Neuroimaging Feature Extraction using a Neural Network Classifier for Imaging Genetics
This work addresses the challenge of linking neuroimaging to genetics for disease prediction, but it is incremental as it applies existing neural network methods to a specific domain problem.
The paper tackled the high dimensionality of neuroimaging data in imaging genetics by using a neural network classifier to extract features relevant for predicting Alzheimer's Disease, achieving competitive predictive power compared to expert-selected features and identifying specific SNPs.
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.