Identifying brain regions that exhibit altered functional connectivity across cognitive or emotional states is a key problem in neuroscience. Existing methods, such as edge-wise testing, seed-based psychophysiological interaction (PPI) analysis, or correlation network comparison, typically suffer from low statistical power, arbitrary thresholding, and limited ability to capture distributed or nonlinear dependence patterns. We propose SpARCD (Spectral Analysis of Revealing Connectivity Differences), a novel statistical framework for detecting differences in brain connectivity between two experimental conditions. SpARCD leverages distance correlation, a dependence measure sensitive to both linear and nonlinear associations, to construct a weighted graph for each condition. It then constructs a differential operator via spectral filtering and uncovers connectivity changes by computing its leading eigenvectors. Inference is achieved via a permutation-based testing scheme that yields interpretable, region-level significance maps. Extensive simulation studies demonstrate that SpARCD achieves superior power relative to conventional edge-wise or univariate approaches, particularly in the presence of complex dependency structures. Application to fMRI data from 113 early PTSD patients performing an emotional face-matching task reveals distinct networks associated with emotional reactivity and regulatory processes. Overall, SpARCD provides a statistically rigorous and computationally efficient framework for comparing high-dimensional connectivity structures, with broad applicability to neuroimaging and other network-based scientific domains.

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