Copy Number Variation Informs fMRI-based Prediction of Autism Spectrum
  Disorder
Nicha C. Dvornek Catherine Sullivan James S. Duncan Abha R. Gupta
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.