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.