Neuroimaging feature extraction using a neural network classifier for imaging genetics
Abstract
Dealing with the high dimension of both neuroimaging data and genetic data is a difficult problem in the association of genetic data to neuroimaging. 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. The neuroimaging-genetic pipeline we propose is comprised of image processing, neuroimaging feature extraction and genetic association steps. We present a neural network classifier for extracting neuroimaging features that are related with the disease. The proposed method is data-driven and requires no expert advice or a priori selection of regions of interest. We further propose a multivariate regression with priors specified in the Bayesian framework that allows for group sparsity at multiple levels including SNPs and genes.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 30, 2023
- Source ID
- 10.1186/s12859-023-05394-x
Entities
People
- Cédric Beaulac
- Erin M. Gibson
- Farouk S. Nathoo
- Jiguo Cao
- Leno Rocha
- Michelle F. Miranda
- Mirza Faisal Beg
- Sidi Wu