Open AccessImproved Prediction of Imminent Progression to Clinically Significant Memory Decline Using Surface Multivariate Morphometry Statistics and Sparse Coding
Author(s) -
Cynthia M. Stonnington,
Jianfeng Wu,
Jie Zhang,
Jie Shi,
Robert Bauer,
Vivek Devadas,
Yi Su,
Dona E.C. Locke,
Eric M. Reiman,
Richard J. Caselli,
Kewei Chen,
Yalin Wang,
Alzheimer’s Disease Neuroimaging Initiative
Publication year - 2021
Publication title -
journal of alzheimer's disease
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.677
H-Index - 139
eISSN - 1875-8908
pISSN - 1387-2877
DOI - 10.3233/jad-200821
Subject(s) - cohort , logistic regression , multivariate statistics , alzheimer's disease neuroimaging initiative , medicine , multivariate analysis , dementia , oncology , psychology , computer science , disease , machine learning
Besides their other roles, brain imaging and other biomarkers of Alzheimer's disease (AD) have the potential to inform a cognitively unimpaired (CU) person's 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.