Distribution-guided Network Thresholding for Functional Connectivity Analysis in fMRI-based Brain Disorder Identification.

Abstract

Brain functional connectivity (FC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely applied to automated identification of brain disorders, such as Alzheimer s disease (AD) and attention deficit hyperactivity disorder (ADHD). To generate compact representations of FC networks, various thresholding strategies have been developed to analyze brain FC networks. However, existing studies usually employ predefined thresholds or percentages of connections to threshold FC networks, thus ignoring the diversity of temporal correlation (particularly strong associations) among brain regions in same/different subject groups. Also, it is usually challenging to decide the optimal threshold or connection percentage in practice. To this end, in this paper, we propose a distribution-guided network thresholding (DNT) method for functional connectivity analysis in brain disorder identification with rs-fMRI. Specifically, for each functional connectivity of a pair of brain regions, we proposed to compute its specific threshold based on the distribution of connection strength (i.e., temporal correlation) between subject groups (e.g., patients and normal controls). The proposed DNT can adaptively yield FC-specific threshold for each connection in brain networks, thus preserving the diversity of temporal correlation among brain regions. Experiment results on both ADNI and ADHD-200 datasets demonstrate the effectiveness of our proposed DNT method in fMRI-based identification of AD and ADHD.