuroscience ABSTRACT In the developing brain, predetermined patterning shapes early neuronal circuits while a later period of heightened experience-dependent synaptic plasticity refines them. In the visual cortex, there is a brief developmental window known as the ocular dominance critical period when neuronal circuits are sensitive to binocular experience. If one eye has disrupted vision during the critical period, the input from that eye weakens and cannot recover later in life. It is unclear both why experience can drive such plasticity during the critical period but not in adulthood, and what aspects of binocular development require this heightened experience-dependent plasticity. Experience drives activity-dependent gene expression, and one of the first transcribed is Arc, a gene required for many types of neuronal plasticity. Indeed, mice lacking Arc do not have an ocular dominance critical period. Arc seems well-positioned to link experience to plasticity, and we, therefore, hypothesized that Arc protein expression gates binocular developmental plasticity in the visual cortex. We first assessed whether Arc expression changes from development to adulthood. We found that visual experience drives Arc expression during the critical period but not in adult animals. We reasoned that lack of Arc could explain why critical period plasticity is absent in the adult. Using electrophysiology, we measured critical period plasticity following unilateral eye closure in mice overexpressing Arc. We found that these adult mice displayed critical period plasticity similar to juvenile animals. Indeed, acute viral overexpression of Arc was sufficient to reopen the critical period. However, it was still unclear if Arc mediated normal experience-dependent binocular development. To examine the development of binocular neurons, we utilized two-photon calcium imaging. We found that few neurons were binocular before the critical period and that early visual experience was required for binocular neurons to emerge and mature. Surprisingly, in the absence of Arc, more binocular neurons emerged. In the adult brain, acutely eliminating Arc expression still led to the appearance of more binocular neurons. Thus, visual experience serves to promote the development of binocular neurons while the experience-dependent transcription of Arc limits their number, potentially to conserve the capacity of the brain for later experience-dependent change. iv TABLE OF CONTENTS ABSTRACT....................................................................................................................... iii LIST OF FIGURES .......................................................................................................... vii ACKNOWLEDGMENTS ................................................................................................. ix Chapters 1. INTRODUCTION .......................................................................................................... 1 1.1 Overview .............................................................................................................. 1 1.2 Anatomy and development of the visual cortex .................................................. 2 1.3 Ocular dominance plasticity .............................................................................. 13 1.4 The immediate early gene Arc ........................................................................... 19 1.5 Hypothesis and overview ................................................................................... 26 1.6 References .......................................................................................................... 27 2. ARC RESTORES JUVENILE PLASTICITY IN ADULT MOUSE VISUAL CORTEX........................................................................................................................... 45 2.1 Abstract .............................................................................................................. 46 2.2 Introduction ........................................................................................................ 46 2.3 Results ................................................................................................................ 46 2.4 Discussion .......................................................................................................... 50 2.5 Materials and methods ....................................................................................... 51 2.6 References .......................................................................................................... 51 3. EXPERIENCE-DEPENDENT DEVELOPMENT AND MAINTENANCE OF BINOCULAR NEURONS IN THE MOUSE VISUAL CORTEX .................................. 52 3.1 Abstract .............................................................................................................. 52 3.2 Introduction ........................................................................................................ 53 3.3 Results ................................................................................................................ 56 3.4 Discussion .......................................................................................................... 70 3.5 Materials and methods ....................................................................................... 78 3.6 References .......................................................................................................... 87 4. CONCLUSIONS AND FUTURE DIRECTIONS ..................................................... 104 4.1 Summary .......................................................................................................... 104 4.2 Conclusions ...................................................................................................... 108 4.3 Future directions .............................................................................................. 111 4.4 References ........................................................................................................ 117 vi LIST OF FIGURES Figures 1.1. Anatomy and selectivity of the mouse binocular visual cortex ................................. 43 1.2. Ocular dominance plasticity ...................................................................................... 44 2.1. Arc-Tg mice exhibit juvenile-like OD plasticity well past the conventional critical period ................................................................................................................................ 47 2.2. Activity-dependent Arc protein, but not mRNA expression, declines with age in WT mouse visual cortex, but not in Arc-Tg mice .................................................................... 48 2.3. Arc and protein translation are required for LTD in layer IV of visual cortex .......... 49 2.4. Acute Arc expression in adult mouse visual cortex is sufficient to restore juvenile OD plasticity ..................................................................................................................... 50 3.1. Visual response properties in layer 2/3 excitatory neurons of binocular V1 rapidly mature after eye-opening .................................................................................................. 93 3.2. Spontaneous activity and binocular probability over development ........................... 95 3.3. Experience-dependent development of binocular neurons. ....................................... 96 3.4. The binocular offset of orientation preference in layer 2/3 neurons .......................... 97 3.5. Experience-dependent increases in correlated binocular responses occur in neuropil during development .......................................................................................................... 98 3.6. Arc limits the emergence of binocular neurons early in development. ..................... 99 3.7. Visual responses in Arc KO mice do not significantly differ from WT mice ......... 100 3.8. Conditional deletion of Arc expression in binocular visual cortex.......................... 101 3.9. Arc is required for the maintenance of binocular neuron number in adult binocular V1.................................................................................................................................... 102 4.1. Chronic imaging of binocular visual cortex............................................................. 122 4.2. Visually evoked responses of dendritic spines ........................................................ 123 4.3. Chronic imaging of ocular dominance plasticity ..................................................... 124 viii ACKNOWLEDGMENTS I am exceptionally fortunate to lead a life surrounded by generous family, friends, and mentors. I would first like to thank my extended family. It takes a village to raise a child, and I credit any success I may have to their love and positive example. I would especially like to thank my parents, who unconditionally supported me and sacrificed every day to make sure I wanted for nothing. Without them, I could never have had the freedom to pursue my dreams. I would also like to thank my sister, who has remained a fantastic person despite growing up with me as an older brother. Jessie, I am so proud of you. I want to acknowledge as well my past mentors. Dr. Winsor Watson taught me the joys of science, and Dr. Gregory Holmes showed me the wonders of the brain. My graduate mentor, Dr. Shepherd, I would like to thank for not only teaching me how to do science but how to be a scientist. It may well have been the most challenging project of his career. Additionally, he brought together the fantastic group of people that it has been my honor to work with these past 6 years. Finally, I would like to thank the person who selflessly stood with me and supported me through all of this, my wife, Lauren. Every dream I have is sweeter because I pursue it with you. CHAPTER 1 INTRODUCTION 1.1 Overview Early postnatal development is a time of rapid refinement of sensory perception as the nervous system interacts with a variety of new sights, smells, sounds, and somatosensations. In brief epochs of development called critical periods, neuronal plasticity peaks and sensory experience can easily induce changes in neuronal circuits that are difficult or impossible to achieve later in life. Elucidating the mechanisms behind critical period plasticity is crucial for understanding the role of these mechanisms in development, how critical period plasticity can go awry in neurodevelopmental disorders, and if critical period plasticity could be reinstated later in life to improve recovery following brain injuries, such as stroke. The goal of this dissertation is to better understand the molecular mechanisms by which sensory experience can modify neuronal circuits within the brain. The visual system has a well-described ocular dominance critical period, wherein the input from the two eyes align in the visual cortex to encode binocular vision. Obstructing vision through one eye during this critical period can drive the rapid loss of synaptic input from the deprived eye to the visual cortex. This easily inducible and quantifiable plasticity makes the binocular visual cortex a perfect model system to study 2 how sensory experience can shape neuronal circuits. This introduction will first summarize the development of the visual system, with a focus on the binocular visual cortex. Next, it will summarize current knowledge of the mechanisms of the ocular dominance critical period. Then the introduction will detail a remarkable plasticityrelated gene, Arc, the mechanisms by which Arc protein regulates synaptic strength in neurons, and the necessity of Arc for critical period plasticity. Finally, the introduction ends with the overarching hypothesis that Arc expression gates binocular plasticity in the visual cortex. 1.2 Anatomy and development of the visual cortex Located at the back of the eye is a multilaminar arrangement of photosensitive cells, called photoreceptors, and neurons, including bipolar cells and retinal ganglion cells, collectively called the retina. The mammalian retina is inverted, with the lightsensitive photoreceptors positioned farthest from the front of the eye, and the retinal ganglion cells, which transmit visual information to the central nervous system, located closest to the front of the eye (Kolb, Fernandez, & Nelson, 2010). The photoreceptors tile the retinal surface, defining the extent of the visual field the eyes can view. Different classes of photoreceptors, including rods and cones, vary in terms of their sensitivity to light and spectrums of light they detect, allowing adaptation from greyscale vision at low light levels to color vision in high light levels. At rest, photoreceptors continuously release the neurotransmitter glutamate onto their postsynaptic partners (Ayoub, Korenbrot, & Copenhagen, 1989). The photoreceptor contains a light-sensitive opsin, formed from a membrane-bound G protein-coupled 3 receptor covalently bound to 11-cis-retinal (Dixon et al., 1986; Wald, 2004). When a photon strikes an opsin, the opsin’s cis-retinal shifts to trans-retinal and the converted opsin then activates roughly 100 transducin G proteins (Fung, Hurley, & Stryer, 1981; Kuhn, Bennett, Michel-Villaz, & Chabre, 1981; Matthews, 1963). Transducin activates the enzyme phosphodiesterase, with each phosphodiesterase catalyzing the hydrolysis of roughly 1,000 cyclic guanosine monophosphates (cGMPs; Kwok-Keung Fung & Stryer, 1980; Yee & Liebman, 1978). The net result of one photon striking an opsin is, therefore, a 100,000 fold amplification in the signal. The decrease in intracellular cGMP closes cyclic nucleotide-gated sodium channels, hyperpolarizing the cell, and decreasing glutamate release (Fesenko, Kolesnikov, & Lyubarsky, 1985). Thus, phototransduction in the nervous system begins with a decrease in neurotransmitter release from photoreceptors. Bipolar cells receive direct input from the photoreceptors (Kolb, 1977). Bipolar cells can either be ON bipolar cells, which activate when their presynaptic photoreceptor becomes hyperpolarized or OFF bipolar cells, which activate when their presynaptic photoreceptor is at rest (Werblin & Dowling, 1969). Retinal ganglion cells then collect summated excitatory and inhibitory input arising from these bipolar cells. The location of the photoreceptors that activate or inhibit those bipolar cells define the ganglion cell’s receptive field, which is the area of visual space to which the ganglion cell is responsive (Nelson, Famiglietti, & Kolb, 1978; Nelson, Kolb, & Freed, 1993). However, while a photoreceptor’s receptive field is a simple cone defining all the angles from which a photon could strike an opsin, a ganglion cell’s receptive field is constructed from many such simple inputs to create “center-surround” receptive field (Hartline, 1978). For an 4 ON-center ganglion cell, ON bipolar cells define the center of the receptive field while OFF bipolar cells define an annulus around that center. For an OFF-center ganglion cell, OFF bipolar cells define the receptive field center while ON bipolar cells define the annulus. This arrangement makes both types of ganglion cells sensitive to contrast arising from light intensity differing between the surround and center of the receptive field. This interpretation of visual space marks one of the earliest computational transformations in the visual system. Retinal ganglion cell axons project out of the eye and into the central nervous system through the optic nerve (Hartline, 1978). Ganglion cell axons predominantly target the lateral geniculate nucleus (LGN) of the thalamus (Bunt, Hendrickson, Lund, Lund, & Fuchs, 1975). The right and left sides of the brain each have an LGN, with the LGN on each side receiving input from the nasal side of the eye contralateral to it, and the temporal side of the eye ipsilateral to it. This arrangement means that the left LGN represents the right side of the visual field, and the right LGN represents the left visual field, with an overlap in the central binocular visual field visible to both eyes. In carnivorans and primates, LGN is a layered structure (Schiller & Malpeli, 1978). Contralateral eye input goes to layers one, four, and six while ipsilateral eye input goes to layers two, three, and five. Layers one and two of LGN are magnocellular layers that receive input from retinal ganglion cells highly sensitive to movement and low levels of light. Layers three through six of LGN receive input from retinal ganglion cells sensitive to color and details (Creutzfeldt, Lee, & Elepfandt, 1979; Dreher, Fukada, & Rodieck, 1976; Hubel & Wiesel, 1966; Wiesel & Hubel, 1966). Each layer is laid out in a map of the visual field, where adjacent cells encode neighboring areas of visual space, much like 5 cells in the retina (Connolly & Van Essen, 1984; Malpeli & Baker, 1975). This organization is termed retinotopic organization. Rodent LGN has a more straightforward organization than that of carnivorans and primates but is still retinotopically patterned (Grubb & Thompson, 2006). Ipsilateral projections segregate to the dorsomedial zone of the LGN (Figure 1.1A), and retinal projections with unique visually selective features, such as selectivity for motion, segregate to the outer shell of the LGN (Cruz-Martín et al., 2014; Krahe, El-Danaf, Dilger, Henderson, & Guido, 2011). Historically it was thought that the role of the cells in the LGN is to relay unmodified information from the retina to the cortex and other brain regions, hence their classification as thalamic relay cells. However, improved techniques for recording neuronal responses within the past decade have demonstrated that thalamic relay cells have response properties that do not exist in the retina, such as binocularity and orientation selectivity (Piscopo, El-Danaf, Huberman, & Niell, 2013; Rompani et al., 2017; Zhao, Liu, & Cang, 2013). Later chapters will discuss how these observations impact the interpretation of the novel findings described in this dissertation. Thalamic relay neurons of the LGN send axons to the primary visual cortex (V1), which is located in the occipital lobe (Kolb et al., 2010). The mammalian cortex has roughly six layers defined by distinct cell types, distinct inputs, and distinct projections that give rise to their characteristic functions in visual processing (Figure 1.1B). Layer one (L1) has few neuronal cell bodies, comprised instead of neuronal axons from lower layers projecting to other brain regions and supporting cells such as astrocytes and microglia (Lund & Yoshioka, 1997). Roughly 80% of the neurons present in L1 are inhibitory (Fitzpatrick, Lund, Schmechel, & Towles, 1987). Layers two and three (L2/3), 6 also known as the supragranular layers, contain predominantly excitatory projection neurons which form long, horizontal connections to cortical neurons within and beyond the boundaries of V1 (Gilbert & Wiesel, 1989). L4, also known as the granular layer, is the principal recipient of input from thalamic relay neurons of the LGN and has mainly excitatory stellate cells (Blasdel & Lund, 1983). L5, along with L6, comprise the infragranular layers. L5 has recurrent projections with L2/3 and projects to subcortical structures (Lund, 1987; Lund & Boothe, 1975). L6 has recurrent connections with L4, as well as with the LGN forming a direct feedback loop to control visual input from LGN arriving in V1 (Henderickson, Wilson, & Ogren, 1978; Lund & Boothe, 1975). Each layer of the visual cortex and the cell types within have distinct receptive fields and functions beyond the scope of this introduction. This introduction will instead focus on the principal cells of L2/3 and L4 that comprise the focus of the later chapters, describing the development of their visual response properties, and evidence for and against visual experience playing a role in that development. V1 maintains retinotopic organization, with the cortical surface of V1 representing a map of the visual field (Tusa, Palmer, & Rosenquist, 1978; Wagor, Mangini, & Pearlman, 1980). During development, retinotopic patterning of V1 occurs before vision even drives retinal responses, precluding a role of visual experience (Wong, Meister, & Shatz, 1993). However, molecular tools in the mouse have helped to dissect the signals that drive V1 retinotopic patterning. Spontaneous waves of correlated activity in the retina activated by cholinergic signaling guide patterning of the visual cortex (Cang et al., 2005; Wong et al., 1993). Additionally, ephrin-A receptor tyrosine kinases along the axons of thalamic relay cells interact with ephrin-A ligands bound to the surface of 7 V1 neurons arranged in a gradient corresponding to the azimuth of the visual field (Cang et al., 2005). Disrupting both spontaneous retinal activity and ephrin-A signaling almost completely abolishes retinotopic organization of azimuth (Cang et al., 2008). Thus, nonexperience dependent mechanisms guide the initial patterning of V1. Selectivity to the orientation of dark light borders, coined orientation selectivity, is a hallmark of receptive fields in mammalian V1 (Dräger, 1975; Hubel & Wiesel, 1962). Orientation-selective cells can be linear, simple cells with well-defined ON-OFF receptive fields in visual space (Figure 1.1C) or complex cells with no defined ON-OFF areas in visual space. Early studies led to the conclusion that orientation selectivity is an emergent response property of V1, constructed from parallel arrangements of simple ONOFF circular receptive fields from LGN (Hubel & Wiesel, 1961, 1962). Cats and primates have orientation-selective cells arranged into pinwheel-like columns grouping similar orientation preferring neurons together (Bonhoeffer & Grinvald, 1991). Rodents also possess orientation-selective neurons despite having only weak spatial clustering of similar orientation preferring neurons, suggesting a spatial organization of orientation preference is only mildly or not at all necessary for orientation selectivity to arise (Niell & Stryker, 2008; Ringach et al., 2016). Orientation selectivity is apparent very early on in development, although neurons are less selective than in the adult, leading to the conclusion that it arises innately without experience (Hoy & Niell, 2015; Hubel, Wiesel, & LeVay, 1976; Wiesel & Hubel, 1963). Findings that dark rearing (depriving animals of visual experience) does not abrogate the posteye-opening strengthening of orientation selectivity suggests neither formation nor maturation of orientation-selectivity requires experience (Crair, Gillespie, & Stryker, 1998; Sherk & Stryker, 1976). In mice, dark 8 rearing or more precise genetic ablation of activity using overexpression of an inward rectifying potassium channel during development also failed to impair the formation and maturation of orientation selectivity (Hagihara, Murakami, Yoshida, Tagawa, & Ohki, 2015; Rochefort et al., 2011). However, computer modeling suggests that weak selectivity can arise simply through the presence of sparse inputs during development and is distinct from selectivity in the mature brain (Ringach, 2007). Cortical application of tetrodotoxin (TTX) to V1 during development in ferrets, which silences neuronal activity, prevents maturation of orientation selectivity, suggesting a role for activity in refining or maintaining selectivity (Chapman & Stryker, 1993; White, Coppola, & Fitzpatrick, 2001). Additionally, prolonged dark rearing of rats into adulthood degrades orientation selectivity (Fagiolini, Pizzorusso, Berardi, Domenici, & Maffei, 1994). From these findings, the consensus appears to be that visual experience is necessary for the maintenance but not the emergence of orientation selectivity. The population of orientation-selective neurons in V1 forms a distribution covering the range of possible orientations. While it may seem intuitive that the brain would devote equal numbers of neurons to represent each orientation, human perception is biased towards detection of the cardinal orientations (horizontal and vertical), which may reflect their overrepresentation in the visual world (Coppola, Purves, McCoy, & Purves, 2002; Girshick, Landy, & Simoncelli, 2011). Cardinal overrepresentation is also present in the ferret, where more cortical surface responds to cardinal orientations than oblique angles (Coppola, White, Fitzpatrick, & Purves, 1998). Observations in the adult mouse suggest there is a bias among selective neurons to encode horizontal orientations (Figure 1.1D; Dräger, 1975; Mank et al., 2008). However, other studies report an early 9 developmental bias for cardinal orientations that later equalizes or is lost with age (Hagihara et al., 2015; Hoy & Niell, 2015; Rochefort et al., 2011). One difference between studies that report an adult orientation bias in mice and those that do not is that the former seems to include the binocular zone while the latter restrict measurements to only the monocular zone. Orientation bias in the mouse binocular zone may mirror development in carnivorans and primates, with their larger binocular field, while the mouse monocular zone follows unique rules. However, quantification of orientation bias over development in the mouse has, as of now, only focused on the monocular zone. The bias of V1 neurons to cardinal orientations, which are over-represented in the visual world, suggests a role for visual experience in the development of this bias. Strikingly, in cats, prolonged exposure to a single orientation using striped lenses maintains selectivity in neurons that match that orientation while other neurons are lost (Stryker, Sherk, Leventhal, & Hirsch, 1978). In mice reared with astigmatic lenses that allow viewing of a narrow range of orientations, a similar change is selectivity occurs, but also new neurons become selective for the over-represented orientation (Kreile, Bonhoeffer, & Hubener, 2011). Indeed, repeated presentation of the same orientation for multiple training sessions increase the response of mouse V1 to the presented orientation but not other orientations, in a phenomenon termed stimulus-response potentiation (Frenkel et al., 2006). This finding highlights that V1 can increase the response to overrepresented orientations, although it is unclear whether this occurs through new neurons encoding the stimulus, or increased activity in neurons that already encoded the stimulus before training. Orientation-selective neurons can also be sensitive to the direction a stimulus is 10 moving (Campbell, Cleland, Cooper, & Enroth-Cugell, 1968). Confusing the interpretation of direction selectivity in V1, however, is the well-documented presence of direction-selective retinal ganglion cells (Barlow & Hill, 1963). The output from direction-selective retinal ganglion cells in the mouse converges onto the dorsolateral LGN shell, and convey direction-selective (and orientation-selective) information to supragranular layers (but see Sun, Tan, Mensh, & Ji, 2016) of V1 (Cruz-Martín et al., 2014). How retinal direction selectivity relates to direction selectivity in orientationselective V1 neurons is unclear. Ferret V1 has little to no direction selectivity at eyeopening, despite the presence of orientation selectivity, and the emergence of direction selectivity requires visual experience (Li, Fitzpatrick, & White, 2006). Early exposure to a moving stimulus can accelerate the development of direction selectivity for the trained stimulus, suggesting cortical direction selectivity depends on experience (Li, Van Hooser, Mazurek, White, & Fitzpatrick, 2008). At eye-opening in the mouse, on the other hand, nearly all orientation-selective neurons are direction-selective (Rochefort et al., 2011). While orientation selectivity increases in the months following eye-opening, mean direction selectivity does not change in L2/3, and the bias in directional preference matches that described in the mouse retina (Elstrott et al., 2008; Rochefort et al., 2011). Subsequent experiments in L2/3 of the mouse show either a transient increase in direction selectivity or even a decrease in direction selectivity from eye-opening to adulthood (Hagihara et al., 2015; Hoy & Niell, 2015). Thus, there exist species-specific differences in the experience-dependent mechanisms of cortical direction selectivity, with evidence in the mouse that V1 may inherit direction selectivity from the retina and thalamus and not require visual experience. However, studies of the development of direction 11 selectivity in the mouse focused on monocular V1, and it is not clear if the development of direction selectivity in binocular V1 resembles that seen in monocular V1 or possibly parallels development in more binocular mammals such as the ferret. V1 in each hemisphere of the brain has inputs from the eye contralateral and ipsilateral to it. In carnivorans and primates, injection of radioactive tracer into a single eye labels stripes through the cortical layers of V1 that alternate with stripes of cortex that serve the opposing eye, forming ocular dominance (OD) columns (Hubel & Wiesel, 1972; Wiesel, Hubel, & Lam, 1974). Investigators initially hypothesized that establishment of OD columns relies on visual experience. Injecting radioactive tracer into one eye of young kittens does not label OD columns, and neurons throughout V1 seem functionally responsive to either eye (Levay, Stryker, & Shatz, 1978). Additionally, retinal silencing of young cats using TTX stops the formation of OD columns (Stryker & Harris, 1986). However, injection of a radioactive tracer into the eye of young animals can sometimes lead to the spillover of the tracer in LGN terminals, making it appear as if OD columns were absent when they are indeed present. Experiments in monkeys before and at birth show present and functional OD columns, precluding a role for visual experience (Des Rosiers et al., 1978; Horton & Hocking, 1996). Additionally, functional recordings in cats show OD columns are present a week before the time when experiments with radioactive tracers can label them (Crair et al., 1998). Further, injection of tracers into eye-specific layers of LGN in the ferret, bypassing the caveats of injecting into the eye, show anatomical OD columns present before eye-opening, and columns for both eyes form even when silencing one eye (Crowley & Katz, 2000). The initial formation of OD columns thus appears to occur independently of vision or retinal 12 activity. However, in young cats, responses to the contralateral eye are stronger and more selective than responses to the ipsilateral eye (Crair et al., 1998). By 3 weeks of age, the ipsilateral responses increase and become nearly identical to contralateral responses, and ipsilateral strengthening does not occur in binocularly deprived animals (Crair et al., 1998). Therefore, while the formation of OD columns occurs independently of visual experience, additional eye-specific refinement requires experience. The anatomical segregation of eye-specific inputs suggests OD columns serve a crucial role in binocular vision. However, New World monkeys lack OD columns, and even within species, the presence or absence of OD columns can vary between individuals (Adams & Horton, 2003; Henderickson et al., 1978; Tigges, Tigges, & Perachio, 1977). Additionally, mice and other rodents lack OD columns. Instead, onethird of V1 is binocular, with overlapping input from the two eyes covering roughly 30° of the visual field (Figure 1.1A; Dräger, 1975). Even in adult mice, the contralateral eye drives population responses that roughly double the magnitude of those of the ipsilateral eye (Coleman, Law, & Bear, 2009; Dräger, 1975; McCurry et al., 2010; Smith & Trachtenberg, 2007). However, eye-specific refinement still occurs after eye-opening. At eye-opening, the contralateral retinotopic map develops first, and the refinement and strengthening of the ipsilateral map follow (Smith & Trachtenberg, 2007). Intriguingly, blocking patterned vision through the contralateral eye disrupts ipsilateral refinement and strengthening, but removing the contralateral eye paradoxically accelerates ipsilateral refinement (Smith & Trachtenberg, 2007). This finding suggests that visual input from the contralateral eye guides ipsilateral development, likely leading to the proper binocular correspondence of the two retinotopic maps. However, contralateral input also limits 13 ipsilateral refinement, and in the absence of binocular vision, the ipsilateral map forms faster without needing to follow the pattern laid down by the other eye. Far from being confined to development, ipsilateral eyes maps could still further refine and strengthen following the removal of the contralateral eye even in adult mice (180 days old; Smith & Trachtenberg, 2010). Based on this, contralateral dominance observed in adult mouse V1 may reflect saturation of the capacity of neuronal circuitry for further refinement. The unique developmental conundrum of balancing input from the two eyes raises interesting questions concerning how other response properties, such as orientation/direction selectivity and orientation preference, might develop differently for responses to the two eyes or, indeed, between monocular and binocular cells. It also remains to be seen how the development of ipsilateral input takes place at the level of individual neurons as opposed to large scale cortical responses and organization. 1.3 Ocular dominance plasticity A critical period is a developmental phase of enhanced neuronal plasticity in response to specific sensory input. Exposure to the specific sensory input during the critical period results in behaviors or abilities that would otherwise not exist, even with exposure later in life, and absence of the specific sensory input during the critical period can lead to lifelong deficits. Hubel and Wiesel were the first to observe that in V1 of kittens, brief closure of one eye (monocular deprivation; Figure 1.2A) during the fourth to eighth week of life induced depression of neuronal responses to the closed eye, and prolonged closure led to the strengthening of the open eye response (Figure 1.2B; Hubel & Wiesel, 1970; Wiesel & Hubel, 1963). Thus, the V1 critical period requires patterned 14 binocular vision in order to achieve proper binocular balance. The critical period of sensitivity to monocular deprivation is called the ocular dominance (OD) critical period, and the shift in cortical responsiveness away from the closed eye is called ocular dominance plasticity (ODP). Without patterned binocular vision during development, such as might occur in children with cataracts or strabismus, permanent deficits in visual acuity, known as amblyopia, occur (Sengpiel, 2014). This loss of acuity is likely related to the necessity of the OD critical period for matching the orientation preference of binocular neurons to visual input from each eye (Wang, Sarnaik, & Cang, 2010). The site of ODP initially appeared uniquely cortical, as Wiesel and Hubel (1963) were not able to observe similar changes in upstream LGN. While more recent experiments do show measurable changes induced in LGN by monocular deprivation, proper expression of ODP in V1 depends on local synaptic changes within V1 itself (Jaepel, Hübener, Bonhoeffer, & Rose, 2017; Sommeijer et al., 2017). The dramatic, quantifiable changes induced by monocular deprivation make ODP a powerful model system for elucidating mechanisms of both critical period timing and neuronal plasticity. The OD critical period seems to be highly conserved across mammals, even in rodents lacking OD columns, suggesting it is a common developmental feature of binocular vision (Horton & Hocking, 1996; Issa, Trachtenberg, Chapman, Zahs, & Stryker, 1999; Maffei, Berardi, Domenici, Parisi, & Pizzorusso, 1992). The age of mouse genetics brought the capacity to knock out (KO) or overexpress genes of interest and gave investigators new tools to dissect the molecular mechanisms of ODP, and how the capacity of the brain to undergo such changes is temporally restricted to the critical period. This introduction will, therefore, focus primarily on current 15 knowledge gathered from experiments performed in mice. The OD critical period in mice starts at postnatal day 21 (P21), peaks at P28, and gradually comes to a close over the next few months of life (Gordon & Stryker, 1996; Lehmann & Löwel, 2008). The start of the OD critical period correlates with the appearance of inhibitory signaling through the neurotransmitter gamma-aminobutyric acid (GABA). Additionally, dark rearing, which delays the start of the OD critical period, also delays the maturation of GABAergic signaling (Iwai, Fagiolini, Obata, & Hensch, 2003; Morales, Choi, & Kirkwood, 2002; Mower, 1991). These findings suggest a link between inhibition and the start of the critical period, and indeed mice in which the GABA synthesis enzyme GAD65 is knocked out have no OD critical period (Figure 1.2C; Hensch et al., 1998). Pharmacologically increasing GABAergic signaling with diazepam can rescue the OD critical period at any age, and even open a precocious OD critical period (Fagiolini & Hensch, 2000). Thus, the initiation of the OD critical period depends on the emergence of inhibition in V1. While inhibition initiates the OD critical period, closed eye depression in excitatory neurons does not seem to take place through changes in inhibition (Khibnik, Cho, & Bear, 2010). Inhibition controls the timing and synchrony of excitatory neuronal firing, which temporally aligns pre-and postsynaptic activity, allowing for spike-timingdependent forms of synaptic strengthening and weakening such as long-term potentiation (LTP) and long-term depression (LTD; Hata, Tsumoto, & Stryker, 1999). Bienenstock, Cooper, and Munro, in a theoretical paper published in 1982, used mathematical modeling to predict that replacing patterned vision with synaptic noise from closing one eye would lead to a loss of synaptic strength from the closed eye through LTD like 16 mechanisms. Their prophetic work guided decades of experimental studies that have generally validated their predictions. Synaptic LTD requires N-methyl-D-aspartate (NMDA) receptor signaling, protein synthesis, and endocytosis of excitatory α-amino-3hydroxy-5-methylisoxazole-4-propionic acid (AMPA) type glutamate receptors (Lee, Liu, Wang, & Sheng, 2002; Philpot, Espinosa, & Bear, 2003). If the mechanism of closed eye depression during ODP is through LTD, closed eye depression should have the same requirements as LTD, and indeed closed eye depression following monocular deprivation does require NMDA receptor signaling, protein synthesis and AMPA receptor endocytosis (Cho, Khibnik, Philpot, & Bear, 2009; Taha & Stryker, 2002; Yoon, Smith, Heynen, Neve, & Bear, 2009). Induction of ODP by monocular deprivation also occludes LTD in ex vivo slices of V1 (Heynen et al., 2003). Closed eye depression, therefore, likely occurs through LTD as initially predicted, but the mechanism behind open-eye potentiation is less clear. Unlike closed eye depression, open eye potentiation still occurs in adult mice following prolonged monocular deprivation, although through seemingly different molecular mechanisms (Ranson, Cheetham, Fox, & Sengpiel, 2012; Sawtell et al., 2003). One possible mechanism of open-eye potentiation is homeostatic plasticity. Homeostatic plasticity lacks the input specificity of LTP and LTD, instead globally scaling up or down the strength of all inputs in response to prolonged decreases or increases in activity respectively (Turrigiano, Leslie, Desai, Rutherford, & Nelson, 1998). Homeostatic scaling of neuronal excitability occurs in the visual cortex following retinal lesion and could explain why open eye input strengthens following monocular deprivation (Keck et al., 2013). However, this seems inconsistent with the input specific, rather than a global 17 increase in synaptic strength and also the NMDA receptor dependence, which indicates a synapse-specific spike-timing dependence rather than global strengthening (Cho et al., 2009). Interestingly, inhibitory currents in neurons of the binocular cortex driven by the open eye are depressed following prolonged monocular deprivation, indicating synapsespecific disinhibition may explain open eye strengthening (Ma, Li, & Tao, 2013). How exactly excitatory and inhibitory changes work cooperatively to mediate open-eye potentiation during ODP remains to be seen. Critical periods allow for long-lasting changes in sensory perception or behavior. However, as observed in amblyopia, impairments of perception during that crucial window in development can lead to lifelong deficits. Additionally, increasing evidence suggests that many genetic mutations that lead to autism impact synaptic plasticity during critical periods, including the OD critical period (Dolen et al., 2007; Krishnan et al., 2015; Leblanc & Fagiolini, 2011; Vlaming, Jonkman, Daalen, Gaag, & Kemner, 2015). Additionally, following traumatic injuries such as stroke, synaptic plasticity is crucial for functional remapping of sensory and motor pathways during recovery (Clarkson, Huang, MacIsaac, Mody, & Carmichael, 2010; Li et al., 2015, 2010). There is, therefore, longstanding clinical interest in understanding both how the critical period comes to a close, and how to restore critical period plasticity in the adult brain to allow treatment of neurodevelopmental disorders and neuronal injury. Experiments that reopen the OD critical period in mice give insight into the mechanisms that lead to the natural closure of the critical period. Synapses are structurally plastic in V1 during the OD critical period and stable in adults (Mataga, Mizuguchi, & Hensch, 2004). The adult stability could be due to physical barriers to 18 structural change in axons and dendrites, such as the extracellular matrix of proteins that fill the space between neurons. Indeed, dissolving the extracellular matrix restores the capability of the adult visual cortex to undergo ODP (Pizzorusso et al., 2002). Interestingly, dense webs of extracellular matrix proteins accrete selectively around inhibitory interneurons in V1 during development, suggesting that dissolving the extracellular matrix could be reopening the critical period through affecting inhibitory neurotransmission as well as structural plasticity (Sugiyama et al., 2008; Ye & Miao, 2013). Indeed, pharmacologically reducing inhibition can also reopen the OD critical period (Harauzov et al., 2010). These findings suggest that while the emergence of inhibition in V1 begins the critical period, the further maturation of inhibition somehow brings it to a close. Supporting this hypothesis, transplanting embryonic inhibitory interneurons into V1 of adult mice opens a second OD critical period when these interneurons would naturally be beginning their critical period (Davis et al., 2015). This finding suggests there is an innately timed change in inhibitory interneurons where their ability to permit ODP in neighboring excitatory neurons appears and is then lost. This change may be through neuromodulation, as acetylcholine during the critical period disinhibits inhibitory signaling, and the developmental increase in Lynx1 protein reduces cholinergic signaling (Morishita, Miwa, Heintz, & Hensch, 2010; Yaeger, Ringach, & Trachtenberg, 2019). Indeed, when Lynx1 is knocked out, the OD critical period does not come to a natural close (Morishita et al., 2010). Simple manipulations such as environmental enrichment and visual deprivation also shift the balance between excitation and inhibition, and can also reopen the OD critical period (Greifzu et al., 2014; He, Hodos, & Quinlan, 2006). Collectively, these findings suggest that changes in 19 inhibition shift the ease with which ODP can occur. However, it is important to note that the adult brain is not aplastic and open-eye potentiation following monocular deprivation still occurs (Sato & Stryker, 2008; Sawtell et al., 2003). How experience differentially affects the qualities of binocular plasticity in critical period aged versus adult mice, and what role ongoing plasticity plays in the adult binocular visual cortex, remains an exciting area of investigation. 1.4 The immediate early gene Arc Cellular stimulation can induce rapid transcription of genes termed immediate early genes that regulate long-lasting changes in cellular anatomy and physiology. In neurons, immediate early genes are hypothesized to regulate long-term plasticity in response to neuronal activity. Two groups independently identified the gene Arc as an immediate early gene in principal neurons upregulated following seizure (Link et al., 1995; Lyford et al., 1995). In the hippocampus and cortex, these principal neurons are exclusively the excitatory population. Shortly afterward, several other groups observed increased Arc transcription induced by spatial exploration, brain-derived neurotrophic factor (BDNF) signaling, and LTP induction (Ramirez-Amaya, 2005; Rodriguez et al., 2005; Ying et al., 2002). Arc transcripts appear as quickly as 5 minutes following exploration. The tight coupling between activity and Arc transcription arises from a small sequence located seven kilobases upstream of the Arc transcription start site. This sequence contains binding motifs for the transcription factors SRE, MEF2, and CREB. Expression of this small sequence, along with a minimal Arc promoter, is sufficient to mimic the tight temporal control of Arc expression in response to activity (Kawashima et 20 al., 2009). The majority of immediate early genes are transcription factors that activate downstream signaling cascades. However, unexpectedly, Arc mRNA is present outside of the cell soma in the neuronal dendrites (Link et al., 1995; Lyford et al., 1995). Indeed, Arc mRNA can traffic out to distal dendrites in less than an hour after transcription, and Arc mRNA localizes to activated segments of dendrites (Guzowski, McNaughton, Barnes, & Worley, 1999). Arc mRNA has a half-life of 47 minutes, suggesting tight regulation of Arc translation (Rao et al., 2006). Indeed, nonsense-mediated decay targets Arc rapidly after the start of translation (Giorgi et al., 2007). Arc translation, much like Arc transcription, is activity-dependent. LTP activates Arc translation in an ERK and NMDA receptor-dependent manner (Panja et al., 2009; Waltereit et al., 2018). Metabotropic glutamate receptor-induced LTD also triggers the translation of preexisting dendritic Arc mRNA under the control of elongation factor 2 and Fragile-X Mental Retardation protein (FMRP; Park et al., 2008; Waung, Pfeiffer, Nosyreva, Ronesi, & Huber, 2008). Translated Arc protein has a half-life of 45 minutes, with the ubiquitinating protein Triad3A controlling the robust and rapid degradation of Arc protein (Mabb et al., 2014). This body of work shows that the production and localization of Arc mRNA is activity dependent, and the availability of Arc mRNA and Arc protein is kept under tight temporal control. The tight regulation and dendritic localization of Arc suggest a vital function in synaptic plasticity following neuronal activity. Indeed, a 60% reduction in Arc expression in rat hippocampus is sufficient to impair both spatial memory and LTP (Guzowski et al., 2000). Generation of Arc KO mice allowed careful testing of the role of Arc in learning and plasticity (Plath et al., 2006). Arc KO mice have intact short-term memory during 21 objection recognition, but when tested 24 hours later can not recall the familiar versus novel objects. Similarly, Arc KO mice have impaired long-term memory during a spatial learning task, operant fear conditioning, and conditioned taste aversion. Arc KO mice seem to have an enhanced early phase, but an impaired late phase, of LTP. However, the effect of Arc KO on LTP varies significantly between studies (Guzowski et al., 2000; Messaoudi et al., 2007; Plath et al., 2006). Arc is, however, required for hippocampal LTD induced by either low-frequency stimulation or metabotropic glutamate receptor activation (Park et al., 2008; Plath et al., 2006; Waung et al., 2008). Arc is also required for LTD in other brain regions, including the cerebellum (Smith-Hicks et al., 2010). Finally, Arc is also required for homeostatic upscaling and downscaling of surface AMPA receptors (Béïque, Na, Kuhl, Worley, & Huganir, 2011; Shepherd et al., 2006). As suggested by the careful temporal control of Arc expression by activity, the presence or absence of Arc has wide-ranging effects on learning and synaptic plasticity in multiple brain regions. Consistent with a role for Arc in LTD, Arc protein interacts with the endocytic proteins endophilin 3, dynamin 2, and clathrin adaptor protein 2, which are all involved in AMPA receptor endocytosis (Chowdhury et al., 2006; DaSilva et al., 2016; Rial Verde, Lee-Osbourne, Worley, Malinow, & Cline, 2006; Shepherd et al., 2006). Additionally, Arc interacts with the AMPA receptor accessory protein, stargazin (Zhang et al., 2015). Overexpression of Arc is sufficient to induce AMPA receptor endocytosis, while overexpression of Arc that cannot bind to endophilin 3 or dynamin 2 does not lead to AMPA receptor endocytosis (Chowdhury et al., 2006). In addition to interactions with endocytic proteins Arc interacts with proteins involved in cytoskeletal remodeling, 22 including Drebin A and WAVE1 (Nair et al., 2017; Zhang et al., 2015). Cytoskeletal remodeling is necessary for dendritic spine growth and shrinkage in LTP and LTD, respectively. Consistent with this, Arc overexpression leads to a loss of large, putatively potentiated spines and an increase in small spines that likely lack AMPA receptors (Peebles et al., 2010). Arc could, therefore, regulate synaptic strength through both AMPA receptor endocytosis and remodeling of the postsynaptic dendritic spine. Arc targeting to activated dendritic segments that are potentiated seems to conflict with a role in LTD. However, after local translation at the base of dendritic spines, Arc protein is targeted to nearby inactive synapses by interaction with inactive calcium/calmodulindependent protein kinase II beta (Okuno et al., 2012). This inverse synaptic tag positions Arc for synapse-specific heterosynaptic depression, decreasing inactive spines next to active spines, increasing the relative signal from potentiated spines and keeping neuronal input from becoming saturated. Indeed, learning requires a delayed reactivation of neurons initially involved in the memory, leading to the translation of proteins, including Arc, and spine elimination. Arc is required for this delayed spine elimination and the persistence of learning (Nakayama et al., 2015). Common variants in the Arc coding region are not associated with cognitive disorders (Myrum et al., 2015). However, several human neurodevelopmental disorders have mutations in Arc regulating proteins. Deletion of the ubiquitin protein ligase E3A (Ube3A) gene cause Angelman syndrome. Ube3A KO mice have upregulated Arc expression and a concomitant decrease in surface AMPA receptors (Greer et al., 2010). Loss of methyl-CpG binding protein 2 (MeCP2) in Rett Syndrome leads to increased RNA polymerase II recruitment to the Arc promoter, causing enhanced Arc transcription 23 (Su, Cha, & West, 2012). Loss of FMRP in Fragile-X syndrome also leads to increased Arc expression (Zalfa et al., 2003). Intriguingly, Angelman syndrome, Rett syndrome, and Fragile-X syndrome mouse models all have impaired ODP, leading to a hypothesis that neurodevelopmental impairments seen in disorders such as autism may arise from alterations in expression or timing of critical period plasticity (Dolen et al., 2007; Greer et al., 2010; Krishnan et al., 2015; Leblanc & Fagiolini, 2011). The findings that misregulated Arc expression is a common underlying feature in these neurodevelopmental disorders suggests a crucial role for Arc in regulating the critical period. Aberrant Arc expression also occurs in adult neurological and neurodegenerative disorders. Mutations in Arc regulating regions in the genome are implicated in schizophrenia (Chuang et al., 2016). Loss of Triad 3A, an Arc ubiquitinating protein, leads to Gordon Holmes syndrome, which is characterized by cognitive decline, dementia, and movement disorders (Husain et al., 2017). Arc may also be involved in Alzheimer’s disease, as Arc binds to presenilin 1, traffics amyloid precursor protein, and regulates activity-dependent amyloid beta generation (Wu et al., 2011). Indeed, crossing an Arc KO mouse line to a mouse model of Alzheimer’s disease decreases pathological amyloid plaque load (Wu et al., 2011). Synthetic amyloid-beta ligands can increase Arc expression, and this is hypothesized to impair memory (Lacor et al., 2004). However, mice expressing human-derived amyloid beta have less exploration induced Arc expression, and also neuronal hyperexcitability, and seizures (Palop, 2005; Palop et al., 2007). While elucidating whether Arc dysregulation is causative in these diverse neurological disorders remains an important step moving forward, proper regulation of 24 Arc expression by experience seems to be crucial for both proper neurodevelopment, and cognitive function in adulthood. The development of a mouse line with a green fluorescent protein (GFP) knocked into the Arc coding region allows in vivo labeling of neurons with active Arc expression. With heterozygous ArcGFP mice (one allele coding GFP, the other coding Arc), researchers observed populations of neurons encoding distinct orientations in V1, with the populations becoming smaller but more reliable with repeated presentations of the same orientation (K. H. Wang et al., 2006). In ArcGFP/GFP mice that are full Arc KOs, there is an increased number of neurons with low orientation selectivity. This study was crucial in linking a plasticity-related, experience-dependent gene to a deficit in sensory encoding. Indeed, many patients with neurodevelopmental disorders have difficulties in visual perception that could be caused by impaired developmental plasticity (Dakin & Frith, 2005; Vlaming et al., 2015). A more in-depth characterization of the visual cortex in Arc KO mice finds normal thalamic organization and cortical retinotopy, as well as preserved visual acuity (McCurry et al., 2010). Expression of inhibitory markers, including GAD65, are normal in Arc KO visual cortex. However, critical period aged Arc KO mice lack both closed eye depression and open eye potentiation following monocular deprivation, as assessed by the population response. Monocular deprivation leads to AMPA receptor internalization from neuron’s cell membrane in wild type mouse V1 but does not lead to internalization in Arc KO mice. This finding suggests that the lack of ODP in Arc KO mice is due to an inability to endocytose AMPA receptors. Additionally, rather than a baseline 2:1 contralateral to ipsilateral response ratio, as seen in wild type controls, Arc 25 KO mice have a 1:1 contralateral to ipsilateral ratio. The aberrant ratio in Arc KO mice suggests the establishment of binocularity in mouse V1 also relies on excitatory plasticity, rather than inhibitory maturation alone, as Arc expression is restricted to the excitatory population in V1 (McCurry et al., 2010). Arc KO mice also lack stimulusresponse potentiation to repeated presentations of a stimulus. Recordings from neurons in ex vivo sections of V1 show basal spontaneous excitatory and inhibitory synaptic transmission are increased (Gao et al., 2010). Two days of visual deprivation lead to upregulated excitatory neurotransmission in WT mouse V1 neurons. However, there was no upregulation in Arc KO mouse neurons. Surprisingly, Arc KO V1 does not have upregulated levels of surface AMPA receptors basally, but acute knockdown of Arc in V1 does lead to increased surface AMPA receptors, impairs heterosynaptic depression, and impairs experience-dependent synaptic plasticity in single neurons (El-Boustani et al., 2018; Gao et al., 2010; McCurry et al., 2010). Compensatory mechanisms, such as the increase in spontaneous inhibitory activity, may stabilize AMPA receptor number in the absence of developmental Arc (Gao et al., 2010). These studies convincingly demonstrate a lack of experience-dependent plasticity in Arc KO V1. However, the necessity of Arc for ODP expression does not address if decreased Arc availability following the close of the OD critical period could be a limiting factor for adult plasticity. If this were the case, increased Arc expression in adult V1 could be sufficient for juvenile like ODP. Additionally, the impaired contralateral to ipsilateral balance in Arc KO mice suggests that Arc is involved in the refinement of ipsilateral connectivity before the critical period. The ipsilateral refinement is thought to reflect the emergence of binocular neurons, but no experiment to date has tested this hypothesis in 26 the mouse. 1.5 Hypothesis and overview The development of V1 and the regulation of the OD critical period provides a window into the mechanisms by which experience sculpts neuronal circuits. Understanding these mechanisms may be the key to correcting developmental or injuryinduced deficits in sensory or cognitive processes. Arc expression is tightly coupled to experience and necessary for ODP. A potential mechanism for the close of the OD critical period is a decrease or loss of experience-induced Arc expression, possibly through a shift in the excitatory/inhibitory balance that makes it harder to induce immediate early gene expression and LTD. Additionally, the disrupted contralateral to ipsilateral balance in Arc KO mice indicates that Arc plays an unknown role in the establishment of binocularity. We hypothesize that Arc is necessary for experiencedependent binocular development and a limiting reagent for adult plasticity in V1. Chapter 2 describes novel findings of Arc’s decreased expression in adult V1, corresponding to a loss of protein synthesis-dependent LTD in L4. Mice overexpressing Arc have persistent LTD into adulthood, as well as persistent ODP indicating a failure to close the OD critical period. Finally, acute overexpression of Arc in adult binocular V1 restored juvenile like ODP. Chapter 3 describes how binocular neurons emerge after eyeopening from an initially monocular population of cells in binocular V1. These binocular neurons have unique tuning of visual response properties compared to monocular neurons, and their emergence depended on experience. Arc KO mice exhibit an increased number of binocular neurons at the peak of the critical period, and acute KO of Arc 27 expression in adult mice recreates this developmental phenotype, suggesting ongoing binocular regulation throughout life. Thus, this dissertation demonstrates that Arc is a crucial regulator of binocular plasticity in V1 throughout life. 1.6 References Adams, D. L., & Horton, J. C. (2003). Capricious expression of cortical columns in the nnnnprimate brain. Nature Neuroscience, 6(2), 113–114. https://doi.org/10.1038/nn1004 Ayoub, G. S., Korenbrot, J. I., & Copenhagen, D. R. (1989). Release of endogenous glutamate from isolated cone photoreceptors of the lizard. Neuroscience Research Supplements, 10(C). https://doi.org/10.1016/0921-8696(89)90008-X Barlow, H. ., & Hill, R. . (1963). Selective sensitivity to direction of movement in ganglion cells of the rabbit retina. Science, 139(3553), 412–414. https://doi.org/10.1126/science.139.3553.412 Béïque, J., Na, Y., Kuhl, D., Worley, P. F., & Huganir, R. L. (2011). Arc-dependent synapse-specific homeostatic plasticity. Proceedings of the National Academy of Sciences, 108(2), 816–821. https://doi.org/10.1073/pnas.1017914108 Bienenstock, E. L., Cooper, L. N., & Munro, P. W. (1982). Theory for the development of neuron selectivity: Orientation specificity and binocular interaction in visual cortex. The Journal of Neuroscience, 2(1), 32–48. Blasdel, G., & Lund, J. (1983). Termination of afferent axons in macaque striate cortex. The Journal of Neuroscience, 3(7), 1389–1413. https://doi.org/10.1523/jneurosci.0307-01389.1983 Bonhoeffer, T., & Grinvald, A. (1991). Iso-orientation domains in cat visual cortex are arranged in pinwheel-like patterns. Nature, 353(6343), 429–431. https://doi.org/10.1038/353429a0 Bunt, A. H., Hendrickson, A. E., Lund, J. S., Lund, R. D., & Fuchs, A. F. (1975). Monkey retinal ganglion cells: Morphometric analysis and tracing of axonal projections, with a consideration of the peroxidase technique. Journal of Comparative Neurology, 164(3), 265–285. https://doi.org/10.1002/cne.901640302 Campbell, F. W., Cleland, B. G., Cooper, G. F., & Enroth-Cugell, C. (1968). The angular selectivity of visual cortical cells to moving gratings. The Journal of Physiology, 198(1), 237–250. https://doi.org/10.1113/jphysiol.1968.sp008604 Cang, J., Kaneko, M., Yamada, J., Woods, G., Stryker, M. P., & Feldheim, D. A. (2005). Ephrin-As guide the formation of functional maps in the visual cortex. Neuron, 28 48(4), 577–589. https://doi.org/10.1016/j.neuron.2005.10.026 Cang, J., Niell, C. M., Liu, X., Pfeiffenberger, C., Feldheim, D. A., & Stryker, M. P. (2008). Selective disruption of one cartesian axis of cortical maps and receptive fields by deficiency in ephrin-As and structured activity. Neuron, 57(4), 511–523. https://doi.org/10.1016/j.neuron.2007.12.025 Cang, J., Rentería, R. C., Kaneko, M., Liu, X., Copenhagen, D. R., & Stryker, M. P. (2005). Development of precise maps in visual cortex requires patterned spontaneous activity in the retina. Neuron, 48(5), 797–809. https://doi.org/10.1016/j.neuron.2005.09.015 Chapman, B., & Stryker, M. P. (1993). Development of orientation selectivity in ferret visual cortex and effects of deprivation. The Journal of Neuroscience, 13(12), 5251– 5262. Cho, K. K. A., Khibnik, L., Philpot, B. D., & Bear, M. F. (2009). The ratio of NR2A/B NMDA receptor subunits determines the qualities of ocular dominance plasticity in visual cortex. Proceedings of the National Academy of Sciences, 106(13), 5377– 5382. https://doi.org/10.1073/pnas.0808104106 Chowdhury, S., Shepherd, J. D., Okuno, H., Lyford, G., Petralia, R. S., Plath, N., … Worley, P. F. (2006). Arc/Arg3.1 interacts with the endocytic machinery to regulate AMPA receptor trafficking. Neuron, 52(3), 445–459. https://doi.org/10.1016/j.neuron.2006.08.033 Chuang, Y., Hu, T., Chen, C., Hsu, S., Tsai, H., & Cheng, M. (2016). Rare mutations and hypermethylation of the ARC gene associated with schizophrenia. Schizophrenia Research, 176(2–3), 106–113. https://doi.org/10.1016/j.schres.2016.07.019 Clarkson, A. N., Huang, B. S., MacIsaac, S. E., Mody, I., & Carmichael, S. T. (2010). Reducing excessive GABA-mediated tonic inhibition promotes functional recovery after stroke. Nature, 468(7321), 305–309. https://doi.org/10.1038/nature09511 Coleman, J. E., Law, K., & Bear, M. F. (2009). Anatomical origins of ocular dominance in mouse primary visual cortex. Neuroscience, 161(2), 561–571. https://doi.org/10.1016/j.neuroscience.2009.03.045 Connolly, M., & Van Essen, D. (1984). The representation of the visual field in parvicellular and magnocellular layers of the lateral geniculate nucleus in the macaque monkey. Journal of Comparative Neurology, 226(4), 544–564. https://doi.org/10.1002/cne.902260408 Coppola, D. M., Purves, H. R., McCoy, A. N., & Purves, D. (2002). The distribution of oriented contours in the real world. Proceedings of the National Academy of Sciences, 95(7), 4002–4006. https://doi.org/10.1073/pnas.95.7.4002 Coppola, D. M., White, L. E., Fitzpatrick, D., & Purves, D. (1998). Unequal 29 representation of cardinal and oblique contours in ferret visual cortex. Proceedings of the National Academy of Sciences, 95(5), 2621–2623. https://doi.org/10.1073/pnas.95.5.2621 Crair, M. C., Gillespie, D. C., & Stryker, M. P. (1998). The role of visual experience in the development of columns in cat visual cortex. Science, 279(5350), 566–570. https://doi.org/10.1126/science.279.5350.566 Creutzfeldt, O. D., Lee, B. B., & Elepfandt, A. (1979). A quantitative study of chromatic organisation and receptive fields of cells in the lateral geniculate body of the rhesus monkey. Experimental Brain Research, 35(3), 527–545. https://doi.org/10.1007/BF00236770 Crowley, J. C., & Katz, L. C. (2000). Early development of ocular dominance columns. Science, 290(5495), 1321–1324. https://doi.org/10.1126/science.290.5495.1321 Cruz-Martín, A., El-Danaf, R. N., Osakada, F., Sriram, B., Dhande, O. S., Nguyen, P. L., … Huberman, A. D. (2014). A dedicated circuit links direction-selective retinal ganglion cells to the primary visual cortex. Nature, 507(7492), 358–361. https://doi.org/10.1038/nature12989 Dakin, S., & Frith, U. (2005). Vagaries of visual perception in autism. Neuron, 48(3), 497–507. https://doi.org/10.1016/j.neuron.2005.10.018 DaSilva, L. L. P., de Almeida, L. P., Januário, Y. C., Wall, M. J., Wauters, S. C., Corrêa, S. A. L., & Müller, J. (2016). Activity-regulated cytoskeleton-associated protein controls AMPAR endocytosis through a direct interaction with clathrin-adaptor protein 2. ENeuro, 3(3), 125–140. https://doi.org/10.1523/ENEURO.0144-15.2016 Davis, M. F., Figueroa Velez, D. X., Guevarra, R. P., Yang, M. C., Habeeb, M., Carathedathu, M. C., & Gandhi, S. P. (2015). Inhibitory neuron transplantation into adult visual cortex creates a new critical period that rescues impaired vision. Neuron, 86(4), 1055–1066. https://doi.org/10.1016/j.neuron.2015.03.062 Des Rosiers, M. H., Sakurada, O., Jehle, J., Shinohara, M., Kennedy, C., & Sokoloff, L. (1978). Functional plasticity in the immature striate cortex of the monkey shown by the [14C]deoxyglucose method. Science, 200(4340), 447–449. https://doi.org/10.1126/science.417397 Dixon, R. A. F., Kobilka, B. K., Strader, D. J., Benovic, J. L., Dohlman, H. G., Frielle, T., … Strader, C. D. (1986). Cloning of the gene and cDNA for mammalian βadrenergic receptor and homology with rhodopsin. Nature, 321(6065), 75–79. https://doi.org/10.1038/321075a0 Dolen, G., Osterweil, E., Rao, B. S. S., Smith, G. B., Auerbach, B. D., Chattarji, S., & Bear, M. F. (2007). Correction of fragile X syndrome in mice. Neuron, 56(6), 955– 962. https://doi.org/10.1016/j.neuron.2007.12.001 30 Dräger, U. C. (1975). Receptive fields of single cells and topography in mouse visual cortex. Journal of Comparative Neurology, 160(3), 269–289. https://doi.org/10.1002/cne.901600302 Dreher, B., Fukada, Y., & Rodieck, R. W. (1976). Identification, classification and anatomical segregation of cells with X‐like and Y‐like properties in the lateral geniculate nucleus of old‐world primates. The Journal of Physiology, 258(2), 433– 452. https://doi.org/10.1113/jphysiol.1976.sp011429 El-Boustani, S., Ip, J. P. K., Breton-Provencher, V., Knott, G. W., Okuno, H., Bito, H., & Sur, M. (2018). Locally Coordinated Cell-wide Synaptic Plasticity of Visual Cortex Neurons in vivo. Science, 360(6395), 1349–1354. https://doi.org/10.1126/science.aao0862 Elstrott, J., Anishchenko, A., Greschner, M., Sher, A., Litke, A. M., Chichilnisky, E. J., & Feller, M. B. (2008). Direction Selectivity in the Retina Is Established Independent of Visual Experience and Cholinergic Retinal Waves. Neuron, 58(4), 499–506. https://doi.org/10.1016/j.neuron.2008.03.013 Fagiolini, M., & Hensch, T. K. (2000). Inhibitory threshold for critical-period activation in primary visual cortex. Nature, 404(6774), 183–186. https://doi.org/10.1038/35004582 Fagiolini, M., Pizzorusso, T., Berardi, N., Domenici, L., & Maffei, L. (1994). Functional postnatal development of the rat primary visual cortex and the role of visual experience: Dark rearing and monocular deprivation. Vision Research, 34(6), 709– 720. https://doi.org/10.1016/0042-6989(94)90210-0 Fesenko, E. E., Kolesnikov, S. S., & Lyubarsky, A. L. (1985). Induction by cyclic GMP of cationic conductance in plasma membrane of retinal rod outer segment. Nature, 313(6000), 310–313. https://doi.org/10.1038/313310a0 Fitzpatrick, D., Lund, J. S., Schmechel, D. E., & Towles, A. C. (1987). Distribution of GABAergic neurons and axon terminals in the macaque striate cortex. Journal of Comparative Neurology, 264(1), 73–91. https://doi.org/10.1002/cne.902640107 Frenkel, M. Y., Sawtell, N. B., Diogo, A. C. M., Yoon, B., Neve, R. L., & Bear, M. F. (2006). Instructive effect of visual experience in mouse visual cortex. Neuron, 51(3), 339–349. https://doi.org/10.1016/j.neuron.2006.06.026 Fung, B. K., Hurley, J. B., & Stryer, L. (1981). Flow of information in the light-triggered cyclic nucleotide cascade of vision. Proceedings of the National Academy of Sciences, 78(1), 152–156. https://doi.org/10.1073/pnas.78.1.152 Gao, M., Sossa, K., Song, L., Errington, L., Cummings, L., Hwang, H., … Lee, H. (2010). A specific requirement of Arc/Arg3.1 for visual experience-induced homeostatic synaptic plasticity in mouse primary visual cortex. The Journal of Neuroscience, 30(21), 7168–7178. https://doi.org/10.1523/JNEUROSCI.1067- 31 10.2010 Gilbert, C. D., & Wiesel, T. N. (1989). Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex. The Journal of Neuroscience, 9(7), 2432–2442. Giorgi, C., Yeo, G. W., Stone, M. E., Katz, D. B., Burge, C., Turrigiano, G., & Moore, M. J. (2007). The EJC factor eIF4AIII modulates synaptic strength and neuronal protein expression. Cell, 130(1), 179–191. https://doi.org/10.1016/j.cell.2007.05.028 Girshick, A. R., Landy, M. S., & Simoncelli, E. P. (2011). Cardinal rules: Visual orientation perception reflects knowledge of environmental statistics. Nature Neuroscience, 14(7), 926–932. https://doi.org/10.1038/nn.2831 Gordon, J. A., & Stryker, M. P. (1996). Experience-dependent plasticity of binocular responses in the primary visual cortex of the mouse. The Journal of Neuroscience, 16(10), 3274–3286. Greer, P. L., Hanayama, R., Bloodgood, B. L., Mardinly, A. R., Lipton, D. M., Flavell, S. W., … Greenberg, M. E. (2010). The angelman syndrome protein Ube3A regulates synapse development by ubiquitinating Arc. Cell, 140(5), 704–716. https://doi.org/10.1016/j.cell.2010.01.026 Greifzu, F., Pielecka-Fortuna, J., Kalogeraki, E., Krempler, K., Favaro, P. D., Schluter, O. M., & Lowel, S. (2014). Environmental enrichment extends ocular dominance plasticity into adulthood and protects from stroke-induced impairments of plasticity. Proceedings of the National Academy of Sciences, 111(3), 1150–1155. https://doi.org/10.1073/pnas.1313385111 Grubb, M. S., & Thompson, I. D. (2006). Quantitative characterization of visual response properties in the mouse dorsal lateral geniculate nucleus. Journal of Neurophysiology, 90(6), 3594–3607. https://doi.org/10.1152/jn.00699.2003 Guzowski, J. F., Lyford, G. L., Stevenson, G. D., Houston, F. P., McGaugh, J. L., Worley, P. F., & Barnes, C. a. (2000). Inhibition of activity-dependent arc protein expression in the rat hippocampus impairs the maintenance of long-term potentiation and the consolidation of long-term memory. The Journal of Neuroscience, 20(11), 3993–4001. Guzowski, J., McNaughton, B., Barnes, C., & Worley, P. (1999). Environment-specific expression of the immediate-early gene Arc in hippocampal neuronal ensembles. Nature Neuroscience, 2(12), 1120–1124. https://doi.org/10.1038/16046 Hagihara, K. M., Murakami, T., Yoshida, T., Tagawa, Y., & Ohki, K. (2015). Neuronal activity is not required for the initial formation and maturation of visual selectivity. Nature Neuroscience, 18(12), 1780–1788. https://doi.org/10.1038/nn.4155 Harauzov, A., Spolidoro, M., DiCristo, G., De Pasquale, R., Cancedda, L., Pizzorusso, T., 32 … Maffei, L. (2010). Reducing intracortical inhibition in the adult visual cortex promotes ocular dominance plasticity. The Journal of Neuroscience, 30(1), 361– 371. https://doi.org/10.1523/JNEUROSCI.2233-09.2010 Hartline, H. K. (1978). The response of single optic nerve fibers of the vertebrate eye to illumination of the retina. American Journal of Physiology, 41(3), 788–797. https://doi.org/10.1152/ajplegacy.1938.121.2.400 Hata, Y., Tsumoto, T., & Stryker, M. P. (1999). Selective pruning of more active afferents when cat visual cortex is pharmacologically inhibited. Neuron, 22(2), 375– 381. https://doi.org/10.1016/S0896-6273(00)81097-1 He, H., Hodos, W., & Quinlan, E. M. (2006). Visual deprivation reactivates rapid ocular dominance plasticity in adult visual cortex. The Journal of Neuroscience, 26(11), 2951–2955. https://doi.org/10.1523/JNEUROSCI.5554-05.2006 Henderickson, A. E., Wilson, J. R., & Ogren, M. P. (1978). The neurological organization of pathways between the dorsal lateral geniculate nucleus and visual cortex in old world and new world primates. Journal of Comparative Neurology, 182(1), 123–136. https://doi.org/10.1002/cne.901820108 Hensch, T., Fagiolini, M., Matega, N., Stryker, M., Baekkeskov, S., & Kash, S. (1998). Local GABA circuit control of experience-dependent plasticity in developing visual cortex. Science, 282(5393), 1504–1508. https://doi.org/10.1126/science.282.5393.1504 Heynen, A. J., Yoon, B., Liu, C., Chung, H. J., Huganir, R. L., & Bear, M. F. (2003). Molecular mechanism for loss of visual cortical responsiveness following brief monocular deprivation. Nature Neuroscience, 6(8), 854–862. https://doi.org/10.1038/nn1100 Horton, J. C., & Hocking, D. R. (1996). An adult-like pattern of ocular dominance columns in striate cortex of newborn monkeys prior to visual experience. The Journal of Neuroscience, 16(5), 1791–1807. Hoy, J. L., & Niell, C. M. (2015). Layer-specific refinement of visual cortex function after eye opening in the awake mouse. Journal of Neuroscience, 35(8), 3370–3383. https://doi.org/10.1523/JNEUROSCI.3174-14.2015 Hubel, D. H., & Wiesel, T. N. (1961). Integrative action in the cat’s lateral geniculate body. The Journal of Physiology, 155(2), 385–398. https://doi.org/10.1113/jphysiol.1961.sp006635 Hubel, D. H., & Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology, 160(1), 106–154. https://doi.org/10.1113/jphysiol.1962.sp006837 Hubel, D. H., & Wiesel, T. N. (1966). Effects of varying stimulus size and color on single 33 lateral geniculate cells in Rhesus monkeys. Proceedings of the National Academy of Sciences, 55(6), 1345–1346. https://doi.org/10.1073/pnas.55.6.1345 Hubel, D. H., Wiesel, T. N., & LeVay, S. (1976). Functional architecture of area 17 in normal and monocularly deprived macaque monkeys. Cold Spring Harbor Symposia on Quantitative Biology, 40, 581–589. https://doi.org/10.1101/SQB.1976.040.01.054 Hubel, D H, & Wiesel, T. N. (1970). The period of susceptibility to the physiological effects of unilateral eye closure in kittens. The Journal of Physiology, 206(2), 419– 436. https://doi.org/10.1113/jphysiol.1970.sp009022 Hubel, David H., & Wiesel, T. N. (1972). Laminar and columnar distribution of geniculocortical fibers in the macaque monkey. Journal of Comparative Neurology, 146(4), 421–450. https://doi.org/10.1002/cne.901460402 Husain, N., Yuan, Q., Yen, Y., Pletnikova, O., Sally, D. Q., Worley, P., … Shawn Je, H. (2017). TRIAD3/RNF216 mutations associated with Gordon Holmes syndrome lead to synaptic and cognitive impairments via Arc misregulation. Aging Cell, 16(2), 281–292. https://doi.org/10.1111/acel.12551 Issa, N. P., Trachtenberg, J. T., Chapman, B., Zahs, K. R., & Stryker, M. P. (1999). The critical period for ocular dominance plasticity in the ferret’s visual cortex. The Journal of Neuroscience, 19(16), 6965–6978. https://doi.org/10.1523/jneurosci.1916-06965.1999 Iwai, Y., Fagiolini, M., Obata, K., & Hensch, T. K. (2003). Rapid critical period induction by tonic inhibition in visual cortex. The Journal of Neuroscience, 23(17), 6695–6702. Kawashima, T., Okuno, H., Nonaka, M., Adachi-Morishima, A., Kyo, N., Okamura, M., … Bito, H. (2009). Synaptic activity-responsive element in the Arc/Arg3.1 promoter essential for synapse-to-nucleus signaling in activated neurons. Proceedings of the National Academy of Sciences, 106(1), 316–321. https://doi.org/10.1073/pnas.0806518106 Keck, T., Keller, G. B., Jacobsen, R. I., Eysel, U. T., Bonhoeffer, T., & Hübener, M. (2013). Synaptic scaling and homeostatic plasticity in the mouse visual cortex in vivo. Neuron, 80(2), 327–334. https://doi.org/10.1016/j.neuron.2013.08.018 Khibnik, L., Cho, K., & Bear, M. F. (2010). Relative contribution of feed-forward excitatory connections to expression of ocular dominance plasticity in layer 4 of visual cortex. Neuron, 66(4), 493–500. https://doi.org/10.1016/j.neuron.2010.04.012.Relative Kolb, H. (1977). The organization of the outer plexiform layer in the retina of the cat: Electron microscopic observations. Journal of Neurocytology, 6(2), 131–153. https://doi.org/10.1007/BF01261502 34 Kolb, H., Fernandez, E., & Nelson, R. (2010). The organization of the retina and visual system. The Organization of the Retina and Visual System, 16(1), 533–579. https://doi.org/10.1146/annurev.cellbio.16.1.521 Krahe, T. E., El-Danaf, R. N., Dilger, E. K., Henderson, S. C., & Guido, W. (2011). Morphologically distinct classes of relay cells exhibit regional preferences in the dorsal lateral geniculate nucleus of the mouse. Journal of Neuroscience, 31(48), 17437–17448. https://doi.org/10.1523/jneurosci.4370-11.2011 Kreile, A. K., Bonhoeffer, T., & Hubener, M. (2011). Altered visual experience induces instructive changes of orientation preference in mouse visual cortex. Journal of Neuroscience, 31(39), 13911–13920. https://doi.org/10.1523/jneurosci.214311.2011 Krishnan, K., Wang, B., Lu, J., Wang, L., Maffei, A., Cang, J., & Huang, Z. J. (2015). MeCP2 regulates the timing of critical period plasticity that shapes functional connectivity in primary visual cortex. Proceedings of the National Academy of Sciences, 112(34), 4782–4791. https://doi.org/10.1073/pnas.1506499112 Kuhn, H., Bennett, N., Michel-Villaz, M., & Chabre, M. (1981). Interactions between photoexcited rhodopsin and GTP-binding protein: kinetic and stoichiometric analyses from light-scattering changes. Proceedings of the National Academy of Sciences, 78(11), 6873–6877. https://doi.org/10.1073/pnas.78.11.6873 Kwok-Keung Fung, B., & Stryer, L. (1980). Photolyzed rhodopsin catalyzes the exchange of GTP for bound GDP in retinal rod outer segments. Proceedings of the National Academy of Sciences, 77(5), 2500–2504. https://doi.org/10.1073/pnas.77.5.2500 Lacor, P. N., Buniel, M., Chang, L., Fernandez, S., Gong, Y., Viola, K., … Klein, W. (2004). Synaptic targeting by Alzheimer’s-related amyloid oligomers. Journal of Neuroscience, 24(45), 10191–10200. https://doi.org/10.1523/jneurosci.343204.2004 Leblanc, J. J., & Fagiolini, M. (2011). Autism: A critical period disorder? Neural Plasticity, 2011. https://doi.org/10.1155/2011/921680 Lee, S. H., Liu, L., Wang, Y. T., & Sheng, M. (2002). Clathrin adaptor AP2 and NSF interact with overlapping sites of GluR2 and play distinct roles in AMPA receptor trafficking and hippocampal LTD. Neuron, 36(4), 661–674. https://doi.org/10.1016/S0896-6273(02)01024-3 Lehmann, K., & Löwel, S. (2008). Age-dependent ocular dominance plasticity in adult mice. PLoS ONE, 3(9). https://doi.org/10.1371/journal.pone.0003120 Levay, S., Stryker, M. P., & Shatz, C. J. (1978). Ocular dominance columns and their development in layer IV of the cat’s visual cortex: A quantitative study. Journal of Comparative Neurology, 179(1), 223–244. https://doi.org/10.1002/cne.901790113 35 Li, S., Nie, E. H., Yin, Y., Benowitz, L. I., Tung, S., Vinters, H. V., … Carmichael, S. T. (2015). GDF10 is a signal for axonal sprouting and functional recovery after stroke. Nature Neuroscience, 18(12), 1737–1745. https://doi.org/10.1038/nn.4146 Li, S., Overman, J. J., Katsman, D., Kozlov, S. V., Donnelly, C. J., Twiss, J. L., … Carmichael, S. T. (2010). An age-related sprouting transcriptome provides molecular control of axonal sprouting after stroke. Nature Neuroscience, 13(12), 1496–1506. https://doi.org/10.1038/nn.2674 Li, Y., Fitzpatrick, D., & White, L. E. (2006). The development of direction selectivity in ferret visual cortex requires early visual experience. Nature Neuroscience, 9(5), 676–681. https://doi.org/10.1038/nn1684 Li, Y., Van Hooser, S. D., Mazurek, M., White, L. E., & Fitzpatrick, D. (2008). Experience with moving visual stimuli drives the early development of cortical direction selectivity. Nature, 456(7224), 952–956. https://doi.org/10.1038/nature07417 Link, W., Konietzko, U., Kauselmann, G., Krug, M., Schwanke, B., Frey, U., & Kuhl, D. (1995). Somatodendritic expression of an immediate early gene is regulated by synaptic activity. Proceedings of the National Academy of Sciences, 92(12), 5734– 5738. https://doi.org/10.1073/pnas.92.12.5734 Lund, J. S. (1987). Local circuit neurons of macaque monkey striate cortex: I. Neurons of laminae 4C and 5A. Journal of Comparative Neurology, 257(1), 60–92. https://doi.org/10.1002/cne.902570106 Lund, J. S., & Boothe, R. G. (1975). Interlaminar connections and pyramidal neuron organisation in the visual cortex, area 17, of the Macaque monkey. Journal of Comparative Neurology, 159(3), 305–334. https://doi.org/10.1002/cne.901590303 Lund, J. S., & Yoshioka, T. (1997). Local circuit neurons of macaque monkey striate cortex: IV. Neurons of laminae 1-3A. Journal of Comparative Neurology, 384(1), 109–126. Lyford, G. L., Yamagata, K., Kaufmann, W. E., Barnes, C. a, Sanders, L. K., Copeland, N. G., … Worley, P. F. (1995). Arc, a growth factor and activity-regulated gene, encodes a novel cytoskeleton-associated protein that is enriched in neuronal dendrites. Neuron, 14(2), 433–445. Ma, W., Li, Y., & Tao, H. W. (2013). Downregulation of cortical inhibition mediates ocular dominance plasticity during the critical period. The Journal of Neuroscience, 33(27), 11276–11280. https://doi.org/10.1523/JNEUROSCI.5598-12.2013 Mabb, A. M., Je, H. S., Wall, M. J., Robinson, C. G., Larsen, R. S., Qiang, Y., … Ehlers, M. D. (2014). Triad3A regulates synaptic strength by ubiquitination of arc. Neuron, 82(6), 1299–1316. https://doi.org/10.1016/j.neuron.2014.05.016 36 Maffei, L., Berardi, N., Domenici, L., Parisi, V., & Pizzorusso, T. (1992). Nerve growth factor (NGF) prevents the shift in ocular dominance distribution of visual cortical neurons in monocularly deprived rats. The Journal of Neuroscience, 12(12), 4651– 4662. Malpeli, J. G., & Baker, F. H. (1975). The representation of the visual field in the lateral geniculate nucleus of Macaca mulatta. Journal of Comparative Neurology, 161(4), 569–594. https://doi.org/10.1002/cne.901610407 Mank, M., Santos, A. F., Direnberger, S., Mrsic-Flogel, T. D., Hofer, S. B., Stein, V., … Griesbeck, O. (2008). A genetically encoded calcium indicator for chronic in vivo two-photon imaging. Nature Methods, 5(9), 805–811. https://doi.org/10.1038/nmeth.1243 Mataga, N., Mizuguchi, Y., & Hensch, T. K. (2004). Experience-dependent pruning of dendritic spines in visual cortex by tissue plasminogen activator. Neuron, 44(6), 1031–1041. https://doi.org/10.1016/j.neuron.2004.11.028 Matthews, R. G. (1963). Tautomeric forms of metarhodopsin. The Journal of General Physiology, 47, 215–240. https://doi.org/10.1085/jgp.47.2.215 McCurry, C. L., Shepherd, J. D., Tropea, D., Wang, K. H., Bear, M. F., & Sur, M. (2010). Loss of Arc renders the visual cortex impervious to the effects of sensory experience or deprivation. Nature Neuroscience, 13(4), 450–457. https://doi.org/10.1038/nn.2508 Messaoudi, E., Kanhema, T., Soulé, J., Tiron, A., Dagyte, G., da Silva, B., & Bramham, C. R. (2007). Sustained Arc/Arg3.1 synthesis controls long-term potentiation consolidation through regulation of local actin polymerization in the dentate gyrus in vivo. The Journal of Neuroscience, 27(39), 10445–10455. https://doi.org/10.1523/JNEUROSCI.2883-07.2007 Morales, B., Choi, S.-Y., & Kirkwood, A. (2002). Dark rearing alters the development of GABAergic transmission in visual cortex. The Journal of Neuroscience, 22(18), 8084–8090. Morishita, H., Miwa, J. M., Heintz, N., & Hensch, T. K. (2010). Lynx1, a cholinergic brake, limits plasticity in adult visual cortex. Science, 330(6008), 1238–1240. https://doi.org/10.1126/science.1195320 Mower, G. D. (1991). The effect of dark rearing on the time course of the critical period in cat visual cortex. Developmental Brain Research, 58(2), 151–158. https://doi.org/10.1016/0165-3806(91)90001-Y Myrum, C., Giddaluru, S., Jacobsen, K., Espeseth, T., Nyberg, L., Lundervold, A. J., … Bramham, C. R. (2015). Common variants in the ARC gene are not associated with cognitive abilities. Brain and Behavior, 5(10). https://doi.org/10.1002/brb3.376 37 Nair, R. R., Patil, S., Tiron, A., Kanhema, T., Panja, D., Schiro, L., … Bramham, C. R. (2017). Dynamic Arc SUMOylation and selective interaction with F-actin-binding protein drebrin a in LTP Consolidation in vivo. Frontiers in Synaptic Neuroscience, 9(8). https://doi.org/10.3389/fnsyn.2017.00008 Nakayama, D., Iwata, H., Teshirogi, C., Ikegaya, Y., Matsuki, N., & Nomura, H. (2015). Long-delayed expression of the immediate early gene Arc/Arg3.1 refines neuronal circuits to perpetuate fear memory. Journal of Neuroscience, 35(2), 819–830. https://doi.org/10.1523/JNEUROSCI.2525-14.2015 Nelson, R, Famiglietti, E. V, & Kolb, H. (2017). Intracellular staining reveals different levels of stratification for on- and off-center ganglion cells in cat retina. Journal of Neurophysiology, 41(2), 472–483. https://doi.org/10.1152/jn.1978.41.2.472 Nelson, Ralph, Kolb, H., & Freed, M. A. (1993). OFF‐alpha and OFF‐beta ganglion cells in cat retina. I: Intracellular electrophysiology and HRP stains. Journal of Comparative Neurology, 329(1), 68–84. https://doi.org/10.1002/cne.903290106 Niell, C. M., & Stryker, M. P. (2008). Highly selective receptive fields in mouse visual cortex. Journal of Neuroscience, 28(30), 7520–7536. https://doi.org/10.1523/JNEUROSCI.0623-08.2008 Okuno, H., Akashi, K., Ishii, Y., Yagishita-Kyo, N., Suzuki, K., Nonaka, M., … Bito, H. (2012). Inverse synaptic tagging of inactive synapses via dynamic interaction of Arc/Arg3.1 with CaMKIIβ. Cell, 149(4), 886–898. https://doi.org/10.1016/j.cell.2012.02.062 Palop, J. J. (2005). Vulnerability of dentate granule cells to disruption of Arc expression in human amyloid precursor protein transgenic mice. Journal of Neuroscience, 25(42), 9686–9693. https://doi.org/10.1523/jneurosci.2829-05.2005 Palop, Jorge J., Chin, J., Roberson, E. D., Wang, J., Thwin, M. T., Bien-Ly, N., … Mucke, L. (2007). Aberrant excitatory neuronal activity and compensatory remodeling of inhibitory hippocampal circuits in mouse models of Alzheimer’s disease. Neuron, 55(5), 697–711. https://doi.org/10.1016/j.neuron.2007.07.025 Panja, D., Dagyte, G., Bidinosti, M., Wibrand, K., Kristiansen, Å. M., Sonenberg, N., & Bramham, C. R. (2009). Novel translational control in arc-dependent long term potentiation consolidation in Vivo. Journal of Biological Chemistry, 284(46), 31498–31511. https://doi.org/10.1074/jbc.M109.056077 Park, S., Park, J. M., Kim, S., Kim, J.-A., Shepherd, J. D., Smith-Hicks, C. L., … Worley, P. F. (2008). Elongation factor 2 and fragile X mental retardation protein control the dynamic translation of Arc/Arg3.1 essential for mGluR-LTD. Neuron, 59(1), 70–83. https://doi.org/10.1016/j.neuron.2008.05.023 Peebles, C. L., Yoo, J., Thwin, M. T., Palop, J. J., Noebels, J. L., & Finkbeiner, S. (2010). Arc regulates spine morphology and maintains network stability in vivo. 38 Proceedings of the National Academy of Sciences, 107(42), 18173–18178. https://doi.org/10.1073/pnas.1006546107 Philpot, B. D., Espinosa, J. S., & Bear, M. F. (2003). Evidence for altered NMDA receptor function as a basis for metaplasticity in visual cortex. The Journal of Neuroscience, 23(13), 5583–5588. https://doi.org/10.1523/jneurosci.23-1305583.2003 Piscopo, D. M., El-Danaf R, Huberman, A. D., & Niell, C. M. (2013). Diverse visual features encoded in mouse LGN. The Journal of Neuroscience, 33(11), 4642–4656. https://doi.org/10.1523/JNEUROSCI.5187-12.2013.Diverse Pizzorusso, T., Medini, P., Nicoletta, B., Chierzi, S., Fawcett, J., & Maffei, L. (2002). Reactivation of ocular dominance plasticity in the adult visual cortex. Science, 298(5596), 1248–1251. https://doi.org/10.1126/science.1072699 Plath, N., Ohana, O., Dammermann, B., Errington, M. L., Schmitz, D., Gross, C., … Kuhl, D. (2006). Arc/Arg3.1 is essential for the consolidation of synaptic plasticity and memories. Neuron, 52(3), 437–444. https://doi.org/10.1016/j.neuron.2006.08.024 Ramirez-Amaya, V. (2005). Spatial exploration-induced Arc mRNA and protein expression: Evidence for selective, network-specific reactivation. Journal of Neuroscience, 25(7), 1761–1768. https://doi.org/10.1523/jneurosci.4342-04.2005 Ranson, A., Cheetham, C. E. J., Fox, K., & Sengpiel, F. (2012). Homeostatic plasticity mechanisms are required for juvenile, but not adult, ocular dominance plasticity. Proceedings of the National Academy of Sciences, 109(4), 1311–1316. https://doi.org/10.1073/pnas.1112204109 Rao, V. R., Pintchovski, S. A., Chin, J., Peebles, C. L., Mitra, S., & Finkbeiner, S. (2006). AMPA receptors regulate transcription of the plasticity-related immediateearly gene Arc. Nature Neuroscience, 9(7), 887–895. https://doi.org/10.1038/nn1708 Rial Verde, E. M., Lee-Osbourne, J., Worley, P. F., Malinow, R., & Cline, H. T. (2006). Increased expression of the immediate-early gene arc/arg3.1 reduces AMPA receptor-mediated synaptic transmission. Neuron, 52(3), 461–474. https://doi.org/10.1016/j.neuron.2006.09.031 Ringach, D. L. (2007). On the origin of the functional architecture of the cortex. PLoS ONE, 2(2). https://doi.org/10.1371/journal.pone.0000251 Ringach, D. L., Mineault, P. J., Tring, E., Olivas, N. D., Garcia-Junco-Clemente, P., & Trachtenberg, J. T. (2016). Spatial clustering of tuning in mouse primary visual cortex. Nature Communications, 7(12270). https://doi.org/10.1038/ncomms12270 Rochefort, N. L., Narushima, M., Grienberger, C., Marandi, N., Hill, D. N., & Konnerth, A. (2011). Development of direction selectivity in mouse cortical neurons. Neuron, 39 71(3), 425–432. https://doi.org/10.1016/j.neuron.2011.06.013 Rodriguez, J. J., Davies, H. A., Silva, A. T., De Souza, I. E. J., Peddie, C. J., Colyer, F. M., … Stewart, M. G. (2005). Long-term potentiation in the rat dentate gyrus is associated with enhanced Arc/Arg3.1 protein expression in spines, dendrites and glia. European Journal of Neuroscience, 21(9), 2384–2396. https://doi.org/10.1111/j.1460-9568.2005.04068.x Rompani, S. B., Müllner, F. E., Wanner, A., Zhang, C., Roth, C. N., Yonehara, K., & Roska, B. (2017). Different modes of visual integration in the lateral geniculate nucleus revealed by single-cell-initiated transsynaptic tracing. Neuron, 93(4), 767776.e6. https://doi.org/10.1016/j.neuron.2017.01.028 Sato, M., & Stryker, M. P. (2008). Distinctive features of adult ocular dominance plasticity. The Journal of Neuroscience, 28(41), 10278–10286. https://doi.org/10.1523/JNEUROSCI.2451-08.2008 Sawtell, N. B., Frenkel, M. Y., Philpot, B. D., Nakazawa, K., Tonegawa, S., & Bear, M. F. (2003). NMDA receptor-dependent ocular dominance plasticity in adult visual cortex. Neuron, 38(6), 977–985. Schiller, P. H., & Malpeli, J. G. (1978). Functional specificity of lateral geniculate nucleus laminae of the rhesus monkey. Journal of Neurophysiology, 41(3), 788–797. https://doi.org/10.1152/jn.1978.41.3.788 Sengpiel, F. (2014). Plasticity of the visual cortex and treatment of amblyopia. Current Biology, 24(18), R936–R940. https://doi.org/10.1016/j.cub.2014.05.063 Shepherd, J., Rumbaugh, G., Wu, J., Chowdhury, S., Plath, N., Kuhl, D., … Worley, P. (2006). Arc/Arg3. 1 mediates homeostatic synaptic scaling of AMPA receptors. Neuron, 52(3), 475–484. https://doi.org/10.1016/j.neuron.2006.08.034 Sherk, H., & Stryker, M. P. (1976). Quantitative study of cortical orientation selectivity in visually inexperienced kitten. Journal of Neurophysiology, 39(1), 63–70. https://doi.org/10.1152/jn.1976.39.1.63 Smith-Hicks, C., Xiao, B., Deng, R., Ji, Y., Zhao, X., Shepherd, J. D., … Linden, D. J. (2010). SRF binding to SRE 6.9 in the Arc promoter is essential for LTD in cultured Purkinje cells. Nature Neuroscience, 13(9), 1082–1089. https://doi.org/10.1038/nn.2611 Smith, S. L., & Trachtenberg, J. T. (2007). Experience-dependent binocular competition in the visual cortex begins at eye opening. Nature Neuroscience, 10(3), 370–375. https://doi.org/10.1038/nn1844 Smith, S. L., & Trachtenberg, J. T. (2010). The refinement of ipsilateral eye retinotopic maps is increased by removing the dominant contralateral eye in adult mice. PLoS ONE, 5(3). https://doi.org/10.1371/journal.pone.0009925 40 Stryker, M., & Harris, W. (1986). Binocular impulse blockade prevents the formation of ocular dominance columns in cat visual cortex. The Journal of Neuroscience, 6(8), 2117–2133. https://doi.org/10.1523/jneurosci.06-08-02117.1986 Stryker, M. P., Sherk, H., Leventhal, A. G., & Hirsch, H. V. (1978). Physiological consequences for the cat’s visual cortex of effectively restricting early visual experience with oriented contours. Journal of Neurophysiology, 41(4), 896–909. https://doi.org/10.1152/jn.1978.41.4.896 Su, D., Cha, Y. M., & West, A. E. (2012). Mutation of Mecp2 alters transcriptional regulation of select immediate-early genes. Epigenetics, 7(2), 146–154. https://doi.org/10.4161/epi.7.2.18907 Sugiyama, S., Di Nardo, A. A., Aizawa, S., Matsuo, I., Volovitch, M., Prochiantz, A., & Hensch, T. K. (2008). Experience-dependent transfer of Otx2 homeoprotein into the visual cortex activates postnatal plasticity. Cell, 134(3), 508–520. https://doi.org/10.1016/j.cell.2008.05.054 Sun, W., Tan, Z., Mensh, B. D., & Ji, N. (2016). Thalamus provides layer 4 of primary visual cortex with orientation- and direction-tuned inputs. Nature Neuroscience, 19(2), 308–315. https://doi.org/10.1038/nn.4196 Taha, S., & Stryker, M. P. (2002). Rapid ocular dominance plasticity requires cortical but not geniculate protein synthesis. Neuron, 34(3), 425–436. https://doi.org/10.1016/S0896-6273(02)00673-6 Tigges, J., Tigges, M., & Perachio, A. A. (1977). Complementary laminar terminations of afferents to area 17 originating in area 18 and in the lateral geniculate nucleus in squirrel monkey. Journal of Comparative Neurology, 176(1), 87–100. https://doi.org/10.1002/cne.901760106 Turrigiano, G. G., Leslie, K. R., Desai, N. S., Rutherford, L. C., & Nelson, S. B. (1998). Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature, 391(6670), 892–896. https://doi.org/10.1038/36103 Tusa, R. J., Palmer, L. A., & Rosenquist, A. C. (1978). The retinotopic organization of area 17 (striate cortex) in the cat. Journal of Comparative Neurology, 177(2), 213– 235. https://doi.org/10.1002/cne.901770204 Vlaming, P., Jonkman, L., Daalen, E., Gaag, R., & Kemner, C. (2015). Basic abnormalities in visual processing affect face processing at an early age in autism spectrum disorder. Discrete Mathematics and Theoretical Computer Science, 17(1), 161–168. https://doi.org/10.1016/j.biopsych.2010.06.024 Wagor, E., Mangini, N. J., & Pearlman, A. L. (1980). Retinotopic organization of striate and extrastriate visual cortex in the mouse. Journal of Comparative Neurology, 193(1), 187–202. https://doi.org/10.1002/cne.901930113 41 Wald, G. (2004). Carotenoids and the visual cycle. The Journal of General Physiology, 19(2), 351–371. https://doi.org/10.1085/jgp.19.2.351 Waltereit, R., Dammermann, B., Wulff, P., Scafidi, J., Staubli, U., Kauselmann, G., … Kuhl, D. (2018). Arg3.1/Arc mRNA induction by Ca 2+ and cAMP requires protein kinase A and mitogen-activated protein kinase/extracellular regulated kinase activation. The Journal of Neuroscience, 21(15), 5484–5493. https://doi.org/10.1523/jneurosci.21-15-05484.2001 Wang, B. S., Sarnaik, R., & Cang, J. (2010). Critical period plasticity matches binocular orientation preference in the visual cortex. Neuron, 65(2), 246–256. https://doi.org/10.1016/j.neuron.2010.01.002 Wang, K. H., Majewska, A., Schummers, J., Farley, B., Hu, C., Sur, M., & Tonegawa, S. (2006). In vivo two-photon imaging reveals a role of arc in enhancing orientation specificity in visual cortex. Cell, 126(2), 389–402. https://doi.org/10.1016/j.cell.2006.06.038 Waung, M. W., Pfeiffer, B. E., Nosyreva, E. D., Ronesi, J. a, & Huber, K. M. (2008). Rapid translation of Arc/Arg3.1 selectively mediates mGluR-dependent LTD through persistent increases in AMPAR endocytosis rate. Neuron, 59(1), 84–97. https://doi.org/10.1016/j.neuron.2008.05.014 Werblin, F. S., & Dowling, J. E. (1969). Organization of the retina of the mudpuppy, Necturus maculosus. II. Intracellular recording. Journal of Neurophysiology, 121, 400–415. https://doi.org/10.1152/jn.1969.32.3.339 White, L. E., Coppola, D. M., & Fitzpatrick, D. (2001). The contribution of sensory experience to the maturation of orientation selectivity in ferret visual cortex. Nature, 411(6841), 1049–1052. https://doi.org/10.1038/35082568 Wiesel, T. N., & Hubel, D. H. (1963). Single-cell responses in striate cortex of kittens deprived of vision in one eye. Journal of Neurophysiology, 26, 1003–1017. https://doi.org/citeulike-article-id:7746240 Wiesel, T. N., & Hubel, D. H. (1966). Spatial and chromatic interactions in the lateral geniculate body of the rhesus monkey. Journal of Neurophysiology, 29(6), 1115– 1156. https://doi.org/10.1152/jn.1966.29.6.1115 Wiesel, T. N., Hubel, D. H., & Lam, D. M. K. (1974). Autoradiographic demonstration of ocular-dominance columns in the monkey striate cortex by means of transneuronal transport. Brain Research, 79(2), 273–279. https://doi.org/10.1016/00068993(74)90416-8 Wong, R. O. L., Meister, M., & Shatz, C. J. (1993). Transient period of correlated bursting activity during development of the mammalian retina. Neuron, 11(5), 923– 938. https://doi.org/10.1016/0896-6273(93)90122-8 42 Wu, J., Petralia, R. S., Kurushima, H., Patel, H., Jung, M., Volk, L., … Worley, P. F. (2011). Arc/Arg3.1 regulates an endosomal pathway essential for activity-dependent β-amyloid generation. Cell, 147(3), 615–628. https://doi.org/10.1016/j.cell.2011.09.036 Yaeger, C. E., Ringach, D. L., & Trachtenberg, J. T. (2019). Neuromodulatory control of localized dendritic spiking in critical period cortex. Nature, 567(7746), 1001–1004. https://doi.org/10.1038/s41586-019-0963-3 Ye, Q., & Miao, Q.-L. (2013). Experience-dependent development of perineuronal nets and chondroitin sulfate proteoglycan receptors in mouse visual cortex. Matrix Biology, 32(6), 352–363. https://doi.org/10.1016/j.matbio.2013.04.001 Yee, R., & Liebman, P. A. (1978). Light-activated phosphodiesterase of the rod outer segment. Kinetics and parameters of activation and deactivation. Journal of Biological Chemistry, 253(24), 8902–8909. Ying, S.-W., Futter, M., Rosenblum, K., Webber, M. J., Hunt, S. P., Bliss, T. V. P., & Bramham, C. R. (2002). Brain-derived neurotrophic factor induces long-term potentiation in intact adult hippocampus: Requirement for ERK activation coupled to CREB and upregulation of Arc synthesis. The Journal of Neuroscience, 22(5), 1532–1540. Yoon, B.-J., Smith, G. B., Heynen, A. J., Neve, R. L., & Bear, M. F. (2009). Essential role for a long-term depression mechanism in ocular dominance plasticity. Proceedings of the National Academy of Sciences, 106(24), 9860–9865. https://doi.org/10.1073/pnas.0901305106 Zalfa, F., Giorgi, M., Primerano, B., Moro, A., Di Penta, A., Reis, S., … Bagni, C. (2003). The fragile X syndrome protein FMRP associates with BC1 RNA and regulates the translation of specific mRNAs at synapses. Cell, 112(3), 317–327. https://doi.org/10.1016/s0092-8674(03)00079-5 Zhang, W., Wu, J., Ward, M. D., Yang, S., Chuang, Y. A., Xiao, M., … Worley, P. F. (2015). Structural basis of arc binding to synaptic proteins: Implications for cognitive disease. Neuron, 86(2), 490–500. https://doi.org/10.1016/j.neuron.2015.03.030 Zhao, X., Liu, M., & Cang, J. (2013). Sublinear binocular integration preserves orientation selectivity in mouse visual cortex. Nature Communications, 4(2088). https://doi.org/10.1038/ncomms3088 43 Figure 1.1 Anatomy and selectivity of the mouse binocular visual cortex. A. Diagram of the visual pathway to the mouse binocular visual cortex. Each hemisphere has a visual cortex that receives input from the eye contralateral (blue) and ipsilateral (yellow) to it. This input from the eye first arrives in the lateral geniculate nucleus of the thalamus. The thalamus then projects to the ipsilateral visual cortex. The majority of the visual cortex (blue) is monocular, encoding the visual field contralateral to it (blue arc). The remainder of the visual cortex is binocular (green), responsive to input from both eyes encoding the central 30° of the visual field (green arc). B. Wiring diagram of the cortical layers in binocular visual cortex. The majority of thalamic input arrives in layer 4 (L4). L4 sends input to L2/3, which projects to other cortical areas, and also to L5. L5 predominantly projects to subcortical structures. L6 has recurrent connections with the thalamus. C. neurons in the visual cortex can be orientation-selective. In simple cells, an arrangement of OFF (black) and ON (red) receptive field inputs from thalamus form a Gabor filter of visual space in the neuron’s receptive field. In this example (left), only a horizontal orientation will optimally stimulate the ON segment of the receptive field while avoiding the OFF. A vertical orientation would activate ON and OFF inputs, leading to no net response. An example neuron selective for horizontal orientations (right) has its peak response (measured as the change from baseline relative to the standard deviation during baseline) to a horizontally oriented bar passing dorsally or ventrally through its receptive field. The X-axis represents the orientation of each stimulus (bar) and the direction it was traveling (arrow) while the gray boxes represent the duration of the stimulus (5 seconds). D. The population of orientation-selective neurons does not have a uniform distribution. Distribution of neurons responsive to drifting grating in the binocular visual cortex of postnatal day 30 mice shows a bias for horizontal orientations moving dorsally or ventrally. 44 Figure 1.2 Ocular dominance plasticity. A. An electrode placed into layer 4 of the binocular visual cortex records population responses whose magnitude reflects the strength of visual input. By stimulating one eye at a time, the relative strength of input from each eye can be determined. Ocular dominance plasticity (ODP) occurs when vision from one eye is occluded (blue input, red arrow). B. Amplitudes are plotted relative to the baseline contralateral (contra) input. At baseline, there is twice as much input from the contra eye (blue) as the ipsilateral (ipsi, yellow) eye. During the critical period, 3 days of monocular deprivation (MD) of the contra eye is sufficient to depress contra response. Prolonged MD for 7 days can also strengthen the ipsi response. MD in adult does not affect the contra response, but the ipsi response will strengthen after prolonged MD. C. Graph of the relative ease of inducing ODP over development in the mouse. The eyes open at 12 to 13 days old, but ODP cannot be induced for several days. The binocular visual cortex becomes increasingly sensitive to the effects of MD, with the largest magnitude of ODP in response to MD occurring roughly at 30 days old, the peak of the critical period. Plasticity gradually declines as the animal matures. Increasing inhibition during development can shift the critical period earlier in development while delaying the appearance of inhibition can delay the critical period. CHAPTER 2 ARC RESTORES JUVENILE PLASTICITY IN ADULT MOUSE VISUAL CORTEX Reprinted with permission from Taekeun Kim, Elissa Pastuzyn, Hiroyuki Okuno, Andrew Taibi, Haruhiko Bito, Mark Bear, Jason Shepherd, 2017. Arc restores juvenile plasticity in adult mouse visual cortex. Proceedings of the National Academy of Sciences, 114(34), 9182-9187.   46   Arc restores juvenile plasticity in adult mouse visual cortex Kyle R. Jenksa,1, Taekeun Kimb,1, Elissa D. Pastuzyna,1, Hiroyuki Okunoc,d, Andrew V. Taibia, Haruhiko Bitod, Mark F. Bearb,2, and Jason D. Shepherda,2 a Department of Neurobiology and Anatomy, University of Utah, Salt Lake City, UT 84112; bThe Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139; cMedical Innovation Center, Kyoto University Graduate School of Medicine, Sakyo-ku, Kyoto 606-8507, Japan; and dDepartment of Neurochemistry, Graduate School of Medicine, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan Edited by Carla J. Shatz, Stanford University, Stanford, CA, and approved July 17, 2017 (received for review January 17, 2017) Arc | synaptic plasticity | visual cortex | ocular dominance | critical period A defining feature of early postnatal brain development is the activity-dependent winnowing of synaptic connections. This process is readily demonstrated by the response of visual cortical circuits to temporary monocular deprivation (MD) during early life. When MD is initiated during an early critical period, the synapses serving the deprived eye in visual cortex lose strength and are eliminated. Deprived-eye depression diminishes with age such that by the onset of adolescence, circuits are less vulnerable to the effects of deprivation. Understanding the molecular mechanisms that underlie the effect of age on this type of ocular dominance (OD) plasticity is considered one of the great challenges in neuroscience (1). It is now well established that OD plasticity after MD occurs through synaptic plasticity of excitatory transmission, using mechanisms that include homosynaptic long-term depression (LTD), metaplasticity, and homeostatic scaling of AMPA-type glutamate receptors (2, 3). Clues to the molecular basis for the decline in juvenile plasticity have come from several diverse experimental treatments that can restore or prolong sensitivity to MD in adult animals. These include genetic manipulations that slow the maturation of cortical inhibition (4, 5), enrichment of animal housing conditions (6), increased exposure to visual stimulation (7), and enhanced modulatory neurotransmission (8). It has been suggested that a common thread connecting these varied treatments might be an increase in the ratio of excitation to inhibition (9, 10). However, it is completely unknown how, at the molecular level, general increases in cortical activity can facilitate deprivation-induced synaptic plasticity in adult visual cortex. Since the immediate early gene Arc is exquisitely sensitive to changes in cortical activity, and is essential for both OD plasticity and modification of excitatory www.pnas.org/cgi/doi/10.1073/pnas.1700866114             synaptic transmission (11–13), we set out to determine whether availability of Arc limits or changes the qualities of plasticity in adults and whether up-regulating Arc levels in adult animals can restore juvenile synaptic plasticity. Results Augmentation of Arc Expression in Adult Mouse Visual Cortex Extends the Critical Period of Juvenile OD Plasticity. In young mice [≤ postnatal day (P) 40], the main consequence of short (3–4 d) MD is the robust loss of cortical responsiveness to stimulation of the deprived eye. A compensatory potentiation of responses to the nondeprived eye may also occur, and is typically observed with longer periods of MD (5–7 d) (14). Importantly, although open-eye potentiation after long-duration MD is common in adult rodents, deprived-eye depression typically is only observed during the juvenile critical period in animals housed under standard laboratory conditions (15, 16). We predicted that augmenting Arc levels would prolong juvenile plasticity, as defined by closed-eye depression, past the conventional critical period in mouse visual cortex. To test this hypothesis, we used a transgenic (Tg) mouse line that expresses an additional allele of Arc tagged with mCherry that is driven by the activity-dependent Arc promoter in a similar manner to the previously characterized ArcGFP Tg mouse line (17, 18) (Fig. S1). We compared the qualities of OD plasticity after short (3–4 d) MD in Arc-Tg mice and wild-type (WT) littermate controls at P30 (juvenile) and P180 (adult) using chronic recordings of NEUROSCIENCE The molecular basis for the decline in experience-dependent neural plasticity over age remains poorly understood. In visual cortex, the robust plasticity induced in juvenile mice by brief monocular deprivation during the critical period is abrogated by genetic deletion of Arc, an activity-dependent regulator of excitatory synaptic modification. Here, we report that augmenting Arc expression in adult mice prolongs juvenile-like plasticity in visual cortex, as assessed by recordings of ocular dominance (OD) plasticity in vivo. A distinguishing characteristic of juvenile OD plasticity is the weakening of deprivedeye responses, believed to be accounted for by the mechanisms of homosynaptic long-term depression (LTD). Accordingly, we also found increased LTD in visual cortex of adult mice with augmented Arc expression and impaired LTD in visual cortex of juvenile mice that lack Arc or have been treated in vivo with a protein synthesis inhibitor. Further, we found that although activity-dependent expression of Arc mRNA does not change with age, expression of Arc protein is maximal during the critical period and declines in adulthood. Finally, we show that acute augmentation of Arc expression in wild-type adult mouse visual cortex is sufficient to restore juvenile-like plasticity. Together, our findings suggest a unifying molecular explanation for the age- and activity-dependent modulation of synaptic sensitivity to deprivation. Significance Neuronal plasticity peaks early in life during critical periods and normally declines with age, but the molecular changes that underlie this decline are not fully understood. Using the mouse visual cortex as a model, we found that activity-dependent expression of the neuronal protein Arc peaks early in life, and that loss of activity-dependent Arc expression parallels loss of synaptic plasticity in the visual cortex. Genetic overexpression of Arc prolongs the critical period of visual cortex plasticity, and acute viral expression of Arc in adult mice can restore juvenile-like plasticity. These findings provide a mechanism for the loss of excitatory plasticity with age, and suggest that Arc may be an exciting therapeutic target for modulation of the malleability of neuronal circuits. Author contributions: M.F.B. and J.D.S. designed research; K.R.J., T.K., E.D.P., H.O., A.V.T., and J.D.S. performed research; H.O. and H.B. contributed new reagents/analytic tools; K.R.J., T.K., E.D.P., H.O., A.V.T., and J.D.S. analyzed data; and K.R.J., M.F.B., and J.D.S. wrote the paper. The authors declare no conflict of interest. This article is a PNAS Direct Submission. 1 K.R.J., T.K., and E.D.P. contributed equally to this work. 2 To whom correspondence may be addressed. Email: [e-mail] or jason.shepherd@ neuro.utah.edu. This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. 1073/pnas.1700866114/-/DCSupplemental. PNAS Early Edition | 1 of 6     47   visually evoked potentials (VEPs) from binocular visual cortex contralateral (contra) to the deprived eye (Fig. 1A) as previously described (11). There was no significant difference between P30 WT and Arc-Tg VEPs before MD, and following MD, both WT and Arc-Tg P30 mice exhibited a significant decrease in contra (closed-eye) VEP amplitudes (WT: n = 7, baseline = 251 ± 28 μV, post-MD = 166 ± 12 μV, P = 0.03; Arc-Tg: n = 10, baseline = 227 ± 21 μV, post-MD = 159 ± 22 μV, P = 0.01 by paired t test; Fig. 1B). As expected, adult P180 WT mice did not exhibit Binocular Zone A Contra eye P30 WT Arc-Tg Contra Ipsi 300 VEP Amplitude (µV) B Ipsi eye 300 200 * 200 * 100 100 0 Baseline P180 * Baseline Post MD 150 100 100 50 50 Post MD 0 * Baseline Post MD Potential Baseline Activity-Dependent Arc Protein Expression Is High During the Critical 200 150 0 Fractional change in contra. eye VEP 0 250 200 D Post MD 250 VE P Amplitude (µV) C * 1.2 1.0 P30 WT 0.8 P30 Arc-Tg 0.6 P180 WT P180 Arc-Tg 0.4 0.2 0.0 0.0 0.5 1.0 1.5 Fractional change in ipsi. eye VEP 2.0 Fig. 1. Arc-Tg mice exhibit juvenile-like OD plasticity well past the conventional critical period. (A) Schematic of recording site for VEPs in layer IV of binocular visual cortex. (B) At P30, both WT and Arc-Tg mice show a significant decrease in contra (closed-eye) VEP amplitude following MD (WT: n = 7, *P = 0.03; Arc-Tg: n = 10, *P = 0.01). Additionally, Arc-Tg mice exhibited a small but significant increase in ipsi (open-eye) VEPs (Arc-Tg: *P = 0.008). There is no significant difference between WT and Arc-Tg animals before or after MD. (C) At P180, only Arc-Tg mice exhibit a significant decrease in contra VEPs (Arc-Tg: n = 6, *P = 0.02). (D) Plot of the fractional change in ipsi (x axis) and contra (y axis) eye VEPs following MD (same data as in B and C). At P30, there is no significant difference between WT and Arc-Tg mice. However, at P180, there is a significant difference between the fractional change of WT and Arc-Tg mice following MD (P = 0.03). Data are represented as mean ± SEM. 2 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1700866114             depression of contra VEP amplitude after MD, reflecting the loss of juvenile plasticity. In sharp contrast, P180 Arc-Tg mice still exhibited a significant decrease in contra VEPs (WT: n = 7, baseline = 184 ± 19 μV, post-MD = 183 ± 20 μV, P = 0.9; Arc-Tg: n = 6, baseline = 208 ± 26 μV, post-MD = 136 ± 20 μV, P = 0.02 by paired t test; Fig. 1C), comparable to the decrease observed in WT juveniles. There was a significant treatment by genotype interaction, indicating that OD plasticity differs in Arc-Tg mice compared with WT mice (P = 0.0092, repeated measures ANOVA). Because the chronic VEP method enables measurements of response strength in the same mouse before and after MD, we can also analyze the qualities of the OD shift by plotting the fractional changes in response magnitude to stimulation of the deprived contra eye and the nondeprived ipsilateral (ipsi) eye (19, 20). This analysis confirms that at P30, both WT and Arc-Tg mice exhibit robust and comparable levels of contra eye depression and a variable potentiation of the nondeprived ipsi eye [Fig. 1D, squares; WT: contra depression = 0.7 ± 0.1, ipsi potentiation = 1.4 ± 0.2; Arc-Tg: contra depression = 0.7 ± 0.1, ipsi potentiation = 1.3 ± 0.1; P = 0.9, multivariate ANOVA (MANOVA)]. There was, however, a significant difference in the qualities of OD plasticity in WT and Arc-Tg adult mice (Fig. 1D, circles). In WT mice, the OD shift was accounted for entirely by ipsi eye potentiation (Fig. 1D, open circles), whereas the shift in Arc-Tg mice (Fig. 1D, filled circles) was solely due to contra eye depression (WT: contra depression = 1.0 ± 0.01, ipsi potentiation = 1.3 ± 0.1; Arc-Tg: contra depression = 0.7 ± 0.1, ipsi potentiation = 0.9 ± 0.2; P = 0.03, MANOVA; Fig. 1D). These data show that augmenting Arc levels in adult mice prolongs juvenile-like OD plasticity, as evidenced by deprivationinduced synaptic depression well past the conventional critical period in mice. Period and Low in Adulthood. We reasoned that if availability of Arc influences the qualities of OD plasticity, Arc expression might decline as the animal ages. In mouse visual cortex, Arc is first detected after eye-opening (∼P14) and expression steadily increases until ∼P30, corresponding to the age of peak sensitivity to MD (21). To determine whether Arc levels decline with age, WT or Arc-Tg mice were killed at P30 or P180. Basal Arc expression in visual cortex is highly variable under standard housing conditions (21); therefore, we housed mice in the dark for 24 h and then either killed them immediately (“dark” condition) or exposed them to light for 2 h (“light” condition) before euthanasia (n = 6 per group) (22). The brain was fixed and sectioned at 30 μm on a cryostat, and immunohistochemistry (IHC) was performed for Arc protein using a custom-made Arc antibody (Fig. S2) on sections of brain containing primary visual cortex. The integrated density of Arc-expressing cells in layer IV of visual cortex, where VEPs and LTD were recorded, was measured with the experimenter blinded to genotype and age (Fig. 2A). A three-way ANOVA comparing genotype (WT or Arc-Tg), age (P30 or P180), and condition (dark or light) revealed a main effect of genotype (P < 0.0001), age (P = 0.02), and condition (P < 0.0001), as well as a genotype × condition interaction (P = 0.02). Post hoc Student’s t tests showed that in P30 mice, light significantly induced Arc expression in both WT and Arc-Tg mice (WT: light > dark; light: 4.5 ± 1.3, dark: 1 ± 0.6, P = 0.02; Arc-Tg: light > dark; light: 8.2 ± 1, dark: 2.7 ± 2.7, P = 0.002). However, Arc-Tg mice expressed significantly more Arc after light exposure than WT mice (P = 0.008). At P180, WT mice no longer exhibited detectable Arc expression, even after light exposure. Arc-Tg mice, on the other hand, exhibited significant Arc expression after light exposure (light: 7.1 ± 0.8, dark: 1.8 ± 1.2; P = 0.001). Furthermore, levels of light-induced Arc in P180 Arc-Tg mice were not significantly different from P30 Arc-Tg mice (P > 0.05), suggesting that activitydependent expression of Arc in Arc-Tg mice does not decline with Jenks et al.   48   4 determine whether endogenous activity-dependent Arc mRNA expression also declines with age. Mice underwent dark and light exposure as described above (n = 3–5 per group). The visual cortex was dissected and qRT-PCR, the most sensitive and quantitative method of RNA detection, was performed on lysates (Fig. 2B). A three-way ANOVA revealed a main effect of genotype (P = 0.002) and condition (P = 0.0002), but not age. Post hoc t tests showed that light-induced Arc mRNA expression was higher in Arc-Tg than WT mice (P30 WT: 2.9 ± 0.9, P30 Arc-Tg: 9.8 ± 1.6, P < 0.0001; P180 WT: 3.3 ± 0.7, P180 Arc-Tg: 16.7 ± 1.1, P < 0.0001). Interestingly, however, levels of activity-induced Arc mRNA expression did not differ with age in either genotype (P > 0.05). Although we cannot rule out the possibility that analysis of layer IV alone would reveal an age effect, we note that Arc protein cannot be detected in any layer of V1 in adult WT mice. These data suggest that availability of endogenous Arc mRNA alone cannot fully explain the differences in Arc protein expression across the lifespan of WT mice and point to the possibility of a decrease in either activity-dependent translation or stability of endogenous Arc protein in adult visual cortex. Nevertheless, the increased expression of Arc mRNA in the active visual cortex of Arc-Tg mice is paralleled by a proportional increase in protein. 2 Augmenting Arc Expression Restores LTD in Adult Visual Cortex. P30 A Dark P180 Light Dark Light WT II/III IV Arc-Tg II/III IV 100 µm * # Integrated Density (norm.) 10 8 6 0 Arc mRNA fold change (norm.) B * WT Arc-Tg Dark P30 Light Dark P180 Light * 20 15 * WT Arc-Tg 10 5 0 Dark Light P30 Dark Light P180 Fig. 2. Activity-dependent Arc protein, but not mRNA expression, declines with age in WT mouse visual cortex, but not in Arc-Tg mice. (A) IHC for Arc expression in layers I–IV of visual cortex after 24 h of being housed in the dark or 24 h of dark housing followed by 2 h of light exposure. Layer IV Arc expression is quantified in the graphs (n = 6 per group). Light increased Arc expression in both WT and Arc-Tg mice at P30 (WT: *P = 0.02, Arc-Tg: *P = 0.002), but Arc levels were higher in Arc-Tg mice (#P = 0.008). At P180, WT mice did not express Arc after light exposure, while Arc-Tg mice exhibited the same light-induced increase in Arc observed at P30 (*P = 0.001). (Scale bar: 100 μm.) (B) WT and Arc-Tg mice were dark-housed for 24 h and then either killed in the dark (dark condition) or exposed to light for 2 h before euthanasia (light condition). qRT-PCR was run on dissected visual cortex to quantify Arc mRNA expression. All values were first normalized to GAPDH to control for total RNA levels. Light-induced Arc mRNA expression was higher in Arc-Tg mice than WT mice at both P30 and P180 (P30: *P < 0.0001, P180: *P < 0.0001). However, light-induced mRNA expression did not decrease with age in WT mice. Plotted data are normalized to P30 WT dark (n = 5 for WT light, n = 4 for Arc-Tg light, and n = 3 for all dark groups). Data are represented as mean ± SEM. age. These data show that activity-dependent Arc protein expression significantly declines with age in WT but not Arc-Tg mice. This loss of endogenous Arc protein over age correlates with the decline of deprived-eye depression following MD. Arc transcription and translation are exquisitely regulated in the brain and are finely tuned to experience and neuronal activity (12). Of particular interest, transcription and translation of Arc can be independently regulated by activity (23). We therefore sought to Jenks et al.             Deprived-eye depression occurs via mechanisms shared with LTD (3), which also diminishes with age (24). In addition to the profound deficit in OD plasticity (11), juvenile (P20–25) Arc knockout (KO) mice exhibit impaired layer IV LTD in visual cortex, induced in slices with low-frequency stimulation (LFS) of the white matter, compared with WT mice, which showed robust LFS LTD (WT: n = 7 slices from four mice, 67.5 ± 5.7%; Arc KO: n = 7 slices from five mice, 90.6 ± 4.6%; P < 0.001, t test; Fig. 3A). We therefore hypothesized that the persistence of juvenile OD plasticity in adult Arc-Tg mice was accompanied (and perhaps accounted for) by continued expression of juvenilelike LTD. To ensure expression of Arc protein in the slices, mice were exposed briefly (30 min) to an enriched environment before euthanasia as described previously (23). We first measured LTD in juvenile mice when both WT and Arc-Tg animals show comparable OD plasticity, characterized by robust deprived-eye depression after MD. Over the age range examined, between P26 and P41, LTD in WT and Arc-Tg mice was also comparable (WT: n = 9 slices from seven mice, 75.4 ± 11.6%; Arc-Tg: n = 7 slices from six mice, 81.3 ± 7.1%; P > 0.5, t test; Fig. 3B). Subgroup analysis of this juvenile cohort revealed no difference in LTD in animals of either genotype at P26–33 or P34–41 (Fig. 3B, circles and inverted triangles, respectively). However, in adult mice (P180–200), we observed significant LTD in Arc-Tg slices but not in WT littermate slices (WT: n = 11 slices from six mice, 102.8% ± 8.7; Arc-Tg: n = 12 slices from six mice, 74.5 ± 7.9%; Fig. 3C). The difference between genotypes was significant (P = 0.04, t test). Thus, augmented expression of Arc in adult visual cortex restores or maintains two features of juvenile plasticity: LTD in vitro (Fig. 3C) and deprivedeye depression following MD in vivo (Fig. 1C). NEUROSCIENCE   Inhibition of Protein Synthesis in Vivo Impairs LTD in Juvenile Visual Cortex. The apparent requirement of Arc translation for deprivedeye depression may offer a partial explanation for why juvenile OD plasticity following brief MD is impaired when the visual cortex is infused locally with the protein synthesis inhibitor cycloheximide (CHX) (25). If this explanation is correct, and the mechanisms of LTD are used for deprived-eye depression following MD, we would also expect to observe reduced LTD ex vivo following microinfusion of CHX into visual cortex. To test this prediction, WT visual cortex was infused in vivo via an osmotic minipump with CHX for 4 d as described (25), and slices were then prepared to conduct LTD experiments. Similar to our observations in the Arc KO visual cortex, there was no LTD in juvenile visual cortex after chronic inhibition PNAS Early Edition | 3 of 6     49   40 20 0 -20 2 LFS 0 20 60 Time (min) WT B 40 1 2 0 -20 20 40 Time (min) 60 WT Arc KO n.s. 100 80 60 40 20 80 2 0 5ms 1 100 80 60 40 20 P26-33 P34-41 140 120 2 LFS 0 fEPSP (% baseline) fEPSP (% baseline) 100 80 60 40 20 1 1 WT Arc-Tg P180-200 100µV 140 120 0 -20 D 50µV 140 120 * 2 5ms Arc-Tg 2 P20-25 140 120 100 80 60 40 20 0 Arc-Tg 1 1 140 120 100 80 60 40 20 0 -20 100 80 60 40 20 LFS 20 40 60 Time (min) Saline 1 2 80 0 Arc-Tg 2 1 2 LFS 20 WT CHX (in vivo) 1 P26-29 100µV 5ms 0 * 140 120 2 0 fEPSP (% baseline) 400µV 5ms 1 WT C 2 40 Time (min) 60 80 fEPSP (% baseline) 1 fEPSP (% baseline) 140 120 100 80 60 Arc KO 2 fEPSP (% baseline) fEPSP (% baseline) 1 fEPSP (% baseline) WT A 140 120 100 80 60 40 20 0 * Saline CHX Fig. 3. Arc and protein translation are required for LTD in layer IV of visual cortex. (A) LFS (900 stimuli at 1 Hz) induces robust LTD in juvenile (P20–25) WT, but not Arc-KO, slices (average of last 5 min of recordings normalized to the baseline; WT: n = 4 mice, KO: n = 5; *P < 0.001). (B) LFS induces LTD to the same degree in young (P26–41) WT and Arc-Tg slices (WT: n = 7, Arc-Tg: n = 6; P > 0.5). The LTD amplitudes of field excitatory postsynaptic potential (fEPSP) of the youngest animals in this group (P26–33, circles) and the older animals (P34–41, inverted triangles) do not show any significant difference (P > 0.5, t test), and were therefore combined. (C) LFS induces robust LTD in adult (P180–200) Arc-Tg slices, but not WT slices (WT: n = 11, Arc-Tg: n = 6; *P = 0.04). (D) LFS induced LTD in juvenile (P25–30) visual cortex previously infused with saline, but not in visual cortex infused with CHX (saline: n = 5, CHX: n = 5; *P = 0.02). Data are represented as mean ± SEM. of protein synthesis (saline: n = 5 slices from five mice, 72.4 ± 8.6%; CHX: n = 7 slices from five mice, 96.2 ± 5.9%; P = 0.02, t test; Fig. 3D). Together, these findings are consistent with the hypothesis that translation of Arc gates the mechanism of deprivation-induced synaptic depression in visual cortex. Acute Expression of Arc in Adult Mouse Visual Cortex Is Sufficient to Reopen the Critical Period of Juvenile OD Plasticity. Augmenting the availability of Arc protein throughout development and into adulthood prolongs the critical period for juvenile OD plasticity (Fig. 1). However, this does not address whether restoring Arc protein expression is sufficient to reopen the critical period of OD plasticity once it has closed. To determine whether acutely increasing Arc protein in adult visual cortex is sufficient to restore juvenile-like plasticity, we expressed Arc using a lentivirus injected into visual cortex of P180 WT mice, which robustly increased Arc levels (Fig. 4A and Fig. S3). Lentivirus containing GFP-Arc or GFP was injected into layer IV of visual cortex, and baseline VEP recordings were conducted 1 wk after virus injection. Unlike the case in Arc-Tg mice, viral Arc overexpression is constitutively driven and not activity-dependent. Based on previous studies (26, 27), we predicted that VEP amplitude might be depressed by constitutive Arc expression since the VEP is mainly a synaptic population response that correlates with surface AMPA receptor expression (11). Indeed, a significant decrease in overall binocular VEP amplitude was observed compared with GFP-injected mice (GFPinjected mice: 197 ± 30 μV, GFP-Arc–injected mice: 75 ± 21 μV; 4 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1700866114             P = 0.005; Fig. 4B). No deprived-eye depression was observed in GFP-injected mice following short (3–4 d) MD (GFP: normalized to baseline contra values: n = 11; contra baseline = 1 ± 0.2, post-MD = 0.9 ± 0.2; P = 0.4, paired t test; Fig. 4C). However, despite a reduction in baseline VEP magnitude, contra VEP responses were further reduced after MD in GFP-Arc–injected mice (GFP-Arc: normalized to baseline contra values: n = 5, contra baseline = 1 ± 0.2, post-MD = 0.6 ± 0.2; P = 0.02; Fig. 4D). Further, when comparing the fractional change in contra eye and ipsi eye visual responses following MD, there was a significant difference between GFP- vs. GFP-Arc–injected mice (GFP: contra depression = 0.9 ± 0.1, ipsi potentiation = 1.4 ± 0.1; GFP-Arc: contra depression = 0.5 ± 0.1, ipsi potentiation = 1.0 ± 0.1; P = 0.01, MANOVA; Fig. 4E). Critically, the fractional OD shift in the P180 GFP-injected mice was the same as in noninjected WT P180 mice (P = 0.3, MANOVA), indicating virus injection had no effect on cortical responses or OD plasticity. Additionally, the fractional OD shift in P180 WT mice injected with GFP-Arc did not significantly differ from age-matched Arc-Tg mice, indicating that acute viral expression of Arc can restore OD plasticity to a degree similar to that achieved by Tg augmentation of Arc throughout life (P = 0.6, MANOVA; Fig. 4E). Intriguingly, not only was contra eye depression observed in Arc-Tg and GFP-Arc–injected mice but a lack of ipsi eye potentiation was also observed, further suggesting that Arc protein levels control the qualitative aspects of OD plasticity. These data show that acutely increasing Arc protein expression in visual cortex is sufficient to restore juvenile OD plasticity Jenks et al.     50   B 150 150 100 100 50 50 100 µm -2 -1 0 1 2 3 4 Day -7 P180 45˚ Habituation Virus Injection Implant Electrodes C VEP Amplitude (µV) I/V GFP DAPI 85µV 0.1s 100 -100 150 0 150 50 100 150 0.1s * 100 50 GFP 60 40 0.1s 40 20 20 0 0 40µV 80 60 60 40 20 0 50 0 100 80 80 40 20 0 200 135˚ 100 80 60 -100 GFP-Arc MD D 100 -50 -50 250 200µV 0 0 II/III in adult visual cortex, suggesting the increased availability of Arc protein is sufficient to allow deprivation-induced synaptic depression in adult visual cortex. -20 0 -40 -20 40 40 40 40 30 30 30 30 20 20 20 20 10 10 10 10 0 0 0 0 -10 -10 -10 -20 -20 -10 -20 -20 -20 -20 -40 -40 -30 -30 -40 -30 -30 -40 -60 -60 Normalized VEP Amplitude 0 Fractional change in contra. eye VEP E 1.5 50 100 150 200 250 300 -60 0 50 Contra 100 150 200 250 -40 300 -80 -80 0 -60 0 50 50 100 150 100 150 200 200 250 300 -40 0 50 100 150 200 e 1 0.5 0.5 Baseline 50 100 150 200 250 300 -40 0 50 100 150 200 250 300 -40 0 50 100 150 200 250 0 50 100 150 200 250 300 300 * 1 0 0 1.5 Tim Tim e 300 250 0.06 0.06 Ipsi 250 0 Post MD * Baseline GFP Post MD GFP-Arc 1.2 1.0 0.8 0.6 GFP 0.4 GFP-Arc Non-Injected WT 0.2 Non-Injected Arc-Tg 0.0 0.0 0.5 1.0 1.5 2.0 Fractional change in ipsi. eye VEP Fig. 4. Acute Arc expression in adult mouse visual cortex is sufficient to restore juvenile OD plasticity. P180 WT mice were injected unilaterally in the visual cortex with lentivirus expressing either GFP alone or GFP-Arc. (A) Representative image of virally driven GFP expression in binocular visual cortex and time line of the experiment. The white dashed lines demarcate the cortical layers, as well as the position of the tip of the recording electrode. (B) GFP- and GFP-Arc–injected P180 mice were visually stimulated before MD with both eyes open to record binocular baseline VEPs. GFP-Arc– injected mice had significantly smaller VEPs than GFP-injected mice (GFP: n = 11, GFP-Arc: n = 5; *P = 0.005). Traces represent average VEPs for GFP- and GFP-Arc–injected mice. (C) Data were normalized to baseline contra values. There was no significant change in contra VEP amplitudes following MD in GFP-injected animals (P > 0.05); however, there was a significant ipsi increase (*P = 0.003). (D) Data were normalized to baseline contra values. GFP-Arc– injected mice exhibited significant contra depression following MD (*P = 0.016) and no change in ipsi responses. Averaged VEP traces are presented above the graphs. (E) Plot of the fractional change in ipsi (x axis) and contra (y axis) eye VEPs following MD (same data as in C and D, noninjected WT and Arc-Tg data are from Fig. 1B). There is a significant difference between the fractional change in visual responses between GFP- and GFP-Arc–injected mice (P < 0.01). GFP-injected mice exhibit the same lack of change as noninjected P180 WT mice (P = 0.3), while GFP-Arc–injected mice exhibit the same degree of change as noninjected P180 Arc-Tg mice (P = 0.6). Data are represented as mean ± SEM. Jenks et al.             Discussion Here, we show that acute or chronic up-regulation of Arc protein in adult mice renders visual cortical synapses sensitive to deprivedeye depression following MD, recapitulating juvenile critical period OD plasticity. In agreement with the prevailing hypothesis that LTD mechanisms mediate deprived-eye depression (3), overexpression of Arc also prolongs juvenile-like LTD in adult visual cortex. Conversely, elimination of Arc expression or inhibition of mRNA translation in juvenile visual cortex prevents deprived-eye depression after MD in vivo (11, 25) and LTD ex vivo. Together, these data indicate that availability of Arc is critical for the expression of juvenile plasticity in visual cortex. Considering the key role for Arc in determining the qualities of OD plasticity in visual cortex of juvenile animals, we predicted that the loss of deprived-eye depression after MD in adult visual cortex correlates with a lack of activity-dependent Arc expression. Indeed, we found that endogenous Arc protein expression in the active visual cortex declines with age, coincident with the loss of juvenile plasticity. Surprisingly, however, we found that activity-dependent Arc mRNA expression is comparable in juvenile (∼P30) and adult (∼P180) WT mouse visual cortex. This finding implies that the normal decline in Arc protein expression in active visual cortex results from a decrease in experiencedependent Arc translation, which can occur via mechanisms that are distinct from those regulating activity-dependent transcription (12, 23). The lack of decline in activity-dependent Arc expression in Arc-Tg mice could be due to the increase in Arc mRNA levels. Alternatively or in addition, the extra Arc allele in the Arc-Tg line does not contain an intron in the 3′ UTR region, which may result in an increase in mRNA stability in dendrites due to a lack of nonsense-mediated decay (28), and would thus potentially have a longer half-life than endogenous Arc mRNA. Restoration of juvenile plasticity in adult mice injected with GFP-Arc suggests that the presence of Arc protein in visual cortex is sufficient for juvenile OD plasticity. Deprived-eye depression after MD is believed to occur via mechanisms revealed by the study of LTD in layer IV. LTD in this layer is triggered by NMDA receptor activation and expressed by internalization of AMPA receptors (29). Although NMDA receptor-dependent LTD is not affected by acute (in vitro) inhibition of protein synthesis (30), we discovered that chronic inhibition of protein synthesis by in vivo microinfusion of CHX, which has been shown to prevent deprived-eye depression (25), impairs layer IV LTD ex vivo. These findings are reminiscent of the recent observation that chronic, but not acute, inhibition of metabotropic glutamate receptor 5 (mGluR5) can disrupt both deprived-eye depression after MD and LTD in layer IV (19). Activity-dependent synthesis of Arc protein occurs downstream of mGluR5 activation (12, 23). Thus, a simple explanation for this constellation of findings is that NMDA receptor-dependent LTD and deprived-eye depression require Arc protein as a necessary cofactor, and are inhibited by chronic block of either mGluR5 or protein synthesis. Decreased availability of Arc, and a consequent down-regulation of the mechanisms of LTD, also offers a simple molecular explanation for the age-dependent loss of synaptic sensitivity to visual deprivation. Inhibition develops later than excitatory transmission in the cortex, and it has been suggested that the consequent decrease in the ratio of excitation to inhibition brings the critical period for juvenile plasticity to a close (10). We propose that decreasing the excitability of the visual cortex ultimately affects OD plasticity by preventing the activity-dependent expression of key activityregulated plasticity proteins at the synapse that are important mediators of excitatory synaptic modification, such as Arc (Fig. S4). NEUROSCIENCE A PNAS Early Edition | 5 of 6     51   Indeed, in addition to manipulations of inhibition, OD plasticity can be restored in adult rodents exposed to an enriched visual environment (6, 7), treated chronically with fluoxetine (8), or genetically engineered to express constitutively active CREB (31), manipulations that also increase Arc protein levels (32). The precise regulation of Arc expression during development therefore provides a potential mechanistic link between the maturation of inhibition and changes in the qualities of excitatory synaptic modification over the lifespan. Materials and Methods Animals. Tg mouse lines harboring the Arc-promoter mCherry-Arc transgene (mCherry-Arc/Arc) were generated as previously described (18). Further details can be found in SI Materials and Methods. Requests for mice should be directly addressed to H.B. or H.O. Arc KO mice were obtained from Kuan Wang, NIH, Bethesda, and were previously described (22). Both male and female mice were used, and the experimenter was blinded to genotype in all experiments. Male C57BL/6 mice (Charles River Laboratories) at the age of P22–25 were used for the Alzet pump implantation experiments. Male C57BL/6 mice (The Jackson Laboratory) at the age of P180 were used for lentiviral VEP experiments. All procedures were approved by the Institutional Animal Care and Use Committees of the Massachusetts Institute of Technology, the University of Utah, and The University of Tokyo Graduate School of Medicine, in conjunction with NIH guidelines. (Ubq)-GFP and Ubq-GFP-Arc. Injections were carried out as previously described (33). VEP recordings, slice electrophysiology, and IHC. VEP recordings, slice electrophysiology, and IHC were carried out as previously described (11, 19). Detailed methods on IHC, qRT-PCR, VEP recordings, and slice electrophysiology can be found in SI Materials and Methods. Statistics. ANOVA/MANOVA tests and post hoc Student’s t tests were performed using JMP Pro software (v12; SAS Institute). For slice electrophysiology experiments, post hoc paired t tests were performed to determine the significance of changes before and after LFS and unpaired t tests were performed to test the differences between groups after LFS. Virus Production/Injection. Virus production. Kimberly Huber, University of Texas Southwestern Medical Center, Dallas, generously donated FUGW lentiviral plasmids for ubiquitin ACKNOWLEDGMENTS. We thank the late Dr. Roger Y. Tsien (University of California, San Diego) for the gift of the mCherry plasmid. We thank Dr. Kimberly Huber (University of Texas Southwestern Medical Center) for the FUGW GFP and Arc-GFP plasmids and Dr. Kuan Wang (NIH) for the Arc KO mouse line. K.R.J. was supported by the University of Utah Neuroscience Training Program (Grant 5T32NS076067) and by NIH National Research Service Award F31 (Grant MH112326). E.D.P. was supported by a developmental biology training grant at the University of Utah (Grant 5T32HD00749117). This work was funded by the Japan Agency for Medical Research and Development– Core Research for Evolutional Science and Technology grant (H.B.), KAKENHI Grants 15H02358, 15H04258, 16H01268 from the Japan Society for the Promotion of Science (to H.O. and H.B.), a grant-in-aid from the Ministry of Health, Labour, and Welfare of Japan (to H.O. and H.B), the NIH (Grants R00-NS076364 and R01-MH112766 to J.D.S.), the E. Matilda Ziegler Foundation (J.D.S.), the Howard Hughes Medical Institute, and The Picower Institute Innovation Fund (M.F.B.). 1. Hübener M, Bonhoeffer T (2014) Neuronal plasticity: Beyond the critical period. Cell 159:727–737. 2. Espinosa JS, Stryker MP (2012) Development and plasticity of the primary visual cortex. Neuron 75:230–249. 3. Cooke SF, Bear MF (2013) How the mechanisms of long-term synaptic potentiation and depression serve experience-dependent plasticity in primary visual cortex. Philos Trans R Soc Lond B Biol Sci 369:20130284. 4. Hensch TK, et al. (1998) Local GABA circuit control of experience-dependent plasticity in developing visual cortex. Science 282:1504–1508. 5. Choi SY, Morales B, Lee HK, Kirkwood A (2002) Absence of long-term depression in the visual cortex of glutamic acid decarboxylase-65 knock-out mice. J Neurosci 22: 5271–5276. 6. Greifzu F, et al. (2014) Environmental enrichment extends ocular dominance plasticity into adulthood and protects from stroke-induced impairments of plasticity. Proc Natl Acad Sci USA 111:1150–1155. 7. Matthies U, Balog J, Lehmann K (2013) Temporally coherent visual stimuli boost ocular dominance plasticity. J Neurosci 33:11774–11778. 8. Maya Vetencourt JF, et al. (2008) The antidepressant fluoxetine restores plasticity in the adult visual cortex. Science 320:385–388. 9. Hensch TK, Fagiolini M (2005) Excitatory-inhibitory balance and critical period plasticity in developing visual cortex. Prog Brain Res 147:115–124. 10. Jiang B, Huang ZJ, Morales B, Kirkwood A (2005) Maturation of GABAergic transmission and the timing of plasticity in visual cortex. Brain Res Brain Res Rev 50: 126–133. 11. McCurry CL, et al. (2010) Loss of Arc renders the visual cortex impervious to the effects of sensory experience or deprivation. Nat Neurosci 13:450–457. 12. Shepherd JD, Bear MF (2011) New views of Arc, a master regulator of synaptic plasticity. Nat Neurosci 14:279–284. 13. Shepherd JD, et al. (2006) Arc/Arg3.1 mediates homeostatic synaptic scaling of AMPA receptors. Neuron 52:475–484. 14. Frenkel MY, Bear MF (2004) How monocular deprivation shifts ocular dominance in visual cortex of young mice. Neuron 44:917–923. 15. Sawtell NB, et al. (2003) NMDA receptor-dependent ocular dominance plasticity in adult visual cortex. Neuron 38:977–985. 16. Sato M, Stryker MP (2008) Distinctive features of adult ocular dominance plasticity. J Neurosci 28:10278–10286. 17. Kawashima T, et al. (2009) Synaptic activity-responsive element in the Arc/ Arg3.1 promoter essential for synapse-to-nucleus signaling in activated neurons. Proc Natl Acad Sci USA 106:316–321. 18. Okuno H, et al. (2012) Inverse synaptic tagging of inactive synapses via dynamic interaction of Arc/Arg3.1 with CaMKIIβ. Cell 149:886–898. 19. Sidorov MS, Kaplan ES, Osterweil EK, Lindemann L, Bear MF (2015) Metabotropic glutamate receptor signaling is required for NMDA receptor-dependent ocular dominance plasticity and LTD in visual cortex. Proc Natl Acad Sci USA 112: 12852–12857. 20. Dölen G, et al. (2007) Correction of fragile X syndrome in mice. Neuron 56:955–962. 21. Tagawa Y, Kanold PO, Majdan M, Shatz CJ (2005) Multiple periods of functional ocular dominance plasticity in mouse visual cortex. Nat Neurosci 8:380–388. 22. Wang KH, et al. (2006) In vivo two-photon imaging reveals a role of arc in enhancing orientation specificity in visual cortex. Cell 126:389–402. 23. Jakkamsetti V, et al. (2013) Experience-induced Arc/Arg3.1 primes CA1 pyramidal neurons for metabotropic glutamate receptor-dependent long-term synaptic depression. Neuron 80:72–79. 24. Dudek SM, Friedlander MJ (1996) Developmental down-regulation of LTD in cortical layer IV and its independence of modulation by inhibition. Neuron 16:1097–1106. 25. Taha S, Stryker MP (2002) Rapid ocular dominance plasticity requires cortical but not geniculate protein synthesis. Neuron 34:425–436. 26. Rial Verde EM, Lee-Osbourne J, Worley PF, Malinow R, Cline HT (2006) Increased expression of the immediate-early gene arc/arg3.1 reduces AMPA receptor-mediated synaptic transmission. Neuron 52:461–474. 27. Chowdhury S, et al. (2006) Arc/Arg3.1 interacts with the endocytic machinery to regulate AMPA receptor trafficking. Neuron 52:445–459. 28. Giorgi C, et al. (2007) The EJC factor eIF4AIII modulates synaptic strength and neuronal protein expression. Cell 130:179–191. 29. Crozier RA, Wang Y, Liu CH, Bear MF (2007) Deprivation-induced synaptic depression by distinct mechanisms in different layers of mouse visual cortex. Proc Natl Acad Sci USA 104:1383–1388. 30. Huber KM, Kayser MS, Bear MF (2000) Role for rapid dendritic protein synthesis in hippocampal mGluR-dependent long-term depression. Science 288:1254–1257. 31. Pham TA, et al. (2004) A semi-persistent adult ocular dominance plasticity in visual cortex is stabilized by activated CREB. Learn Mem 11:738–747. 32. Li Y, et al. (2015) Reversal of age-associated cognitive deficits is accompanied by increased plasticity-related gene expression after chronic antidepressant administration in middle-aged mice. Pharmacol Biochem Behav 135:70–82. 33. Kaplan ES, et al. (2016) Contrasting roles for parvalbumin-expressing inhibitory neurons in two forms of adult visual cortical plasticity. Elife 5:e11450. 6 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1700866114 Jenks et al.   CHAPTER 3 EXPERIENCE-DEPENDENT DEVELOPMENT AND MAINTENANCE OF BINOCULAR NEURONS IN THE MOUSE VISUAL CORTEX 3.1 Abstract The development of neuronal circuits requires both hard-wired gene expression and experience. Sensory processing, such as in the visual system, is especially sensitive to perturbations of experience. The binocular visual cortex has to process information from each eye to form a coherent image, and occlusion of binocular vision during development can lead to lifelong deficits. To determine the contribution of experience to the development of visual response properties in binocular visual cortex in vivo, we used single-cell resolution two-photon calcium imaging in awake mice at multiple time-points after eye-opening, and in mice raised without visual experience. Few neurons are binocularly responsive immediately after eye-opening and respond solely to either the contralateral or ipsilateral eye. Binocular neurons emerge during development and acquire specific visual response properties, such as a preference for horizontal orientations, which requires visual experience. As binocular neurons emerge, activity between the two eyes becomes more correlated in the neuropil, and this correlation requires experience. We determined whether the plasticity gene Arc mediates the 53 development of experience-dependent visual response properties in binocular neurons. Surprisingly, rather than mirroring the effects of visual deprivation, mice that lack Arc show increased numbers of binocular neurons and correspondingly less ocular dominance of the contralateral eye’s input relative to the ipsilateral eye. Strikingly, removing Arc in adult binocular visual cortex recapitulates the developmental phenotype, suggesting that maintenance of binocular circuits requires ongoing Arc expression. Thus, visual experience and activity-dependent gene expression are both crucial for the development and maintenance of circuits required to process binocular vision. 3.2 Introduction Sensory processing requires the development of organized neuronal circuits that can also adapt to environmental stimuli. Thus, the shaping of sensory circuits requires both hard-wired patterning and experience-dependent plasticity. These experiencedependent processes predominately occur early in development during critical periods of heightened plasticity. In V1, manipulation of visual experience during one such critical period can lead to dramatic changes in neuronal function and structure (Espinosa & Stryker, 2012). Closing one eye for several days drives the loss of input from the deprived eye in V1, resulting in a shift in ocular dominance (OD). While the mechanisms of this OD plasticity are well studied (Levelt & Hübener, 2012), it remains unclear whether similar mechanisms mediate the development of visual response properties in binocular V1. V1 receives visual input from both eyes and needs to integrate this information to resolve an accurate representation of visual space. In cats and most primates, which share 54 forward-facing eyes and large binocular fields, eye-specific input into V1 form alternating OD columns. These columns appear perpendicular to orientation columns that correspond to the orientation of edges (dark-light borders) in the neuron’s receptive field and are dominated by the contralateral eye early in development (Crair, Gillespie, & Stryker, 1998; Hubel, Wiesel, & LeVay, 1976). This structural organization seems to arise independent of visual experience, although other visual response properties, such as direction selectivity and the strengthening of ipsilateral input seem to require experience (Crair et al., 1998; Li, Fitzpatrick, & White, 2006; Rakic, 1976; Sherk & Stryker, 1976). Mice have a smaller binocular visual field than cats and primates and lack distinct OD columns (Dräger, 1975). However, neurons within mouse binocular V1 still display a range of orientation selectivity, have OD bias towards the contralateral eye, and later experience-dependent strengthening of ipsilateral input (Salinas, Figueroa Velez, Zeitoun, Kim, & Gandhi, 2017; Smith & Trachtenberg, 2007). Recent studies have also uncovered a spatial organization of orientation selectivity as a function of cortical distance and cortical depth, consistent with a primitive columnar organization (Ringach et al., 2016). Studies using intrinsic imaging in mouse V1 suggest that eye-specific plasticity begins at eye-opening, especially in the establishment of ipsilateral eye connectivity (Smith & Trachtenberg, 2007). The contralateral eye drives the majority of initial input into V1, and the refinement and strengthening of ipsilateral eye responsiveness require patterned contralateral visual input, although it is unclear how this large-scale refinement of ipsilateral input relates to the integration of binocular input at the level of single neurons. One vital function of binocular V1 refinement is to match specific visual 55 responses from each eye onto binocular neurons. Initially, inputs from each eye onto one neuron can encode different orientations, but these inputs become matched during the critical period (Wang, Sarnaik, & Cang, 2010). This binocular matching requires experience and NMDA receptor activity during a critical period in development (Wang et al., 2010; Wang, Feng, Liu, Liu, & Cang, 2013). These mechanisms resemble those of OD plasticity, and monocular deprivation can disrupt binocular matching, suggesting the enhanced plasticity of the OD critical period regulates binocular matching (Levine, Chen, Gu, & Cang, 2017). In monocular V1, using electrophysiological recordings, layer-specific maturation of orientation and direction selectivity is observed rapidly after eye-opening (Hoy & Niell, 2015). Dark rearing or genetically reducing neuronal activity in monocular V1 by expressing the potassium channel kir2.1 had little effect on the initial formation of orientation and direction selectivity (Hagihara, Murakami, Yoshida, Tagawa, & Ohki, 2015; Rochefort et al., 2011). While experience is unnecessary for monocular V1 development, it remains unclear whether these findings also generalize to binocular V1. Given the demonstrable role of experience in the development of binocular V1 during the critical period, distinct processes might govern the development of monocular and binocular V1, or even of eye-specific responses within binocular V1. Molecular mechanisms reminiscent of cellular models of plasticity such as longterm potentiation and depression (LTP/LTD) mediate OD plasticity (Yoon, Smith, Heynen, Neve, & Bear, 2009). However, little is known about the contribution of these mechanisms to the development and refinement of visual response properties. Visual experience induces expression of the immediate early gene Arc in excitatory neurons in 56 V1 (Tagawa, Kanold, Majdan, & Shatz, 2005), and Arc is necessary for OD plasticity and LTD in binocular V1 (Jenks et al., 2017; McCurry et al., 2010). The induction of Arc expression declines past the close of the critical period and increasing Arc expression in the adult brain can restore juvenile like OD plasticity (Jenks et al., 2017). Thus, Arc may also have a role in experience-dependent development of binocular visual response properties. To determine how visual response properties develop in binocular V1, we used two-photon in vivo calcium imaging to examine the visual response properties of individual neurons at various points early after eye-opening. In order to determine the contribution of experience and synaptic plasticity, we also imaged dark-reared mice and Arc knock out (KO) mice. Finally, we tested whether Arc’s role in vision is only during development by knocking out Arc in adult V1. 3.3 Results 3.3.1 Visually responsive neurons emerge after eye-opening To measure visual response properties of single neurons, we conducted acute, two-photon calcium imaging through cranial windows implanted over binocular V1 (Figure 3.1A) in cohorts of mice expressing GCaMP6s driven by the Thy1 promoter, which labels the majority of excitatory neurons in L2/3 (Dana et al., 2014; Feng et al., 2000; Lee, Meyer, Park, & Smirnakis, 2017; Sun, Tan, Mensh, & Ji, 2016). To capture the development of response properties after eye-opening, we imaged at three points: postnatal day (P)14 corresponding to 0-2 days after eye-opening, P20 immediately before the start of the canonical OD plasticity critical period, and P30, the peak of the critical 57 period. We presented visual stimuli in the form of drifting, sinusoidal gratings with various orientations (0-330°) in 30° increments at 0.05 cycles/degree (previously shown to maximally stimulate V1; Gu et al., 2013; Hoy & Niell, 2015; Niell & Stryker, 2008), interleaved with grey screen presentations, to each eye independently in pseudorandom order while recording in the same hemisphere. Regions of interest (ROIs) corresponding to neuronal soma were selected using the program Suite2P (Jeon, Swain, Good, Chase, & Kuhlman, 2018) and manually annotated (see methods). We subtracted the neuropil fluorescence as previously described (Peron, Freeman, Iyer, Guo, & Svoboda, 2015) around the soma to remove contaminating signal from surrounding processes (Figure 3.1B). Raw fluorescent traces were then converted to a Z-score as previously described (El-Boustani et al., 2018) using the mean and standard deviation of activity during the grey screen presentations (Figure 3.1C). In this study, a neuron is visually responsive if the mean response amplitude (from 0-5 second poststimulus onset) to a visual stimulus was 0.5 standard deviations above the mean of the responses to the grey screen, and if the responses to individual presentations of that stimulus were significantly larger in mean amplitude (p<=0.05, paired t test) than the preceding grey screen periods. Spontaneous activity during the grey screen periods did not significantly differ across age (Figure 3.2A: P14=0.66±0.07 (ΔF/F)/S, P20=0.61±0.02 (ΔF/F)/S, P30=0.56±0.04 (ΔF/F)/S, F2,13=1.1, p=0.3758, ANOVA). However, the percent of visually responsive neurons increased significantly from 13±3% (mean±standard error of the group) at P14 to 40±1% in P30 mice wild type (WT; Figure 3.1D. P14=27±7 total responsive neurons/mouse N=5; P20=59±7 neurons/mouse N=5, P30=60±3 neurons/mouse N=6. Percent responsive; F2,13=30.8, 58 p<0.0001, ANOVA). For this comparison and all comparisons made in this study, a single measure is reported per animal (generally taken as the mean of individual cells in that animal), and N is the number of animals per group. Visually responsive neurons were further classified as either binocular, where the neuron showed significant visual responses to stimulation of both the contralateral or ipsilateral eye independently, or monocularly responsive if the neurons responded solely to either the contralateral eye or ipsilateral eye (Figure 3.1E). The spatial distribution of binocular and ipsilateral responses appeared widely dispersed across the field of view, discounting the possibility that our recordings were in or near the boundary of monocular V1. We quantified the ocular dominance index (ODI) of neurons on a scale of 0 (purely contralateral responsive) to 1 (purely ipsilateral responsive) and averaged all neurons recorded per mouse before averaging within each age group (Figure 3.1F). The ODI of the population did not vary across age groups (P14=0.39±0.11, P20=0.30±0.05, P30=0.30±0.02, F2,13=0.6, p=0.5888, ANOVA). 3.3.2 Distinct development of eye-specific response properties To determine how visual response properties develop in binocular V1, we quantified orientation preference, orientation selectivity, and direction selectivity across age as these properties are important for visual feature detection (Figure 3.1G). We analyzed the eye-specific responses in binocular and monocular neurons separately. We binned orientation preference in 45° bins centered on 0°, 45°, 90°, and 135°. In P14 mice, binocular neurons prefer vertical orientations (90°), while in P20 and P30 mice, binocular neurons prefer horizontal (0°) orientations (Figure 3.1H and I). To quantify the degree of 59 bias for cardinal orientations, we measured the horizontal bias index (HBI) of neurons, which ranks the bias of neurons from -1 (vertical) to 1 (horizontal), with 0 representing no cardinal bias, and averaged the population of neurons in each mouse and then averaged these means for each age group (Figure 3.1H and I; Hagihara et al., 2015). Contralateral responses of binocular neurons in P14 mice show a significant bias for vertical orientations (Figure 3.1H: P14=-0.53±0.14, t=-3.8, p=0.0326, t test), while P20 and P30 mice show a significant bias for horizontal orientations (P20=0.27±0.05, t=5.2, p=0.0066. P30=0.35±0.06, t=5.9, p=0.0020, t test). Contralateral HBI of binocular neurons is significantly different between P14 mice and P20/P30 mice (F2,12=31.8, p<0.0001, ANOVA). Ipsilateral responses of binocular neurons have a bias for vertical orientations at P14 (Figure 3.1I: P14 HBI: -0.75±0.16, t=-4.7, p=0.0184, t test) and also show a shift towards horizontal orientations in P20/30 animals (F2,12=22.5, p<0.0001, ANOVA), although the bias at P20/P30 is not significant. Interestingly, monocular neurons have no orientation bias at P14 and do not switch to a horizontal bias during development in either contralateral or ipsilateral responsive neurons (Figure 3.1J and K). Thus, a developmental switch in orientation bias is only observed in binocular neurons. We next measured orientation selectivity for neurons, quantified as an orientation selectivity index (OSI, see methods). Both monocular and binocular neuron OSI for contralateral responses increased significantly from P14 to P20 and P30 (Figure 3.1L: contralateral monocular; P14=0.45±0.05, P20=0.75±0.02, P30=0.73±0.02, F2,13=25.1, p<0.0001, ANOVA. Contralateral binocular; P14=0.44±0.08, P20=0.72±0.04, P30=0.71±0.04, F2,12=8.7, p=0.0047, ANOVA). In contrast, OSI to the ipsilateral eye did not significantly change with age in monocular or binocular neurons (Ipsilateral 60 monocular; P14=0.61±0.09, P20=0.60±0.08, P30=0.70±0.02, F2,13=0.7, p=0.5154, ANOVA. Ipsilateral binocular; P14=0.68±0.12, P20=0.75±0.05, P30=0.63±0.04, F2,12=0.9, p=0.4390, ANOVA). This suggests that OSI tuning improves specifically in contralateral eye inputs. The direction selectivity index (DSI) was calculated for all neurons (see methods). Both monocular and binocular neuron DSI of contralateral responses decreased significantly over age (Figure 3.1M: contralateral monocular; P14=0.50±0.03, P20=0.37±0.03, P30=0.39±0.02, F2,13=5.9, p=0.0152, ANOVA. Contralateral binocular; P14=0.45±0.04, P20=0.28±0.04, P30=0.28±0.03, F2,12=6.0, p=0.0157, ANOVA), while DSI for ipsilateral responses were unchanged (Figure 3.1M: Ipsilateral monocular; P14=0.46±0.07, P20=0.35±0.03, P30=0.36±0.03, F2,13=1.7, p=0.2253, ANOVA. Ipsilateral binocular; P14=0.45±0.12, P20=0.32±0.04, P30=0.30±0.02, F2,12=1.5, p=0.2534, ANOVA). Thus, similar to OSI, developmental tuning of DSI occurs specifically in contralateral inputs. Interestingly, the tuning of visual responses did not change significantly from P20 to P30, suggesting many aspects of visual development in binocular V1 mature before the start of the classical OD critical period in mice. Tuning of visual response properties occurs mostly through contralateral eye input refinement and binocular neurons become uniquely biased towards horizontal orientations, indicating that these neurons may mediate distinct processing of visual information compared to monocular neurons. 3.3.3 Binocular development requires visual experience Our results suggest that binocular neurons have specific visual response properties that differ from monocular neurons; therefore, we investigated these neurons in more 61 detail. Only a small percentage of visually responsive cells were binocular at P14, with most cells responding only to the contralateral or ipsilateral eye (Figure 3.3A and B). However, the percentage of binocular cells significantly increased with age, seeming to peak at P20 (Figure 3.3B: P14=8±3%, P20=25±3%, P30=17±2%, F2,13=9.0, p=0.0036, ANOVA). The binocular increase was not simply due to more neurons becoming visually responsive, as the probability of a visually responsive neuron being binocular by chance does not change over age (Figure 3.2B: P14=0.20±0.03, P20=0.22±0.01, P30=0.22±0.01, F2,13=0.6, p=0.5456, ANOVA). We also assessed the relative strength of contralateral and ipsilateral inputs to binocular neurons. The ODI of binocular neurons alone did not change over age (Figure 3.3C: P14=0.39±0.08, P20=0.45±0.04, P30=0.38±0.03, F2,12=0.5, p=0.6229, ANOVA). These findings suggest that binocularity in L2/3 neurons in mouse V1 is initially represented at the population level mostly by monocularly driven cells, while individual cells become binocular later in development. Previous studies have reported a significant neuronal mismatch in orientation preference between the eyes early in development (Wang, Feng, Liu, Liu, & Cang, 2013; Wang, Sarnaik, & Cang, 2010). We examined binocular matching of orientation preference across age, measured as the binocular offset between the orientation preference of the contralateral and ipsilateral responses in binocular neurons. We found, however, that in L2/3 neurons binocular offset was low for all ages examined and did not change across age (Figure 3.4A: P14=19.3±4.4°, P20=22.6±3.7°, P30=23.6±2.4°, F2,12=0.4, p=0.6826, ANOVA). The contribution of visual experience to the maturation of visual responses in binocular neurons is unclear. We, therefore, investigated whether the emergence of 62 binocular neurons or changes in their selectivity requires visual experience. We darkreared WT mice from birth in complete darkness (DR) until P30, with controls raised on a standard 12:12 h light/dark cycle (NR). On the day of surgery and recording, all mice were transported, anesthetized, and allowed to recover in darkness until immediately before recording. NR and DR mice did not differ in the total percentage of neurons that were visually responsive (Figure 3.3D: NR=41±4 neurons/mouse N=7, 33.2±8.4% responsive; DR=67±18 neurons/mouse N=4, 26.5±2.2% responsive, F1,9=1.0, p=0.3463, ANOVA). We examined the distribution of binocular neurons within the population of responsive neurons (Figure 3.3E). At P30, DR mice had significantly more monocular contralateral neurons (Figure 3.3E: NR=50.8±4.6%, DR=68.8±1.7%, F1,9=8.1, p=0.0191, ANOVA) and significantly fewer binocular neurons (NR=31.7±1.9%, DR=11.7±1.9%, F1,9=46.1, p<0.0001, ANOVA) than NR mice, reminiscent of P14 NR animals (Figure 3.3B). Despite the change in the number of visually responsive neurons that were binocular versus monocular, the ODI of the total population of neurons was not different (Figure 3.3F: NR=0.28±0.04, DR=0.23±0.03, F1,9=0.7, p=0.4285, ANOVA). Additionally, the ODI of binocular neurons did not differ between NR and DR mice (Figure 3.3G: NR=0.33±0.02, DR=0.32±0.03, F1,9=0.1, p=0.7977, ANOVA). We also did not observe a significant difference in binocular offset between NR and DR mice, although there was a trend towards higher offsets in DR mice (Figure 3.4B: NR=20.4±2.6°, DR=33.7±7.4°, F1,9=4.3, p=0.0683, ANOVA). These findings indicate that visual experience is crucial to the emergence of binocular neurons. We measured orientation preference, contralateral OSI and contralateral DSI in NR and DR binocular neurons to see if experience regulates their development in 63 binocular neurons. Binocular neurons in NR mice show a bias to horizontal orientations for contralateral and ipsilateral responses (Figure 3.3H: NR contralateral HBI: 0.23±0.08, t=2.8, p=0.0327; ipsilateral NR HBI: 0.22±0.07, t=2.9, p=0.0262, t test), similar to the results in P30 WT mice. Neither the binocular neurons’ contralateral (DR HBI=0.14±0.20, t=-0.7, p=0.5161, t test) nor ipsilateral (DR HBI=0.01±0.03, t=0.31, p=0.7775, t test) responses in DR mice showed a significant bias, although the difference between NR and DR mice is not significant (Contralateral, F1,9=4.2, p=0.0702. Ipsilateral, F1,9=4.0, p=0.0755, ANOVA). The contralateral OSI in binocular neurons was not significantly different between NR and DR mice (Figure 3.3I: NR=0.71±0.05, DR=0.59±0.08, F1,9=1.8, p=0.2074, ANOVA). The mean contralateral DSI of binocular neurons, however, was significantly lower in NR mice than DR mice, indicating the developmental decrease in DSI is experience-dependent (Figure 3.3J: NR=0.23±0.02, DR=0.39±0.09, F1,9=5.2, p=0.0485, ANOVA). These results suggest that, except for OSI, development of visual response properties in binocular neurons requires visual experience. 3.3.4 Development of binocular correlation in the neuropil The L2/3 neuropil in V1 is comprised of intracortical axons and dendrites from L2/3, L4, and L5, as well as thalamic projections into V1, and is densely labeled by GCaMP6s in the Thy1-GCaMP6s mouse (Dana et al., 2014). Despite most neurons being monocular in binocular V1, we predicted that the neuropil signal would reflect the binocularity of the population response. We also wondered whether neuropil responses show any developmental-dependent tuning in visual responses or binocularity, as small 64 (7-15 µm radius) neuropil patches in L2/3 of V1 have distinct stimulus-specific preferences and perform as well as neurons in encoding stimulus direction in adult mice (Lee et al., 2017). We examined visual responses of neuropil patches taken as an annulus around the soma of all detected neurons three times the size of the neuron’s radius (see methods), and we found that many patches displayed significant visual responses evoked by either eye (Figure 3.5A). Similar to neurons, the percentage of neuropil patches with significant visual responses increased over age (Figure 3.5B: P14=66±11.7%, P20=97.8±0.9%, P30=95.5±2.2%, F2,13=7.2, p=0.0079, ANOVA). Since binocular neurons only emerge after eye-opening, we predicted that correlated binocular tuning in the neuropil would also emerge after eye-opening. To test this, we measured signal correlation between mean contralateral and ipsilateral responses in binocular neuropil (Jeon et al., 2018; Ko et al., 2013). Correlation of the contralateral and ipsilateral responses was extremely low at P14 and P20 but increased significantly at P30 (Figure 3.5C: P14=-0.08±0.1, P20=0.09±0.07, P30=0.33±0.06, F2,13=7.8, p=0.0060, ANOVA). We next examined whether this increase in correlated activity between the eyes over development is experience-dependent. Correlation between contralateral and ipsilateral responses in neuropil patches was 0.39±0.07 in NR mice, but absent in DR mice at 0.03 ±0.03 (Figure 3.5D: F1,9=14.0, p=0.0046, ANOVA). We next examined orientation preference of neuropil patches across development to determine whether neuropil preference showed similar biases to binocular neurons. At P14, an orientation bias for vertical orientations is present for the ipsilateral but not the contralateral eye (Figure 3.5E: Contralateral=-0.05±0.28, t=-0.2, p=0.8564; Ipsilateral=-0.40±0.14, t=-2.8, p=0.0470, t test). However, at P20 (Figure 3.5E: 65 Contralateral=0.41±0.11, t=3.7, p=0.0200; Ipsilateral=0.63±0.10, t=6.2, p=0.0035, t test) and P30 (Figure 3.5E: Contralateral=0.67±0.07, t=9.2, p=0.0003; Ipsilateral=0.59±0.07, t=8.8, p=0.0003, t test) responses showed a significant bias for horizontal orientations. The horizontal orientation bias increased significantly over age for contralateral (F2,13=4.8, p=0.0269, ANOVA) and ipsilateral (F2,13=30.6, p<0.0001, ANOVA) responses. To see if this change in the neuropil is experience-dependent, we measured orientation bias in the neuropil of NR and DR mice. P30 NR mice show a significant horizontal orientation bias for both contralateral and ipsilateral neuropil responses (Figure 3.5F: contralateral=0.53±0.09, t=6.1, p=0.0009; Ipsilateral=0.45±0.09, t=5.1, p=0.0022, t test). In contrast, DR mice show no horizontal bias (Figure 3.5F: Contralateral=0.12±0.18, t=-0.7, p=0.5563; Ipsilateral=-0.19±0.27, t=-0.7, p=0.5333, t test) and were significantly different than NR mice (Contralateral, F1,9=13.7, p=0.0049; Ipsilateral, F=7.9, p=0.0202, ANOVA). These results suggest that axons and dendrites within local patches of binocular neuropil develop correlated tuning to visual input from both eyes and a horizontal orientation preference that both changes require experience, similar to the emergence and development of binocular neurons. 3.3.5 Arc tunes the development of binocular neurons To gain molecular insight into the processes that regulate the development of binocularity, we investigated the role of the activity-dependent gene Arc. Given Arc’s role in experience-dependent plasticity in V1 (Gao et al., 2010; McCurry et al., 2010), we hypothesized that Arc KO mice would mimic aspects of DR WT mice. We measured visual responses in binocular V1 of Arc KO mice at P14, P20, and P30 alongside their 66 WT littermates described above (Figure 3.6A: KO P14=34±6 neurons/mouse N=6; P20=47±14 neurons/mouse N=3; P30=59±8 neurons/mouse N=4). Since this Arc KO line was created by knock-in of a destabilized GFP (Wang et al., 2006), we examined the fluorescence during grey screen presentations to determine if the GFP signal would lead to higher baseline fluorescence (see methods), possibly biasing analysis of changes in fluorescence. However, the baseline fluorescence in Arc KO mice did not significantly differ from the WT P14, P20 and P30 data (Figure 3.7A: P14; WT=214±30, KO=203±17, p=0.7603. P20; WT=315±62, KO=244±3, p=0.3203. P30; WT=278±33, KO=231±6, p=0.2432, t test). In KO mice, the percent of visually responsive neurons responsive solely to the contralateral eye decreased significantly over development from 85.9±1.2% at P14 to 41.5±3.2% at P30 (Figure 3.6B: P20=70.9±4.2%, F2,10=88.3, p<0.0001, ANOVA), while binocular responsive neurons increased significantly from 2.0±1.0% at P14 to 29.2±3.9% at P30 (P20=11.5±4.4%, F2,10=27.3, p<0.0001, ANOVA). Monocular ipsilateral responsive neurons also increased significantly from 12.0±1.9% at P14 to 29.3±2.3% at P30 (Figure 3.6B: P20=17.6±1.5, F2,10=19.5, p=0.0004, ANOVA). The population ODI increased significantly from P14 to P20, and from P20 to P30 (Figure 3.6C: P14=0.13±0.02, P20=0.22±0.02, P30=0.41±0.02, F2,10=61.5, p<0.0001, ANOVA). ODI of binocular neurons in Arc KO mice did not significantly change over development (Figure 3.6D: P14=0.33±0.07, P20=0.38±0.03, P30=0.41±0.02, F2,7=0.8, p=0.4719, ANOVA). However, unlike WT mice, binocular offset in Arc KO mice was high in P14 animals and decreased over the course of development (Figure 3.7B: P14=52.7±2.5°, P20=22.6±4.7°, P30=16.9±3.2°, F2,7=28.2, p=0.0004, ANOVA). When directly compared, no difference in the percentage of visually responsive 67 neurons was observed between KO and WT mice at P30 (Figure 3.7C: WT=39.6±0.9%, KO=41.1±2.8%, F1,8=0.3, p=0.5825, ANOVA). However, the percent of visually responsive neurons that were binocular is significantly higher in Arc KO mice than in WT at P30 (Figure 3.6E: WT=16.6±2.1%, KO=29.2±3.9%, F1,8=9.8, p=0.0141, ANOVA). Also, the percentage of visually responsive neurons responding solely to the contralateral eye was significantly lower in Arc KO mice compared to WT mice (Figure 3.6E: WT=60.0±3.5%, KO=41.5±3.2%, F1,8=13.7, p=0.0060, ANOVA). The ODI of the neuronal population was significantly higher in Arc KO mice compared to WT (Figure 3.6F: WT=0.30±0.02, KO=0.41±0.02, F1,8=11.6, p=0.0093, ANOVA), while ODI in binocular neurons did not significantly differ (Figure 3.6G: WT=0.38±0.03, KO=0.41±0.02, F1,8=0.3, p=0.6174, ANOVA). We next compared the visual response properties of binocular neurons in P30 WT and Arc KO mice. The orientation preference of contralateral (Figure 3.6H: WT=0.35±0.06, KO=0.34±0.11, F1,8=0.0, p=0.8825, ANOVA) and ipsilateral (Figure 3.6H: WT=0.24±0.10, KO=0.33±0.12, F1,8=0.4, p=0.5670, ANOVA) responses in binocular neurons did not differ between WT and Arc KO mice. Similarly, neither contralateral OSI (Figure 3.6I: WT=0.71±0.04, KO=0.71±0.05, F1,8=0.0, p=0.9987, ANOVA) or contralateral DSI (Figure 3.6J: WT=0.28±0.03, KO=0.23±0.01, F1,8=2.7, p=0.1384, ANOVA) of binocular neurons differed between P30 WT and KO mice. Finally, binocular offset did not differ between P30 WT and Arc KO mice (Figure 3.7D: WT=23.6±2.4°, KO=16.9±3.2°, F1,8=2.8, p=0.1333, ANOVA). In contrast to our original hypothesis, these results show that Arc KO mice do not mirror the effect of dark rearing. Loss of Arc results in an increase in binocular and a 68 decrease in contralateral monocular neurons without impacting other response properties. This finding suggests that Arc may tune cortical circuits during development by mediating eye-specific synaptic plasticity. 3.3.6 Adult binocularity requires ongoing maintenance Arc protein expression peaks during the critical period (Jenks et al., 2017); thus, we predicted that Arc’s role in regulating binocular neurons would be constrained to early development. However, OD plasticity still occurs in adult mouse V1, although primarily through potentiation of open-eye/ipsilateral responses (Sawtell et al., 2003), suggesting the balance of input between the two eyes is still plastic and could require ongoing maintenance. To determine whether Arc is required for the maintenance of binocular cells in adult binocular V1, we used a floxed Arc line (Arc cKO) mice (Chen, Campos, Jarvie, & Palmiter, 2018) that was crossed into the Thy1-GCaMP6s line. At P180 we injected either AAV5-hSyn-mCherry (Control) or AAV5-hSyn-mCherry-Cre (Cre) virus into L2/3 of binocular V1. Two weeks following injection, we performed a craniotomy over the injection site and imaged as above. Immunohistochemistry of the injected hemisphere confirmed effective recombination and deletion of Arc after Cre expression (Figure 3.8A). The total percentage of responsive neurons did not differ between Control and Cre mice (Figure 3.8B: Control=55±7 neurons/mouse, 37.2±2.9%, N=5; Cre=56±6 neurons/mouse, 33.5±3.5%, N=5, F1,8=0.7, p=0.4403, ANOVA). As in P30 mice, a mix of monocular and binocular neurons was visible in L2/3 of Cre injected mice (Figure 3.9A). Strikingly, Cre mediated knockdown of Arc in adult binocular V1 was sufficient to alter the percentage of binocular neurons as compared to control 69 injected mice (Figure 3.9B: Control=12.6±2.5%, Cre=26.6±5.3%, F1,8=5.7, p=0.0442, ANOVA). Additionally, Cre injected mice showed a decrease in the number of monocular contralateral neurons (Figure 3.9B: Control=70.7±4.8%, Cre=40.6±6.2%, F1,8=14.7, p=0.0050, ANOVA) and an increase in monocular ipsilateral neurons (Figure 3.9B: Control=16.6±4.1%, Cre=32.8±3.6%, F1,8=8.8, p=0.0180, ANOVA). Consistent with an overall shift in binocularity, Cre injected mice also showed an increase in the ODI of the neuronal population (Figure 3.9C, Control=0.24±0.06, Cre=0.43±0.05, F1,8=6.4, p=0.0356, ANOVA). The ODI of binocular neurons was unaffected (Figure 3.9D: Control=0.41±0.02, Cre=0.35±0.03, F1,8=2.6, p=0.1442, ANOVA). We next compared the response properties of binocular neurons in Control and Cre injected mice. As expected, given our previous results in P30 and NR mice, binocular neurons in control injected mice had an orientation preference bias for horizontal orientations (Figure 3.9E: Contralateral=0.39±0.12, t=3.2, p=0.0331; Ipsilateral=0.44±0.09, t=4.7, p=0.0096, t test). While this bias was still present in contralateral responses of Cre mice (Cre=0.18±0.05, t=3.4, p=0.0261, t test. Comparison to control, F1,8=2.5, p=0.1514, ANOVA), it was absent in ipsilateral responses (Cre=0.09±0.04, t=2.0, p=0.1209, t test) and significantly differed from Control mice (F1,8=11.6, p=0.0092, ANOVA). Neither contralateral OSI (Figure 3.9F: Control=0.81±0.02, Cre=0.74±0.04, F1,8=2.6, p=0.1466, ANOVA) or contralateral DSI (Figure 3.9G: Control=0.24±0.05, Cre=0.26±0.02, F1,8=0.1, p=0.7430, ANOVA) differed between Control and Cre mice. Binocular offset was also not significantly different between Control and Cre injected mice, although there is a trend for a larger offset in Cre mice (Figure 3.8C: Control=19.7±0.9°, Cre=25.9±2.7°, F1,8=4.6, p=0.0642, ANOVA). 70 Table 3.1 reports all response properties across genotype and manipulation for both the binocular and monocular populations of neurons. These results show that acutely reducing Arc levels in adult binocular V1, surprisingly, recapitulates the developmental phenotype observed in full Arc KO mice and suggests that the precise regulation of binocularity in V1 requires ongoing maintenance and plasticity, even in adult mice. 3.4 Discussion The interplay between experience and genetic hardwiring of circuits in the brain results in optimal processing of information from the outside world. Here, we show that at eye-opening mouse binocular V1 is mostly driven by monocular responding cells, while binocular cells rapidly emerge after eye-opening in an experience-dependent process that is, in part, regulated by the activity-dependent gene Arc. These binocular neurons convey specific visual response properties, including a bias for horizontal orientations. The emergence of binocular responding neurons is associated with an increase in total correlated activity between the two eyes in the neuropil, which is also experience-dependent. These data solidify the role of experience as a crucial component of refining sensory processing and binocular vision early in development. While it is clear that there are critical windows of plasticity during development that are essential for the proper wiring of neuronal circuits, it has also become evident that plasticity still occurs in adult brains (Hubener & Bonhoeffer, 2014). Indeed, many studies have shown that adult brains can have juvenile-like plasticity reinstated (Greifzu et al., 2014; Jenks et al., 2017; Matthies, Balog, & Lehmann, 2013; Pizzorusso, 2002; Vetencourt et al., 2008), and that visual deprivation can alter plasticity rules in adult 71 animals (He, Hodos, & Quinlan, 2006). However, few studies have shown a direct role for plasticity in regulating or maintaining circuits required for sensory processing in adults. Unexpectedly, we found that knock-down of Arc in adult mouse binocular V1 alters the distribution of monocular and binocular neurons; recapitulating the developmental phenotype observed in young Arc KO mice. This suggests that sensory circuits may require ongoing plasticity that refines information processing in response to experience even in adult brains. Previous studies that have evaluated binocular mouse V1 have predominately used methods that lack single-cell resolution (intrinsic or wide-field imaging, local field potentials) or have issues with sampling bias for deeper layers (single unit electrophysiology). We found, at the single-cell level, that at eye-opening binocular V1 is mostly comprised of monocular responding cells (~90%) and that by P30 there are ~80% monocular/~20% binocular cells, which remains constant into adulthood. This high prevalence of monocular cells in L2/3 of binocular V1 is similar to what has been previously observed using GCaMP6 recordings (Jaepel, Hübener, Bonhoeffer, & Rose, 2017; Salinas et al., 2017). However, it is possible that because the transgenic Thy1GCaMP6s expression in single neurons is lower than that a viral expression of GCaMP6s we may miss weak neuronal responses, especially from the ipsilateral eye, which would lead to under-estimation of binocular cell numbers (Dana et al., 2014). Also, there may be some bias in the population of cells expressing GCaMP6s as not every L2/3 neuron expresses the transgene. However, Thy1-GCaMP6s GP4.3 mice have been used in several studies to examine visual responses of excitatory neurons (Dana et al., 2014; Lee et al., 2017; Park, Kong, Zhou, & Cui, 2017; Sun et al., 2016). Sun et al. (2016) report 72 that of 2,608 L2/3 neurons analyzed from 6 Thy1-GCaMP6s GP4.3 mice, 1,279 (49%) were visually responsive, consistent with our findings in P30 mice, and Dana et al. (2014) report that ~80% of all L2/3 neurons are labeled in V1, indicating that the labeled neurons make up the majority of excitatory neurons. While the percentage of binocular single-cell responses is low, the emergence of binocular cells is correlated with an increase in tuning between contralateral and ipsilateral input in the neuropil. Calcium signals in large (~10,000 µm2) sections of neuropil correlate well with electrocorticogram measures of sensory-evoked population activity (Kerr, Greenberg, & Helmchen, 2005). However, smaller sections of neuropil can report stimulus-specific information that has response properties comparable to neurons in the same area (Lee et al., 2017). While this local specificity is incompatible with the view of mouse V1 as a “salt and pepper” mix of orientation preference, this finding agrees with the observation that neurons with similar tuning are arranged in minicolumns and that thalamocortical projections from LGN to V1 are arranged in patches rather than having no spatial organization (Ji et al., 2015; Ringach et al., 2016). Similarly tuned neurons also preferentially wire together, and the probability for similarly tuned neurons to be connected doubles over development from P13-15 to P22-26 (Ko et al., 2013). The axons and dendrites that form local patches of neuropil, therefore, likely respond to similar stimuli even though they arise from different cell bodies. Our findings demonstrate that similar local responses also emerge between the two eyes throughout development, mirroring the emergence of binocular neurons. The neuropil activity of the mice imaged in this study are a heterogeneous mix of axons and dendrites from different layers and brain regions. It will be of interest to examine the refinement of visual 73 response properties specifically in and projecting to L2/3 of binocular V1 using a layer or region-specific labeling. The development of inhibitory neurons plays an essential role in regulating V1 plasticity (Hensch, 2005). Parvalbumin-positive interneurons make up the majority of cortical interneurons, and from the precritical to critical period in binocular V1 display an experience-dependent increase in firing rates coinciding with a decrease in inhibitory orientation selectivity that is thought to be necessary for initiation of the critical period (Kuhlman, Tring, & Trachtenberg, 2011). This low selectivity reflects integration from the local (~100 µm) excitatory neuronal population, leading to a strongly binocular, but nonselective inhibitory population (Scholl, Pattadkal, Dilly, Priebe, & Zemelman, 2015). The experience-dependent emergence of excitatory binocular neurons we observe may be what initiates this net binocular drive onto inhibitory neurons. Since our recordings are acute, we were not able to follow the same neurons over time. It, therefore, remains to be seen if monocular neurons could become binocular by the addition or strengthening of ipsilateral eye input into V1 (i.e., a switch from monocular contralateral to binocular) or if neurons that start as monocularly ipsilateral could switch to binocular by the addition or strengthening of contralateral input. Alternatively, the majority of binocular neurons could emerge from the available pool of nonresponsive neurons. Chronically tracking neurons from the time of eye-opening to adulthood will help to address many of our questions concerning how visual responsiveness emerges in the cortex. Surprisingly, we did not find a developmental change in binocular matching in WT mice after eye-opening. In our recordings, binocular offset at eye-opening was 74 already close to the previously identified offset average in adult mice (Wang et al., 2010). This may be due to the previous study using electrophysiological recordings, which may be able to detect weaker mismatched inputs than we could detect. This conclusion assumes that the majority of mismatched neurons early in development have weak binocular responses. However, to our knowledge, the only other studies showing mismatched orientation preferences in layer 2/3 have been following monocular deprivation (Levine et al., 2017) or silencing of interneurons (Yaeger, Ringach, & Trachtenberg, 2019), not during normal development. The development of binocular matching may occur mostly in thalamic inputs (which precedes the development of matching in L4) and L4 neurons that innervate L2/3, rather than in L2/3 itself (Gu & Cang, 2016). Thus, the binocular neurons that develop in L2/3 may already receive matched input that does not undergo further refinement. Interestingly, we do see a trend towards higher offset in dark-reared mice. In addition, Arc KO mice have higher offset values at P14, although this decreases to WT levels by P30. The offset observed in Arc KO mice and in dark-reared L2/3 may be the result of poorly tuned L4 inputs, and later refined in upper cortical layers by Arc independent mechanisms. We find that binocular neurons have different response properties to those of monocular neurons. In particular, binocular cells rapidly develop an orientation preference bias towards horizontal stimuli that we do not observe in monocular neurons. Interestingly, we only observed orientation and direction selectivity changes during development in contralateral eye responses, both in monocular and binocular neurons. This suggests both eye-specific information and plasticity is required to refine binocular V1. We conclude that the emergence of binocular responding cells in binocular V1 is 75 vital for processing of input from each eye. Much of what is known about experience-dependent plasticity in V1 comes from monocular deprivation studies. Less is known about the normal activity-dependent processes that are required for visual response properties, especially in binocular V1. In cats, where the majority of neurons in V1 are binocularly responsive, spontaneous activity originating in the retina is sufficient to guide the formation of OD bands (Stryker & Harris, 1986). However, visual experience is necessary during the critical period in the cat to strengthen ipsilateral responsiveness (Crair et al., 1998). We find that the development and tuning of binocular neurons are experience-dependent in mouse V1. Both the increase in binocular neurons and the horizontal orientation preference in the few existing binocular neurons are absent in dark-reared mice. Moreover, the increase in correlated binocular activity in the neuropil is also experience-dependent. Interestingly, orientation selectivity does not seem to be experience-dependent. This may be due to processes that occur before eye-opening or that tuning occurs predominantly in L4 or even thalamic input from the lateral geniculate nucleus (Jaepel et al., 2017). It remains to be seen how conserved these mechanisms are in other species that have predominantly binocular vision. Experience leads to activity-dependent gene expression. The immediate early gene Arc is necessary for experience-dependent synaptic plasticity in mouse V1, and Arc expression is sufficient to prolong or reopen the critical period of OD plasticity (Jenks et al., 2017). However, it was not clear whether Arc regulates the normal development of visual response properties. Here, we find that Arc expression controls the normal distribution of monocular and binocular neurons in binocular V1. Surprisingly, Arc KO 76 mice show a significant increase in binocular neurons as compared with WT mice, which is the opposite of what occurs in DR mice. Also, Arc KO mice show decreased contralateral monocular neurons. Thus, at the population level, there is a change in overall OD where contra- and ipsilateral drive is more equivalent than the contralateral dominance observed in WT mice. Consistent with this, ODI derived from visually evoked potentials shows that Arc KO mice at the population level have ~1:1 contra to ipsi response amplitude ratio as opposed to ~2:1 in WT mice (McCurry et al., 2010). Many forms of synaptic plasticity require Arc, such as LTP/LTD and homeostatic scaling (Shepherd & Bear, 2011). Arc KO mice lack ODP following monocular deprivation (McCurry et al., 2010) and the scaling of mini excitatory postsynaptic currents (mEPSCs) following dark adaptation (Gao et al., 2010). Both phenotypes suggest deficits in downscaling noncorrelated/inactive synaptic inputs. Indeed, mEPSCs in Arc KO V1 are increased compared to WT mice. Contralateral input matures before ipsilateral input in binocular V1 (Smith & Trachtenberg, 2007) and is thought to guide, but also compete with ipsilateral input as contralateral eye silencing accelerates ipsilateral maturation. We hypothesize that one way that contralateral input limits ipsilateral development is through homeostatic regulation of total output of the cell, leading to synaptic downscaling and suppressing the addition of new, ipsilateral inputs. Since downscaling requires Arc, neurons that lack Arc could form additional ipsilateral inputs during development that lead to more binocular neurons and an overall shift in OD. An alternative model is that binocular neurons initially arise from potentiation or an increase in ipsilateral input during development that is then subsequently refined by LTD-like processes. Since Arc is also required for LTD in V1 (Jenks et al., 2017), neurons that lack 77 Arc may end up with excess ipsilateral input due to a lack of LTD or pruning of uncorrelated ipsilateral input. In addition to cell-autonomous processes, Arc may also have a systems-wide role in regulating experience-dependent correlated activity in different brain regions during development (Kraft et al., 2017), which may occur through noncell-autonomous regulation of synaptic plasticity through the intercellular spread of Arc protein capsids (Pastuzyn et al., 2018). Further studies will be required to determine precisely how Arc regulates cellular changes in V1 during development. Experience-dependent plasticity is well documented in the adult brain, although in general, the extent and ease of induction are limited compared with the juvenile brain (Hubener & Bonhoeffer, 2014). Therefore, the role of experience-dependent plasticity in regulating adult sensory processing is thought to be restricted. However, manipulations of experience such as dark exposure can reactivate developmental plasticity and allow recovery from developmental deficits such as amblyopia (He, Ray, Dennis, & Quinlan, 2007), suggesting that adult cortex is still primed to undergo experience-dependent plasticity. Here, we find that reducing Arc levels only in the adult cortex and well-beyond the classical critical period in mice resulted in a significant change in the distribution of monocular and binocular neurons. These changes were observed in a relatively short time course of Arc knockdown (~2 weeks) and recapitulate the developmental phenotype observed in Arc KO mice. These results suggest that Arc-dependent, and presumably experience-dependent, plasticity is required for ongoing maintenance of binocular circuits in adult V1. These results also suggest that despite a reduction in the induction of Arc protein expression over age (Jenks et al., 2017), cortical neurons are still sensitive to 78 changes in Arc expression. Periods of visual deprivation or visual enrichment in adult animals could, conceivably, also change the percentage of responsive neurons that are binocular through decreases or increases in Arc expression. Such changes could underlie the susceptibility of adult V1 to ODP following monocular deprivation. Taken together, we conclude that experience is required for the emergence and tuning of binocular neurons in V1 during development and ongoing maintenance in adult V1. 3.5 Materials and methods 3.5.1 Animals All procedures were performed in compliance with the Institutional Animal Care and Use Committee at the University of Utah. The mouse line C57BL/6J-Tg(Thy1GCaMP6s)GP4.3Dkim/J (Stock No. 024275, The Jackson Laboratory) was used for dark rearing experiments and crossed to Arc KO and Arc cKO mouse lines in all other experiments. The Arc KO (GFP knock-in) mouse line C57BL/6-Arctm1Stl/J (Stock No. 007662, The Jackson Laboratory) was crossed with Thy1 GCaMP6s mice, and resulting ArcWT/GFP Thy1 GCaMP6s mice crossed to yield littermate WT and Arc KO (ArcGFP/GFP) Thy1 GCaMP6s mice. Arc conditional knock out (cKO) mice were generously provided by Dr. Richard Palmiter (Chen et al., 2018). All mouse lines were maintained on a C57BL/6 background. Both male and female mice were used for all experiments. Mice were group-housed until the day of recording. P14 and P20 mice were kept with their dams until the day of recording. All P30 and P180 mice were weaned at P21. All mice, except dark-reared mice, were kept on a 12 hour:12 hour light/dark cycle. Dark-reared mice were housed in a separate dark room inside a dark cabinet from birth and checked daily using a red LED light. 79 3.5.2 Cranial window surgeries Cranial window surgeries were performed identically for all ages and genotypes used in the study. On the day of recording, mice were anesthetized with 2% isoflurane and injected subcutaneously with enrofloxacin (VETONE), Carprofen (Zoetis), and Dexamethasone (VETONE). The fur over the scalp was trimmed and mice secured in a stereotaxic (Kopf). Lidocaine (VETONE) was injected subcutaneously under the scalp and the scalp sterilized with alternating swabs of iodine and 70% ethanol. The skin over the skull surface was removed using scissors, and the skull scraped clean using a scalpel and then dried. A 3 mm circle was drawn over the right binocular V1, centered 3mm lateral of midline and 1 mm anterior lambda. The skull outside the 3mm circle and the skin margins were covered by a layer of vetbond (3M) and then dental cement (Lang) mixed with black acrylic paint powder (Sargent Art) and allowed to dry. A 3 mm craniotomy was carefully drilled using a high-speed drill and 0.5 mm drill bit. Ice cold ACSF (124 mM NaCl, 3.2 mM KCl, 1.25 mM NaH2PO4, 2 mM CaCl) was periodically placed on the site of drilling to cool the brain and clean the skull surface. When only a thin layer of bone remained at the margins of the craniotomy, forceps were used to remove the circle of bone under a drop of ACSF. ACSF was used to wash the brain surface until surface bleeding was controlled. In some cases, Gelfoam (Pfizer) was placed on the brain surface to remove excess blood. When bleeding had stopped, a 3 mm circular coverslip (No. 0 coverglass, Warner Instruments) was placed on the brain surface and held down using a needle attached to the arm of the stereotax. Vetbond and dental cement were used to attach the edges of the coverglass to the skull. A custom stainless steel head plate was attached to the skull over the coverglass using dental cement. 80 Animals were allowed to recover on a heating pad and given a postsurgery injection of carprofen subcutaneously to control pain. All animals were allowed to recover for 3 hours minimum before recording. In the case of dark-reared and normally reared mice, this recovery took place in a dark cabinet and transportation was carried out in the dark with no light exposure until the animal was under anesthesia. 3.5.3 Two-photon calcium imaging and visual stimulation Calcium responses were recorded using a raster-scanning two-photon microscope (Bruker Ultima) controlled by Prairie View software. A Coherent Chameleon laser was used for fluorophore excitation, fixed at 920 nm for GCaMP6s imaging and 1020 nm for mCherry imaging. Laser power was ~80 mW at the sample. A 20x water immersion objective (Olympus, one numerical aperture) was used in all experiments. Images were acquired at 2.64-2.94 Hz over a field of view 594x594 µm at 2.32 µm/pixel. All imaging was performed at a depth of 150-210 µm below the pia as measured from the center of the field of view. A dichroic mirror was used to separate red and green fluorescence. All testing was done in awake head-fixed mice held stationary in a plastic tube. Mice were anesthetized with 2% isoflurane and placed below the objective and allowed to habituate for 30 minutes before imaging. Normally reared and dark-reared mice did not have their monitor turned on until 5 minutes before recording. Visual stimuli were presented on an LCD monitor centered perpendicular to the mouse’s midline, 36 cm from the mouse’s head. Visual stimuli consisted of drifting, sinusoidal grating of spatial frequency 0.05 cycles/degree moving one cycle/second generated by the MATLAB (Mathworks) toolbox, Psychtoolbox. 6 orientations from 0-150°, each with two directions of movement orthogonal to their orientation, were used to elicit calcium responses. Each 81 direction was repeated five times in pseudorandom order. Visual stimuli were each presented for 5 seconds with 5 seconds of a grey screen between each presentation. Visual stimuli were time-locked to data acquisition using a 5-volt square wave pulse generated using a Measurement Computing USB-1208FS-Plus data acquisition board and recorded by Prairie View software. Visual stimuli were presented independently to the contralateral or ipsilateral eye by placing an opaque eye block in front of the opposing eye to temporarily occlude vision. The eye stimulated first (contralateral or ipsilateral) were randomized between animals and recordings. 3.5.4 Analysis of two-photon imaging data Only one FOV was quantified for each animal. Collected frames were opened in ImageJ (NIH) using the Prairie Reader plugin, and contralateral and ipsilateral recordings concatenated. The two-photon calcium imaging analysis pipeline, Suite2P (https://github.com/MouseLand/suite2p) was used to register and detect cells in recorded data. Default settings for the December 2018 distribution of Suite2P were used except for the following: diameter of 9, Tau of 2, frames per second 2.64 or 2.94, 3 to 1 neuropil to cell ratio, and 191 minimum neuropil pixels. Detected ROIs were manually curated to discard ROIs in which a cell body was not visible in the mean image. Remaining ROIs were classified as putative neurons active during the recording period. All further analysis was carried out using custom functions written in MATLAB (https://github.com/Shepherdlab/2Photon). We measured spontaneous activity in the contralateral ΔF/F ((F-F0)/F0 trace with a neuropil correction factor set to 0.7 (Stringer et al., 2019) by measuring the area under the curve of events above 0.1 ΔF/F during the 82 blank screen period, excluding the first second to avoid residual decay from the preceding stimulus (as analyzed previously, Keck et al., 2013). For all other comparisons, the analysis was run on Z-scored traces. Due to the high neuropil signal in the densely labeled Thy1-GCaMP6s line, mean F0 after neuropil subtraction can in some cases be close to 0, causing misleading fold changes in fluorescence. Therefore, we used Z-scores ((F-F0)/FSTD), which report the change in fluorescence (ΔF) normalized to the variance in baseline (standard deviation of grey screen period) rather than the mean fluorescence of baseline (mean of F0; El-Boustani et al., 2018). Following Z-scoring, the period preceding and including the five presentations of each unique stimulus was then segmented. The mean Z-score during each presentation and its corresponding prestimulus period were statistically compared using a paired sample t test. The five stimulus presentations were then averaged together as a robust mean, and the mean Z-score of the mean trace during the stimulus calculated. Only neurons with a significant response to at least one stimulus (during stimulation of either the contralateral or ipsilateral eye) were included in further analysis. The criteria used to determine a significant response was a p value of the paired t test below 0.05, and a mean Z-score of the mean response above 0.5 for the same unique stimulus. This amplitude threshold was determined by taking the 99.58th percentile of mean responses during the grey screen periods and was the same for all ages. To determine the false discovery rate of this method, we used random timestamps from the recording in place of the timestamps of stimulus onset for P14, P20, and P30 WT mice. Mean false discovery for each eye was 1.0% for P14, 2.3% for P20 and 1.2% for P30. Mean total false discovery (contra+ipsi) for each age group is 1.9% for P14, 4.5% for P20, and 2.4% for P30. A ratio 83 of the number of neurons responding to the contralateral eye, and the number of neurons responding to the ipsilateral eye was calculated for each mouse. Within each group (genotype/age), this measure was used to remove outliers more than one standard deviation from the mean. Responsive neurons were classified as binocular or monocular based on whether there was a significant response to stimulation of both the contralateral and ipsilateral eye, or only to one eye. For all responsive neurons, their direction selectivity was calculated using a global measure of direction selectivity equal to DSI= 𝑎𝑎𝑎𝑎𝑎𝑎((∑𝑘𝑘 𝑅𝑅(𝜃𝜃𝜃𝜃)exp(𝑖𝑖𝑖𝑖𝑖𝑖)) ∑𝑘𝑘 𝑅𝑅(𝜃𝜃𝜃𝜃) (3.1) where R(θk) is the response to the direction θk. Orientation selectivity was calculated in a similar manner, where OSI= 𝑎𝑎𝑎𝑎𝑎𝑎((∑𝑘𝑘 𝑅𝑅(𝜃𝜃𝜃𝜃)exp(2𝑖𝑖𝑖𝑖𝑖𝑖)) ∑𝑘𝑘 𝑅𝑅(𝜃𝜃𝜃𝜃) (3.2) where R(θk) is the response to the orientation θk. Orientation preference is derived during the above equation as 𝑎𝑎𝑎𝑎𝑎𝑎((∑𝑘𝑘 𝑅𝑅(𝜃𝜃𝜃𝜃)exp(2𝑖𝑖𝑖𝑖𝑖𝑖))/2 (3.3) and converted to degrees. The horizontal bias index was calculated for each neuron (or eye-specific response for binocular neurons) as 84 1 - (min(abs(pref-0°), abs(180°-(pref-0°)))/45° (3.4) The binocular ocular dominance index (ODI), taken as a ratio of their ipsilateral response over the sum of their ipsilateral and contralateral response for binocular neurons, and for each eye utilized the mean response to the direction which elicited the largest mean response. This was not necessarily the same direction for each eye. Population ODI used an ODI score of 0 for contralateral monocular neurons and 1 for ipsilateral monocular neurons. All data are first averaged within the animal and then between animals in a group (age/genotype). All response properties calculated for all groups used in this study are reported in Table 3.1. For analysis of neuropil patches, in each recording, a blood vessel passing perpendicular to the plane of imaging was manually selected, and the mean fluorescent signal of the blood vessel in each frame calculated and subtracted from the trace of each neuropil patch. This subtraction controls for any light contamination related to the visual stimuli, and assures neuropil signal reflects true local calcium fluctuations. Calculation of Z-score and defining significant visual responses were done identically for neuropil and neurons. Signal correlation between the contralateral and ipsilateral responses was calculated as a linear Pearson correlation between vectors of the mean response to each direction in MATLAB (Jeon et al., 2018; Ko et al., 2013). 3.5.5 Virus injections For Arc conditional knock out studies, either rAAV5/hSyn-mCherry or rAAV5/hSyn-mCherry-Cre were injected at concentrations of 1012 GC/mL (University of 85 North Carolina Viral Vector Core). Imaging in Thy1 GCaMP6s Arc cKO mice was performed 2 weeks after virus injection. Mice were anesthetized with 2% isoflurane (VETONE) and injected subcutaneously with enrofloxacin (7mg/kg, VETONE), Carprofen (5mg/kg, Zoetis), and Dexamethasone (0.2 mg/kg, VETONE). The fur over the scalp was trimmed and mice secured in a stereotax (Kopf). Lidocaine (VETONE) was injected subcutaneously under the scalp and the scalp sterilized with alternating swabs of iodine and ethanol. A small incision was then made to expose the skull surface and the position of the right, binocular V1 marked (3 mm lateral midline, 1 mm anterior Lambda). A high-speed drill (Foredom) with a 0.5 mm drill bit was used to drill a burr hole over the marked site. The virus was loaded into a pulled, glass pipette fitted to a Nanoject II system (Drummond Scientific). The pipette tip was lowered 200 µm from the brain surface and allowed to rest for 5 minutes. 9.2 nLs of the virus was injected every 15 seconds for 25 minutes for a total volume of 920 nLs. Following injection, the pipette tip remained in position for 5 minutes before being carefully being withdrawn. The injection site was then sealed with a small drop of Vetbond (3M), and the scalp sewn up using vicryl sutures (Ethicon). Animals recovered on a warm heating pad before being returned to their cage. Carprofen tablets (2 mg/tablet) were used postoperatively to control pain. Injected mice were returned to their cage with gender-matched siblings following surgery. 3.5.6 Immunohistochemistry To confirm effective knock out of Arc using the mCherry-Cre virus, an Arc cKO mouse was injected at P20 and sacrificed at P34, then perfused with phosphate-buffered 86 saline (PBS) followed by 4% ice-cold paraformaldehyde in PBS. The brain was removed and stored in 4% paraformaldehyde in PBS for a minimum of 24 hours before being transferred to 30% sucrose in PBS. The brain was cut into 40 µm sections on a Leica 1950 CM cryostat and stored in PBS at 4°C. Sections containing binocular V1 were blocked in 5% normal donkey serum (Jackson ImmunoResearch)/0.1% Triton X-100 (Amresco) in PBS for 1 hour and then transferred into 1:1000 custom made rabbit antiArc antibody (Proteintech) diluted in blocking buffer overnight. Sections were then washed three times for 10 minutes each time in PBS, before being placed in secondary donkey anti-rabbit Alexa Fluor 488 (Jackson ImmunoResearch) for 4 hours. Sections were then washed three times for 10 minutes each time in PBS before mounting on slides with ProlongGold Antifade reagent (Invitrogen). 3.5.7 Statistics N in all comparisons is the number of animals. The number of neurons is not used as statistical N for any comparison between groups in this study to avoid bias from biological replicates. Calculation of paired t test between pre- and poststimulation means on individual trials was calculated using built-in MATLAB functions. All other statistics were calculated using JMP (SAS). Significance level was set at α<0.05 for all within/two group comparisons using a two-sample ANOVA or t test. Otherwise, an ANOVA was used to calculate an F value and p value followed by a posthoc Tukey’s Honest Significant Difference test to determine significant differences between groups. 87 3.6 References Chen, J. Y., Campos, C. A., Jarvie, B. C., & Palmiter, R. D. (2018). Parabrachial CGRP neurons establish and sustain aversive taste memories. Neuron, 100(4), 891–899. https://doi.org/10.1016/j.neuron.2018.09.032 Crair, M. C., Gillespie, D. C., & Stryker, M. P. (1998). The role of visual experience in the development of columns in cat visual cortex. Science, 279(5350), 566–570. https://doi.org/10.1126/science.279.5350.566 Dana, H., Chen, T., Hu, A., Shields, B. C., Guo, C., Looger, L. L., … Svoboda, K. (2014). Thy1-GCaMP6 transgenic mice for neuronal population imaging in vivo. PLOS One, 9(9). https://doi.org/10.1371/journal.pone.0108697 Dräger, U. C. (1975). Receptive fields of single cells and topography in mouse visual cortex. Journal of Comparative Neurology, 160(3), 269–289. https://doi.org/10.1002/cne.901600302 El-Boustani, S., Ip, J. P. K., Breton-Provencher, V., Knott, G. W., Okuno, H., Bito, H., & Sur, M. (2018). Locally coordinated cell-wide synaptic plasticity of visual cortex neurons in vivo. Science, 360(6395), 1349–1354. https://doi.org/10.1126/science.aao0862 Espinosa, J. S., & Stryker, M. P. (2012). Development and plasticity of the primary visual cortex. Neuron, 75(2), 230–249. https://doi.org/10.1016/j.neuron.2012.06.009 Feng, G., Mellor, R. H., Bernstein, M., Keller-Peck, C., Nguyen, Q. T., Wallace, M., … Sanes, J. R. (2000). Imaging neuronal subsets in transgenic mice expressing multiple spectral variants of GFP. Neuron, 28(1), 41–51. https://doi.org/10.1016/S08966273(00)00084-2 Gao, M., Sossa, K., Song, L., Errington, L., Cummings, L., Hwang, H., … Lee, H. (2010). A specific requirement of Arc/Arg3.1 for visual experience-induced homeostatic synaptic plasticity in mouse primary visual cortex. The Journal of Neuroscience, 30(21), 7168–7178. https://doi.org/10.1523/JNEUROSCI.106710.2010 Greifzu, F., Pielecka-Fortuna, J., Kalogeraki, E., Krempler, K., Favaro, P. D., Schluter, O. M., & Lowel, S. (2014). Environmental enrichment extends ocular dominance plasticity into adulthood and protects from stroke-induced impairments of plasticity. Proceedings of the National Academy of Sciences, 111(3), 1150–1155. https://doi.org/10.1073/pnas.1313385111 Gu, Y., & Cang, J. (2016). Binocular matching of thalamocortical and intracortical circuits in the mouse visual cortex. ELife, 5. https://doi.org/10.7554/eLife.22032 Gu, Y., Huang, S., Chang, M. C., Worley, P., Kirkwood, A., & Quinlan, E. M. (2013). Obligatory role for the immediate early gene NARP in critical period plasticity. 88 Neuron, 79(2), 335–346. https://doi.org/10.1016/j.neuron.2013.05.016 Hagihara, K. M., Murakami, T., Yoshida, T., Tagawa, Y., & Ohki, K. (2015). Neuronal activity is not required for the initial formation and maturation of visual selectivity. Nature Neuroscience, 18(12), 1780–1788. https://doi.org/10.1038/nn.4155 He, H., Hodos, W., & Quinlan, E. M. (2006). Visual deprivation reactivates rapid ocular dominance plasticity in adult visual cortex. The Journal of Neuroscience, 26(11), 2951–2955. https://doi.org/10.1523/JNEUROSCI.5554-05.2006 He, H. Y., Ray, B., Dennis, K., & Quinlan, E. M. (2007). Experience-dependent recovery of vision following chronic deprivation amblyopia. Nature Neuroscience, 10(9), 1134–1136. https://doi.org/10.1038/nn1965 Hensch, T. K. (2005). Critical period plasticity in local cortical circuits. Nature Reviews. Neuroscience, 6(11), 877–888. https://doi.org/10.1038/nrn1787 Hoy, J. L., & Niell, C. M. (2015). Layer-specific refinement of visual cortex function after eye opening in the awake mouse. Journal of Neuroscience, 35(8), 3370–3383. https://doi.org/10.1523/JNEUROSCI.3174-14.2015 Hubel, D. H., Wiesel, T. N., & LeVay, S. (1976). Functional architecture of area 17 in normal and monocularly deprived macaque monkeys. Cold Spring Harbor Symposia on Quantitative Biology, 40, 581–589. https://doi.org/10.1101/SQB.1976.040.01.054 Hubener, M., & Bonhoeffer, T. (2014). Neuronal plasticity: Beyond the critical period. Cell, 159(4), 727–737. https://doi.org/10.1016/j.cell.2014.10.035 Jaepel, J., Hübener, M., Bonhoeffer, T., & Rose, T. (2017). Lateral geniculate neurons projecting to primary visual cortex show ocular dominance plasticity in adult mice. Nature Neuroscience, 20(12), 1708–1714. https://doi.org/10.1038/s41593-017-00210 Jenks, K.R., Kim, T., Pastuzyn, E. D., Okuno, H., Taibi, A. V., Bito, H., … Shepherd, J. D. (2017). Arc restores juvenile plasticity in adult mouse visual cortex. Proceedings of the National Academy of Sciences of the United States of America, 114(34). https://doi.org/10.1073/pnas.1700866114 Jenks, K.R., Kim, T., Pastuzyn, E. D., Okuno, H., Taibi, A. V., Bito, H., … Shepherd, J. D. (2017). Arc restores juvenile plasticity in adult mouse visual cortex. Proceedings of the National Academy of Sciences, 114(34), 9182–9187. https://doi.org/10.1073/pnas.1700866114 Jeon, B. B., Swain, A. D., Good, J. T., Chase, S. M., & Kuhlman, S. J. (2018). Feature selectivity is stable in primary visual cortex across a range of spatial frequencies. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-33633-2 Ji, W., Gămănuţ, R., Bista, P., D’Souza, R. D., Wang, Q., & Burkhalter, A. (2015). 89 Modularity in the organization of mouse primary visual cortex. Neuron, 87(3), 632– 643. https://doi.org/10.1016/j.neuron.2015.07.004 Keck, T., Keller, G. B., Jacobsen, R. I., Eysel, U. T., Bonhoeffer, T., & Hübener, M. (2013). Synaptic scaling and homeostatic plasticity in the mouse visual cortex in vivo. Neuron, 80(2), 327–334. https://doi.org/10.1016/j.neuron.2013.08.018 Kerr, J. N. D., Greenberg, D., & Helmchen, F. (2005). From The Cover: Imaging input and output of neocortical networks in vivo. Proceedings of the National Academy of Sciences, 102(39), 14063–14068. https://doi.org/10.1073/pnas.0506029102 Ko, H., Cossell, L., Baragli, C., Antolik, J., Clopath, C., Hofer, S. B., & Mrsic-Flogel, T. D. (2013). The emergence of functional microcircuits in visual cortex. Nature, 496(7443), 96–100. https://doi.org/10.1038/nature12015 Kraft, A. W., Mitra, A., Bauer, A. Q., Snyder, A. Z., Raichle, M. E., Culver, J. P., & Lee, J.-M. (2017). Visual experience sculpts whole-cortex spontaneous infraslow activity patterns through an Arc-dependent mechanism. Proceedings of the National Academy of Sciences, 114(46), E9952–E9961. https://doi.org/10.1073/pnas.1711789114 Kuhlman, S. J., Tring, E., & Trachtenberg, J. T. (2011). Fast-spiking interneurons have an initial orientation bias that is lost with vision. Nature Neuroscience, 14(9), 1121– 1123. https://doi.org/10.1038/nn.2890 Lee, S., Meyer, J. F., Park, J., & Smirnakis, S. M. (2017). Visually driven neuropil activity and information encoding in mouse primary visual cortex. Frontiers in Neural Circuits, 11(50). https://doi.org/10.3389/fncir.2017.00050 Levelt, C. N., & Hübener, M. (2012). Critical-period plasticity in the visual cortex. Annual Review of Neuroscience, 35, 309–330. https://doi.org/10.1146/annurevneuro-061010-113813 Levine, J. N., Chen, H., Gu, Y., & Cang, J. (2017). Environmental enrichment rescues binocular matching of orientation preference in the mouse visual cortex. The Journal of Neuroscience, 37(24), 5822–5833. https://doi.org/10.1523/jneurosci.353416.2017 Li, Y., Fitzpatrick, D., & White, L. E. (2006). The development of direction selectivity in ferret visual cortex requires early visual experience. Nature Neuroscience, 9(5), 676–681. https://doi.org/10.1038/nn1684 Matthies, U., Balog, J., & Lehmann, K. (2013). Temporally coherent visual stimuli boost ocular dominance plasticity. Journal of Neuroscience, 33(29), 11774–11778. https://doi.org/10.1523/jneurosci.4262-12.2013 McCurry, C. L., Shepherd, J. D., Tropea, D., Wang, K. H., Bear, M. F., & Sur, M. (2010). Loss of Arc renders the visual cortex impervious to the effects of sensory 90 experience or deprivation. Nature Neuroscience, 13(4), 450–457. https://doi.org/10.1038/nn.2508 Niell, C. M., & Stryker, M. P. (2008). Highly selective receptive fields in mouse visual cortex. Journal of Neuroscience, 28(30), 7520–7536. https://doi.org/10.1523/JNEUROSCI.0623-08.2008 Park, J. H., Kong, L., Zhou, Y., & Cui, M. (2017). Large-field-of-view imaging by multipupil adaptive optics. Nature Methods, 14(6), 581–583. https://doi.org/10.1038/nmeth.4290 Pastuzyn, E. D., Day, C. E., Kearns, R. B., Kyrke-Smith, M., Taibi, A. V., McCormick, J., … Shepherd, J. D. (2018). The neuronal gene Arc encodes a repurposed retrotransposon Gag protein that mediates intercellular RNA transfer. Cell, 172(1– 2), 275-288.e18. https://doi.org/10.1016/j.cell.2017.12.024 Peron, S. P., Freeman, J., Iyer, V., Guo, C., & Svoboda, K. (2015). A cellular resolution map of barrel cortex activity during tactile behavior. Neuron, 86(3), 783–799. https://doi.org/10.1016/j.neuron.2015.03.027 Pizzorusso, T. (2002). Reactivation of ocular dominance plasticity in the adult visual cortex. Science, 298(5596), 1248–1251. https://doi.org/10.1126/science.1072699 Rakic, P. (1976). Prenatal genesis of connections subserving ocular dominance in the rhesus Monkey. Nature, 261(5560), 467–471. https://doi.org/10.1038/261467a0 Ringach, D. L., Mineault, P. J., Tring, E., Olivas, N. D., Garcia-Junco-Clemente, P., & Trachtenberg, J. T. (2016). Spatial clustering of tuning in mouse primary visual cortex. Nature Communications, 7(12270). https://doi.org/10.1038/ncomms12270 Rochefort, N. L., Narushima, M., Grienberger, C., Marandi, N., Hill, D. N., & Konnerth, A. (2011). Development of direction selectivity in mouse cortical neurons. Neuron, 71(3), 425–432. https://doi.org/10.1016/j.neuron.2011.06.013 Salinas, K. J., Figueroa Velez, D. X., Zeitoun, J. H., Kim, H., & Gandhi, S. P. (2017). Contralateral bias of high spatial frequency tuning and cardinal direction selectivity in mouse visual cortex. The Journal of Neuroscience, 37(42), 1484–17. https://doi.org/10.1523/JNEUROSCI.1484-17.2017 Sawtell, N. B., Frenkel, M. Y., Philpot, B. D., Nakazawa, K., Tonegawa, S., & Bear, M. F. (2003). NMDA receptor-dependent ocular dominance plasticity in adult visual cortex. Neuron, 38(6), 977–985. Scholl, B., Pattadkal, J. J., Dilly, G. A., Priebe, N. J., & Zemelman, B. V. (2015). Local integration accounts for weak selectivity of mouse neocortical parvalbumin interneurons. Neuron, 87(2), 424–436. https://doi.org/10.1016/j.neuron.2015.06.030 Shepherd, J. D., & Bear, M. F. (2011). New views of Arc, a master regulator of synaptic 91 plasticity. Nature Neuroscience, 14(3), 279–284. https://doi.org/10.1038/nn.2708 Sherk, H., & Stryker, M. P. (1976). Quantitative study of cortical orientation selectivity in visually inexperienced kitten. Journal of Neurophysiology, 39(1), 63–70. https://doi.org/10.1152/jn.1976.39.1.63 Smith, S. L., & Trachtenberg, J. T. (2007). Experience-dependent binocular competition in the visual cortex begins at eye opening. Nature Neuroscience, 10(3), 370–375. https://doi.org/10.1038/nn1844 Stringer, C., Pachitariu, M., Steinmetz, N., Reddy, C. B., Carandini, M., & Harris, K. D. (2019). Spontaneous behaviors drive multidimensional, brainwide activity. Science, 364(6437). https://doi.org/10.1126/science.aav7893 Stryker, M., & Harris, W. (1986). Binocular impulse blockade prevents the formation of ocular dominance columns in cat visual cortex. The Journal of Neuroscience, 6(8), 2117–2133. https://doi.org/10.1523/jneurosci.06-08-02117.1986 Sun, W., Tan, Z., Mensh, B. D., & Ji, N. (2016). Thalamus provides layer 4 of primary visual cortex with orientation- and direction-tuned inputs. Nature Neuroscience, 19(2), 308–315. https://doi.org/10.1038/nn.4196 Tagawa, Y., Kanold, P. O., Majdan, M., & Shatz, C. J. (2005). Multiple periods of functional ocular dominance plasticity in mouse visual cortex. Nature Neuroscience, 8(3), 380–388. https://doi.org/10.1038/nn1410 Vetencourt, J. F. M., Sale, A., Viegi, A., Baroncelli, L., De Pasquale, R., O’Leary, O. F., … Maffei, L. (2008). The antidepressant fluoxetine restores plasticity in the adult visual cortex. Science, 320(5874), 385–388. https://doi.org/10.1126/science.1150516 Wang, B. S., Feng, L., Liu, M., Liu, X., & Cang, J. (2013). Environmental enrichment rescues binocular matching of orientation preference in mice that have a precocious critical period. Neuron, 80(1), 198–209. https://doi.org/10.1016/j.neuron.2013.07.023 Wang, B. S., Sarnaik, R., & Cang, J. (2010). Critical period plasticity matches binocular orientation preference in the visual cortex. Neuron, 65(2), 246–256. https://doi.org/10.1016/j.neuron.2010.01.002 Wang, K. H., Majewska, A., Schummers, J., Farley, B., Hu, C., Sur, M., & Tonegawa, S. (2006). In vivo two-photon imaging reveals a role of arc in enhancing orientation specificity in visual cortex. Cell, 126(2), 389–402. https://doi.org/10.1016/j.cell.2006.06.038 Yaeger, C. E., Ringach, D. L., & Trachtenberg, J. T. (2019). Neuromodulatory control of localized dendritic spiking in critical period cortex. Nature, 567(7746), 1001–1004. https://doi.org/10.1038/s41586-019-0963-3 92 Yoon, B.-J., Smith, G. B., Heynen, A. J., Neve, R. L., & Bear, M. F. (2009). Essential role for a long-term depression mechanism in ocular dominance plasticity. Proceedings of the National Academy of Sciences, 106(24), 9860–9865. https://doi.org/10.1073/pnas.0901305106 93 Figure 3.1 Visual response properties in layer 2/3 excitatory neurons of binocular V1 rapidly mature after eye-opening. A. Imaging was performed in mouse binocular V1 (green), which receives input from both the contralateral (contra, blue) and ipsilateral (ispi, yellow) eyes. B. Regions of interest (ROIs) corresponding to the soma of a neuron (green outline) and the surrounding area of neuropil (red outline). C. The averaged responses of an example neuron to stimuli (grey bars show the period of each visual stimulation) through the contra (blue) and ipsi (yellow) eye, after neuropil subtraction and z-scoring. The thick bars below the X-axis show the orientation of each stimulus, while the arrow bisecting it indicates the direction of movement. D. Percentage of neurons that are visually responsive at each time point. E. Representative image of layer 2/3 excitatory neurons during the recording of visually evoked calcium responses. Neurons that respond solely to the contralateral eye are labeled blue, neurons that respond solely to the ipsilateral eye are labeled yellow, and neurons that respond to both eyes (binoc) are labeled green. F. Ocular dominance index (ODI) of the total population of visually responsive neurons. An ODI of 0 is purely contralateral and 1 purely ipsilateral. G. Example tuning curves for two neurons showing: orientation preference (Pref), orientation selectivity index (OSI), and direction selectivity index (DSI). The neuron on the left is weakly orientation-selective for 161° and weakly direction selective. The neuron on the right is highly orientation-selective for 2° and modestly direction selective. H. Distribution of orientation preference in binocular neurons for contralateral responses at each age (left). A horizontal bias index (HBI) quantifies the bias of the population for horizontal (1) or vertical (-1) orientations, or whether no bias is present (0). I. Distribution of orientation preference in binocular neurons for ipsilateral response at each age. J. Distribution of orientation preference for contralateral monocular neurons. K. Distribution of orientation preference for ipsilateral monocular neurons. L. Quantification of OSI. M. Quantification of DSI. (* indicates a significant difference between groups, p<0.05. # indicates a significant difference within a group from the expected mean, p<0.05. Error bars represent standard error of the mean. Open circles indicate a data point from an individual mouse. N=number of animals for all statistics). 94 95 Figure 3.2 Spontaneous activity and binocular probability over development. A. Measure of spontaneous activity during the interstimulus interval determined as the area under the curve of the ΔF/F trace over time. The spontaneous activity does not change over development. B. The theoretical probability of a visually responsive neuron being binocular given the number of contralaterally and ipsilaterally responsive neurons at each age. The mean probability does not change over development. (Error bars represent standard error of the mean. Open circles indicate a data point from an individual mouse. N=number of animals for all statistics). 96 Figure 3.3 Experience-dependent development of binocular neurons. A. Representative images from a two-photon recording of visually evoked calcium responses from a P14 (left) and a P30 (right) WT mouse. B. Percentage of visually responsive neurons that respond only to the contralateral eye (contra), to the contralateral and ipsilateral eye (binoc), or only to the ipsilateral eye (ipsi). C. Ocular dominance index (ODI) of binocular neurons across development. D. Percentage of neurons responsive to visual stimulation in normally reared (NR) and dark-reared (DR) mice. E. The percentage of contralateral, binocular, and ipsilateral neurons in DR and NR mice. F. ODI of the total visually responsive neuron population in NR and DR mice. G. ODI of binocular neurons in NR and DR mice. H. Horizontal bias index (HBI) of the contralateral (left) and ipsilateral (right) responses of binocular neurons in NR and DR mice. I. Contralateral OSI of binocular neurons in NR and DR mice. J. DSI of the contralateral responses in binocular neurons. (* indicates a significant difference between groups, p<0.05. # indicates a significant difference within a group from the expected mean, p<0.05. Error bars represent standard error of the mean. Open circles indicate a data point from an individual mouse. N=number of animals for all statistics). 97 Figure 3.4 The binocular offset of orientation preference in layer 2/3 neurons. A. Average offset in orientation preference of the contralateral and ipsilateral responses in binocular neurons of WT mice over development. Offset does not significantly change over age. B. Average binocular offset in normally reared (NR) and dark-reared (DR) mice. Binocular offset does not significantly change with dark rearing. (Error bars represent standard error of the mean. Open circles indicate a data point from an individual mouse. N=number of animals for all statistics). 98 Figure 3.5 Experience-dependent increases in correlated binocular responses occur in neuropil during development. A. Example calcium responses of a binocular region of neuropil to the contralateral (blue trace) and ipsilateral (yellow trace) eye. B. Percentage of neuropil surrounding neurons that respond to visual stimuli increases over age. C. Signal correlation of contralateral and ipsilateral activity in binocular neuropil. D. Binocular signal correlation in DR mice. E. Horizontal bias index (HBI) of the contralateral (left) and ipsilateral (right) responses in binocular neuropil. F. HBI of the contra- (left) and ipsilateral (right) responses in binocular neuropil of NR and DR mice. (* indicates a significant difference between groups, p<0.05. # indicates a significant difference within a group from the expected mean, p<0.05. Error bars represent standard error of the mean. Open circles indicate a data point from an individual mouse. N=number of animals for all statistics). 99 Figure 3.6 Arc limits the emergence of binocular neurons early in development. A. Representative images from two-photon recordings of visually evoked calcium responses in Arc knock out (KO) P14 and P30 mice. B. Percentage of visually responsive neurons that respond only to the contralateral eye, to the contralateral and ipsilateral eye (binoc), or only to the ipsilateral eye in Arc KO mice. C. Ocular dominance index (ODI) of the total visually responsive neuron population in Arc KO mice. D. ODI of binocular neurons in Arc KO mice. E. Percentage of visually responsive neurons that are contralateral, binocular, or ipsilateral responsive in P30 WT and Arc KO mice. F. ODI of the neuronal population in P30 WT and Arc KO mice. G. ODI in binocular neurons of WT and Arc KO mice. H. Horizontal bias index (HBI) of the contra- (left) and ipsilateral (right) responses in binocular neurons of P30 WT and Arc KO mice. I. OSI of contralateral responses in binocular neurons of P30 WT and Arc KO mice. J. DSI of contralateral responses in binocular neurons of P30 WT and Arc KO mice. (* indicates a significant difference between groups, p<0.05. # indicates a significant difference within a group from the expected mean, p<0.05. Error bars represent standard error of the mean. Open circles indicate a data point from an individual mouse. N=number of animals for all statistics). 100 Figure 3.7 Visual responses in Arc KO mice do not significantly differ from WT mice. A. The lowest fifth percentile of fluorescence during the interstimulus period does not differ between WT and Arc KO mice at any age. B. Average offset in orientation preference of the contralateral and ipsilateral responses of binocular neurons of Arc KO mice across age. Binocular offset significantly decreases over age. C. Percentage of visually responsive neurons in P30 WT and Arc KO mice does not differ. D. The binocular offset of P30 WT and Arc KO binocular neurons. Offset at P30 does not significantly differ between WT and Arc KO mice. (* indicates a significant difference between groups, p<0.05. Error bars represent standard error of the mean. Open circles indicate a data point from an individual mouse. N=number of animals for all statistics). 101 Figure 3.8 Conditional deletion of Arc expression in binocular visual cortex. A. Immunohistochemistry performed in a mCherry-Cre injected Arc cKO mouse. Arc protein staining (green, left panel) is decreased in the region of mCherry expression (red, middle panel. Right panel, merge). B. Percentage of visually responsive neurons does not differ between Control and Cre mice. C. Binocular offset does not significantly differ between Control and Cre mice. (Error bars represent standard error of the mean. Open circles indicate a data point from an individual mouse. N=number of animals for all statistics). 102 Figure 3.9 Arc is required for the maintenance of binocular neuron number in adult binocular V1. A. Representative image from a two-photon recording of visually evoked calcium responses from neurons in a Cre injected mouse. B. Percentage of visually responsive neurons that are contralateral, binocular, and ipsilateral responsive. C. Ocular dominance index (ODI) of the total population of neurons in Control and Cre mice. D. ODI of binocular neurons in Control and Cre mice. E. Horizontal bias index (HBI) of the contralateral and ipsilateral responses in binocular neurons. F. OSI of contralateral responses in binocular neurons of Control and Cre mice. G. DSI of contralateral responses in binocular neurons of Control and Cre mice. (* indicates a significant difference between groups, p<0.05. # indicates a significant difference within a group from the expected mean, p<0.05. Error bars represent standard error of the mean. Open circles indicate a data point from an individual mouse. N=number of animals for all statistics). 103 Table 3.1 Response properties of all groups used in the study Monocular Contra OSI Ipsi OSI Contra DSI Ipsi DSI WT P14 (5) 0.45±0.05 0.61±0.09 0.50±0.03 0.46±0.07 WT P20 (5) 0.75±0.02 0.60±0.08 0.37±0.03 0.35±0.03 WT P30 (6) 0.73±0.02 0.70±0.02 0.39±0.02 0.36±0.03 Arc KO P14 (6) 0.55±0.05 0.48±0.08 0.49±0.02 0.41±0.03 Arc KO P20 (3) 0.73±0.03 0.56±0.06 0.40±0.02 0.40±0.06 Arc KO P30 (4) 0.73±0.05 0.67±0.02 0.32±0.05 0.34±0.04 NR WT (7) 0.75±0.02 0.58±0.04 0.33±0.02 0.35±0.03 DR WT (4) 0.68±0.05 0.67±0.05 0.60±0.05 0.43±0.04 Control cKO P180 (5) 0.78±0.01 0.68±0.06 0.37±0.06 0.34±0.05 Cre cKO P180 (5) 0.78±0.01 0.68±0.03 0.34±0.03 0.32±0.02 Genotype/Age (N) Binocular Contra OSI Ipsi OSI Contra DSI Ipsi DSI WT P14 (4) 0.44±0.08 0.68±0.12 0.45±0.04 0.45±0.12 WT P20 (5) 0.72±0.04 0.75±0.05 0.28±0.04 0.32±0.04 WT P30 (6) 0.71±0.04 0.63±0.04 0.28±0.03 0.30±0.02 Arc KO P14 (3) 0.46±0.23 0.51±0.22 0.46±0.03 0.56±0.10 Arc KO P20 (3) 0.80±0.04 0.76±0.07 0.37±0.05 0.36±0.14 Arc KO P30 (4) 0.71±0.05 0.70±0.05 0.23±0.01 0.27±0.01 NR WT (7) 0.71±0.05 0.63±0.08 0.23±0.02 0.36±0.03 DR WT (4) 0.59±0.08 0.55±0.06 0.39±0.09 0.27±0.02 Control cKO P180 (5) 0.81±0.02 0.73±0.01 0.24±0.05 0.26±0.03 Cre cKO P180 (5) 0.74±0.04 0.66±0.06 0.26±0.02 0.22±0.02 Genotype/Age (N) CHAPTER 4 CONCLUSIONS AND FUTURE DIRECTIONS 4.1 Summary The goal of this dissertation was to determine if Arc expression controls binocular developmental plasticity in V1. In binocular V1, brief monocular deprivation during the OD critical period leads to a loss of responsiveness to the deprived eye. The loss of responsiveness to the deprived eye shares many of the same requirements as LTD, including a requirement for expression of the immediate early gene, Arc. Closed eye depression following MD does not occur in adult mice. Loss of necessary factors for LTD, such as Arc, as the brain transitions from the critical period to the adult state, could explain why the adult brain lacks the capacity for ODP. If Arc is indeed the limiting factor for ODP, restoring Arc expression to critical period levels would be sufficient to restore the capacity for ODP. While binocularity during the OD critical period is sensitive to manipulations of visual experience, as evidenced by the effects of monocular deprivation, it is unclear what the role of visual experience is in the development of neuronal visual response properties. Studies in the mouse suggest that neurons in dark-reared mice (who have no visual experience) have similar visual response properties to neurons in normally reared mice, suggesting that there is no role for visual experience. However, the majority of 105 these studies focus on the monocular visual cortex, where the added complication of integrating input from the two eyes is not a factor. Population measures of neuronal activity show that ipsilateral responses develop after, and never equal, contralateral responses. However, it is unclear how the timing of response property development differs between contralaterally and ipsilaterally driven neurons, between monocular and binocular neurons, and how ipsilateral strengthening relates to the emergence of binocular neurons. Additionally, the altered baseline ratio in Arc KO mice between the strength of contralateral and ipsilateral population response hints that aspects of binocular development require Arc before the OD critical period begins. As visual experience induces Arc expression, experience-dependent binocular development may require Arc. Collectively, these observations inform the hypothesis that decreased experiencedependent Arc translation in adulthood explains the loss of ODP and that Arc expression shapes the development of experience-dependent visual response properties in binocular V1. Chapter 2 is published work that tests the hypothesis that decreased experiencedependent Arc expression in adulthood explains the closure of the critical period and the resulting loss of ODP. It first shows that transgenic mice overexpressing Arc have juvenile like ODP well into adulthood. The absence of ODP in adult mice is concomitant with a loss of experience-induced Arc protein, but not Arc mRNA, expression. Adult transgenic mice overexpressing Arc have no loss of experience-induced Arc protein expression. It then shows that LTD, the synaptic correlate of closed eye depression during monocular deprivation, requires Arc and protein synthesis during the critical period. The transgenic mice overexpressing Arc have juvenile like long term depression 106 during adulthood while wild type mice do not, which agrees with the well-described link between LTD and closed eye depression following monocular deprivation and confirms that both require Arc. Finally, virally overexpressing Arc in adult mouse V1 restores juvenile like closed eye depression following monocular deprivation. Thus, Arc expression is lower in adult mice than in critical period aged mice, and elevated Arc expression in adulthood is sufficient to keep the OD critical period from closing and to reopen the OD critical period. Chapter 3 tests the hypothesis that experience-dependent development of binocular visual response properties requires Arc. Given that, to date, most studies of visual development in the mouse focus on the monocular region of V1, this chapter first characterized visual response properties from mouse binocular V1 at several points in early development. In agreement with previous findings from L2/3 of monocular V1, orientation and direction-selective neurons are present at eye-opening and the percentage of neurons responsive to drifting grating increases rapidly after eye-opening (Rochefort et al., 2011). The population of binocular neurons soon after eye-opening is biased almost exclusively for vertical orientations. However, the population of binocular neurons in mice 1-2 weeks older have a horizontal bias. Despite the bias present in neighboring binocular neurons, the monocular population of neurons in binocular V1 does not display a bias at any age. Orientation selectivity index of contralateral responses in both binocular and monocular cells increases from eye-opening to the later ages, while average direction selectivity is instead decreasing in these same neurons. Ipsilateral responses of binocular and monocular neurons have no such change in orientation or direction selectivity. These findings highlight developmental differences in the binocular 107 and monocular neuronal population, as well as eye-specific differences in neuronal selectivity. Soon after eye-opening, very few neurons are binocularly responsive. Instead, the majority of neurons are monocularly responsive to either the contralateral or ipsilateral eye. The percentage of binocular neurons increases in a similar time course to the overall increase in the percentage of neurons that are responsive to the drifting grating. Darkreared mice develop neurons responsive to drifting grating but have fewer binocular neurons than normally reared mice. The binocular neurons in dark-reared mice do not have a horizontal orientation bias and have higher direction selectivity than normally reared mice. The neuropil surrounding neurons in binocular V1 has visually evoked responses with local tuning that reflects the orientation bias of the binocular population. Soon after eye-opening, neuropil responses to drifting grating is uncorrelated between the two eyes but becomes correlated 1-2 weeks later. Dark rearing prevents binocular correlation from emerging. The emergence of binocular neurons with a horizontal bias and low direction selectivity depends on experience, and thus, the hypothesis is that this emergence also required Arc. However, Arc KO mice also have few binocular neurons at eye-opening, and binocular neurons make up a more significant percentage of neurons responsive to drifting grating by 1-2 weeks after eye-opening. Indeed, at P30, the percentage of binocular neurons in Arc KO mice is higher than in wild type mice. P30 Arc KO mice do not differ from wild type mice in other response properties. The previous finding that experience induced Arc expression declines in adulthood suggests that there is no endogenous role for Arc in adult binocular plasticity 108 and that Arc is only required for the development of the binocular visual cortex and not the maintenance of any of these properties. However, Arc mRNA is still prevalent in adulthood and can play a role in rapid activity-induced plasticity, refuting that assumption (El-Boustani et al., 2018; Jakkamsetti et al., 2013). Injection of a Cre containing virus into binocular V1 of adult Arc conditional KO mice selectively ablates Arc expression in V1. Two weeks following injection of Cre, binocular neurons represent a more significant percentage of neurons responsive to drifting grating than mice injected with a control virus. The percentage of neurons responsive solely to the ipsilateral eye increases as well, which suggests that basal levels of Arc expression in adulthood maintain suppression of ipsilateral development (Smith & Trachtenberg, 2010). 4.2 Conclusions The results of this dissertation show that Arc gates juvenile like ODP following monocular deprivation and suggest that, in adulthood, the loss of experience-induced Arc translation underlies the closure of the OD critical period. It is not clear why Arc translation declines in adulthood; however, the lack of a corresponding decline in Arc mRNA suggests the regulation is not through epigenetic silencing of Arc transcription. Local decrease of dendritic mRNA translation or increase of protein degradation could underlie the decrease in Arc protein (Gozdz et al., 2017; Mabb et al., 2014; Tognini et al., 2015). Inhibiting translation blocks LTD in ex vivo slices of V1 in young mice and inhibiting translation in vivo can block ODP in young mice (Taha & Stryker, 2002). Interestingly, in the hippocampus of aged, cognitively impaired rats, the opposite phenomena occur to what we observe in healthy adults. Experience-induced Arc mRNA 109 transcription fails, and basal Arc proteins increase (Fletcher et al., 2014). Similar increases in Arc protein expression occur in both neurodevelopmental and neurodegenerative disorders (Greer et al., 2010; Lacor et al., 2004). From this, we conclude that both too much or too little Arc protein can be maladaptive. Restoring the proper balance of experience-dependent Arc expression in adulthood may be a therapeutic avenue in forms of cognitive impairment. Alternatively, increasing Arc expression could be therapeutic in brief windows where increased plasticity is beneficial. To see if restoring Arc was indeed sufficient to increase plasticity, we injected an Arc containing lentivirus into adult binocular V1. Overexpression of Arc using viral transduction could indeed restore ODP, showing that Arc is sufficient for ODP even when the OD critical period has closed. Given Arc’s necessity and sufficiency for gating binocular plasticity following monocular deprivation during the OD critical period, the hypothesis was that Arc would also be necessary for experience-dependent development of visual response properties in binocular cells. However, the developmental increase in the percentage of visually responsive cells that are binocular does not require Arc. Indeed the increase seems to be limited by Arc as Arc KO mice have an increased percentage of visually responsive neurons that are binocular. Why would Arc limit the percentage of neurons in binocular V1 that have binocular responses? The contralateral responses in binocular V1 mature before the ipsilateral, and while patterned vision through the contralateral eye guides ipsilateral development, it also limits the extent of ipsilateral development (Smith & Trachtenberg, 2007). The contralateral eye limiting ipsilateral development is thought to reflect competition between the two eyes for limited resources, possibly available 110 synaptic connections (Trachtenberg, 2015). As new synapses strengthen, further synaptic strengthening cannot occur, and the development of the ipsilateral input never catches up to that of the contralateral input. This process would require a mechanism whereby neurons can control the addition of new synapses based on the number of active synapses they already have. Metaplasticity, or the plasticity of plasticity, is a phenomenon whereby neurons bidirectionally shift the ease with which activity drives synaptic strengthening or weakening based on prior experience to maintain optimal levels of output (Abraham & Bear, 1996), and in the visual cortex, this requires NMDA receptor signaling, which is known to lead to Arc transcription (Kirkwood, Rioult, & Bear, 1996; Philpot, Espinosa, & Bear, 2003). In the hippocampus, a novel experience induces Arc transcription in some neurons, but rather than translate this mRNA; the Arc mRNA traffics out to the dendrites, where a second round of the same experience induces metabotropic glutamate receptordependent Arc translation and LTD (Jakkamsetti et al., 2013). This delayed translation is a form of metaplasticity linking past and present experience. As ipsilateral input develops and drives neuronal firing, Arc mRNA present from existing contralateral activity would dampen ipsilateral activity heterosynaptically, preventing runaway excitation but also bringing the period of ipsilateral refinement and strengthening to a close. When there is no Arc present, the ipsilateral input can strengthen further. The lack of experienceinduced plasticity in the Arc KO V1 could be because uncontrolled ipsilateral strengthening saturates the capacity of the binocular visual cortex to undergo further change by shifting the synaptic requirement for plasticity beyond what physiologically occurs (El-Boustani et al., 2018; McCurry et al., 2010). Limiting ipsilateral refinement through Arc could, therefore, be biologically beneficial to conserve capacity for further 111 change. When we conditionally delete Arc from neurons in adult binocular V1, the ipsilateral eye input strengthens and both the percent of binocular and ipsilateral monocular visually responsive neuron increases. As the total percentage of visually responsive neurons does not change, the increase in binocular neurons likely occurs through contralateral monocular neurons becoming binocular. Thus, in the course of binocular development continuous Arc translation maintains the dominance of the contralateral eye over ipsilateral eye input. Collectively, these findings support a model wherein the presence of Arc mRNA in dendrites is sufficient to limit the extent of neuronal binocularity through the heterosynaptic depression of ipsilateral strengthening. Drastic changes in sensory input, such as through monocular deprivation, disrupt this balance and lead to bidirectional, eye-specific changes in neuronal responses. 4.3 Future directions Most developmental studies in the visual cortex, including those detailed in this dissertation, rely on comparisons between groups of animals recorded at different ages (Hagihara, Murakami, Yoshida, Tagawa, & Ohki, 2015; Hoy & Niell, 2015; Rochefort et al., 2011). While these studies are invaluable, they can only resolve what changes occur in the overall population of neurons and not what changes occur in the response of individual neurons during development. We can therefore not say for sure whether the differences we and others observe between different ages reflect changes in the same population of visually responsive neurons, whether responsive neurons change or keep the same selectivity over development, or even if the responsive population at P30 is 112 entirely separate from those neurons responsive at eye-opening. Chronic two-photon imaging allows tracking of activity in individual neurons at multiple time points. In adult V1, researchers have used chronic two-photon imaging to measure changes in neuronal activity over the course of visually guided learning tasks, during monocular deprivation and postdeprivation recovery, and even to identify and later reactivate neurons encoding a learned stimulus to control an animal’s perception and behavior (Carrillo-Reid, Han, Yang, Akrouh, & Yuste, 2019; Minderer, Brown, & Harvey, 2019; Poort et al., 2015; Rose, Jaepel, Hübener, & Bonhoeffer, 2016). During development, there is the added technical limitation of maintaining the stability of the cranial window with the rapid skull thickening in the first month of life. However, methods of imaging through the intact or thinned skull could conceivably allow repeated imaging of the same neurons chronically for a period of days to weeks. Using our coverslip method, we have preliminary data from neurons tracked chronically from P14 to P30 (Figure 4.1A). However, further methodological refinement will help to improve the success rate of these recordings. Using these chronic methods, we plan to ascertain whether monocular neurons present at eye-opening become binocular, or if the binocular population at P30 are neurons that were not visually responsive at eye-opening (Figure 4.1B). Experience-dependent elimination of poorly tuned or weak dendritic spines during the development of the cortex is hypothesized to underlie the functional maturation of neuronal selectivity and responsiveness in V1 (Chen, Lu, & Zuo, 2014; Stevens et al., 2007). However, the mechanisms that link visual experience to spine elimination are not well understood. Experiments in the hippocampus demonstrate that LTD triggers the selective elimination of weak synapses (Wiegert & Oertner, 2013). 113 Similarly, monocular deprivation weakens spines from the deprived eye through LTDlike mechanisms and eventually leads to spine elimination (Mataga, Mizuguchi, & Hensch, 2004). Arc is necessary for LTD in V1, and Arc KO mice also have altered dendritic spine density in the hippocampus (Peebles et al., 2010). Thus, Arc may serve as the link between experience and selective elimination of weak spines through LTD (ElBoustani et al., 2018; Okuno et al., 2012). Given our observation that Arc KO mice have more binocular neurons than control mice, we hypothesize that Arc expression regulates the number of binocular neurons through selective elimination of ipsilaterally responsive, weak spines during development. Using a bicistronic viral construct that contains the functional reporter GCaMP6s and the cell filling fluorophore mRuby, we plan to image dendritic calcium responses during visual stimulation to quantify the percentage of contralaterally and ipsilaterally responsive spines in control and Arc KO mice, as well as spine density and morphology (Figure 4.2A). In preliminary experiments, we can resolve individual synaptic events in response to visual stimulation using this method (Figure 4.2B). L2/3 neurons receive input from L4 and thalamus and have recurrent horizontal connections with other L2/3 neurons (Antonini, Fagiolini, & Stryker, 1999; Gilbert & Wiesel, 1989). The main feedforward drive to L2/3 is thought to be connections from L4. However, thalamic input develops before local horizontal and vertical connections within the cortex (Burkhalter, Bernardo, & Charles, 1993). Additionally, in mouse V1, maturation of L4 to L2/3 connectivity is delayed compared to other brain regions (Cheetham & Fox, 2010). Visually-driven responses of L2/3 neurons in P14 mice are, therefore, likely reflecting direct thalamic inputs. This would explain why, at P14, L2/3 114 neurons have a 90° orientation preference bias and low selectivity, as these are features also seen in thalamic boutons in L2/3 (Marshel, Kaye, Nauhaus, & Callaway, 2012; Sun, Tan, Mensh, & Ji, 2016). Additionally, L2/3 neurons at P14 have high direction selectivity, and direction-selective input from thalamus preferentially projects to L2/3 of V1 (Cruz-Martín et al., 2014). To directly test whether L2/3 neurons at P14 inherit their visual response properties from direct thalamic input rather than intracortical connections, we will record visually evoked responses before and after optogenetically silencing L4 neurons (Gu & Cang, 2016). I hypothesize silencing L4 input at P14 will not affect L2/3 response properties while silencing L4 in adult V1 will revert L2/3 responses to a P14 like responsiveness. These experiments could resolve the confusion surrounding the transition from juvenile to adult sensory cortices, by demonstrating that the transition that occurs is in the input (thalamocortical vs. corticocortical) that optimally drives neuronal firing rather than a continuum of development in the same feedforward circuit. In this dissertation, we found that Arc expression in V1 decreases in adulthood. However, it is unclear what changes within V1 lead to a decrease of Arc expression past the close of the OD critical period. While we observe no direct changes in Arc mRNA, epigenetic changes in expression of Arc regulating proteins or interacting partners could lead to a loss of Arc translation (Putignano et al., 2007; Vierci, Pannunzio, Bornia, & Rossi, 2016). Maturation of inhibitory signaling could also downregulate Arc translation. During the OD critical period, cholinergic signaling from the basal forebrain selectivity activates somatostatin interneurons (Yaeger, Ringach, & Trachtenberg, 2019). Somatostatin interneurons inhibit parvalbumin interneurons that target excitatory somas, leading to increased excitatory neuron firing. However, somatostatin interneurons also 115 inhibit sections of excitatory dendrite promoting branch-specific rather than global dendritic activity. In adult V1, acetylcholine no longer activates somatostatin interneurons and excitatory neurons therefore lose branch-specific activity. The loss of branch-specific activity likely leads to less local Arc translation and impairs eye-specific heterosynaptic plasticity. I hypothesize that increasing somatostatin interneuron activity would effectively restore juvenile like ODP in the adult V1, and that this restoration is dependent on increased Arc expression. Adult binocular V1 lacks the capacity for closed eye depression but retains the capacity for open eye strengthening following monocular deprivation. Interestingly, while critical period ODP can occur regardless of which eye is deprived, adult ODP only occurs when the stronger, contralateral eye is deprived (Sato & Stryker, 2008). This distinction suggests that the adult brain requires a larger change in activity for plasticity to occur than the critical period aged brain. Removing input from the contralateral eye, which provides the majority of excitatory input to binocular V1, could theoretically drive the strengthening of the ipsilateral eye input by decreasing local Arc translation and heterosynaptic depression. I hypothesize that adult ODP requires a decrease in Arc expression for open-eye potentiation to occur. This finding would unveil a role for bidirectional shifts in Arc expression in gating V1 plasticity. While Arc’s role in plasticity was long assumed to be intracellular, recent findings uncovered a novel role for Arc in intercellular RNA transfer through the assembly of oligomers resembling viral capsids (Pastuzyn et al., 2018). This finding brings the long-standing assumption that Arc’s role in ODP is through AMPA receptor endocytosis into question (McCurry et al., 2010). Monocular or binocular deprivation has 116 not only local effects on synaptic strength, but effects in functional connectivity between brain regions (Kraft et al., 2017). Changes in functional connectivity following monocular deprivation require Arc, possibly through spreading Arc oligomers across brain regions. Our finding that local KO of Arc in V1 of Arc cKO mice is sufficient to replicate the constitutive Arc KO mouse phenotype suggests (but does not conclusively show) that transfer of Arc from outside of V1 does not control local binocular plasticity. However, local retrograde transfer of signaling factors from the postsynapse to presynapse regulates plasticity in many brain regions, including some forms of LTD in the visual cortex (Crozier, Wang, Liu, & Bear, 2007; Hulme, Jones, Raymond, & Abraham, 2014). An Arc homolog in Drosophila transfers RNA transsynaptically from motor neurons to muscles (Ashley et al., 2018). However, it is unclear if mammalian Arc is also transferred transsynaptically to regulate plasticity. To test, if Arc’s role in ODP is endogenous to the cell undergoing ODP, we will sparsely delete Arc in V1 preserving the majority of extracellular Arc and putative transsynaptic Arc transfer to KO cells. If transsynaptic Arc controls ODP, ODP should still occur in Arc KO neurons surrounded by neighboring neurons expressing Arc. If we find that ODP requires endogenous Arc, extracellular heterosynaptic transfer between spines still cannot be ruled out. Experiments within the lab are underway to find mutants of Arc that cannot form capsids. It may be possible in the future to derive mutants of Arc that are separably capable of AMPA receptor endocytosis and RNA transfer to directly test this hypothesis. We have feasibility data from WT mice, demonstrating that we can reliably image from the same population of neurons before and after monocular deprivation (Figure 4.3A). Using this method in Arc cKO mice and the mCherry-Cre virus, we can effectively track control and 117 Arc KO neurons within the same animal. We observe a decrease in the percentage of responsive neurons following monocular deprivation (Figure 4.3B), as described previously (Rose et al., 2016). However, we do not observe significant changes in the distribution of monocular and binocular neurons (Figure 4.3C). 4.4 References Abraham, W. C., & Bear, M. F. (1996). Metaplasticity: The plasticity of synaptic plasticity. Trends in Neurosciences, 19(4), 126–130. Antonini, A., Fagiolini, M., & Stryker, M. P. (1999). Anatomical correlates of functional plasticity in mouse visual cortex. The Journal of Neuroscience, 19(11), 4388–4406. Ashley, J., Cordy, B., Lucia, D., Fradkin, L. G., Budnik, V., & Thomson, T. (2018). Retrovirus-like Gag protein Arc1 binds RNA and traffics across synaptic boutons. Cell, 172(1–2), 262-274.e11. https://doi.org/10.1016/j.cell.2017.12.022 Burkhalter, A., Bernardo, K., & Charles, V. (1993). Development of local circuits in human visual cortex. The Journal of Neuroscience, 13(5), 1916–1931. https://doi.org/10.1523/jneurosci.13-05-01916.1993 Carrillo-Reid, L., Han, S., Yang, W., Akrouh, A., & Yuste, R. (2019). Controlling visually guided behavior by holographic recalling of cortical ensembles. Cell, 178(2), 447–457. https://doi.org/10.1016/j.cell.2019.05.045 Cheetham, C. E. J., & Fox, K. (2010). Presynaptic development at L4 to L2/3 excitatory synapses follows different time courses in visual and somatosensory cortex. Journal of Neuroscience, 30(38), 12566–12571. https://doi.org/10.1523/jneurosci.254410.2010 Chen, C.-C., Lu, J., & Zuo, Y. (2014). Spatiotemporal dynamics of dendritic spines in the living brain. Frontiers in Neuroanatomy, 8(May), 28. https://doi.org/10.3389/fnana.2014.00028 Crozier, R. a, Wang, Y., Liu, C.-H., & Bear, M. F. (2007). Deprivation-induced synaptic depression by distinct mechanisms in different layers of mouse visual cortex. Proceedings of the National Academy of Sciences, 104(4), 1383–1388. https://doi.org/10.1073/pnas.0609596104 Cruz-Martín, A., El-Danaf, R. N., Osakada, F., Sriram, B., Dhande, O. S., Nguyen, P. L., … Huberman, A. D. (2014). A dedicated circuit links direction-selective retinal ganglion cells to the primary visual cortex. Nature, 507(7492), 358–361. https://doi.org/10.1038/nature12989 118 El-Boustani, S., Ip, J. P. K., Breton-Provencher, V., Knott, G. W., Okuno, H., Bito, H., & Sur, M. (2018). Locally coordinated cell-wide synaptic plasticity of visual cortex neurons in vivo. Science, 360(6395), 1349–1354. https://doi.org/10.1126/science.aao0862 Fletcher, B. R., Hill, G. S., Long, J. M., Gallagher, M., Shapiro, M. L., & Rapp, P. R. (2014). A fine balance: Regulation of hippocampal Arc/Arg3.1 transcription, translation and degradation in a rat model of normal cognitive aging. Neurobiology of Learning and Memory, 115, 58–67. https://doi.org/10.1016/j.nlm.2014.08.007 Gilbert, C. D., & Wiesel, T. N. (1989). Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex. The Journal of Neuroscience, 9(7), 2432–2442. Gozdz, A., Nikolaienko, O., Urbanska, M., Cymerman, I. A., Sitkiewicz, E., Blazejczyk, M., … Jaworski, J. (2017). GSK3α and GSK3β phosphorylate Arc and regulate its degradation. Frontiers in Molecular Neuroscience, 10(192). https://doi.org/10.3389/fnmol.2017.00192 Greer, P. L., Hanayama, R., Bloodgood, B. L., Mardinly, A. R., Lipton, D. M., Flavell, S. W., … Greenberg, M. E. (2010). The angelman syndrome protein Ube3A regulates synapse development by ubiquitinating Arc. Cell, 140(5), 704–716. https://doi.org/10.1016/j.cell.2010.01.026 Gu, Y., & Cang, J. (2016). Binocular matching of thalamocortical and intracortical circuits in the mouse visual cortex. ELife, 5. https://doi.org/10.7554/eLife.22032 Hagihara, K. M., Murakami, T., Yoshida, T., Tagawa, Y., & Ohki, K. (2015). Neuronal activity is not required for the initial formation and maturation of visual selectivity. Nature Neuroscience, 18(12), 1780–1788. https://doi.org/10.1038/nn.4155 Hoy, J. L., & Niell, C. M. (2015). Layer-specific refinement of visual cortex function after eye opening in the awake mouse. Journal of Neuroscience, 35(8), 3370–3383. https://doi.org/10.1523/JNEUROSCI.3174-14.2015 Hulme, S., Jones, O., Raymond, C., & Abraham, W. (2014). Mechanisms of heterosynaptic metaplasticity. Philosophical Transactions of the Royal Society Biological Sciences, 369. https://doi.org/10.1098/rstb.2013.0148 Jakkamsetti, V., Tsai, N., Gross, C., Molinaro, G., Collins, K. a, Nicoletti, F., … Huber, K. M. (2013). Experience-induced Arc/Arg3.1 primes CA1 pyramidal neurons for metabotropic glutamate receptor-dependent long-term synaptic depression. Neuron, 80(1), 72–79. https://doi.org/10.1016/j.neuron.2013.07.020 Kirkwood, A., Rioult, M. G., & Bear, M. F. (1996). Experience-dependent modification of synaptic plasticity in visual cortex. Nature, 381(6582), 526–528. https://doi.org/10.1038/381526a0 119 Kraft, A. W., Mitra, A., Bauer, A. Q., Snyder, A. Z., Raichle, M. E., Culver, J. P., & Lee, J.-M. (2017). Visual experience sculpts whole-cortex spontaneous infraslow activity patterns through an Arc-dependent mechanism. Proceedings of the National Academy of Sciences, 114(46), E9952–E9961. https://doi.org/10.1073/pnas.1711789114 Lacor, P. N., Buniel, M., Chang, L., Fernandez, S., Gong, Y., Viola, K., … Klein, W. (2004). Synaptic targeting by Alzheimer’s-related amyloid oligomers. Journal of Neuroscience, 24(45), 10191–10200. https://doi.org/10.1523/jneurosci.343204.2004 Mabb, A. M., Kullmann, P. H. M., Twomey, M. a, Miriyala, J., Philpot, B. D., & Zylka, M. J. (2014). Topoisomerase 1 inhibition reversibly impairs synaptic function. Proceedings of the National Academy of Sciences, 111(48), 17290–17295. https://doi.org/10.1073/pnas.1413204111 Marshel, J. H., Kaye, A. P., Nauhaus, I., & Callaway, E. M. (2012). Anterior-posterior direction opponency in the superficial mouse lateral geniculate nucleus. Neuron, 76(4), 713–720. https://doi.org/10.1016/j.neuron.2012.09.021 Mataga, N., Mizuguchi, Y., & Hensch, T. K. (2004). Experience-dependent pruning of dendritic spines in visual cortex by tissue plasminogen activator. Neuron, 44(6), 1031–1041. https://doi.org/10.1016/j.neuron.2004.11.028 McCurry, C. L., Shepherd, J. D., Tropea, D., Wang, K. H., Bear, M. F., & Sur, M. (2010). Loss of Arc renders the visual cortex impervious to the effects of sensory experience or deprivation. Nature Neuroscience, 13(4), 450–457. https://doi.org/10.1038/nn.2508 Minderer, M., Brown, K. D., & Harvey, C. D. (2019). The spatial structure of neural encoding in mouse posterior cortex during navigation. Neuron, 102(1), 232-248.e11. https://doi.org/10.1016/j.neuron.2019.01.029 Okuno, H., Akashi, K., Ishii, Y., Yagishita-Kyo, N., Suzuki, K., Nonaka, M., … Bito, H. (2012). Inverse synaptic tagging of inactive synapses via dynamic interaction of Arc/Arg3.1 with CaMKIIβ. Cell, 149(4), 886–898. https://doi.org/10.1016/j.cell.2012.02.062 Pastuzyn, E. D., Day, C. E., Kearns, R. B., Kyrke-Smith, M., Taibi, A. V., McCormick, J., … Shepherd, J. D. (2018). The neuronal gene Arc encodes a repurposed retrotransposon Gag protein that mediates intercellular RNA transfer. Cell, 172(1– 2), 275-288.e18. https://doi.org/10.1016/j.cell.2017.12.024 Peebles, C. L., Yoo, J., Thwin, M. T., Palop, J. J., Noebels, J. L., & Finkbeiner, S. (2010). Arc regulates spine morphology and maintains network stability in vivo. Proceedings of the National Academy of Sciences, 107(42), 18173–18178. https://doi.org/10.1073/pnas.1006546107 120 Philpot, B. D., Espinosa, J. S., & Bear, M. F. (2003). Evidence for altered NMDA receptor function as a basis for metaplasticity in visual cortex. The Journal of Neuroscience, 23(13), 5583–5588. https://doi.org/10.1523/jneurosci.23-1305583.2003 Poort, J., Khan, A. G., Pachitariu, M., Nemri, A., Orsolic, I., Krupic, J., … Hofer, S. B. (2015). Learning enhances sensory and multiple non-sensory representations in primary visual cortex. Neuron, 86(6), 1478–1490. https://doi.org/10.1016/j.neuron.2015.05.037 Putignano, E., Lonetti, G., Cancedda, L., Ratto, G., Costa, M., Maffei, L., & Pizzorusso, T. (2007). Developmental downregulation of histone posttranslational modifications regulates visual cortical plasticity. Neuron, 53(5), 747–759. https://doi.org/10.1016/j.neuron.2007.02.007 Rochefort, N. L., Narushima, M., Grienberger, C., Marandi, N., Hill, D. N., & Konnerth, A. (2011). Development of direction selectivity in mouse cortical neurons. Neuron, 71(3), 425–432. https://doi.org/10.1016/j.neuron.2011.06.013 Rose, T., Jaepel, J., Hübener, M., & Bonhoeffer, T. (2016). Cell-specific restoration of stimulus preference after monocular deprivation in the visual cortex. Science, 352(6291). https://doi.org/10.1126/science.aad3358 Sato, M., & Stryker, M. P. (2008). Distinctive features of adult ocular dominance plasticity. The Journal of Neuroscience, 28(41), 10278–10286. https://doi.org/10.1523/JNEUROSCI.2451-08.2008 Smith, S. L., & Trachtenberg, J. T. (2007). Experience-dependent binocular competition in the visual cortex begins at eye opening. Nature Neuroscience, 10(3), 370–375. https://doi.org/10.1038/nn1844 Smith, S. L., & Trachtenberg, J. T. (2010). The refinement of ipsilateral eye retinotopic maps is increased by removing the dominant contralateral eye in adult mice. PLoS ONE, 5(3). https://doi.org/10.1371/journal.pone.0009925 Stevens, B., Allen, N. J., Vazquez, L. E., Howell, G. R., Christopherson, K. S., Nouri, N., … Barres, B. A. (2007). The classical complement cascade mediates CNS synapse elimination. Cell, 131(6), 1164–1178. https://doi.org/10.1016/j.cell.2007.10.036 Sun, W., Tan, Z., Mensh, B. D., & Ji, N. (2016). Thalamus provides layer 4 of primary visual cortex with orientation- and direction-tuned inputs. Nature Neuroscience, 19(2), 308–315. https://doi.org/10.1038/nn.4196 Taha, S., & Stryker, M. P. (2002). Rapid ocular dominance plasticity requires cortical but not geniculate protein synthesis. Neuron, 34(3), 425–436. https://doi.org/10.1016/S0896-6273(02)00673-6 Tognini, P., Napoli, D., Tola, J., Silingardi, D., Della Ragione, F., D’Esposito, M., & 121 Pizzorusso, T. (2015). Experience-dependent DNA methylation regulates plasticity in the developing visual cortex. Nature Neuroscience, 18(7), 956–958. https://doi.org/10.1038/nn.4026 Trachtenberg, J. T. (2015). Competition, inhibition, and critical periods of cortical plasticity. Current Opinion in Neurobiology, 35, 44-48. https://doi.org/10.1016/j.conb.2015.06.006 Vierci, G., Pannunzio, B., Bornia, N., & Rossi, F. M. (2016). H3 and H4 lysine acetylation correlates with developmental and experimentally induced adult experience-dependent plasticity in the mouse visual cortex. Journal of Experimental Neuroscience, 10, 49–64. https://doi.org/10.4137/JEN.S39888.TYPE Wiegert, J. S., & Oertner, T. G. (2013). Long-term depression triggers the selective elimination of weakly integrated synapses. Proceedings of the National Academy of Sciences, 110(47), E4510–E4519. https://doi.org/10.1073/pnas.1315926110 Yaeger, C. E., Ringach, D. L., & Trachtenberg, J. T. (2019). Neuromodulatory control of localized dendritic spiking in critical period cortex. Nature, 567(7746), 1001–1004. https://doi.org/10.1038/s41586-019-0963-3 122 Figure 4.1 Chronic imaging of binocular visual cortex. A. Visually responsive neurons over development. We imaged binocular visual cortex in a WT mouse at P14 (left), the same day as the placement of the cranial window (see methods, Chapter 3). 6 days later, the same area was identified using the pattern of the brain vasculature, and visually evoked responses were recorded using the same stimuli as at P14. 10 days later, at P30 (right), the final imaging session was conducted in the same area using the same set of stimuli. B. At P14, the majority of visually responsive neurons only respond to the contralateral eye. In the same area imaged again at P20, the percentage of contralateral only neurons decrease while more binocular and ipsilateral only responding neurons appear. In the final imaging session at P30, the percentage of binocularly responsive neurons peaks. 123 Figure 4.2 Visually evoked responses of dendritic spines. A. Structure of dendritic spines. At P20, a mouse was injected with a mixture of dilute AAV-CamKII-Cre and AAV-hSyn-GCaMP6s-mRuby to sparsely label excitatory neurons. We created a window to record in vivo responses from dendritic spines 2 weeks postinjection (see methods, Chapter 3). From the mRuby fluorescence, we can resolve the structure of dendritic branches and their spines. B. Responses of dendritic spines. Averaged GCaMP6s responses for the dendritic spine, highlighted by the red box in panel A. This spine has significant responses to several visual stimuli. 124 Figure 4.3 Chronic imaging of ocular dominance plasticity. A. 2 weeks following P20 or P30 acute imaging, we reassessed WT mouse cranial windows for clarity during a baseline imaging session. If the window was clear and visually responsive neurons present, we sutured shut the eye contralateral to the cranial window for 4 days, reopened the eye and reimaged the same population of neurons in a postmonocular deprivation (post-MD) imaging session (see methods, Chapters 2 and 3). B. We imaged 6 WT mice at baseline (age at baseline P37-P49) and Post MD. Following MD, there was a significant decrease in the percent of responsive neurons (t=6.7, p=0.0011, paired t test). C. Percentage of visually responsive neurons that respond to only the contralateral eye (Contra) only the ipsilateral eye (Ipsi) or both eyes (Binoc). There is no significant difference between mice at Baseline and Post MD, although there is a trend for a decrease in Contra neurons and an increase in Ipsi neurons. (* indicates a significant difference, p<0.05. Error bars represent standard error of the mean. Open circles indicate a data point from an individual mouse. N=number of animals for all statistics).