Whole brain white matter connectivity analysis using machine learning: An application to autism.

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TitleWhole brain white matter connectivity analysis using machine learning: An application to autism.
Publication TypeJournal Article
Year of Publication2018
AuthorsZhang, F, Savadjiev, P, Caí, W, Song, Y, Rathi, Y, Tunc, B, Parker, D, Kapur, T, Schultz, RT, Makris, N, Verma, R, O'Donnell, LJ
Date Published2018 05 15
KeywordsAdolescent, Autistic Disorder, Brain Mapping, Child, Diffusion Tensor Imaging, Humans, Machine Learning, Male, Neural Pathways, White Matter

In this paper, we propose an automated white matter connectivity analysis method for machine learning classification and characterization of white matter abnormality via identification of discriminative fiber tracts. The proposed method uses diffusion MRI tractography and a data-driven approach to find fiber clusters corresponding to subdivisions of the white matter anatomy. Features extracted from each fiber cluster describe its diffusion properties and are used for machine learning. The method is demonstrated by application to a pediatric neuroimaging dataset from 149 individuals, including 70 children with autism spectrum disorder (ASD) and 79 typically developing controls (TDC). A classification accuracy of 78.33% is achieved in this cross-validation study. We investigate the discriminative diffusion features based on a two-tensor fiber tracking model. We observe that the mean fractional anisotropy from the second tensor (associated with crossing fibers) is most affected in ASD. We also find that local along-tract (central cores and endpoint regions) differences between ASD and TDC are helpful in differentiating the two groups. These altered diffusion properties in ASD are associated with multiple robustly discriminative fiber clusters, which belong to several major white matter tracts including the corpus callosum, arcuate fasciculus, uncinate fasciculus and aslant tract; and the white matter structures related to the cerebellum, brain stem, and ventral diencephalon. These discriminative fiber clusters, a small part of the whole brain tractography, represent the white matter connections that could be most affected in ASD. Our results indicate the potential of a machine learning pipeline based on white matter fiber clustering.

Alternate JournalNeuroimage
PubMed ID29079524
PubMed Central IDPMC5910272
Grant ListR03 NS088301 / NS / NINDS NIH HHS / United States
R01 MH092862 / MH / NIMH NIH HHS / United States
P41 EB015898 / EB / NIBIB NIH HHS / United States
R01 MH097979 / MH / NIMH NIH HHS / United States
RC1 MH088791 / MH / NIMH NIH HHS / United States
R01 MH074794 / MH / NIMH NIH HHS / United States
U54 HD086984 / HD / NICHD NIH HHS / United States
U01 CA199459 / CA / NCI NIH HHS / United States
P41 EB015902 / EB / NIBIB NIH HHS / United States