ArticleCell reports. Medicine2021
Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns.
Article in Cell reports. Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Elucidating the neuropathological and molecular heterogeneity of amyloid beta and tau in Alzheimer's disease through machine learning and transcriptomic integration.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Diagnosis and management of subjects with mild cognitive impairment in clinical practice: an Italian delphi consensus study.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Article
- Mapping heterogeneous brain structural subtypes in alzheimer's disease and mild cognitive impairment using normative models.Translational psychiatry · 2026Article
- Resolving Heterogeneity in the Diagnosis of Alzheimer's Disease and its Progression Using Multimodal Data.Journal of molecular neuroscience : MN · 2026Article
- Functional Connectome of Superagers Reveals Early Markers of Resilience and Vulnerability to Alzheimer's Disease.bioRxiv : the preprint server for biology · 2025Article
- Understanding heterogeneity in psychiatric disorders: A method for identifying subtypes and parsing comorbidity.Psychiatry and clinical neurosciences · 2025Article
- Revealing heterogeneity in mild cognitive impairment based on individualized structural covariance network.Alzheimer's research & therapy · 2025Article
- CSEPC: a deep learning framework for classifying small-sample multimodal medical image data in Alzheimer's disease.BMC geriatrics · 2025Article
- Merging multimodal digital biomarkers into "Digital Neuro Fingerprints" for precision neurology in dementias: the promise of the right treatment for the right patient at the right time in the age of AI.Frontiers in digital health · 2025Article
- Machine learning on longitudinal multi-modal data enables the understanding and prognosis of Alzheimer's disease progression.iScience · 2024Article
- Application of artificial intelligence-based magnetic resonance imaging in diagnosis of cerebral small vessel disease.CNS neuroscience & therapeutics · 2024Review
- Machine Learning Classification of Patients with Amnestic Mild Cognitive Impairment and Non-Amnestic Mild Cognitive Impairment from Written Picture Description Tasks.Brain sciences · 2024Article
- Predicting Progression From Mild Cognitive Impairment to Alzheimer's Dementia With Adversarial Attacks.IEEE journal of biomedical and health informatics · 2024Article
- The spatial distribution of coupling between tau and neurodegeneration in amyloid-β positive mild cognitive impairment.Neurobiology of aging · 2024Article
- Article
- Definition and analysis of gray matter atrophy subtypes in mild cognitive impairment based on data-driven methods.Frontiers in aging neuroscience · 2024Article
- Distinct subtypes of spatial brain metabolism patterns in Alzheimer's disease identified by deep learning-based FDG PET clusters.European journal of nuclear medicine and molecular imaging · 2024Article
- Functional Imaging Derived ADHD Biotypes Based on Deep Clustering May Guide Personalized Medication Therapy.Research square · 2023Article
- Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach.Alzheimer's research & therapy · 2023Article
- Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression.Diagnostics (Basel, Switzerland) · 2023Review
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6 authors.
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Abstract
Trajectories of cognitive decline vary considerably among individuals with mild cognitive impairment (MCI). To address this heterogeneity, subtyping approaches have been developed, with the objective of identifying more homogeneous subgroups. To date, subtyping of MCI has been based primarily on cognitive measures, often resulting in indistinct boundaries between subgroups and limited validity. Here, we introduce a subtyping method for MCI based solely upon brain atrophy. We train a deep learning model to differentiate between Alzheimer's disease (AD) and cognitively normal (CN) subjects based on whole-brain MRI features. We then deploy the trained model to classify MCI subjects based on whole-brain gray matter resemblance to AD-like or CN-like patterns. We subsequently validate the subtyping approach using cognitive, clinical, fluid biomarker, and molecular imaging data. Overall, the results suggest that atrophy patterns in MCI are sufficiently heterogeneous and can thus be used to subtype individuals into biologically and clinically meaningful subgroups.
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