multivariate characterization of white matter heterogeneity in autism spectrum disorder
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ID: 201801
2017
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Abstract
The complexity and heterogeneity of neuroimaging findings in individuals with autism spectrum disorder has suggested that many of the underlying alterations are subtle and involve many brain regions and networks. The ability to account for multivariate brain features and identify neuroimaging measures that can be used to characterize individual variation have thus become increasingly important for interpreting and understanding the neurobiological mechanisms of autism. In the present study, we utilize the Mahalanobis distance, a multidimensional counterpart of the Euclidean distance, as an informative index to characterize individual brain variation and deviation in autism. Longitudinal diffusion tensor imaging data from 149 participants (92 diagnosed with autism spectrum disorder and 57 typically developing controls) between 3.1 and 36.83 years of age were acquired over a roughly 10-year period and used to construct the Mahalanobis distance from regional measures of white matter microstructure. Mahalanobis distances were significantly greater and more variable in the autistic individuals as compared to control participants, demonstrating increased atypicalities and variation in the group of individuals diagnosed with autism spectrum disorder. Distributions of multivariate measures were also found to provide greater discrimination and more sensitive delineation between autistic and typically developing individuals than conventional univariate measures, while also being significantly associated with observed traits of the autism group. These results help substantiate autism as a truly heterogeneous neurodevelopmental disorder, while also suggesting that collectively considering neuroimaging measures from multiple brain regions provides improved insight into the diversity of brain measures in autism that is not observed when considering the same regions separately. Distinguishing multidimensional brain relationships may thus be informative for identifying neuroimaging-based phenotypes, as well as help elucidate underlying neural mechanisms of brain variation in autism spectrum disorders.
| Reference Key |
iii2017neuroimage:multivariate
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| Authors | ;D.C. Dean, III;N. Lange;B.G. Travers;M.B. Prigge;N. Matsunami;K.A. Kellett;A. Freeman;K.L. Kane;N. Adluru;D.P.M. Tromp;D.J. Destiche;D. Samsin;B.A. Zielinski;P.T. Fletcher;J.S. Anderson;A.L. Froehlich;M.F. Leppert;E.D. Bigler;J.E. Lainhart;A.L. Alexander |
| Journal | chemical engineering journal |
| Year | 2017 |
| DOI |
10.1016/j.nicl.2017.01.002
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| URL | |
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