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We present a computational framework for analysis and visualization ofnon-linear functional connectivity in the human brain from resting statefunctional MRI (fMRI) data for purposes of recovering the underlying networkcommunity structure and exploring causality between network components. Ourproposed methodology of non-linear mutual connectivity analysis (MCA) involvestwo computational steps. First, the pair-wise cross-prediction performancebetween resting state fMRI pixel time series within the brain is evaluated. Theunderlying network structure is subsequently recovered from the affinity matrixconstructed through MCA using non-metric network partitioning/clustering withthe so-called Louvain method. We demonstrate our methodology in the task ofidentifying regions of the motor cortex associated with hand movement onresting state fMRI data acquired from eight slice locations in four subjects.For comparison, we also localized regions of the motor cortex through atask-based fMRI sequence involving a finger-tapping stimulus paradigm. Finally,we integrate convergent cross mapping (CCM) into the first step of MCA forinvestigating causality between regions of the motor cortex. Results regardingcausation between regions of the motor cortex revealed a significantdirectional variability and were not readily interpretable in a consistentmanner across all subjects. However, our results on whole-slice fMRI analysisdemonstrate that MCA-based model-free recovery of regions associated with theprimary motor cortex and supplementary motor area are in close agreement withlocalization of similar regions achieved with a task-based fMRI acquisition.Thus, we conclude that our computational framework MCA can extract andvisualize valuable information concerning the underlying network structure andcausation between different regions of the brain in resting state fMRI.

A Framework for Exploring Non-Linear Functional Connectivity and  Causality in the Human Brain: Mutual Connectivity Analysis (MCA) of  Resting-State Functional MRI with Convergent Cross-Mapping and Non-Metric  Clustering