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

Citation:

Fan Zhang, Peter Savadjev, Weidong Cai, Yang Song, Ragini Verma, Carl-Fredrik Westin, and Lauren J O'Donnell. 2016. “Fiber Clustering Based White Matter Connectivity Analysis for Prediction of Autism Spectrum Disorder using Diffusion Tensor Imaging.” In IEEE International Symposium on Biomedical Imaging, Pp. 564-7. Date Presented: 13 April. Copy at http://www.tinyurl.com/y59p5qw3
Zhang ISBI 2016 Paper757 KB

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