Analysis of diffusion tensor imaging for subjects with Down Syndrome

Authors: Neda Sadeghi1, Clement Vachet1, Marcel Prastawa1, Julie Korenberg1, Guido Gerig1 Institutions: 1University of Utah, Salt Lake City, UT Introduction: Down syndrome (DS) is the most common chromosome abnormality in humans. It is typically associated with delayed cognitive development and physical growth. DS is also associated with Alzheimer-like dementia [1]. In this study we analyze the white matter integrity of individuals with DS compared to control as is reflected in the diffusion parameters derived from Diffusion Tensor Imaging. DTI provides relevant information about the underlying tissue, which correlates with cognitive function [2]. We present a cross-sectional analysis of white matter tracts of subjects with DS compared to control. Methods: We study a population of 25 adults composed of 12 healthy controls (age 21 +/- 3.97) and 13 subjects diagnosed with Down syndrome (age 26.2 +/- 5.12). Each subject has diffusion tensor imaging (DTI) scans at different ages that capture different stages of development. We construct an unbiased atlas of adult brains as a population template using the method described by Joshi et al. [3]. Diffusion tensor images of the subjects were mapped to the reference space defined by this template. White matter label maps developed and disseminated by Mori et al. [4] were also registered to this template [5][6]. The labeling of regions in the atlas space allows for automatic partitioning of each subject's scans. Fractional anisotropy (FA) were extracted from each region and compared between subjects with Down syndrome and control. False discovery rate was used to adjust for multiple comparisons. Results: We performed white matter analysis on the following brain regions: anterior limb of internal capsule (ALIC, right and left), posterior limb of internal capsule (PLIC, right and left), body of corpus callosum (BCC), genu, splenium, external capsule (ExCap, right and left), and posterior thalamic radiation (PTR), which includes the optic radiation. Cross-sectional FA trajectories differ significantly in controls and DS subjects in the intercept term for ALIC R, Genu, ExCap L, PTR R and PTR L. Opposite patterns of development between controls and DS were observed in almost all the regions except for the left ALIC (ALIC L). FA tends to increase with age for the control group, whereas FA for subjects with Down syndrome tends to decrease with age with the exception of ALIC L which showed increasing trend for both populations. However, we didn't observe any significant differences in the slopes between these two groups which could be due to the small sample size and high variability between subjects. We observed only group-by-age differences for left ExCap (ExCap L). Estimated parameters for linear regression along with p-values are shown in Table 1 and 2.

OHBM 1/11/13 12:36 AM https://ww4.aievolution.com/hbm1301/index.cfm?do=abs.viewAbs&subView=1&abs=3430 Page 1 of 4 Analysis of Diffusion Tensor Imaging for Subjects with Down Syndrome Abstract Submission No: 3896 Authors: Neda Sadeghi1, Clement Vachet1, Marcel Prastawa1, Julie Korenberg1, Guido Gerig1 Institutions: 1University of Utah, Salt Lake City, UT Introduction: Down syndrome (DS) is the most common chromosome abnormality in humans. It is typically associated with delayed cognitive development and physical growth. DS is also associated with Alzheimer-like dementia [1]. In this study we analyze the white matter integrity of individuals with DS compared to control as is reflected in the diffusion parameters derived from Diffusion Tensor Imaging. DTI provides relevant information about the underlying tissue, which correlates with cognitive function [2]. We present a cross-sectional analysis of white matter tracts of subjects with DS compared to control. Methods: We study a population of 25 adults composed of 12 healthy controls (age 21 +/- 3.97) and 13 subjects diagnosed with Down syndrome (age 26.2 +/- 5.12). Each subject has diffusion tensor imaging (DTI) scans at different ages that capture different stages of development. We construct an unbiased atlas of adult brains as a population template using the method described by Joshi et al. [3]. Diffusion tensor images of the subjects were mapped to the reference space defined by this template. White matter label maps developed and disseminated by Mori et al. [4] were also registered to this template [5][6]. The labeling of regions in the atlas space allows for automatic partitioning of each subject's scans. Fractional anisotropy (FA) were extracted from each region and compared between subjects with Down syndrome and control. False discovery rate was used to adjust for multiple comparisons. Results: We performed white matter analysis on the following brain regions: anterior limb of internal capsule (ALIC, right and left), posterior limb of internal capsule (PLIC, right and left), body of corpus callosum (BCC), genu, splenium, external capsule (ExCap, right and left), and posterior thalamic radiation (PTR), which includes the optic radiation. Cross-sectional FA trajectories differ significantly in controls and DS subjects in the intercept term for ALIC R, Genu, ExCap L, PTR R and PTR L. Opposite patterns of development between controls and DS were observed in almost all the regions except for the left ALIC (ALIC L). FA tends to increase with age for the control group, whereas FA for subjects with Down syndrome tends to decrease with age with the exception of ALIC L which showed increasing trend for both populations. However, we didn't observe any significant differences in the slopes between these two groups which could be due to the small sample size and high variability between subjects. We observed only group-by-age differences for left ExCap (ExCap L). Estimated parameters for linear regression along with p-values are shown in Table 1 and 2. OHBM 1/11/13 12:36 AM https://ww4.aievolution.com/hbm1301/index.cfm?do=abs.viewAbs&subView=1&abs=3430 Page 2 of 4 OHBM 1/11/13 12:36 AM https://ww4.aievolution.com/hbm1301/index.cfm?do=abs.viewAbs&subView=1&abs=3430 Page 3 of 4 OHBM 1/11/13 12:36 AM https://ww4.aievolution.com/hbm1301/index.cfm?do=abs.viewAbs&subView=1&abs=3430 Page 4 of 4 Conclusions: Our study shows a decrease in FA values of subjects with DS compared to control, which may be due to an earlier aging process in subjects with Down syndrome. DS subjects show cognitive differences that can be characterized in the white matter integrity of specific brain connectivity pathways. Diffusion tensors in white matter can serve as a surrogate biomarker of specific aspects of cognitive development in DS. Imaging Methods: Diffusion MRI [1] Korenberg, J. R., X. N. Chen, R. Schipper, Z. Sun, R. Gonsky, S. Gerwehr, N. Carpenter, C. Daumer, P. Dignan, and C. Disteche. "Down syndrome phenotypes: the consequences of chromosomal imbalance." Proceedings of the National Academy of Sciences 91, no. 11 (1994): 4997-5001. [2] J. Dubois, L. Hertz-Pannier, Ghislaine Dehaene-Lambertz, Y. Cointepas, and D. Le Bihan. Assessment of the early organization and maturation of infants' cerebral white matter fiber bundles: a feasibility study using quantitative diffusion tensor imaging and tractography. Neuroimage, 30(4):1121--1132, May 2006 [3] Joshi S, Davis B, Jomier M, Gerig G: Unbiased diffeomorphic atlas construction for computational anatomy. Neuroimage 2004; 23(suppl 1):S151-S160 [4] Mori, S., Oishi, K., Jiang, H., Jiang, L., Li, X., Akhter, K., Hua, K., Faria, A., Mahmood, A., Woods, R., Toga, A., Pike, G., Neto, P., Evans, A., Zhang, J., Huang, H., Miller, M., van Zijl, P., Mazziotta, J., Apr 2008. Stereotaxic white matter atlas based on diffu- sion tensor imaging in an ICBM template. NeuroImage 40, 570-582. [5] Avants, B. B.; Epstein, C. L.; Grossman, M. & Gee, J. C. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal, Department of Radiology, University of Pennsylvania, 3600 Market Street, Philadelphia, PA 19104, United States., 2008, 12, 26-41 [6] Klein, A.; Andersson, J.; Ardekani, B. A.; Ashburner, J.; Avants, B.; Chiang, M.; Christensen, G. E.; Collins, L.; Hellier, P.; Song, J. H.; Jenkinson, M.; Lepage, C.; Rueckert, D.; Thompson, P.; Vercauteren, T.; Woods, R. P.; Mann, J. J. & Parsey, R. V. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage, New York State Psychiatric Institute, Columbia University, NY, NY 10032, USA., 2009