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

ABSTRACT: 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 discrimin

SUBMITTER: Zhang F 

PROVIDER: S-EPMC5910272 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature