Spatiotemporal feature extraction and classification of Alzheimer's disease using deep learning 3D-CNN for fMRI data

Abstract

Purpose: Through the last three decades, functional magnetic resonance imaging (fMRI) has provided immense quantities of information about the dynamics of the brain, functional brain mapping, and resting-state brain networks. Despite providing such rich functional information, fMRI is still not a commonly used clinical technique due to inaccuracy involved in analysis of extremely noisy data. However, ongoing developments in deep learning techniques suggest potential improvements and better performance in many different domains. Our main purpose is to utilize the potentials of deep learning techniques for fMRI data for clinical use. Approach: We present one such synergy of fMRI and deep learning, where we apply a simplified yet accurate method using a modified 3D convolutional neural networks (CNN) to resting-state fMRI data for feature extraction and classification of Alzheimer's disease (AD). The CNN is designed in such a way that it uses the fMRI data with much less preprocessing, preserving both spatial and temporal information. Results: Once trained, the network is successfully able to classify between fMRI data from healthy controls and AD subjects, including subjects in the mild cognitive impairment (MCI) stage. We have also extracted spatiotemporal features useful for classification. Conclusion: This CNN can detect and differentiate between the earlier and later stages of MCI and AD and hence, it may have potential clinical applications in both early detection and better diagnosis of Alzheimer's disease.

Keywords: 3D convolutional neural networks; Alzheimer’s disease; clinical functional magnetic resonance imaging; deep learning; spatiotemporal functional magnetic resonance imaging features.