A Statistical Characterization of Dynamic Brain Functional Connectivity

Abstract

This study examined the statistical underpinnings of dynamic functional connectivity in mental disorders, using resting-state fMRI signals. Notably, there has been an absence of research demonstrating the non-stationarity of the empirical probability distribution of functional connectivity. This gap has prompted debate on the existence of dynamic functional connectivity, leading skeptics to question its relevance and the reliability of research findings. Our aim was to fill this gap by conducting a comprehensive empirical distribution analysis of functional connectivity, using Pearson's correlation as a measure. We conducted our analysis on a set of preprocessed resting-state fMRI samples obtained from 186 subjects selected from the UCLA Consortium for Neuropsychiatric Phenomics dataset. Departing from conventional methods that aggregated signals over voxels within a region of interest, our approach leveraged individual voxel signals. Specifically, our approach offered a precise characterization of the empirical probability distribution of resting-state fMRI signals by evaluating the temporal variations and non-stationarity in dynamic functional connectivity, as measured by Pearson's correlation. Our study investigated functional connectivity patterns across 49 regions of interest, comparing healthy control subjects with patients diagnosed with ADHD, bipolar disorder, and schizophrenia. Our analysis revealed that (1) the empirical distribution of the correlation coefficient exhibited non-stationarity, (2) the beta distribution was an accurate approximation of the exact correlation coefficient distribution, and (3) the empirical distribution of means derived from the fitted beta distributions, unraveled distinctive dynamic functional connectivity patterns with potential as biomarkers associated with different mental disorders. A key contribution of our study was the presentation of the first comprehensive empirical distribution analysis of dynamic functional connectivity, thus providing compelling evidence for its existence. Overall, our study presented an innovative statistical approach that advances our understanding of the dynamic nature of functional connectivity patterns derived from resting-state fMRI. Our examination of the empirical distribution of dynamic functional connectivity provided solid evidence supporting its existence. The distinctive dynamic functional connectivity patterns we identified across various mental disorders hold promise as potential biomarkers for further development.