用贝叶斯模型融合脑电与行为数据，提升隐性联想测试对心理症状的预测能力
Bayesian Inference of Psychometric Variables From Brain and Behavior in Implicit Association Tests
- 构建稀疏分层贝叶斯模型，融合多模态数据提升小样本下预测性能
- 在自杀倾向和精神病相关测试中分别达到0.73和0.76的AUC，显著优于传统D-score
- 适用于心理疾病早期筛查，尤其适合小样本研究场景
目的：提出一种基于隐性联想测试（IAT）的神经与行为数据，推断心理健康相关心理测量变量的系统方法，旨在克服仅依赖反应时的黄金标准D-score方法（通常AUC低于0.7）预测性能不足的问题。方法：设计了一种稀疏分层贝叶斯模型，利用多模态数据预测新参与者的精神疾病症状体验。该模型是D-score的多变量推广，具有可训练参数，专为小样本队列（典型IAT研究规模）下的参数效率优化。分析了两种IAT变体数据：与自杀意念相关的E-IAT（n=39）和与精神病相关的PSY-IAT（n=34）。主要结果：该方法有效应对个体间高变异性及会话内效应量低的问题，在最佳模态配置下，分别获得0.73（E-IAT）和0.76（PSY-IAT）的AUC，尽管校正后的95%置信区间较宽（±0.18），且经FDR校正后结果仅边缘显著（q=0.10）。将E-IAT限制于重度抑郁障碍（MDD）参与者后，AUC提升至0.79 [0.62, 0.97]，且在q=0.05水平显著。性能与最优参考方法（收缩LDA和EEGNet）相当，即使后者针对任务进行了适配，而本方法未做调整。在两项任务中均显著高于接近随机的D-score（0.50–0.53 AUC），且跨任务表现更稳定。意义：该框架有望增强基于IAT的心理状态评估，尤其在困顿感和精神病相关体验方面，但需更大、独立队列验证以确立临床应用价值。
原文摘要 · Abstract (English)
Objective. We establish a principled method for inferring mental health related psychometric variables from neural and behavioral data using the Implicit Association Test (IAT) as the data generation engine, aiming to overcome the limited predictive performance (typically under 0.7 AUC) of the gold-standard D-score method, which relies solely on reaction times. Approach. We propose a sparse hierarchical Bayesian model that leverages multi-modal data to predict experiences related to mental illness symptoms in new participants. The model is a multivariate generalization of the D-score with trainable parameters, engineered for parameter efficiency in the small-cohort regime typical of IAT studies. Data from two IAT variants were analyzed: a suicidality-related E-IAT ($n=39$) and a psychosis-related PSY-IAT ($n=34$). Main Results. Our approach overcomes a high inter-individual variability and low within-session effect size in the dataset, reaching AUCs of 0.73 (E-IAT) and 0.76 (PSY-IAT) in the best modality configurations, though corrected 95% confidence intervals are wide ($\pm 0.18$) and results are marginally significant after FDR correction ($q=0.10$). Restricting the E-IAT to MDD participants improves AUC to 0.79 $[0.62, 0.97]$ (significant at $q=0.05$). Performance is on par with the best reference methods (shrinkage LDA and EEGNet) for each task, even when the latter were adapted to the task, while the proposed method was not. Accuracy was substantially above near-chance D-scores (0.50-0.53 AUC) in both tasks, with more consistent cross-task performance than any single reference method. Significance. Our framework shows promise for enhancing IAT-based assessment of experiences related to entrapment and psychosis, and potentially other mental health conditions, though further validation on larger and independent cohorts will be needed to establish clinical utility.
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