神经心理症状偏离规范轮廓：一种基于MRI的早期阿尔茨海默病检测标志物
Neuropsychiatric Deviations From Normative Profiles: An MRI-Derived Marker for Early Alzheimer's Disease Detection
AI总结：
本文提出基于深度学习的规范模型，利用结构MRI识别神经心理症状偏离，通过DNPI指标预测阿尔茨海默病转化，其预测准确度与脑脊液AB42相当。
摘要：
神经心理症状（如抑郁和迟钝）在阿尔茨海默病（AD）中常见，并常在认知下降前出现。NPS评估因其与疾病进展的相关性及非侵入性而有潜力作为早期检测标志物。然而，现有工具无法区分NPS是衰老部分还是AD早期迹象，限制了其应用。本文提出基于深度学习的规范建模框架，利用结构MRI识别异常NPS负担。3D卷积神经网络在认知稳定的阿尔茨海默病神经影像计划参与者上训练，学习大脑解剖结构与神经心理调查问卷（NPIQ）分数之间的映射。预测与观察分数之间的差异定义为NPIQ分数的偏离（DNPI）。更高的DNPI与未来AD转化相关（调整OR=2.5；p < 0.01），其预测准确度与脑脊液AB42相当（AUC=0.74 vs 0.75）。本方法支持可扩展的、非侵入性的早期AD检测策略。
英文摘要：
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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