Neuroimaging Feature Extraction using a Neural Network Classifier for Imaging Genetics

TL;DR

This paper presents a neural network-based method for extracting neuroimaging features relevant to Alzheimer's Disease, improving disease prediction and genetic association analysis.

Contribution

It introduces a neural network classifier for neuroimaging feature extraction tailored for disease prediction and genetic association, advancing neuroimaging-genetics analysis.

Findings

Neural network features outperform expert-selected features in disease prediction.

Identified SNPs show stronger association with neuroimaging features.

Enhanced understanding of genetic influences on neuroimaging biomarkers.

Abstract

A major issue in the association of genes to neuroimaging phenotypes is the high dimension of both genetic data and neuroimaging data. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer's Disease (AD) for subsequent relation to genetics. Our neuroimaging-genetic pipeline is comprised of image processing, neuroimaging feature extraction and genetic association steps. We propose a neural network classifier for extracting neuroimaging features that are related with disease and a multivariate Bayesian group sparse regression model for genetic association. We compare the predictive power of these features to expert selected features and take a closer look at the SNPs identified with the new neuroimaging features.