ArticleHuman brain mapping2023
Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data.
Article in Human brain mapping, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Frontotemporal dementia: a systematic review of artificial intelligence approaches in differential diagnosis.Frontiers in aging neuroscience · 2025Pooled it
- A computational model to describe multi-regional brain architecture during neurodegeneration in Alzheimer's disease.Scientific reports · 2026Article
- Cortical asymmetry in autosomal dominant Alzheimer's disease progression.Brain communications · 2026Article
- Hybrid multimodal late fusion frameworks for bvFTD classification in imbalanced dementia datasets.Frontiers in aging neuroscience · 2026Article
- The potential role of machine learning and deep learning in differential diagnosis of Alzheimer's disease and FTD using imaging biomarkers: A review.The neuroradiology journal · 2025Review
- Distinctive Progression Patterns of Brain Structural Damage Aid Classification of Frontotemporal Dementia Variants.European journal of neurology · 2025Article
- The Diagnostic Landscape of Behavioral Variant Frontotemporal Dementia.Rhode Island medical journal (2013) · 2025Review
- Predicting incident dementia in community-dwelling older adults using primary and secondary care data from electronic health records.Brain communications · 2025Article
- Caregiver perspectives enable accurate diagnosis of neurodegenerative disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Sink-index: a network-based EEG marker for frontotemporal dementia and Alzheimer's disease.Brain communications · 2025Article
- Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis.Journal of medical Internet research · 2024Article
- Metal Toxicity and Dementia Including Frontotemporal Dementia: Current State of Knowledge.Antioxidants (Basel, Switzerland) · 2024Review
- Cortical thickness modeling and variability in Alzheimer's disease and frontotemporal dementia.Journal of neurology · 2024Article
- Temporal atrophy together with verbal encoding impairment is highly predictive for cognitive decline in typical Alzheimer's dementia - a retrospective follow-up study.Frontiers in psychiatry · 2024Article
- Automated brain segmentation and volumetry in dementia diagnostics: a narrative review with emphasis on FreeSurfer.Frontiers in aging neuroscience · 2024Review
- The pursuit for markers of disease progression in behavioral variant frontotemporal dementia: a scoping review to optimize outcome measures for clinical trials.Frontiers in aging neuroscience · 2024Article
- Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data.Human brain mapping · 2023Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T-T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2-year follow-up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross-sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross-sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross-sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross-sectional and longitudinal data.
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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.