Predictive Models of Resting State Networks for Assessment of Altered Functional Connectivity in MCI

Abstract

Due to the difficulties in establishing accurate correspondences of brain network nodes across individual subjects, systematic elucidation of possible functional connectivity (FC) alterations in mild cognitive impairment (MCI) compared with normal controls (NC) is a challenging problem. To address this challenge, in this paper, we develop and apply novel predictive models of resting state networks (RSNs) learned from multimodal resting state fMRI (R-fMRI) and DTI data to assess large-scale FC alterations in MCI. Our rationale is that some RSNs in MCI are substantially altered and can hardly be directly compared with those in NC. Instead, structural landmarks derived from DTI data are much more consistent and correspondent across MCI/NC brains, and therefore can be employed to encode RSNs in NC and serve as the predictive models of RSNs for MCI. To derive these predictive models, RSNs in NC are constructed by group-wise ICA clustering and employed to functionally annotate corresponding structural landmarks. Afterwards, these functionally-annotated structural landmarks are predicted in MCI based on DTI data and used to assess FC alterations in MCI. Experimental results demonstrated that the predictive models of RSNs are effective and can comprehensively reveal widespread FC alterations in MCI.