{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T12:33:54Z","timestamp":1781181234408,"version":"3.54.1"},"reference-count":37,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T00:00:00Z","timestamp":1637625600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAD"],"published-print":{"date-parts":[[2021,11,23]]},"abstract":"<jats:p>Background: While both cognitive and magnetic resonance imaging (MRI) data has been used to predict progression in Alzheimer\u2019s disease, heterogeneity between patients makes it challenging to predict the rate of cognitive and functional decline for individual subjects. Objective: To investigate prognostic power of MRI-based biomarkers of medial temporal lobe atrophy and macroscopic tissue change to predict cognitive decline in individual patients in clinical trials of early Alzheimer\u2019s disease. Methods: Data used in this study included 312 patients with mild cognitive impairment from the ADNI dataset with baseline MRI, cerebrospinal fluid amyloid-\u03b2, cognitive test scores, and a minimum of two-year follow-up information available. We built a prognostic model using baseline cognitive scores and MRI-based features to determine which subjects remain stable and which functionally decline over 2 and 3-year follow-up periods. Results: Combining both sets of features yields 77%accuracy (81%sensitivity and 75%specificity) to predict cognitive decline at 2 years (74%accuracy at 3 years with 75%sensitivity and 73%specificity). When used to select trial participants, this tool yields a 3.8-fold decrease in the required sample size for a 2-year study (2.8-fold decrease for a 3-year study) for a hypothesized 25%treatment effect to reduce cognitive decline. Conclusion: When used in clinical trials for cohort enrichment, this tool could accelerate development of new treatments by significantly increasing statistical power to detect differences in cognitive decline between arms. In addition, detection of future decline can help clinicians improve patient management strategies that will slow or delay symptom progression.<\/jats:p>","DOI":"10.3233\/jad-210664","type":"journal-article","created":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T22:37:05Z","timestamp":1633127825000},"page":"1071-1078","source":"Crossref","is-referenced-by-count":7,"title":["Automatic Prediction of Cognitive and Functional Decline Can Significantly Decrease the Number of Subjects Required for Clinical Trials in Early Alzheimer\u2019s Disease"],"prefix":"10.1177","volume":"84","author":[{"given":"Neda","family":"Shafiee","sequence":"first","affiliation":[{"name":"McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec (QC), Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahsa","family":"Dadar","sequence":"additional","affiliation":[{"name":"CERVO Brain Research Center, Centre Int\u00e9gr\u00e9 Universitaire Sant\u00e9 et Services Sociaux de la Capitale Nationale, Quebec (QC), Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"Ducharme","sequence":"additional","affiliation":[{"name":"McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec (QC), Canada"},{"name":"Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec (QC), Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D. 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