  Functional Connectivity between brain regions is known to be altered in
Alzheimer's disease, and promises to be a biomarker for early diagnosis of the
disease. While several approaches for functional connectivity obtain an
un-directed network representing stochastic associations (correlations) between
brain regions, association does not necessarily imply causation. In contrast,
Causal Functional Connectivity is more informative, providing a directed
network representing causal relationships between brain regions. In this paper,
we obtained the causal functional connectome for the whole brain from
recordings of resting-state functional magnetic resonance imaging (rs-fMRI) for
subjects from three clinical groups: cognitively normal, mild cognitive
impairment, and Alzheimer's disease. We applied the recently developed
Time-aware PC (TPC) algorithm to infer the causal functional connectome for the
whole brain. TPC supports model-free estimation of whole brain causal
functional connectivity based on directed graphical modeling in a time series
setting. We then perform an exploratory analysis to identify the causal brain
connections between brain regions which have altered strengths between pairs of
subject groups, and over the three subject groups, based on edge-wise p-values
from statistical tests. We used the altered causal brain connections thus
obtained to compile a comprehensive list of brain regions impacted by
Alzheimer's disease according to the current data set. The brain regions thus
identified are found to be in agreement with literature on brain regions
impacted by Alzheimer's disease, published by researchers from clinical/medical
institutions.
