Deep learning analysis of fMRI data for predicting Alzheimer's Disease: A focus on convolutional neural networks and model interpretability.

Journal: PloS one
Published Date: Dec 4, 2024

Abstract

The early detection of Alzheimer's Disease (AD) is thought to be important for effective intervention and management. Here, we explore deep learning methods for the early detection of AD. We consider both genetic risk factors and functional magnetic resonance imaging (fMRI) data. However, we found that the genetic factors do not notably enhance the AD prediction by imaging. Thus, we focus on building an effective imaging-only model. In particular, we utilize data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), employing a 3D Convolutional Neural Network (CNN) to analyze fMRI scans. Despite the limitations posed by our dataset (small size and imbalanced nature), our CNN model demonstrates accuracy levels reaching 92.8% and an ROC of 0.95. Our research highlights the complexities inherent in integrating multimodal medical datasets. It also demonstrates the potential of deep learning in medical imaging for AD prediction.

Authors

  • Xiao Zhou
    College of Environmental Science and Engineering, Tongji University, 200092, Shanghai, China; Centre for Water Systems, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QF, UK.
  • Sanchita Kedia
    Department of Computer Science, Yale University, New Haven, CT, United States of America.
  • Ran Meng
    Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, CT, United States of America.
  • Mark Gerstein
    Program of Computational Biology and Bioinformatics and Department of Molecular Biophysics and Biochemistry and Department of Computer Science, Yale University, New Haven, CT 06511, USA.

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