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DOI 10.1371/journal.pcbi.1013486

Медицинская библиотека · PLoS computational biology · 2025

Effects of tDCS of the DLPFC on brain networks: A hybrid brain modeling study.

Dong Yanqing, Wei Jing, Peng Songjun, Wu Xinran, Xu Yaru, Feng Jianfeng, Zhang Jie, Jirsa Viktor, Xiang Jie

СтатьяПоддержка исследования: вне правительства СШАПеревод пока не подготовлен

Аннотация

Transcranial direct current stimulation (tDCS) has shown promise in treating neurological disorders, particularly through dorsolateral prefrontal cortex (DLPFC) targeting. However, the effects of DLPFC-tDCS on brain functional networks and the underlying propagation mechanisms remain poorly understood. We present a novel tDCS hybrid brain model (tDCS-HBM) that incorporates tDCS-induced gray matter electric fields into a large-scale brain network model, considering their relationship with membrane potential to effectively predict spatiotemporal dynamics. Using this model, we simulated brain activity in response to tDCS over the left (F3-Fp2) and right DLPFC (F4-Fp1). Our results demonstrate that tDCS enhances brain complexity and flexibility, leading to increased functional connectivity (FC) across the whole brain and an improvement in global network efficiency. Dynamic analysis reveals an initial FC decline, followed by widespread enhancement originating from inferior and orbital frontal regions. Importantly, right DLPFC-tDCS induces strong FC associated with the ventral attention network. These changes in topological metrics and spatiotemporal patterns are consistent with prior modeling and empirical findings, validating the utility of our tDCS-HBM in understanding propagation mechanisms. Our hybrid model holds the potential to predict the stimulation effects of modulation protocols, providing precise guidance for clinical neuromodulation interventions.

Для цитирования: Dong Yanqing, Wei Jing, Peng Songjun, Wu Xinran, Xu Yaru, Feng Jianfeng, Zhang Jie, Jirsa Viktor, Xiang Jie. Effects of tDCS of the DLPFC on brain networks: A hybrid brain modeling study.. PLoS computational biology. 2025. DOI: 10.1371/journal.pcbi.1013486.

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