Erin W Dickie1, Stephanie H Ameis2, Saba Shahab3, Navona Calarco1, Dawn E Smith1, Dayton Miranda1, Joseph D Viviano1, Aristotle N Voineskos4. 1. Kimel Family Translational Imaging Genetics Research Laboratory, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada. 2. Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Margaret and Wallace McCain Centre for Child, Youth, and Family Mental Health, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Centre for Brain and Mental Health, the Hospital for Sick Children, Toronto, Canada. 3. Kimel Family Translational Imaging Genetics Research Laboratory, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada. 4. Kimel Family Translational Imaging Genetics Research Laboratory, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada. Electronic address: aristotle.voineskos@camh.ca.
Abstract
BACKGROUND: Recent advances in techniques using functional magnetic resonance imaging data demonstrate individually specific variation in brain architecture in healthy individuals. To our knowledge, the effects of individually specific variation in complex brain disorders have not been previously reported. METHODS: We developed a novel approach (Personalized Intrinsic Network Topography, PINT) for localizing individually specific resting-state networks using conventional resting-state functional magnetic resonance imaging scans. Using cross-sectional data from participants with autism spectrum disorder (ASD; n = 393) and typically developing (TD) control participants (n = 496) across 15 sites, we tested: 1) effect of diagnosis and age on the variability of intrinsic network locations and 2) whether prior findings of functional connectivity differences in persons with ASD compared with TD persons remain after PINT application. RESULTS: We found greater variability in the spatial locations of resting-state networks within individuals with ASD compared with those in TD individuals. For TD persons, variability decreased from childhood into adulthood and increased in late life, following a U-shaped pattern that was not present in those with ASD. Comparison of intrinsic connectivity between groups revealed that the application of PINT decreased the number of hypoconnected regions in ASD. CONCLUSIONS: Our results provide a new framework for measuring altered brain functioning in neurodevelopmental disorders that may have implications for tracking developmental course, phenotypic heterogeneity, and ultimately treatment response. We underscore the importance of accounting for individual variation in the study of complex brain disorders. Crown
BACKGROUND: Recent advances in techniques using functional magnetic resonance imaging data demonstrate individually specific variation in brain architecture in healthy individuals. To our knowledge, the effects of individually specific variation in complex brain disorders have not been previously reported. METHODS: We developed a novel approach (Personalized Intrinsic Network Topography, PINT) for localizing individually specific resting-state networks using conventional resting-state functional magnetic resonance imaging scans. Using cross-sectional data from participants with autism spectrum disorder (ASD; n = 393) and typically developing (TD) control participants (n = 496) across 15 sites, we tested: 1) effect of diagnosis and age on the variability of intrinsic network locations and 2) whether prior findings of functional connectivity differences in persons with ASD compared with TDpersons remain after PINT application. RESULTS: We found greater variability in the spatial locations of resting-state networks within individuals with ASD compared with those in TD individuals. For TDpersons, variability decreased from childhood into adulthood and increased in late life, following a U-shaped pattern that was not present in those with ASD. Comparison of intrinsic connectivity between groups revealed that the application of PINT decreased the number of hypoconnected regions in ASD. CONCLUSIONS: Our results provide a new framework for measuring altered brain functioning in neurodevelopmental disorders that may have implications for tracking developmental course, phenotypic heterogeneity, and ultimately treatment response. We underscore the importance of accounting for individual variation in the study of complex brain disorders. Crown
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