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<p>The heterogeneity of depression complicates treatment. Identifying stable biological subtypes could advance precision-targeted interventions. This study aims to identify stable depression subtypes using functional connectome normative deviation models and to assess their response to repetitive transcranial magnetic stimulation (rTMS). We analyzed 1204 patients spanning different states of depression, together with 1636 healthy controls. Functional connectome normative models were derived from healthy controls to generate individual deviation maps for patients with depression, which were clustered using k-means to identify biologically informed subtypes. Subtype-specific responses to dorsolateral prefrontal cortex rTMS were evaluated, and putative neurobiological mechanisms underlying differential rTMS responsiveness were investigated. Two reproducible subtypes emerged across various clinical and methodological conditions: subtype-1 exhibited hyperconnectivity in somatomotor and ventral attention networks and hypoconnectivity in frontoparietal and default mode networks, whereas subtype-2 showed the opposite pattern. Only subtype-2 showed significant improvement in anhedonia following rTMS treatment (SHAPS: z =− 2.92, P = 0.001, FDR), which was significantly greater than that of subtype-1 (SHAPS, subtype-1 vs. subtype-2 efficacy: z = −2.43, P = 0.046, FDR). Patients whose connectome deviation patterns more closely resembled subtype-2 had better anhedonia improvement (r = 0.48, P = 0.012), while those closer to subtype-1 had less improvement (r = −0.46, P = 0.016). Only the pattern of deviation changes in subtype-2 was positively correlated with the anhedonia-related functional connectivity network mapping (r = 0.43, P &lt; 0.001). These preliminary findings highlight potential avenues for subtype-targeted interventions in depression and warrant validation in larger randomized controlled trials.</p>

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Stable depression subtypes identified using functional connectome normative deviation models and their response to rTMS

  • 期刊论文

  • 发表日期：

    2026年5月7日

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  • Chengfeng Chen,
  • Liyuan Lin,
  • Yuan Liu,
  • Shiying Wang,
  • Jiang Wang,
  • Yufeng Zang,
  • Jijun Wang,
  • Wen Qin,
  • Bin Zhang

摘要

The heterogeneity of depression complicates treatment. Identifying stable biological subtypes could advance precision-targeted interventions. This study aims to identify stable depression subtypes using functional connectome normative deviation models and to assess their response to repetitive transcranial magnetic stimulation (rTMS). We analyzed 1204 patients spanning different states of depression, together with 1636 healthy controls. Functional connectome normative models were derived from healthy controls to generate individual deviation maps for patients with depression, which were clustered using k-means to identify biologically informed subtypes. Subtype-specific responses to dorsolateral prefrontal cortex rTMS were evaluated, and putative neurobiological mechanisms underlying differential rTMS responsiveness were investigated. Two reproducible subtypes emerged across various clinical and methodological conditions: subtype-1 exhibited hyperconnectivity in somatomotor and ventral attention networks and hypoconnectivity in frontoparietal and default mode networks, whereas subtype-2 showed the opposite pattern. Only subtype-2 showed significant improvement in anhedonia following rTMS treatment (SHAPS: z =− 2.92, P = 0.001, FDR), which was significantly greater than that of subtype-1 (SHAPS, subtype-1 vs. subtype-2 efficacy: z = −2.43, P = 0.046, FDR). Patients whose connectome deviation patterns more closely resembled subtype-2 had better anhedonia improvement (r = 0.48, P = 0.012), while those closer to subtype-1 had less improvement (r = −0.46, P = 0.016). Only the pattern of deviation changes in subtype-2 was positively correlated with the anhedonia-related functional connectivity network mapping (r = 0.43, P < 0.001). These preliminary findings highlight potential avenues for subtype-targeted interventions in depression and warrant validation in larger randomized controlled trials.