Abstract
In recent years, understanding functional brain connectivity has become increasingly important as a scientific tool with potential clinical implications. Statistical methods, such as graphical models and network analysis, have been adopted to construct functional connectivity networks for single subjects. Here we focus on studying the association between functional connectivity networks and clinical characteristics such as psychiatric symptoms and diagnoses. Utilizing machine learning algorithms, we propose a method to examine predictability of functional connectivity networks from clinical characteristics. Our methods can identify salient clinical characteristics predictive of the whole brain network or specific subnetworks. We illustrate our methods on the analysis of fMRI data in the Philadelphia Neurodevelopmental Cohort study, demonstrating clinically meaningful results.