Madhura Ingalhalikar1, Bilwaj Gaonkar1, Alex R. Smith1, Robert T. Schultz2, Ragini Verma1
1Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; 2Center for Autism Research, Childrens Hospital of Philadelphia, Philadelphia, PA, United States
The study employs a novel method using an analytic approach to permutation tests on support vector machines for computing statistical significance maps on connectivity matrices. Permutation tests are critical for interpreting SVM output for high dimensional data. However performing these tests is time consuming and computationally expensive. However, the analytical approximation to these tests can yield the results quickly. We apply this method to investigate the differences between patients with autism and typically developing controls based on their structural connectivity networks. We find that patients with autism show lower connectivity mainly in the connections initiating from temporal regions like fusiform gyrus and insula as well between fronto-parietal tracts.