Inference of functional connectivity from structural brain connectivity

Abstract

Studies that examine the relationship of functional and structural connectivity are tremendously important in interpreting neurophysiological data. Although, the relationship between functional and structural connectivity has been explored with a number of statistical tools [1, 2], there is no explicit attempt to quantitatively measure how well functional data can be predicted from structural data. Here, we predict functional connectivity from structural connectivity, explicitly, by utilizing a predictive model based on PCA and CCA. The combination of these techniques allowed the reduction of dimensionality and modeling of inter-correlations, successfully. We provide both qualitative and quantitative results based on a leave-one-out validation.