Functional and Clinical: An Explainable Deep Learning Model for Multimodal Alzheimer's Disease Classification
Our model was highly accurate and highlighted the importance of combining fMRI and clinical data for AD classification. These findings have implications for the refinement of multimodal deep learning models; however, our small sample and need for external validation are also noted.
Authors
- Ahmed Abdelrehem (ORCID: https://orcid.org/0000-0003-0523-3009)
- Samuel L. Warren (ORCID: https://orcid.org/0000-0001-6473-1791)
Institutions
- Bond University (AU)
- University of Johannesburg (ZA)
Publication Details
- Journal
- Brain and Behavior
- Published
- 2026-01-30
- DOI
- https://doi.org/10.1002/brb3.71240
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Defense
- Alzheimer's Association
- Bristol-Myers Squibb
- Eli Lilly and Company
- Pfizer
- Biogen
- University of Southern California
- Meso Scale Diagnostics
- BioClinica
- Novartis Pharmaceuticals Corporation
- Northern California Institute for Research and Education
- Australian Government
- Bond University
- Eisai
- Servier
- H. Lundbeck A/S
- IXICO
- National Institutes of Health
- Genentech
- Canadian Institutes of Health Research