Multiple Kernel Learning for Modeling Resting State EEG Connectomes using Structural Connectivity of the Brain

Abstract

An active area of research in cognitive science is characterizing the relationship between brain structure and the observed functional activations. Recent graph diffusion models have had great success in mapping whole-brain, resting-state dynamics measured using functional Magnetic Resonance Imaging (fMRI) to the brain structure derived using diffusion and T1 brain imaging. Here we test the application of one such graph diffusion method called the Multiple Kernel Learning (MKL) model. MKL model, formulated as a reaction-diffusion system using Wilson-Cowan equations, combines multiple diffusion kernels at different scales to predict functional connectome (FC) arising from a fixed structural connectome (SC). Our simulation results demonstrate that the MKL model successfully mapped the relationship between SC and FC from five different Electroen-cephalogram (EEG) bands (delta, theta, alpha, beta, and gamma). We used simultaneously acquired EEG-fMRI and NODDI dataset of 17 participants. The correlation between predicted FC and ground truth FC was higher for EEG bands than for fMRI data. The prediction accuracy peaked for the alpha band, and the highest frequency band, gamma had the lowest prediction accuracy. To the best of our knowledge, this is the first such end-to-end application of multiple kernel graph diffusion framework for modeling EEG data. One of the important features of MKL model is its ability to incorporate structural connectivity features into the generative model that predicts the EEG functional connectivity.