Integrating Neuroimaging and Genetics via Contrastive Learning for Working Memory
Keywords: Working memory, Genetic variants, Neuroimaging, Contrastive learning, Sparse Canonical Correlation Analysis
Abstract: Understanding working memory's genetic and neural bases is crucial for advancing cognitive neuroscience and identifying biomarkers for cognitive impairments, particularly in the older population. This study integrates SNP and neuroimaging data from the UK biobank to improve the classification of high vs. low working memory capacity and reveal genetic factors associated with brain structure. 1060 SNPs belonging to Protein-Protein Interaction networks of amyloid precursor protein and Aβ of Alzheimer's disease were integrated with latent features of whole brain gray matter density, extracted by a pre-trained CNN, via supervised contrastive learning. Our model effectively extracts latent representations of both modalities through enhancing genetic-imaging relation within individuals and within working memory groups, in contrast to across individuals and groups. Features derived from contrastive learning outperformed other baseline models in terms of classification. Sparse canonical correlation analysis was applied to the latent representations and uncovered significantly related genetic variants and brain regions. Genetic components highlight SNPs in genes FYN, RPL28, MAPT, enriched in the pathways of dendrite and synapse, among others. The linked brain regions support the cerebellum and striatum's role in cognitive functions. These findings provide new insights into the genetic and neural mechanisms underlying working memory, potentially guiding future research and therapeutic strategies for cognitive impairment.
Track: 6. AI for biomarker discovery and drug design
Registration Id: 67NLQ645SRX
Submission Number: 297
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