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Cited by 50Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsArtificial Intelligence in Healthcare

Machine learning based multimodal neuroimaging genomics dementia score for predicting future conversion to alzheimer’s disease

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Mirabnahrazam, Ghazal, the Alzheimer’s Disease Neuroimaging Initiative, Ma, Da, Lee, Sieun, Popuri, Karteek, Lee, Hyunwoo, Cao, Jiguo, Wang, Lei +2 more·2022·Journal of Alzheimer's Disease
DOI 10.3233/jad-220021PMC PMC9195128
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Abstract

Background: The increasing availability of databases containing both magnetic resonance imaging (MRI) and genetic data allows researchers to utilize multimodal data to better understand the characteristics of dementia of Alzheimer’s type (DAT). Objective: The goal of this study was to develop and analyze novel biomarkers that can help predict the development and progression of DAT. Methods: We used feature selection and ensemble learning classifier to develop an image/genotype-based DAT score that represents a subject’s likelihood of developing DAT in the future. Three feature types were used: MRI only, genetic only, and combined multimodal data. We used a novel data stratification method to better represent different stages of DAT. Using a pre-defined 0.5 threshold on DAT scores, we predicted whether a subject would develop DAT in the future. Results: Our results on Alzheimer’s Disease Neuroimaging Initiative (ADNI) database showed that dementia scores using genetic data could better predict future DAT progression for currently normal control subjects (Accuracy = 0.857) compared to MRI (Accuracy = 0.143), while MRI can better characterize subjects with stable mild cognitive impairment (Accuracy = 0.614) compared to genetics (Accuracy = 0.356). Combining MRI and genetic data showed improved classification performance in the remaining stratified groups. Conclusion: MRI and genetic data can contribute to DAT prediction in different ways. MRI data reflects anatomical changes in the brain, while genetic data can detect the risk of DAT progression prior to the symptomatic onset. Combining information from multimodal data appropriately can improve prediction performance.

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Authors
Mirabnahrazam, G., Initiative, T. A. D. N., Ma, D., Lee, S., Popuri, K., Lee, H., Cao, J., Wang, L., Galvin, J. E., & Beg, M. F.
Affiliations
  • Simon Fraser University (CA)
  • University of Nottingham (GB)
  • University of British Columbia (CA)
  • The Ohio State University Wexner Medical Center (US)
  • University of Miami (US)
Corresponding author
Beg, Mirza Faisal
ORCID
Venue
Journal of Alzheimer's Disease
Publisher
SAGE Publications
Published
2022-05-31
Volume
87
Issue
3
Pages
1345-1365
ISSN
  • 1387-2877 (ISSN-L)
  • 1875-8908
DOI
10.3233/jad-220021
PMC
PMC9195128
OpenAlex
W4221154077
Cited by
50
References
4
Citations by year
2026: 19 · 2025: 12 · 2024: 9 · 2023: 9 · 2022: 1
FWCI
5.05
Type
journal article
Language
en
Open access
Locations
  • Journal of Alzheimer s Disease (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Institutes of Health (UL1TR001420, 5u01ag024904-10)
  • National Institute on Aging (U01-AG-024904, R01AG069765, R01AG055121, K24AG045333, P01AG019724, R01AG071514)
  • National Institute of Neurological Disorders and Stroke (R01NS101483)
  • National Center for Advancing Translational Sciences
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
Alzheimer's disease
MeSH terms
Machine Learning, Alzheimer Disease, Brain, Humans, Magnetic Resonance Imaging, Disease Progression, Genomics, Neuroimaging, Cognitive Dysfunction

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