ArticleFrontiers in human neuroscience2025
Dynamic brain network reconfiguration following rTMS in males with cocaine use disorder.
Article in Frontiers in human neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Cocaine use disorder (CUD) is characterized by cortico-striatal circuit dysregulation and high relapse rates, with repetitive transcranial magnetic stimulation (rTMS) emerging as a potential neuromodulatory intervention. This study investigates rTMS-induced dynamic brain network reconfigurations in 30 CUD patients using longitudinal resting-state fMRI from the SUDMEX-TMS cohort. Applying Leading Eigenvector Dynamics Analysis (LEiDA) to phase-locking states, we identified four metastable network configurations mapped to canonical resting-state networks. Post-rTMS analyses revealed selective modulation of visual network (VIS)-dominant states, showing increased duration and occupancy, alongside reduced self-transition probabilities in frontoparietal control network (FPCN) states after rTMS therapy. Temporal dynamics of these states correlated with subjective craving intensity: increased duration of the VIS-dominant state was associated with lower craving severity (CCQ-N) post-treatment. These findings suggest that increased VIS metastability strengthened bottom-up sensory gating that attenuates drug-cue salience through perceptual desensitization. Although FPCN-state self-transition decreased significantly following stimulation, it was not directly linked to craving improvement, indicating a potentially supportive but nonspecific role in perceptual recalibration. Together, these dynamic markers highlight the relevance of network-level flexibility in mediating rTMS treatment efficacy for cocaine addiction. By establishing dynamic network state reconfiguration as a mechanism linking rTMS to symptom evolution, this work provides a framework for optimizing neuromodulation protocols and developing neurodynamics-dependent biomarkers in addiction therapeutics.
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