ArticleRadiology. Artificial intelligence2021
Accelerated Brain Aging in Amnestic Mild Cognitive Impairment: Relationships with Individual Cognitive Decline, Risk Factors for Alzheimer Disease, and Clinical Progression.
Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 21 citations in OpenAlex.
- The Therapeutic Efficacy of Gold Needle "Regulating Spirit" Acupuncture for Amnestic Mild Cognitive Impairment: Protocol for a Randomized Controlled Trial.JMIR research protocols · 2026Article
- A Comparative Study of Plasma Biomarkers of Neurodegeneration in Rhesus Monkeys (bioRxiv : the preprint server for biology · 2026Article
- Aging beyond diagnosis: the MRI brain age gap across disorders.GeroScience · 2026Review
- Functional system-specific brain aging across the Alzheimer's disease continuum.Translational psychiatry · 2026Article
- Brain age gap is associated with cognitive abilities in captive chimpanzees.Scientific reports · 2025Article
- Morphologic and functional alterations in the parasagittal dural space in mild cognitive impairment.Scientific reports · 2025Article
- AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.Cell reports. Medicine · 2025Review
- Decoding brain aging trajectory: predictive discrepancies, genetic susceptibilities, and emerging therapeutic strategies.Frontiers in aging neuroscience · 2025Review
- A deep neural network estimation of brain age is sensitive to cognitive impairment and decline.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Review
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- Diagnostic accuracy study of automated stratification of Alzheimer's disease and mild cognitive impairment via deep learning based on MRI.Annals of translational medicine · 2022Article
- Confounds in neuroimaging: A clear case of sex as a confound in brain-based prediction.Frontiers in neurology · 2022Article
- Assessment of cognitive impairment after acute cerebral infarction with T1 relaxation time measured by MP2RAGE sequence and cerebral hemodynamic by transcranial Doppler.Frontiers in neurology · 2022Article
- Assessing brain and biological aging trajectories associated with Alzheimer's disease.Frontiers in neuroscience · 2022Review
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo determine whether a brain age prediction model could quantify individual deviations from a healthy brain-aging trajectory (predicted age difference [PAD]) in patients with amnestic mild cognitive impairment (aMCI) and to determine if PAD was associated with individual cognitive impairment. MATERIALS AND
methodsIn this retrospective study, a machine learning approach was trained to determine brain age based on T1-weighted MRI scans. Two datasets were used for model training and testing-the Beijing Aging Brain Rejuvenation Initiative (BABRI) (616 healthy controls and 80 patients with aMCI, 2010-2018) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) (589 healthy controls and 144 patients with aMCI, 2010-2018). A total of 974 healthy controls were used for model training (490 from BABRI and 484 from ADNI; age range, 49-95 years). The trained model was then tested on both healthy controls (126 from BABRI and 105 from ADNI) and patients with aMCI (80 from BABRI and 144 from ADNI) to estimate PAD (predicted age - actual age). Furthermore, the associations between PAD with cognitive impairment, genetic risk factors and pathologic markers of Alzheimer disease (AD), and clinical progression in patients with aMCI were examined using a partial correlation analysis, a two-way analysis of covariance, and a general linear model, respectively.
resultsBased on the prediction model, patients with aMCI were found to have higher PADs than those of healthy controls (BABRI: 2.65 ± 4.91 [standard deviation] vs 0.18 ± 4.79 [
conclusionThe PAD is a sensitive imaging marker related to individual cognitive differences in patients with aMCI.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.