Abstract #0520

Identification of Neural Connectivity Signatures of Autism Using Machine Learning

Gopikrishna Deshpande¹,², Karthik Ramakrishnan Sreenivasan³, Hrishikesh Deshpande⁴, Rajesk K. Kana⁵

¹AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States; ²Department of Psychology, Auburn University, Auburn, AL, United States; ³ AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States; ⁴Department of Biomedical Engineering, University of Alabama, Birmingham, AL, United States; ⁵Department of Psychology, University of Alabama, Birmingham, AL, United States

The current study focuses on effective connectivity (EC) in autism, demonstrating the use of machine learning for identification of metrics which can be used to predict a novel subjects group membership. fMRI time-series were de-convolved using a cubature Kalman filter and the resultant neuronal variables were input into a multivariate autoregressive model (MVAR) to obtain the EC paths. These metrics were then input into a recursive cluster elimination based support vector machine (RCE-SVM) classifier which showed a prediction accuracy of 94.3% based only on causal connectivity weights indicating that EC could serve as a potential non-invasive neuroimaging biomarker for autism.