Fiber Clustering Based White Matter Connectivity Analysis for Prediction of Autism Spectrum Disorder using Diffusion Tensor Imaging

Citation:

Zhang F, Savadjev P, Cai W, Song Y, Verma R, Westin C-F, O'Donnell LJ. Fiber Clustering Based White Matter Connectivity Analysis for Prediction of Autism Spectrum Disorder using Diffusion Tensor Imaging, in IEEE International Symposium on Biomedical Imaging. ; 2016 :564-7. Copy at http://www.tinyurl.com/zpcwqdt
Zhang ISBI 2016 Paper757 KB

Date Presented:

13 April

Abstract:

Autism Spectrum Disorder (ASD) has been suggested to associate with alterations 
in brain connectivity. In this study, we focus on a fiber clustering tractography segmentation 
strategy to observe white matter connectivity alterations in ASD. Compared to another 
popular parcellation-based approach for tractography segmentation based on cortical 
regions, we hypothesized that the clustering-based method could provide a more 
anatomically correspondent division of white matter. We applied this strategy to conduct a population-based group statistical analysis for the automated prediction of ASD. We obtained a maximum classification accuracy of 81.33% be- tween ASDs and controls, compared to the results of 78.00% from the parcellation-based method.

Last updated on 03/22/2017