AN INTERPRETABLE GENERATIVE MULTIMODAL NEUROIMAGING-GENOMICS FRAMEWORK FOR DECODING ALZHEIMER'S DISEASE.

ABSTRACT:

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.

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). Explainabl

SUBMITTER: Dolci G 

PROVIDER: S-EPMC11213156 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature