ArticleHuman brain mapping2020
Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases.
Article in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 2 of them syntheses that pooled it.
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Who cites it
49 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of brain health in adults with chronic pain.Anaesthesia · 2026Pooled it
- Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer's Disease.International journal of molecular sciences · 2021Pooled it
- Comparative Study of Anatomical and Learned Features in AI Models for Structural Brain MRI.Research square · 2026Article
- GLP-1 agonists in neurodegeneration: a multimodal biomarker-guided approach.Trends in molecular medicine · 2026Review
- Classification of familial and non-familial ADHD using auto-encoding network and binary hypothesis testing.Brain research bulletin · 2026Article
- MRI-to-PET synthesis via deep learning for amyloid-β quantification in Alzheimer's disease.European radiology · 2026Article
- Predictive Cellular Signatures from Live Human Motor Neurons Distinguish TDP-43 ALS and Enable ALS Subtype Stratification.bioRxiv : the preprint server for biology · 2026Article
- Sex-dependent grey matter atrophy in Alzheimer's disease progression.Brain communications · 2026Article
- Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.PeerJ. Computer science · 2026Article
- Spatiotemporal Network Dynamics Reveal Alzheimer's Disease Progression.bioRxiv : the preprint server for biology · 2025Article
- Alzheimer's Disease Prediction Using Fisher Mantis Optimization and Hybrid Deep Learning Models.Diagnostics (Basel, Switzerland) · 2025Article
- U-Net-Based Prediction of Cerebrospinal Fluid Distribution and Ventricular Reflux Grading.NMR in biomedicine · 2025Article
- Alzheimer's Prediction Methods with Harris Hawks Optimization (HHO) and Deep Learning-Based Approach Using an MLP-LSTM Hybrid Network.Diagnostics (Basel, Switzerland) · 2025Article
- Effective deep convolutional neural network with attention mechanism for Alzheimer disease classification.Frontiers in radiology · 2025Article
- Prediction, prognosis and monitoring of neurodegeneration at biobank-scale via machine learning and imaging.medRxiv : the preprint server for health sciences · 2024Article
- Machine Learning-Driven Prediction of Brain Age for Alzheimer's Risk: APOE4 Genotype and Gender Effects.Bioengineering (Basel, Switzerland) · 2024Article
- Biophysical models applied to dementia patients reveal links between geographical origin, gender, disease duration, and loss of neural inhibition.Alzheimer's research & therapy · 2024Article
- Brain structural indicators of β-amyloid neuropathology.Neurobiology of aging · 2024Article
- Leveraging electronic health records and knowledge networks for Alzheimer's disease prediction and sex-specific biological insights.Nature aging · 2024Article
- AI in imaging: the regulatory landscape.The British journal of radiology · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Biomarkers for dementia of Alzheimer's type (DAT) are sought to facilitate accurate prediction of the disease onset, ideally predating the onset of cognitive deterioration. T1-weighted magnetic resonance imaging (MRI) is a commonly used neuroimaging modality for measuring brain structure in vivo, potentially providing information enabling the design of biomarkers for DAT. We propose a novel biomarker using structural MRI volume-based features to compute a similarity score for the individual's structural patterns relative to those observed in the DAT group. We employed ensemble-learning framework that combines structural features in most discriminative ROIs to create an aggregate measure of neurodegeneration in the brain. This classifier is trained on 423 stable normal control (NC) and 330 DAT subjects, where clinical diagnosis is likely to have the highest certainty. Independent validation on 8,834 unseen images from ADNI, AIBL, OASIS, and MIRIAD Alzheimer's disease (AD) databases showed promising potential to predict the development of DAT depending on the time-to-conversion (TTC). Classification performance on stable versus progressive mild cognitive impairment (MCI) groups achieved an AUC of 0.81 for TTC of 6 months and 0.73 for TTC of up to 7 years, achieving state-of-the-art results. The output score, indicating similarity to patterns seen in DAT, provides an intuitive measure of how closely the individual's brain features resemble the DAT group. This score can be used for assessing the presence of AD structural atrophy patterns in normal aging and MCI stages, as well as monitoring the progression of the individual's brain along with the disease course.
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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.