ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2025
Integrated cerebellar radiomic-network model for predicting mild cognitive impairment in Alzheimer's disease.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- Interpretable multimodal MRI fusion in Alzheimer's disease classification using extended parallel multilink joint ICA and 3D ResNet.iScience · 2026Article
- Integration of glymphatic system function and hippocampal radiomics for diagnosis and conversion prediction of Alzheimer's disease.BMC medical imaging · 2026Article
- Decoupling MCI-specific signatures from shared neurobiological substrates of cognitive aging via deep learning.NPJ digital medicine · 2026Article
- Clinical impact of the Alzheimer's Disease Neuroimaging Initiative: A review of studies using ADNI data (2023 to June 2025).Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Review
- Integrating standard and native spaces for radiomics and brain network analysis in Alzheimer's disease diagnosis and prognosis.Journal of neurology · 2026Article
- Feasibility study of differentiating patients with levodopa-induced dyskinesia using cerebellar gray and white matter radiomics features from 3DT1WI images.Frontiers in aging neuroscience · 2026Article
- Machine learning-based radiomics modeling of combined subcortical nuclei from quantitative susceptibility mapping for Parkinson's disease diagnosis.Frontiers in aging neuroscience · 2026Article
- Comment on "Association of peripheral immune markers with brain age and dementia risk estimated using deep learning method".International journal of surgery (London, England) · 2026Article
- Hippocampal texture asymmetry features onFrontiers in immunology · 2026Article
- Interpretable machine learning model based on multimodal MRI radiomics for Alzheimer's disease diagnosis.Frontiers in aging neuroscience · 2026Article
- Cerebellar subregional structural changes across the Alzheimer's disease continuum: a longitudinal analysis of cognitive and behavioural correlates.Brain communications · 2026Article
- Early diagnosis of Alzheimer's Disease: Graph theoretical analysis of cerebellar network features based on 18F-AV45 PET.PloS one · 2026Article
- Non-invasive brain stimulation: current and future applications in neurology.Nature reviews. Neurology · 2025Review
- Spotlight on Alzheimer's disease and related dementias research in East Asia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- The Advancements of Marine Natural Products in the Treatment of Alzheimer's Disease: A Study Based on Cell and Animal Experiments.Marine drugs · 2025Review
- Integrated cerebellar radiomic-network model for predicting mild cognitive impairment in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- The application of radiomics in the diagnosis and evaluation of cognitive impairment related to neurological diseases.Frontiers in neuroscience · 2025Review
- Multimodal radiomics of cerebellar subregions for machine learning-driven Alzheimer's disease diagnosis.Frontiers in aging neuroscience · 2025Article
- Integrating genetic and immune profiles for personalized immunotherapy in Alzheimer's disease.Frontiers in medicine · 2025Review
- Advancements in multi-omics research to address challenges in Alzheimer's disease: a systems biology approach utilizing molecular biomarkers and innovative strategies.Frontiers in aging neuroscience · 2025Review
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13 authors.
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Abstract
introductionPathological and neuroimaging alterations in the cerebellum of Alzheimer's disease (AD) patients have been documented. However, the role of cerebellum-derived radiomic and structural connectome modeling in the prediction of AD progression remains unclear.
methodsRadiomic features were extracted from magnetic resonance imaging (MRI) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 1319) and an in-house dataset (n = 308). Integrated machine learning models were developed to predict the conversion risk of normal cognition (NC) to mild cognitive impairment (MCI) over a 6-year follow-up.
resultsThe cerebellar models outperformed hippocampal models in distinguishing MCI from NC and in predicting transitions from NC to MCI across both cohorts. Key predictors included textural features in the right III and left I and II lobules, and network properties in Vermis I and II, which were associated with cognitive decline in AD. DISCUSSION: Cerebellum-derived radiomic-network modeling shows promise as a tool for early identification and prediction of disease progression during the preclinical stage of AD. HIGHLIGHTS: Altered cerebellar radiomic features and topological networks were identified in the subjects with mild cognitive impairment (MCI). The cerebellar radiomic-network integrated models outperformed hippocampal models in distinguishing MCI from normal cognition. The cerebellar radiomic model effectively predicts MCI risk and can stratify individuals into distinct risk categories. Specific cerebellar radiomic features are associated with cognitive impairment across various stages of amyloid beta and tau pathology.
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