Douglas C Dean III1, Nicholas Lange2, Brittany Travers1, Nagesh Adluru1, Do Tromp1, Daniel Destiche1, Abigail Freeman1, Danica Samsin1, Brandon Zielinski3, Molly Prigge3, P.T. Fletcher3, Jeffery Anderson3, Erin Bigler4, Janet Lainhart1, and Andrew Alexander1
1Waisman Center, University of Wisconsin-Madison, Madison, WI, United States, 2McLean Hospital, Boston, MA, United States, 3University of Utah, Salt Lake City, UT, United States, 4Brigham Young University, Provo, UT, United States
To date, the heterogeneity of neuroimaging
findings has made it challenging to identify specific brain-related phenotypes
within autism spectrum disorder (ASD). In particular, a quantitative index of
individual deviation across a set of brain measurements may be informative for
constructing distributions of brain variation and identifying individuals who
may or may not have abnormal brain structure. To this end, we investigated the
use of the Mahalanobis distance to characterize multidimensional brain measures
in individuals with and without ASD and to demonstrate that patterns of brain
features distinguishing individuals with ASD are multidimensional and likely
encompass differing cortical and sub-cortical characteristics.
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