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This makes it critically important to identify the symptoms of Alzheimer\u2019s disease in its early stages before significant cognitive deterioration has taken hold and even before any brain morphology and neuropathology are noticeable. In this study, five different multimodal deep neural networks (MDNN), with different architectures, in search of an optimal model for predicting the cognitive test scores for the Mini-Mental State Examination (MMSE) and the modified Alzheimer\u2019s Disease Assessment Scale (ADAS-CoG13) over a span of 60\u00a0months (5\u00a0years). The multimodal data utilized to train and test the proposed models were obtained from the Alzheimer\u2019s Disease Neuroimaging Initiative study and includes cerebrospinal fluid (CSF) levels of tau and beta-amyloid, structural measures from magnetic resonance imaging (MRI), functional and metabolic measures from positron emission tomography (PET), and cognitive scores from the neuropsychological tests (Cog). The models developed herein delve into two main issues: (1) application merits of single-task vs. multitask for predicting future cognitive scores and (2) whether time-varying input data are better suited than specific timepoints for optimizing prediction results. This model yields a high of 90.27% (SD\u2009=\u20091.36) prediction accuracy (correlation) at 6\u00a0months after the initial visit to a lower 79.91% (SD\u2009=\u20098.84) prediction accuracy at 60\u00a0months. The analysis provided is comprehensive as it determines the predictions at all other timepoints and all MDNN models include converters in the CN and MCI groups (CNc, MCIc) and all the unstable groups in the CN and MCI groups (CNun and MCIun) that reverted to CN from MCI and to MCI from AD, so as not to bias the results. The results show that the best performance is achieved by a multimodal combined single-task long short-term memory (LSTM) regressor with an input sequence length of 2 data points (2 visits, 6\u00a0months apart) augmented with a pretrained Neural Network Estimator to fill in for the missing values.\n<\/jats:p>","DOI":"10.1007\/s12559-023-10169-w","type":"journal-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T12:02:23Z","timestamp":1689768143000},"page":"2062-2086","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Prediction of Cognitive Test Scores from Variable Length Multimodal Data in Alzheimer\u2019s Disease"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8283-7055","authenticated-orcid":false,"given":"Ulyana","family":"Morar","sequence":"first","affiliation":[]},{"given":"Harold","family":"Martin","sequence":"additional","affiliation":[]},{"given":"Robin P.","family":"M.","sequence":"additional","affiliation":[]},{"given":"Walter","family":"Izquierdo","sequence":"additional","affiliation":[]},{"given":"Elaheh","family":"Zarafshan","sequence":"additional","affiliation":[]},{"given":"Parisa","family":"Forouzannezhad","sequence":"additional","affiliation":[]},{"given":"Elona","family":"Unger","sequence":"additional","affiliation":[]},{"given":"Mercedes","family":"Cabrerizo","sequence":"additional","affiliation":[]},{"given":"Rosie E.","family":"Curiel Cid","sequence":"additional","affiliation":[]},{"given":"Monica","family":"Rosselli","sequence":"additional","affiliation":[]},{"given":"Armando","family":"Barreto","sequence":"additional","affiliation":[]},{"given":"Naphtali","family":"Rishe","sequence":"additional","affiliation":[]},{"given":"David E.","family":"Vaillancourt","sequence":"additional","affiliation":[]},{"given":"Steven T.","family":"DeKosky","sequence":"additional","affiliation":[]},{"given":"David","family":"Loewenstein","sequence":"additional","affiliation":[]},{"given":"Ranjan","family":"Duara","sequence":"additional","affiliation":[]},{"given":"Malek","family":"Adjouadi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,19]]},"reference":[{"issue":"4","key":"10169_CR1","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1056\/NEJMra0909142","volume":"362","author":"HW Querfurth","year":"2010","unstructured":"Querfurth HW, LaFerla FM. 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Malek Adjouadi received support from the National Science Foundation (FIU), National Institute of Health (UM), and the NIH-1Florida Alzheimer\u2019s Disease Research Center (UF), Consulting from UM, and a Speaker Fee from FAMU. David Loewenstein received support from NIH, Statistical Consulting (FIU), and Grand Grounds-Dell Medical Center (Austin Texas). Armando Barreto received support from the National Science Foundation -NSF (FIU) and royalties for his two books from CRC press (Taylor & Francis). David E. Vaillancourt has received research support from NIH, and serves as manager of Neuroimaging Solutions, LLC. Steven T. DeKosky has served as editor (dementia section) and as associate editor for Neurotherapeutics, has served as a consultant on advisory boards, or on data monitoring committees for Acumen Pharmaceuticals, Biogen Pharmaceuticals, Cognition Therapeutics, Prevail Pharmaceuticals, and Vaccinex Pharmaceuticals. Ranjan Duara has received research support from Oregon Health Science University. Authors Ulyana Morar, Walter Izquierdo, Harold Martin, Robin P. M., Parisa Forouzannezhad, and Elaheh Zarafshan received student support from NSF (FIU). Author Elona Unger declares no conflicts of interest with regard to this manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}