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Various brain atrophy measures are essential in research of Alzheimer\u2019 disease (AD) and related dementias. This study aims to forecast future annual percentage changes in hippocampal, ventricular, and total gray matter (TGM) volumes in individuals with varying cognitive statuses, from healthy to dementia. We developed a machine learning model using elastic net linear regression and tested two approaches: (1) a baseline model using predictors from a single-time-point and (2) a longitudinal model using predictors derived from longitudinal MRI. Both approaches were evaluated with MRI-only models and models that combined MRI with additional risk factors (age, sex, APOE4, and baseline diagnosis). Cross-validated Pearson correlation scores between predicted and actual annual percentage changes were 0.62 for the hippocampus, 0.51 for the ventricles, and 0.41 for TGM, using the longitudinal MRI + risk factor model. Longitudinal models consistently outperformed baseline models, and models including risk factors outperformed the MRI only model. Validation using an external dataset confirmed these findings, highlighting the value of predictors derived based on longitudinal data. We further studied the value of the predicted atrophy\/enlargement rates for clinical status progression prediction across three different datasets. Predicted atrophy was a consistently better indicator of progression to mild cognitive impairment and dementia than present-day regional volumes, with the longitudinal atrophy prediction model typically outperforming the baseline model in terms of clinical status prediction. Future atrophy prediction has significant potential for assessing the risk of cognitive decline, even in cognitively unimpaired individuals, and can aid in selecting participants for clinical trials of disease-modifying drugs for AD.<\/jats:p>","DOI":"10.1101\/2025.04.09.25325520","type":"posted-content","created":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T19:00:21Z","timestamp":1744484421000},"source":"Crossref","is-referenced-by-count":0,"title":["Predicting Future Brain Atrophy Based on Longitudinal MRI"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5486-7513","authenticated-orcid":false,"given":"Maryam","family":"Hadji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elaheh","family":"Moradi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jussi","family":"Tohka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025101301050905000_2025.04.09.25325520v2.1","first-page":"051496","article-title":"The estimation of the global prevalence of dementia from 1990-2019 and forecasted prevalence through 2050: an analysis for the global burden of disease (gbd) study 2019","volume":"17","year":"2021","journal-title":"Alzheimer\u2019s & Dementia"},{"issue":"1","key":"2025101301050905000_2025.04.09.25325520v2.2","doi-asserted-by":"crossref","first-page":"97","DOI":"10.14283\/jpad.2023.61","article-title":"Burden of illness in people with alzheimer\u2019s disease: a systematic review of epidemiology, comorbidities and mortality","volume":"11","year":"2024","journal-title":"The Journal of Prevention of Alzheimer\u2019s Disease"},{"key":"2025101301050905000_2025.04.09.25325520v2.3","doi-asserted-by":"crossref","unstructured":"Colliot, O. : Machine Learning for Brain Disorders (2023)","DOI":"10.1007\/978-1-0716-3195-9"},{"key":"2025101301050905000_2025.04.09.25325520v2.4","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101848"},{"key":"2025101301050905000_2025.04.09.25325520v2.5","doi-asserted-by":"crossref","unstructured":"Frisoni, G.B. , Altomare, D. , et al.: Dementia prevention in memory clinics: recommendations from the European task force for brain health services. 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