Late-life depression (LLD) is a highly prevalent mood disorder occurring in older adults and is frequently accompanied by cognitive impairment (CI). Studies have shown that LLD may increase the risk of Alzheimer’s disease (AD). However, the heterogeneity of presentation of geriatric depression suggests that multiple biological mechanisms may underlie it. Current biological research on LLD progression incorporates machine learning that combines neuroimaging data with clinical observations. There are few studies on incident cognitive diagnostic outcomes in LLD based on structural MRI (sMRI). In this paper, we describe the development of a hybrid representation learning (HRL) framework for predicting cognitive diagnosis over 5 years based on T1-weighted sMRI data. Specifically, we first extract prediction-oriented MRI features via a deep neural network, and then integrate them with handcrafted MRI features via a Transformer encoder for cognitive diagnosis prediction. Two tasks are investigated in this work, including (1) identifying cognitively normal subjects with LLD and never-depressed older healthy subjects, and (2) identifying LLD subjects who developed CI (or even AD) and those who stayed cognitively normal over five years. We validate the proposed HRL on 294 subjects with T1-weighted MRIs from two clinically harmonized studies. Experimental results suggest that the HRL outperforms several classical machine learning and state-of-the-art deep learning methods in LLD identification and prediction tasks.

中文翻译：

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使用结构 MRI 进行 5 年以上晚年抑郁症认知诊断的混合表征学习

晚年抑郁症（LLD）是一种在老年人中非常普遍的情绪障碍，并且经常伴有认知障碍（CI）。研究表明，LLD 可能会增加患阿尔茨海默病 (AD) 的风险。然而，老年抑郁症表现的异质性表明其背后可能存在多种生物学机制。目前关于 LLD 进展的生物学研究结合了机器学习，将神经影像数据与临床观察相结合。基于结构 MRI (sMRI) 的 LLD 事件认知诊断结果的研究很少。在本文中，我们描述了混合表征学习 (HRL) 框架的开发，该框架用于基于 T1 加权 sMRI 数据预测 5 年以上的认知诊断。具体来说，我们首先通过深度神经网络提取面向预测的 MRI 特征，然后通过 Transformer 编码器将它们与手工制作的 MRI 特征集成以进行认知诊断预测。这项工作研究了两项任务，包括（1）识别患有 LLD 的认知正常受试者和从未抑郁的老年健康受试者，以及（2）识别出现 CI（甚至 AD）的 LLD 受试者和五年内保持认知正常的受试者。我们对来自两项临床协调研究的 294 名受试者使用 T1 加权 MRI 验证了拟议的 HRL。实验结果表明，HRL 在 LLD 识别和预测任务中优于多种经典机器学习和最先进的深度学习方法。