ArticleAlzheimer's research & therapy2024
Predicting progression from subjective cognitive decline to mild cognitive impairment or dementia based on brain atrophy patterns.
Article in Alzheimer's research & therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Scalable CT-based prognostic modeling of dementia conversion in mild cognitive impairment.Scientific reports · 2026Article
- Association of Staphylococcus aureus bloodstream infection with 7-day incident delirium in critically Ill adults: a propensity-weighted competing risk analysis.BMC infectious diseases · 2026Article
- Individualized prediction of transition from subjective cognitive decline to mild cognitive impairment based on multimodal MRI: a 10-year follow-up study.The journal of prevention of Alzheimer's disease · 2026Article
- Effect of CPAP treatment on subjective cognitive decline in patients with mild obstructive sleep apnea syndrome.Sleep and biological rhythms · 2026Article
- Normalizing flow based neural processes for Alzheimer's disease progression prediction.PloS one · 2026Article
- Data-driven subtypes of subjective cognitive decline: neuropsychological profiles, Alzheimer's disease biomarkers, and clinical trajectories.Journal of neurology · 2025Article
- Development of a Predictive Model for the Progression of Subjective Cognitive Decline: A Longitudinal Study.Brain and behavior · 2025Article
- Anxious-depressive symptoms and sleep disturbances across the Alzheimer disease spectrum.Nature. Mental health · 2025Article
- Computational Risk Stratification of Preclinical Alzheimer's in Younger Adults.Diagnostics (Basel, Switzerland) · 2025Article
- Structural and microstructural changes in white and gray matter across the Alzheimer's disease continuum.Frontiers in aging neuroscience · 2025Review
- From amyloid PET to plasma biomarkers: A multimodal imaging framework for early diagnosis and staging of Alzheimer's disease.Alzheimer's & dementia (Amsterdam, Netherlands)Review
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10 authors.
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Abstract
backgroundAlzheimer's disease (AD) is a progressive neurodegenerative disorder where pathophysiological changes begin decades before the onset of clinical symptoms. Analysis of brain atrophy patterns using structural MRI and multivariate data analysis are an effective tool in identifying patients with subjective cognitive decline (SCD) at higher risk of progression to AD dementia. Atrophy patterns obtained from models trained to classify advanced AD versus normal subjects, may not be optimal for subjects at an early stage, like SCD. In this study, we compared the accuracy of the SCD progression prediction using the 'severity index' generated using a standard classification model trained on patients with AD dementia versus a new model trained on β-amyloid (Aβ) positive patients with amnestic mild cognitive impairment (aMCI).
methodsWe used structural MRI data of 504 patients from the Swedish BioFINDER-1 study cohort (cognitively normal (CN), Aβ-negative = 220; SCD, Aβ positive and negative = 139; aMCI, Aβ-positive = 106; AD dementia = 39). We applied multivariate data analysis to create two predictive models trained to discriminate CN individuals from either individuals with Aβ positive aMCI or AD dementia. Models were applied to individuals with SCD to classify their atrophy patterns as either high-risk "disease-like" or low-risk "CN-like". Clinical trajectory and model accuracy were evaluated using 8 years of longitudinal data.
resultsIn predicting progression from SCD to MCI or dementia, the standard, dementia-based model, reached 100% specificity but only 10.6% sensitivity, while the new, aMCI-based model, reached 72.3% sensitivity and 60.9% specificity. The aMCI-based model was superior in predicting progression from SCD to MCI or dementia, reaching a higher receiver operating characteristic area under curve (AUC = 0.72; P = 0.037) in comparison with the dementia-based model (AUC = 0.57).
conclusionWhen predicting conversion from SCD to MCI or dementia using structural MRI data, prediction models based on individuals with milder levels of atrophy (i.e. aMCI) may offer superior clinical value compared to standard dementia-based models.
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