Year: 2024
Type: preprint
Abstract: Alzheimer's disease (AD) is the most prevalent neurodegenerative disease; yet its currently available treatments are limited to stopping disease progression. Moreover, effectiveness of these treatments is not guaranteed due to the heterogenetiy of the disease. Therefore, it is essential to be able to identify the disease subtypes at a very early stage. Current data driven approaches are able to classify the subtypes at later stages of AD or related disorders, but struggle when predicting at the asymptomatic or prodromal stage. Moreover, most existing models either lack explainability behind the classification or only use a single modality for the assessment, limiting scope of its analysis. Thus, we propose a multimodal framework that uses early-stage indicators such as imaging, genetics and clinical assessments to classify AD patients into subtypes at early stages. Similarly, we build prompts and use large language models, such as ChatGPT, to interpret the findings of our model. In our framework, we propose a tri-modal co-attention mechanism (Tri-COAT) to explicitly learn the cross-modal feature associations. Our proposed model outperforms baseline models and provides insight into key cross-modal feature associations supported by known biological mechanisms.
Source: arXiv (Cornell University)
Institutions: Rensselaer Polytechnic Institute, University of Pennsylvania
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: National Science Foundation, U.S. Department of Defense, Alzheimer's Association, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, Biogen, University of Southern California, Meso Scale Diagnostics, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Novartis Pharmaceuticals Corporation, Northern California Institute for Research and Education, Eisai, Servier, H. Lundbeck A/S, IXICO, National Institutes of Health, Genentech, Canadian Institutes of Health Research, National Institute on Aging
Awards: G1298807586, G1554142629, G1593117279, G1650819308, G1880935189, Alzheimer's Disease Neuroimaging Initiative, G2100670906, G2152643862, G2189010064, G2340207221, G2423387114, G2471646051, G2522063444, G2835601961, G29951154, G3131374986, G3216574877, G3553156159, G3710456697, G3752451908, G3870774570, G3908963977, G4119534169, G43180419, G4475667458, G4501223628, G4800177743, CAREER: Systematic Mitigation of Deep Learning Adversaries in Medical Imaging, G5207404921, G5209990037, G528601746, G5365730499, EFFECTS OF TRAUMATIC BRAIN INJURY (TBI) AND POST TRAUMATIC STRESS DISORDER (PTSD) ON ALZHEIMER'S DISEASE (AD) IN VETERANS USING IMAGING AND BIOMARKERS IN THE AD NEUROIMAGING INITIATIVE (ADNI)", G5654639750, G5740349702, G5850682332, G613619302, G6173816828, G6210868510, G6371410416, G6412110892, G6447063303, BIGDATA: IA: Collaborative Research: Asynchronous Distributed Machine Learning Framework for Multi-Site Collaborative Brain Big Data Mining, G6671297155, G6719828067, G6792506723, G6899163935, G72415811, G7298697282, G764699806, G7799614282, G781725486, G8228018387, G8281378126, G8301434335, G839208290, G8852128998, Advancing Analysis of Multi-omics Data in Alzheimer's Disease Research, G8895907669, G8897063270, G8957659042, G9000039018, G9000122335, G9000222778, Artificial Intelligence Strategies for Alzheimer's Disease Research, Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks
OpenAlex ID: W4391505289