Abstract
The application of deep learning techniques to the detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention. The rapid progress in neuroimaging and sequencing techniques has enabled the generation of large-scale imaging genetic data for AD research. In this study, we developed a deep learning approach, IGnet, for automated AD classification using both magnetic resonance imaging (MRI) data and genetic sequencing data. The proposed approach integrates computer vision (CV) and natural language processing (NLP) techniques, with a deep three-dimensional convolutional network (3D CNN) being used to handle the three-dimensional MRI input and a Transformer encoder being used to manage the genetic sequence input. The proposed approach has been applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set. Using baseline MRI scans and selected single-nucleotide polymorphisms on chromosome 19, it achieved a classification accuracy of 83.78% and an area under the receiver operating characteristic curve (AUC-ROC) of 0.924 with the test set. The results demonstrate the great potential of using multi-disciplinary AI approaches to integrate imaging genetic data for the automated classification of AD.
Highlights
Alzheimer’s disease (AD), the most common cause of dementia, is the sixth leading cause of death in the United States
The main contributions of this article are as follows: (a) we present imaging genetic deep neural network system (IGnet), which combines computer vision (CV) and natural language processing (NLP) deep learning techniques to use 3D brain image data and genetic sequence data jointly to predict AD diagnosis; (b) we show that fusing brain magnetic resonance imaging (MRI) and genetic information together can provide predictions that are more accurate than those obtained with a single data modality; and (c) we show that the proposed method can serve as a baseline for related imaging genetic tasks and that it can be generalized
We evaluated the performance of IGnet on the test set by calculating the accuracy, the precision, the recall, the F1 score, the area under the receiver operating characteristic curve (AUC-ROC), and the AUC-PRC
Summary
Alzheimer’s disease (AD), the most common cause of dementia, is the sixth leading cause of death in the United States It is an irreversible and progressive brain disorder that slowly destroys memory, thinking skills, eventually, and the ability to carry out the simplest tasks. In addition to the well-established effects of APOE, genome-wide association studies (GWASs) have identified more than 30 genomic loci that are associated with increased risk of AD (MacArthur et al, 2017). These advances in AD genetics have provided important assistance in AD diagnosis but have encouraged current endeavors in translational research and personalized treatment of AD. It is important and beneficial to facilitate AD diagnosis by leveraging both imaging and genetic data
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