{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:20:15Z","timestamp":1792790415266,"version":"4.5.0"},"reference-count":55,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAD"],"published-print":{"date-parts":[[2022,5,31]]},"abstract":"<jats:p>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\u2019s 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\u2019s 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\u2019s Disease Neuroimaging Initiative (ADNI) database showed that dementia scores using genetic data could better predict future DAT progression for currently normal control subjects (Accuracy\u200a=\u200a0.857) compared to MRI (Accuracy\u200a=\u200a0.143), while MRI can better characterize subjects with stable mild cognitive impairment (Accuracy\u200a=\u200a0.614) compared to genetics (Accuracy\u200a=\u200a0.356). 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.<\/jats:p>","DOI":"10.3233\/jad-220021","type":"journal-article","created":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T12:10:57Z","timestamp":1650370257000},"page":"1345-1365","source":"Crossref","is-referenced-by-count":51,"title":["Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer\u2019s Disease"],"prefix":"10.1177","volume":"87","author":[{"given":"Ghazal","family":"Mirabnahrazam","sequence":"first","affiliation":[{"name":"School of Engineering, Simon Fraser University, Burnaby, BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Da","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Medicine, Wake Forest University, 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