Neuroimaging feature extraction using a neural network classifier for imaging genetics

BMC Bioinformatics

Research

Leno Rocha¹  Michelle F. Miranda¹  Farouk S. Nathoo¹  Sidi Wu²  Jiguo Cao²  Erin Gibson³  Mirza Faisal Beg³  Cédric Beaulac⁴ 

[1] Department of Mathematics and Statistics, University of Victoria, Victoria, Canada;Department of Statistics and Actuarial Sciences, Simon Fraser University, Burnaby, Canada;School of Engineering Science, Simon Fraser University, Burnaby, Canada;School of Engineering Science, Simon Fraser University, Burnaby, Canada;Department of Mathematics and Statistics, University of Victoria, Victoria, Canada;

关键词: Dimensionality reduction;    Feature extraction;    Neural Network Classifier;    Bayesian Hierarchical Modelling;    Imaging genetics;   

DOI  :  10.1186/s12859-023-05394-x

 received in 2022-09-28, accepted in 2023-06-21,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundDealing 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.ResultsWe find the features extracted with our proposed method are better predictors of AD than features used previously in the literature suggesting that single nucleotide polymorphisms (SNPs) related to the features extracted by our proposed method are also more relevant for AD. Our neuroimaging-genetic pipeline lead to the identification of some overlapping and more importantly some different SNPs when compared to those identified with previously used features.ConclusionsThe pipeline we propose combines machine learning and statistical methods to benefit from the strong predictive performance of blackbox models to extract relevant features while preserving the interpretation provided by Bayesian models for genetic association. Finally, we argue in favour of using automatic feature extraction, such as the method we propose, in addition to ROI or voxelwise analysis to find potentially novel disease-relevant SNPs that may not be detected when using ROIs or voxels alone.

【 授权许可】

CC BY   
© The Author(s) 2023

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