Investigating the predictive models of efficacy of accelerated neuronavigation-guided rTMS for suicidal depression based on multimodal large-scale brain networks

• Fen Pan ^[1] ; Junle Li ^[2] ; Suhui Jin ^[2] ; Chensheng Hou ^[2] ; Yan Gui ^[1] ; Xinyi Ye ^[1] ; Haoyang Zhao ^[1] ; Kaiqi Wang ^[3] ; Desheng Shang ^[1] ; Shangda Li ^[1] ; Jinhui Wang ^[2] ; Manli Huang ^[1]
  1. [1] Zhejiang University

     Zhejiang University

     China

     
  2. [2] South China Normal University

     South China Normal University

     China

     
  3. [3] Ningbo Psychiatric Hospital, Ningbo, China

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• Localización: International journal of clinical and health psychology, ISSN 1697-2600, Vol. 25, Nº. 1, 2025, págs. 291-300
• Idioma: inglés
• DOI: 10.1016/j.ijchp.2025.100564
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• Resumen

  • Background Accelerated neuronavigation-guided high-dose repetitive transcranial magnetic stimulation (NH-rTMS) can rapidly reduce suicidal ideation and alleviate depressive symptoms in one week. Exploring accelerated NH-rTMS-related biomarkers will enhance the precision of treatment decisions for patients with major depressive disorder (MDD). This study aimed to establish predictive models of treatment response to accelerated NH-rTMS in MDD based on multimodal large-scale brain networks.

    Method In this study, morphological, structural, and functional brain networks were constructed for untreated MDD patients with suicidal ideation before accelerated NH-rTMS treatment. Linear support vector regression methods were utilized to examine the ability of multimodal brain networks in predicting antidepressant and anti-suicidal effects of accelerated NH-rTMS.

    Results We found that both the morphological and structural networks predicted the percentage changes of total Beck Scale of Suicidal Ideation and 24-item Hamilton Depression Rating Scale (HAMD-24) scores. Additionally, the functional networks predicted the percentage changes of total HAMD-24 scores. Further analyses revealed that the structural networks outperformed the morphological and functional networks and the somatomotor module outperformed other subnetworks in the prediction.

    Conclusions In summary, our study provides brain connectome-based predictive models of treatment response to accelerated NH-rTMS in MDD patients with suicidal ideation.

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