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Functional Connectivity between brain regions is known to be altered inAlzheimer's disease, and promises to be a biomarker for early diagnosis of thedisease. While several approaches for functional connectivity obtain anun-directed network representing stochastic associations (correlations) betweenbrain regions, association does not necessarily imply causation. In contrast,Causal Functional Connectivity is more informative, providing a directednetwork representing causal relationships between brain regions. In this paper,we obtained the causal functional connectome for the whole brain fromrecordings of resting-state functional magnetic resonance imaging (rs-fMRI) forsubjects from three clinical groups: cognitively normal, mild cognitiveimpairment, and Alzheimer's disease. We applied the recently developedTime-aware PC (TPC) algorithm to infer the causal functional connectome for thewhole brain. TPC supports model-free estimation of whole brain causalfunctional connectivity based on directed graphical modeling in a time seriessetting. We then identified the causal brain connections between brain regionswhich have significantly different strengths between pairs of subject groups,and over the three subject groups. We used the significant causal brainconnections thus obtained to compile a comprehensive list of brain regionsimpacted by Alzheimer's disease according to the current data set. The obtainedbrain regions are in agreement with existing literature published byresearchers from clinical/medical institutions thus validating the approach. Wethen discuss the soundness and completeness of the results and the potentialfor using causal functional connectivity obtained using this methodology as abasis for the prognosis and diagnosis of Alzheimer's disease.

Causal Functional Connectivity in Alzheimer's Disease Computed from Time Series fMRI data