Journal of psychiatric research · 2026
Regional and network-level convergent structural effects of electroconvulsive therapy in depression: a neuroimaging meta-analysis.
СтатьяNetwork Meta-AnalysisПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Meng Huiqing, Yang Yahui, Cui Qian, Long Zhiliang
Аннотация
Neuroimaging studies have suggested macroscale effects of electroconvulsive therapy (ECT) on brain structures in depression, but findings remain inconsistent. The current study aims to investigate whether ECT exhibits convergence in the treatment effect at the regional and network levels and to explore the relationship between ECT-related structural effect and neurotransmitter systems. We conducted a comprehensive search in PubMed and Web of Science, identifying eleven studies on ECT effects related to whole-brain gray matter volume (GMV) in depression. A coordinate-based meta-analysis named activation likelihood estimation (ALE) was performed to examine regional convergence of ECT treatment effects. Additionally, we further employed a network-based meta-analysis to assess convergent structural connectivity patterns of reported treatment-related coordinates using data from the Human Connectome Project. Finally, we explored the association of the treatment effect with neurotransmitter receptor/transporter maps. We observed that ECT consistently increased GMV in the bilateral hippocampus, parahippocampus and amygdala, right insula, superior temporal gyrus, and temporal pole in patients with depression. Morerover, the coordinates showing increased GMV following ECT had convergent structural connectivity with the limbic and subcortical networks. Furthermore, the convergent structural connectivity was associated with several neurotransmitter receptors and transporters, while none of the receptors/transporters were significantly over or under expressed within the ALE convergent clusters. These results suggested that ECT treatment efficacy in depression may be associated with the structural neuroplasticity within limbic-subcortical networks.