ArticleJournal of central nervous system disease2024
MRI-informed machine learning-driven brain age models for classifying mild cognitive impairment converters.
Article in Journal of central nervous system disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Harnessing Brain Age-Specific Effects on the Associations Between Sleep Quality, Glymphatic Function, and Cognition in Normal Ageing Adults: Insights for Gerotherapeutics.Neurology and therapy · 2026Article
- HK-ASAP study: protocol for a prospective cohort study on sleep quality, brain reserve and cognitive phenotypes in community-dwelling older adults.BMC geriatrics · 2026Article
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Abstract
backgroundBrain age model, including estimated brain age and brain-predicted age difference (brain-PAD), has shown great potentials for serving as imaging markers for monitoring normal ageing, as well as for identifying the individuals in the pre-diagnostic phase of neurodegenerative diseases. PURPOSE: This study aimed to investigate the brain age models in normal ageing and mild cognitive impairments (MCI) converters and their values in classifying MCI conversion.
methodsPre-trained brain age model was constructed using the structural magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) project (N = 609). The tested brain age model was built using the baseline, 1-year and 3-year follow-up MRI data from normal ageing (NA) adults (n = 32) and MCI converters (n = 22) drew from the Open Access Series of Imaging Studies (OASIS-2). The quantitative measures of morphometry included total intracranial volume (TIV), gray matter volume (GMV) and cortical thickness. Brain age models were calculated based on the individual's morphometric features using the support vector machine (SVM) algorithm.
resultsWith comparable chronological age, MCI converters showed significant increased TIV-based (Baseline:
conclusionsThis is the first demonstration that MRI-informed brain age models exhibit feature-specific patterns. The greater GMV-based brain age observed in MCI converters may provide new evidence for identifying the individuals at the early stage of neurodegeneration. Our findings added value to existing quantitative imaging markers and might help to improve disease monitoring and accelerate personalized treatments in clinical practice.
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