Structural Connectivity Guided Sparse Effective Connectivity for MCI Identification

Abstract

Recent advances in network modelling techniques have enabled the study of neurological disorders at a whole-brain level based on functional connectivity inferred from resting-state magnetic resonance imaging (rs-fMRI) scan possible. However, constructing a directed effective connectivity, which provides a more comprehensive characterization of functional interactions among the brain regions, is still a challenging task particularly when the ultimate goal is to identify disease associated brain functional interaction anomalies. In this paper, we propose a novel method for inferring effective connectivity from multimodal neuroimaging data for brain disease classification. Specifically, we apply a newly devised weighted sparse regression model on rs-fMRI data to determine the network structure of effective connectivity with the guidance from diffusion tensor imaging (DTI) data. We further employ a regression algorithm to estimate the effective connectivity strengths based on the previously identified network structure. We finally utilize a bagging classifier to evaluate the performance of the proposed sparse effective connectivity network through identifying mild cognitive impairment from healthy aging.

Structural Connectivity Enriched Functional Brain Network Using Simplex Regression with GraphNet

“…Pineda Pardo et al applied adaptive GL to estimate an MEG connectivity network guided by a structural connectivity network [15]. Li et al proposed an ultra-weighted-LASSO approach to efficiently estimate functional networks by considering structural connectivity and derivatives of the temporal signal [10]. These methods incorporated the adaptive LASSO regularization approach to incorporate multi-modal information.…”