Uncovering Brain Network Modules in Major Depression During Naturalistic Music Perception With Block Term Decomposition

Major depressive disorder (MDD) has been linked to altered brain networks and might be relieved by music therapy. Yet, the neurophysiological basis, especially the functional network mechanism, of music therapy on depression remains poorly understood. Here, we apply a novel dynamic module detection method based on block term decomposition (BTD) to examine the reorganization of time-varying topological network structures during music listening in MDD using electroencephalography (EEG). Specifically, temporal adjacency matrices generated using a sliding-window technique form a three-way tensor. The multilinear rank-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(L,L,1)$</tex-math></inline-formula> BTD is applied to directly derive hidden network modules with specific time evolution from the temporally concatenated tensors for each frequency band. After temporal correlation analysis with musical features extracted from music stimuli, we identify several frequency-dependent network modules with specific temporal patterns modulated by musical features. These modular networks encompass subnetworks of default mode, frontoparietal, language, and sensorimotor networks involved in the delta, alpha, and beta bands, exhibiting significantly different modulations by music between healthy control and MDD groups. These results implicate that the altered oscillatory modular networks might affect the dynamic processing of musical features for MDD patients, which could offer valuable perspectives on revealing the neural mechanisms of music therapy for MDD.