找出精神疾病根源性脑活动，精准关联症状与脑区异常
Extracting Root-Causal Brain Activity Driving Psychopathology from Resting State fMRI
- 用双层因果模型连接症状结构与静息态脑成像
- 识别出与症状维度直接相关的局部脑区异常活动
- 方法更可解释，适合研究精神疾病机制的学者
精神疾病神经影像研究常将影像模式与诊断标签或综合症状评分相关联，导致结果模糊，难以揭示潜在机制。本文旨在识别根因性脑图谱——即引发病理连锁反应的局部血氧水平依赖（BOLD）异常，并将其与症状维度精准关联。提出一种双层结构因果模型，通过独立潜变量建立跨被试症状结构与被试内静息态功能磁共振（fMRI）之间的联系，具有局部直接效应。基于该模型，开发了SOURCE（症状导向的根因元素发现）方法，可将可解释的症状轴与一组精简的局部驱动因素关联。实验表明，SOURCE恢复的脑图谱与根因性BOLD驱动因素一致，相比现有方法显著提升可解释性与解剖特异性。
原文摘要 · Abstract (English)
Neuroimaging studies of psychiatric disorders often correlate imaging patterns with diagnostic labels or composite symptom scores, yielding diffuse associations that obscure underlying mechanisms. We instead seek to identify root-causal maps -- localized BOLD disturbances that initiate pathological cascades -- and to link them selectively to symptom dimensions. We introduce a bilevel structural causal model that connects between-subject symptom structure to within-subject resting-state fMRI via independent latent sources with localized direct effects. Based on this model, we develop SOURCE (Symptom-Oriented Uncovering of Root-Causal Elements), a procedure that links interpretable symptom axes to a parsimonious set of localized drivers. Experiments show that SOURCE recovers localized maps consistent with root-causal BOLD drivers and increases interpretability and anatomical specificity relative to existing comparators.
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