Copy Number Variation Informs fMRI-based Prediction of Autism Spectrum Disorder

• URL: http://arxiv.org/abs/2308.05122v1
• Date: Tue, 8 Aug 2023 19:53:43 GMT
• Title: Copy Number Variation Informs fMRI-based Prediction of Autism Spectrum Disorder
• Authors: Nicha C. Dvornek, Catherine Sullivan, James S. Duncan, Abha R. Gupta
• Abstract summary: We develop a more integrative model for combining genetic, demographic, and neuroimaging data. Inspired by the influence of genotype on phenotype, we propose using an attention-based approach. We evaluate the proposed approach on ASD classification and severity prediction tasks, using a sex-balanced dataset of 228 ASD.
• Score: 9.544191399458954
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The multifactorial etiology of autism spectrum disorder (ASD) suggests that its study would benefit greatly from multimodal approaches that combine data from widely varying platforms, e.g., neuroimaging, genetics, and clinical characterization. Prior neuroimaging-genetic analyses often apply naive feature concatenation approaches in data-driven work or use the findings from one modality to guide posthoc analysis of another, missing the opportunity to analyze the paired multimodal data in a truly unified approach. In this paper, we develop a more integrative model for combining genetic, demographic, and neuroimaging data. Inspired by the influence of genotype on phenotype, we propose using an attention-based approach where the genetic data guides attention to neuroimaging features of importance for model prediction. The genetic data is derived from copy number variation parameters, while the neuroimaging data is from functional magnetic resonance imaging. We evaluate the proposed approach on ASD classification and severity prediction tasks, using a sex-balanced dataset of 228 ASD and typically developing subjects in a 10-fold cross-validation framework. We demonstrate that our attention-based model combining genetic information, demographic data, and functional magnetic resonance imaging results in superior prediction performance compared to other multimodal approaches.

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