{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T16:55:11Z","timestamp":1777395311999,"version":"3.51.4"},"reference-count":50,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAD"],"published-print":{"date-parts":[[2021,5,4]]},"abstract":"<jats:p>Background: Besides their other roles, brain imaging and other biomarkers of Alzheimer\u2019s disease (AD) have the potential to inform a cognitively unimpaired (CU) person\u2019s likelihood of progression to mild cognitive impairment (MCI) and benefit subject selection when evaluating promising prevention therapies. We previously described that among baseline FDG-PET and MRI measures known to be preferentially affected in the preclinical and clinical stages of AD, hippocampal volume was the best predictor of incident MCI within 2 years (79%sensitivity\/78%specificity), using standard automated MRI volumetric algorithmic programs, binary logistic regression, and leave-one-out procedures. Objective: To improve the same prediction by using different hippocampal features and machine learning methods, cross-validated via two independent and prospective cohorts (Arizona and ADNI). Methods: Patch-based sparse coding algorithms were applied to hippocampal surface features of baseline TI-MRIs from 78 CU adults who subsequently progressed to amnestic MCI in approximately 2 years (\u201cprogressors\u201d) and 80 matched adults who remained CU for at least 4 years (\u201cnonprogressors\u201d). Nonprogressors and progressors were matched for age, sex, education, and apolipoprotein E4 allele dose. We did not include amyloid or tau biomarkers in defining MCI. Results: We achieved 92%prediction accuracy in the Arizona cohort, 92%prediction accuracy in the ADNI cohort, and 90%prediction accuracy when combining the two demographically distinct cohorts, as compared to 79%(Arizona) and 72%(ADNI) prediction accuracy using hippocampal volume. Conclusion: Surface multivariate morphometry and sparse coding, applied to individual MRIs, may accurately predict imminent progression to MCI even in the absence of other AD biomarkers.<\/jats:p>","DOI":"10.3233\/jad-200821","type":"journal-article","created":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T13:59:31Z","timestamp":1615903171000},"page":"209-220","source":"Crossref","is-referenced-by-count":7,"title":["Improved Prediction of Imminent Progression to Clinically Significant Memory Decline Using Surface Multivariate Morphometry Statistics and Sparse Coding"],"prefix":"10.1177","volume":"81","author":[{"given":"Cynthia M.","family":"Stonnington","sequence":"first","affiliation":[{"name":"Department of Psychiatry and Psychology, Mayo Clinic, Scottsdale, AZ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging 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