用于解码阿尔茨海默病的可解释生成式多模态神经成像基因组学框架。

ArXiv Pub Date : 2025-02-04
Giorgio Dolci, Federica Cruciani, Md Abdur Rahaman, Anees Abrol, Jiayu Chen, Zening Fu, Ilaria Boscolo Galazzo, Gloria Menegaz, Vince D Calhoun
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引用次数: 0

摘要

阿尔茨海默病(AD)是最常见的痴呆症,患者的认知能力会逐渐下降。阿兹海默病的连续过程包括一个被称为轻度认知功能障碍(MCI)的前驱阶段,在这一阶段,患者既可能发展为阿兹海默病,也可能保持稳定。在这项研究中,我们利用结构性和功能性核磁共振成像来研究疾病引起的灰质和功能性网络连接变化。此外,考虑到注意力缺失症具有很强的遗传因素,我们还引入了 SNPs 作为第三通道。鉴于输入的多样性,遗漏一种或多种模式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,其中的生成模块采用了循环 GAN,以弥补潜在空间中的缺失数据。此外,我们还采用了一种可解释的人工智能方法--集成梯度(Integrated Gradients)来提取输入特征的相关性,从而增强我们对所学表征的理解。我们完成了两项关键任务:注意力缺失检测和 MCI 转换预测。实验结果表明,我们的模型在CN/AD分类中达到了SOA,平均测试准确率为0.926/pm0.02$。在 MCI 任务中,我们使用预先训练好的 CN/AD 模型达到了 0.711/pm0.01 美元的平均预测准确率。可解释性分析表明,皮层和皮层下脑区的灰质发生了明显的改变,而这些区域众所周知与注意力缺失症有关。此外,感觉-运动和视觉静息态网络连接沿疾病连续性的损伤,以及定义与淀粉样蛋白-β和胆固醇形成清除和调控相关的生物过程的 SNPs 突变,也被确定为影响所取得成绩的因素。总之,我们的综合深度学习方法在揭示重要的生物学见解的同时,也显示出了对注意力缺失症检测和 MCI 预测的前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease.

\textbf{Objective:} Alzheimer's disease (AD) is the most prevalent form of dementia worldwide, encompassing a prodromal stage known as Mild Cognitive Impairment (MCI), where patients may either progress to AD or remain stable. The objective of the work was to capture structural and functional modulations of brain structure and function relying on multimodal MRI data and Single Nucleotide Polymorphisms, also in case of missing views, with the twofold goal of classifying AD patients versus healthy controls and detecting MCI converters. % in two distinct tasks, dealing with also missing data.\\ \textbf{Approach:} We propose a multimodal DL-based classification framework where a generative module employing Cycle Generative Adversarial Networks was introduced in the latent space for imputing missing data (a common issue of multimodal approaches). Explainable AI method was then used to extract input features' relevance allowing for post-hoc validation and enhancing the interpretability of the learned representations. \textbf{Main results:} Experimental results on two tasks, AD detection and MCI conversion, showed that our framework reached competitive performance in the state-of-the-art with an accuracy of $0.926\pm0.02$ and $0.711\pm0.01$ in the two tasks, respectively. The interpretability analysis revealed gray matter modulations in cortical and subcortical brain areas typically associated with AD. Moreover, impairments in sensory-motor and visual resting state networks along the disease continuum, as well as genetic mutations defining biological processes linked to endocytosis, amyloid-beta, and cholesterol, were identified. \textbf{Significance:} Our integrative and interpretable DL approach shows promising performance for AD detection and MCI prediction while shedding light on important biological insights.

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