用MRI预测精神症状异常，提前发现阿尔茨海默病风险
Neuropsychiatric Deviations From Normative Profiles: An MRI-Derived Marker for Early Alzheimer's Disease Detection
- 基于3D卷积神经网络，从脑结构影像预测精神症状评分
- 异常偏离分（DNPI）越高，未来转为阿尔茨海默病概率增加2.5倍
- 无需侵入性检测，适合大规模早期筛查
阿尔茨海默病（AD）常见抑郁、冷漠等精神症状，常早于认知下降出现。这些症状评估具有作为早期检测标志物的潜力，因其与疾病进展相关且无创。但现有工具难以区分症状是正常衰老还是早期AD表现，限制了应用。本文提出一种基于深度学习的范式建模框架，通过结构磁共振成像（MRI）识别异常的精神症状负担。使用来自阿尔茨海默病神经影像计划（ADNI）的认知稳定参与者数据，训练3D卷积神经网络，学习脑解剖结构与神经精神量表问卷（NPIQ）评分之间的映射关系。预测得分与实际得分之间的偏差定义为偏离分数（DNPI）。DNPI值越高，越可能在未来转化为阿尔茨海默病（调整后比值比=2.5；p<0.01），其预测效能接近脑脊液Aβ42（曲线下面积AUC=0.74 vs 0.75）。该方法支持可扩展、非侵入性的早期阿尔茨海默病检测策略。
原文摘要 · Abstract (English)
Neuropsychiatric symptoms (NPS) such as depression and apathy are common in Alzheimer's disease (AD) and often precede cognitive decline. NPS assessments hold promise as early detection markers due to their correlation with disease progression and their non-invasive nature. Yet current tools cannot distinguish whether NPS are part of aging or early signs of AD, limiting their utility. We present a deep learning-based normative modelling framework to identify atypical NPS burden from structural MRI. A 3D convolutional neural network was trained on cognitively stable participants from the Alzheimer's Disease Neuroimaging Initiative, learning the mapping between brain anatomy and Neuropsychiatric Inventory Questionnaire (NPIQ) scores. Deviations between predicted and observed scores defined the Divergence from NPIQ scores (DNPI). Higher DNPI was associated with future AD conversion (adjusted OR=2.5; p < 0.01) and achieved predictive accuracy comparable to cerebrospinal fluid AB42 (AUC=0.74 vs 0.75). Our approach supports scalable, non-invasive strategies for early AD detection.
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