Improved Prediction of Imminent Progression to Clinically Significant Memory Decline Using Surface Multivariate Morphometry Statistics and Sparse Coding | Zendy

Cynthia M. Stonnington | Zendy; Jianfeng Wu | Zendy; Jie Zhang | Zendy; Jie Shi | Zendy; Robert Bauer | Zendy; Vivek Devadas | Zendy; Yi Su | Zendy; Dona E.C. Locke | Zendy; Eric M. Reiman | Zendy; Richard J. Caselli | Zendy; Kewei Chen | Zendy; Yalin Wang | Zendy

Open Access

Improved 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

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) - logistic regression , cohort , multivariate statistics , alzheimer's disease neuroimaging initiative , dementia , medicine , multivariate analysis , oncology , psychology , disease , computer science , 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.

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