Network Analysis Based on Analytic Solution to Permutation Tests on Support Vector Machines

Madhura Ingalhalikar¹, Bilwaj Gaonkar¹, Alex R. Smith¹, Robert T. Schultz², Ragini Verma¹

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.