Functional connectivity of emotional well-being: over-connectivity between default and attentional networks is associated with attitudes of anger and aggression

Functional MRI connectivity has identified neurophysiology relevant to cognition and personality, but correlates between brain architecture and emotional health and well-being remain unclear. Two approaches were used to asses functional connectivity correlates in emotional health and well-being. The first approach used principal component analysis. We evaluated resting-state functional magnetic resonance imaging data from 1003 subjects (534 female, 469 male) of the Human Connectome Project. Pairwise functional connectivity measurements were obtained for each subject across 6923 x 6923 regions of interest. Principal components were calculated for individuals and across the group mean connectivity data and compared to obtain typicality, which was then compared to reported emotional health metrics using a linear regression model. The second approach calculated functional connectivity between each pair of networks from a 17-resting-state network cortical parcellation. Typicality of connectivity showed significant correlation across the population to emotional metrics corresponding to aggression in 3 of 10 principal components. These components included features corresponding to association cortical networks including the default and attentional networks. Additionally, functional connectivity between the default and attentional networks was positively correlated with scores of attitudes of anger and aggression. Atypical functional connectivity corresponding to increased synchrony of default network and brain attentional networks is associated with attitudes of anger and aggression. These findings suggest a mechanism of impaired effortful control and decreased inhibition related to control of impulsivity.

FUNCTIONAL CONNECTIVITY OF EMOTIONAL WELL-BEING: OVER-CONNECTIVITY BETWEEN DEFAULT AND ATTENTIONAL NETWORKS IS ASSOCIATED WITH ATTITUDES OF ANGER AND AGGRESSION by Fiona L. Weathersby A thesis submitted to the faculty of The University of Utah in partial fulfillment of the requirements for the degree of Master of Science in Bioengineering Department of Biomedical Engineering The University of Utah May 2019 Copyright c Fiona L. Weathersby 2019 All Rights Reserved The University of Utah Graduate School STATEMENT OF THESIS APPROVAL The thesis of Fiona L. Weathersby has been approved by the following supervisory committee members: Jeffrey S. Anderson , Chair(s) 6 December 2018 Date Approved Alan D. Dorval , Member 6 December 2018 Date Approved Christopher R. Butson , Member 6 December 2018 Date Approved by David W. Grainger , Chair/Dean of the Department/College/School of Biomedical Engineering and by David B. Kieda , Dean of The Graduate School. ABSTRACT Functional MRI connectivity has identified neurophysiology relevant to cognition and personality, but correlates between brain architecture and emotional health and well-being remain unclear. Two approaches were used to asses functional connectivity correlates in emotional health and well-being. The first approach used principal component analysis. We evaluated resting-state functional magnetic resonance imaging data from 1003 subjects (534 female, 469 male) of the Human Connectome Project. Pairwise functional connectivity measurements were obtained for each subject across 6923 x 6923 regions of interest. Principal components were calculated for individuals and across the group mean connectivity data and compared to obtain typicality, which was then compared to reported emotional health metrics using a linear regression model. The second approach calculated functional connectivity between each pair of networks from a 17-resting-state network cortical parcellation. Typicality of connectivity showed significant correlation across the population to emotional metrics corresponding to aggression in 3 of 10 principal components. These components included features corresponding to association cortical networks including the default and attentional networks. Additionally, functional connectivity between the default and attentional networks was positively correlated with scores of attitudes of anger and aggression. Atypical functional connectivity corresponding to increased synchrony of default network and brain attentional networks is associated with attitudes of anger and aggression. These findings suggest a mechanism of impaired effortful control and decreased inhibition related to control of impulsivity. For my family CONTENTS ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii CHAPTERS 1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2. METHODS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Approach 1: Principal Component Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Approach 2: Functional Connectivity across a Resting State 17-Network Parcellation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Functional Connectivity Correlation to Metrics of Emotional Well-being . . . . 3 3 4 4 3. RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 4. DISCUSSION AND CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.1 Emotion and Brain Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Effortful Control and Aggression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Default and Attentional Brain Networks Overconnectivity, Aggression, and Effortful Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 14 16 17 17 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 LIST OF FIGURES 3.1 Principal Components in Anatomical Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2 Principal Component - Eigen Value Relationship . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.3 Emotional Health Behavioral Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.4 Principal Components Related to Behavioral Metrics . . . . . . . . . . . . . . . . . . . . . 11 3.5 Functional Connectivity of Anger and Aggression . . . . . . . . . . . . . . . . . . . . . . . 12 LIST OF TABLES 2.1 Behavioral Metrics Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 ACKNOWLEDGEMENTS I would like to thank first and foremost my adviser, Jeff. Thank you for the opportunity to work in your lab, for the incredible mentorship you have provided, and for your time and expertise on this project. To my committee members, Chris and Chuck, your mentorship over the years has helped me get to where I am now; thank you. Thanks to Jace King for his help and guidance. Thanks to the Brain Network Lab and all of the people at the Utah Center for Advanced Imaging and Radiology. To my family, Mum, Dad, and Mary, you have always stood by me regardless of the situation or circumstances – I love you all so much. To the countless friends who have offered support, advice, or simply just a friendly cup of coffee, I am so grateful to all of you and am blessed to have you in my life. To Alyssa, Susanna, and Isabella, thank you for all that you do, I could not have done this without you three in my life. Ps. 25:4-5 CHAPTER 1 INTRODUCTION The notion of a structure-function relationship between emotion, personality, or social function, and intrinsic brain networks or structures has long been hypothesized. Beginning with the MacLean limbic system theory of emotion [56, 57], there has been extensive research to determine anatomical or neurosystem underpinnings of emotion. There are commonly established and well-studied associations with regard to regional activation during emotion induction or recall [64, 76]. Some well-known associations, both within the context of healthy individuals and disease, include the role of the amygdala in fear induction and conditioning, an association between sadness and stress and activity in the subcallosal cingulate (particularly in major depressive disorder), and a general role in emotional processing for the medial prefrontal cortex [1, 8, 24, 27, 33, 45, 52, 60, 77]. More than traditional regional activation in emotion processing, connectivity between different regions of interest (ROIs) may affect and modulate function. Since Ogawa et al. showed that the paramagnetic properties of deoxyhemoglobin produced gradients resulting in blood oxygen level dependent (BOLD) contrast [69–71], the possibility of mapping cortex by functional regions has become increasingly and continues to be studied and utilized [10, 72, 81, 91]. It has become a tool in clinical settings for mapping cortex to plan surgery or to differentiate brain function in nonresponsive vs. minimally conscious patients [25, 82] and provides potentially powerful tools in psychology and behavioral studies [11]. Analysis of low-frequency fluctuations in BOLD signal at rest has proven a useful tool for assessing how connectivity within and between brain regions contributes to everything from personality to cognition and intelligence [12, 17–19, 30]. The relationship between functional connectivity and emotional traits has been explored most commonly in the scope of disorders, namely in the realm of mood, anxiety, psychotic, and personality dis- 2 orders, through examining the role of the amygdala, frontal cortex, and anterior cingulate [14, 38, 46, 49, 50, 65–67, 74, 85, 96]. Functional connectivity correlates of emotional health and well-being in healthy controls have been studied, but remain uncertain [48, 75, 89]. The Human Connectome Project data releases provide an ideal population in which to further probe functional connectivity correlates of emotional health and well-being. This large-scale, minimally preprocessed data set consists of resting state functional connectivity data, task fMRI, and diffusion MRI for over 1000 subjects and gave rise to several tools, studies, and strategies for using and exploring the data [37, 58, 86, 93]. This data set provides standardized behavioral measures that have the potential to covary across subjects in meaningful and interesting ways [92]. The emotion metrics include measures of emotion recognition, negative affect, psychological well-being, social relationships, and stress and self-efficacy [6, 42, 43]. Using the Human Connectome Project 1200 subjects data release, we proposed to probe functional connectivity correlates in emotional health and well-being. Our hypothesis was that differences in emotional well-being metrics across subjects that showed significant associations with functional connectivity differences would be associated with atypical functional connectivity compared to population mean results. We assessed these correlates by comparing typicality of connectivity to reported behavioral covariates across the sample. Typicality of connectivity was established as a correlation of an individual’s sources of variance during a resting state functional connectivity scan compared to the mean sources of variance across the sample. We further examined connectivity across intrinsic brain network pairs as a function of behavioral metrics of interest. We propose that this technique can be used to establish a basic threshold for assessing idiosyncrasy within emotional health and well-being, and, further, to assess how connectivity between brain ROI pairs may contribute to observed idiosyncrasy. CHAPTER 2 METHODS Two approaches were used to assess functional connectivity correlates with emotional health and well-being in the Human Connectome Project data. Here, these two methods and the supporting analyses are described. 2.1 Participants From the Human Connectome Project 1200 Subjects Release, 1003 subjects of 1206 were used in this analysis (mean age = 28.7 years; SD = 3.7 years; age range: 22-37; 534 female subjects). These subjects had 4 complete scans (60 minutes per subject) of FIX ICA cleaned Multiband BOLD resting state data [41, 62, 92]. 2.2 Approach 1: Principal Component Analysis Resting state functional connectivity data were analyzed using two separate approaches. The first approach was designed to identify common patterns of variation of functional connectivity across subjects and used principal components of resting state functional connectivity. To test our hypothesis, we performed principal component analysis using singular value decomposition on correlation matrices from a gray matter parcellation consisting of 6923 regions of interest covering cortical, subcortical, and cerebellar gray matter at 5 mm spatial resolution as previously described [83]. Principal components are the eigenvectors of resting state functional connectivity matrices and identify covariance patterns in the functional brain data. Thus, the principal components from resting state functional connectivity matrices represent a set of intrinsic brain networks that are hierarchically organized by the amount of signal variance within each component. Functional connectivity matrices for 6923 x 6923 ROIs were averaged across all 1003 subjects to obtain group mean functional connectivity, and principal components from the group averaged data were also obtained. The networks for the first 10 principal components of the group 4 mean were back-projected onto anatomical space in order to visualize the networks that contribute strongly to signal variance within resting state functional connectivity. For each subject, 6923 x 6923 ROI functional connectivity matrices were averaged for each of the 4 scans for that subject and 20 principal components were extracted. The first 20 principal components of each subject were compared to the first 20 principal components of the group averaged data using Pearson correlation coefficient across brain regions. For each group level component, the individual component exhibiting the highest absolute value of correlation was selected as the best match for that group level component in that individual and the absolute value of correlation was recorded. This produced a measure of typicality of individuals functional connectivity pattern for each component to the population averaged connectivity: specifically, how correlated an individuals principal components were to population-averaged principal components. 2.3 Approach 2: Functional Connectivity across a Resting State 17-Network Parcellation The second approach calculated functional connectivity between each pair of networks for each subject from the 17 network cortical parcellation of Yeo et al. [97]. This approach was designed to evaluate the spatial distribution of functional connectivity differences for metrics of emotional well-being correlated with atypical functional connectivity from the first approach. Average time series were extracted from each of the 17 distributed brain networks described by this parcellation and each network was treated as a single region of interest (ROI). Each ROI pair was compared and correlation coefficients were estimated. These results were Fisher transformed to improve normality and a matrix consisting of the correlation coefficients for the group mean was reported. These correlation coefficients are representative of functional connectivity between different networks. 2.4 Functional Connectivity Correlation to Metrics of Emotional Well-being In order to assess the correlative nature of principal component analysis to emotional well-being and health, comparison of metrics of emotional well-being to the measure of correlation to the group mean was performed for each principal component. We used the Human Connectome behavioral measures related to emotion for this analysis. The mea- 5 sures included NIH Toolbox measures and the Penn Emotion Recognition Test. For further information, please see Table 2.1. Typicality of functional connectivity (correlation between individual and group principal components for each of the first 10 group-averaged principal components) was correlated with subject-level scores for each metric using a generalized linear model that included age, sex, and head motion as covariates. These results were then corrected for multiple comparison using false discovery rate (q(FDR) <0.05) across all 10 components and all metrics of emotional well-being. For metrics showing significant correlation to functional connectivity typicality, correlations were also performed between those metrics and functional connectivity between pairs of the 17 intrinsic connectivity networks for the Yeo et al. parcellation, also corrected for multiple comparisons using false discovery rate. 6 Table 2.1. Behavioral Metrics Descriptive Statistics Metric Anger Hostility Aggression Fear Somatic Arousal Sadness Life Satisfaction Life Purpose Psychological Health Relationships Loneliness Social Distress Social Rejection Social Support Social Network Perceived Stress Self-Efficacy Emotion Recognition Response Time E.R. Anger E.R. Fear E.R. Happiness E.R. Neutral E.R. Sad Age Female N = 1003 M 47.71 50.33 51.82 50.09 51.83 46.12 54.76 52.03 50.22 50.48 50.96 48.60 48.33 51.47 48.03 48.12 51.06 35.59 1831 ms 6.782 6.901 7.956 7.152 6.788 28.7 yrs SD 8.158 8.546 8.748 7.872 8.130 7.827 9.196 8.757 7.855 9.060 8.559 8.470 8.669 9.462 9.023 9.056 8.292 2.531 336.5 ms 1.022 1.159 0.233 1.228 1.147 3.7 yrs N = 534 CHAPTER 3 RESULTS We used principal component analysis to analyze how typicality of connectivity may align with emotional health and well-being. Figure 3.1 shows the first 10 principal components of the group-averaged resting state functional connectivity data mapped onto anatomical space. As can be seen, each principal component corresponds to patterns from more familiar intrinsic connectivity networks obtained from singular-value decomposition. Many of the components show patterns associated with multiple intrinsic connectivity networks, for example in component 4 where both visual and somatomotor networks are represented, but have opposite sign. Principal component 9 contains information on brain lateralization. Principal component 2 shows strong resemblance to the canonical default network, while principal component 5 also contains elements of the default network as well as opposite sign elements of the dorsal attention network. It may be advantageous to evaluate principal components that include information both about between network interactions and as well as robustness of within network organization as a complementary approach to analyzing networks identified by independent component analysis. Figure 3.2 shows the connection between the first 20 principal components and their respective eigenvalues. These eigenvalues decrease steadily as the principal components approach 20. While the number of principal components to include is to some extent arbitrary, it has been previously demonstrated that individual variability of the order and architecture of principal components becomes large beyond about the first 10 components, and consistent individual-level patterns are less reliable [28]. For this reason, we limited analysis to the first 10 principal components as defined by group-averaged functional connectivity. We assessed how metrics corresponding to emotional well-being and health covaried across the population prior to assessing correlation to group mean principal components. 8 Metrics associated with anger, fear, and sadness show relatively high correlation to each other and to metrics corresponding to loneliness and stress (which are also correlated to each other). Psychological metrics (psychological well-being and life-outlook) show similar distribution across subjects and exhibit an association with metrics corresponding to perception of social interactions and perception of self, which also exhibit a correlation to one another. These results are displayed in Figure 3.3. After establishing typicality of connectivity (i.e., how correlated individuals were to the group mean principal components), we assessed correlation between typicality of connectivity and metrics of emotional health and well-being (Figure 3.4). The NIH Toolbox measure for anger corresponding to physical aggression showed a significantly negative correlation to typicality of functional connectivity for components 2, 5, and 9. This indicates that in healthy populations, individuals exhibiting connectivity patterns similar to population mean patterns for these components are less likely to report aggression. PC component 10 showed a negative correlation with the metric assessing an individuals perceived life purpose. To further evaluate this result, we evaluated assessed functional connectivity between 17 pairs of intrinsic connectivity networks for correlation across subjects with scores on the anger-aggression metric, shown in Figure 3.5. Significant associations were thresholded for q(FDR)<0.05 across all network pairs. Positive correlations were noted between functional connectivity primarily between the default network and sensory and attention networks (visual, somatomotor, frontoparietal, cinguloinsular networks), as well as between the anterior cinguloinsular and lateral somatomotor network. No significant correlates were found after multiple comparison correction for connectivity between the 17 network pairs and scores of perceived life purpose. 9 Figure 3.1. Principal Components in Anatomical Space: Here the first 10 principal components are back-projected into anatomical space representing combinations of network activity corresponding to sources of variance in resting state data. 10 Figure 3.2. Principal Component - Eigen Value Relationship: Here principal components are related to their respective eigenvalues. Figure 3.3. Emotional Health Behavioral Metrics: Correlation between metrics of emotional health, well-being, and insight across the population. Pearson correlation of the emotional metrics was used to compare each pair of metrics to identify how similar scores were across the population (E.R. = Emotion Recognition). 11 T-Statistic Figure 3.4. Principal Components Related to Behavioral Metrics: FDR corrected functional connectivity correlated to reported emotional metrics. Note that aggression is significantly, negatively correlated across the population to principal components 2, 5, and 9. Psychological health is negatively associated with principal component 10. 12 T-Statistic Figure 3.5. Functional Connectivity of Anger and Aggression: FDR corrected functional connectivity across 17 brain networks for Anger Aggression. Note that functional connectivity associated with Anger and Aggression shows marked correlation along the Default Mode Network and Attention Networks. CHAPTER 4 DISCUSSION AND CONCLUSIONS To assess functional connectivity correlates for emotional health and well-being, we examined typicality of functional connectivity across the Human Connectome Project 1200 data release. We found that typicality of functional connectivity in principal components 2, 5, and 9 was negatively correlated to the NIH toolbox assessment of anger pertaining to aggression. Typicality of functional connectivity in component 10 was negatively correlated with reported life purpose. In order to better understand functional connectivity underpinnings for these associations observed, we then assessed correlation of functional connectivity and anger and aggression across 17 brain networks. We found a positive association between high scores of anger and aggression and connectivity between default mode and attention networks. Anger and aggression was negatively correlated with typicality of connectivity in principal components 2, 5, and 9. This association indicates that regions represented in these principal components may disproportionately contribute to regulation or processing of anger and aggression. In component 2, the default mode network is clearly shown. This network is composed of several core regions [39, 40, 63, 73, 95], which in turn are commonly attributed to key functional hubs corresponding to different aspects of internal thought processes [4]. These regions include the ventral medial and dorsal prefrontal cortex and posterior cingulate clearly seen in component 2. The dorsolateral prefrontal cortex, superior occipital gyrus, and superior parietal lobule all appear in component 5. These are well-characterized components of the dorsal attention network [20, 31]. Component 5 also shows aspects of the visual network and lacks some features of the dorsal attention networks. Component 9 is characterized by lateralization of the frontoparietal ventral attention network (or executive control network, Figure 3.1). These anatomical regions have been well defined and characterized [97], but this does not immediately explain the 14 observed association between anger and aggression and components 2, 5, and 9. 4.1 Emotion and Brain Imaging Lindquist et al. found that discrete emotions could not be limited to distinct brain regional localization, but rather a set of interacting brain regions mediating emotional experience and processing [53]. In the same way, it is unlikely that a single component or region described by the principal components correlated to anger and aggression can explain the relationship between anger and aggression scores and components 2, 5, and 9. This is further supported by a positive correlation between anger and aggression scores and functional connectivity between the default and attention networks shown in Figure 3.5. Connectivity between these two regions has been typically described as anticorrelated [31, 32], and individuals exhibiting high scores on anger and aggression metrics may therefore exhibit decreased anticorrelation between the default and attentional networks. The default mode network is activated during spontaneous, unconstrained events such as mind-wandering, imagining ones future, recollecting personal past, or self-reference [15, 21, 44, 80, 87, 88]. The attention network is actively engaged with directed attention and working memory [20, 29]. These two networks are considered to be anticorrelates of one another, and the mediation of this relationship has been attributed to the frontal parietal cortex [34, 90]. Essentially, what this may mean is that there is discrimination between how an individual takes in signals from the environment, and how an individual thinks about those signals in reference to self. 4.2 Effortful Control and Aggression Negative emotions corresponding to sadness, fear, and anger both along normal and extreme continuums of these emotions can be measured based on specific attitudes and experiences. Anger, is traditionally associated with hostile and cynical attitudes and can be measured in terms of behavior (aggression) or emotion/attitude (hostility and anger affect) [36]. Aggression, therefore, is not an emotion but a manifestation and behavior of anger. Aggression can be described using two distinctive patterns, reactive and proactive; reactive aggression is most commonly associated with response to stimuli causing anger or involving threat, while proactive aggression is seen more to be a learned trait resulting 15 in personal gain in exchange for aggression [94]. If aggression is essentially the physical manifestation of anger, then the question arises: How is aggression regulated? Certainly anger can be experienced without outward physical aggression, so there must be a mechanism by which aggression is controlled and exhibited. A strong candidate for this regulation is effortful control, which is essentially the suppression of instinctive reactions to environmental stimuli [35, 55]. Effortful control recruits frontal cortex areas to constrain reactive, reflexive emotional responses to stimuli (such as an amygdala-triggered fear response to a strange noise) [3, 5]. Critical regions within the frontal cortex that are associated with effortful control are the prefrontal cortex and the orbitofrontal cortex [78, 79]. Two aspects of effortful control are useful for the current discussion: the suppression of impulsivity and the regulation of negative emotions. Davidson, Putman, and Larson found that the orbitofrontal cortex and the anterior cingulate cortex, through connection to the amygdala, are implicated in the ability to inhibit impulsivity [22]. Essentially, activation results in inhibition of emotional behavior, and deficits in this connection can result in increased likelihood of impulsive aggression [22, 23]. Amygdala-orbitofrontal cortex coupling seems to be critically important in the suppression of impulsive aggression. In individuals with intermittent explosive disorder, there is weak amygdala-orbitofrontal cortex coupling in response to presented angry faces [16]. The prefrontal cortex, particularly the orbitofrontal cortex, exhibits connections to the ventral anterior cingulate cortex and the amygdala and plays a key role in the regulation of negative emotion [7, 9]. Activity within the ventral anterior cingulate cortex is associated with regulation of anger when imagining anger-evoking scripts, and is shown to be more active during tasks involving focused attention for individuals with increased social insight, or better control in social situations [2, 13, 26]. The prefrontal cortex and the amygdala are both recruited for up and down regulation of negative emotion, but the orbitofrontal cortex is recruited primarily in the down regulation of negative emotions [68]; this finding has been supported using surface EEG and suppression tests [23, 47]. 16 4.3 Default and Attentional Brain Networks Overconnectivity, Aggression, and Effortful Control Studies examining the default network have found reduction of activity during effortful control [44, 84]. In fact, Knyazev et al. showed that as children develop, there is increased discrimination between the default mode network and networks closely associated with processing and acting on external signals (namely the executive control and salience networks) [51]. They examined cohorts of school children at three different developmental stages using EEG at rest, in regions found in fMRI, and compared this to adults under the same conditions. It was found that for higher discrimination between the default and the attentional/executive networks, parents reported higher effortful control scores for the children [51]. The current study exhibits an increase in connectivity between the default and attentional networks corresponding to increased scores on aggressiveness self-report metrics. This is further supported by the lack of activity for the orbitofrontal cortex seen in the principal components of interest (principal components 2, 5, 9). Our results may be consistent with weaker suppression of impulsivity and aberrant regulation of negative emotions. It is possible that the over-connectivity between the default mode and attention networks is representative of aberrant regulation of negative emotions while the lack of orbitofrontal cortex involvement in brain components relative to aggressiveness may be indicative of both the lack of regulation of negative emotions and of the lack of suppression of impulsivity. The default mode network exhibits negatively correlated connections with brain attentional networks, which may facilitate a division of cognitive resources between networks processing stimulus-independent cognition and internal narrative from those processing attention to external stimuli [4, 20, 31, 59]. Coactivation of these two broad networks may represent either intrusive stimuli interrupting introspection or difficulty silencing internal narrative when directing attention to external stimuli. This may contribute to abnormalities in response inhibition [61]. This supports our finding that coactivation of these two regions promotes physical aggression via an inability to suppress impulsivity. 17 4.4 Limitations While typicality at the principal component level provided a useful threshold for assessing general emotional health and well-being, it is a screening assessment and may miss important results. Here we observed that typicality of connectivity, or correlation of individual principal components to group mean principal components, was negatively associated with physical aggression, a measure of trait anger, indicating that individuals most like the group mean were less likely to exhibit physical aggression. This was the only meaningful result of typicality screening, but it is likely that other emotional metrics are associated with more specific, localized brain activation patterns, as opposed to large-scale patterns shown in principal component analysis. Future work may assess the difference between broad range measurements such as principal component analysis and more acute measures of brain activation across the population. The second limitation of this work is that we did not have a direct measure of effortful control in these metrics. Measuring base aggressiveness is an indirect measure of lack of effortful control in this study, but future work could include standardized measures of effortful control [79]. The third limitation of this work is the small effect size observed. There are over 1000 subjects in this study and the correlation values are relatively small. This is likely due to the large amount of noise in baseline fMRI scans [54]. 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