Alzheimer's disease (AD) is the most prevalent form of dementia with a progressive decline in cognitive abilities. The AD continuum encompasses a prodromal stage known as MCI, where patients may either progress to AD (MCIc) or remain stable (MCInc). Understanding AD mechanisms requires complementary analyses relying on different data sources, leading to the development of multimodal DL models. We leveraged structural and functional MRI to investigate the disease-induced GM and functional network connectivity changes. Moreover, considering AD's strong genetic component, we introduced SNPs as a third channel. Missing one or more modalities is a typical concern of multimodal methods. We hence propose a novel DL-based classification framework where a generative module employing Cycle GAN was adopted for imputing missing data in the latent space. Additionally, we adopted an XAI method, Integrated Gradients, to extract features' relevance, enhancing our understanding of the learned representations. Two tasks were addressed: AD detection and MCI conversion prediction. Experimental results showed that our framework reached the SOA in the classification of CN/AD with an average test accuracy of $0.926\pm0.02$. For the MCInc/MCIc task, we achieved an average prediction accuracy of $0.711\pm0.01$ using the pre-trained model for CN and AD. The interpretability analysis revealed that significant GM modulations led the classification performance in cortical and subcortical brain areas well known for their association with AD. Impairments in sensory-motor and visual functional network connectivity along AD, as well as mutations in SNPs defining biological processes linked to endocytosis, amyloid-beta, and cholesterol, were identified as contributors to the results. Overall, our integrative DL model shows promise for AD detection and MCI prediction, while shading light on important biological insights.

翻译：阿尔茨海默病（AD）是最常见的痴呆症类型，其特征是认知能力进行性衰退。AD疾病谱包含一个被称为轻度认知障碍（MCI）的前驱阶段，此阶段患者可能进展为AD（MCIc）或保持稳定（MCInc）。理解AD机制需要依赖不同数据源的互补性分析，这推动了多模态深度学习模型的发展。我们利用结构和功能磁共振成像（MRI）来研究疾病引起的灰质（GM）和功能网络连接变化。此外，考虑到AD具有强烈的遗传成分，我们引入了单核苷酸多态性（SNP）作为第三模态。缺失一种或多种模态是多模态方法的典型问题。因此，我们提出了一种新颖的基于深度学习的分类框架，其中采用基于Cycle GAN的生成模块在潜在空间中对缺失数据进行填补。另外，我们采用了一种可解释人工智能（XAI）方法——积分梯度法，以提取特征相关性，从而增强我们对所学表征的理解。本研究解决了两个任务：AD检测和MCI转化预测。实验结果表明，我们的框架在认知正常（CN）/AD分类中达到了最先进水平（SOA），平均测试准确率为$0.926\pm0.02$。对于MCInc/MCIc任务，我们使用针对CN和AD预训练的模型实现了$0.711\pm0.01$的平均预测准确率。可解释性分析表明，显著的灰质调制主导了分类性能，这些调制发生在已知与AD相关的皮层和皮层下脑区。感觉运动与视觉功能网络连接在AD病程中的损伤，以及与内吞作用、β-淀粉样蛋白和胆固醇相关的生物学过程所定义的SNP突变，被确认为影响结果的因素。总体而言，我们的集成深度学习模型在AD检测和MCI预测方面展现出潜力，同时为重要的生物学见解提供了线索。