Application of Time-Aware PC algorithm to compute Causal Functional Connectivity in Alzheimer's Disease from fMRI data

TL;DR

This study applies the Time-aware PC algorithm to rs-fMRI data to infer causal brain networks in Alzheimer's disease, revealing altered causal connections that align with known disease-affected regions.

Contribution

It introduces the use of the Time-aware PC algorithm for causal functional connectivity analysis in Alzheimer's, providing a model-free, directed network approach from fMRI data.

Findings

Altered causal connections identified between specific brain regions in Alzheimer's.

The causal connectome findings agree with existing literature on affected regions.

The method distinguishes between different clinical groups based on causal network differences.

Abstract

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.