Bidirectional prognostic predictions of conversion and reversion in amnestic mild cognitive impairment

Amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) are closely related. While some aMCI patients convert to AD (conversion), some revert to age-appropriate cognitive functioning (reversion). Early identification of these bidirectional trajectories informs aMCI-AD pathology and aids patient management. Data from 129 aMCI participants in the Alzheimer's Disease Neuroimaging Initiative, who either converted or reverted, were analyzed. Baseline and longitudinal data relative to exact conversion and reversion times were incorporated to evaluate predictive power using support vector machine analysis. The accuracy of three models, each utilizing different data modalities, was compared. Neurobiological markers associated with these clinical trajectories were examined. The model incorporating brain volumetric changes achieved 92.31% accuracy in classifying conversion vs. reversion. Key neural markers included left inferior lateral ventricle volume for conversion times, left inferior temporal gyrus volume for cognitive status at conversion, and hippocampal and amygdala volumes for memory performance at conversion. The models offer bidirectional, prognostic predictions in aMCI patients. Identified markers provide valuable insights for early intervention. This study informs strategies to reduce AD incidence and optimize resource allocation, contributing to a more comprehensive understanding of disease trajectories and effective management.

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Publication Details

Journal
GeroScience
Published
2026-03-03
DOI
https://doi.org/10.1007/s11357-026-02155-5
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Bidirectional prognostic predictions of conversion and reversion in amnestic mild cognitive impairment

Rachel R. Jin, Yue Gu, T. M. C. Lee, Esther Zhiwei Zheng et al.
GeroScience
Dementia and Cognitive Impairment Research
article

Bidirectional prognostic predictions of conversion and reversion in amnestic mild cognitive impairment

Rachel R. Jin, Yue Gu, T. M. C. Lee, Esther Zhiwei Zheng, for the Alzheimer’s Disease Neuroimaging Initiative
article en

Abstract

Amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) are closely related. While some aMCI patients convert to AD (conversion), some revert to age-appropriate cognitive functioning (reversion). Early identification of these bidirectional trajectories informs aMCI-AD pathology and aids patient management. Data from 129 aMCI participants in the Alzheimer's Disease Neuroimaging Initiative, who either converted or reverted, were analyzed. Baseline and longitudinal data relative to exact conversion and reversion times were incorporated to evaluate predictive power using support vector machine analysis. The accuracy of three models, each utilizing different data modalities, was compared. Neurobiological markers associated with these clinical trajectories were examined. The model incorporating brain volumetric changes achieved 92.31% accuracy in classifying conversion vs. reversion. Key neural markers included left inferior lateral ventricle volume for conversion times, left inferior temporal gyrus volume for cognitive status at conversion, and hippocampal and amygdala volumes for memory performance at conversion. The models offer bidirectional, prognostic predictions in aMCI patients. Identified markers provide valuable insights for early intervention. This study informs strategies to reduce AD incidence and optimize resource allocation, contributing to a more comprehensive understanding of disease trajectories and effective management.

GeroScience
Innopage (China) (CN), University of Hong Kong - Shenzhen Hospital (CN), University of Hong Kong (HK)
Alzheimer's Disease Neuroimaging Initiative, University of Hong Kong
Openalex Percentile: Top 43%
Dementia and Cognitive Impairment Research
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Bidirectional prognostic predictions of conversion and reversion in amnestic mild cognitive impairment — Rachel R. Jin, Yue Gu, et al. · GeroScience (2026) | TGRS Research Map | TGRS