ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2019
Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging.
Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Frontotemporal Dementias in Latin America: History, Epidemiology, Genetics, and Clinical Research.Frontiers in neurology · 2021Pooled it
- Hybrid multimodal late fusion frameworks for bvFTD classification in imbalanced dementia datasets.Frontiers in aging neuroscience · 2026Article
- Cardiovascular risk factors and the allostatic interoceptive network in dementia.Cardiovascular research · 2025Article
- AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.Cell reports. Medicine · 2025Review
- Structural inequality linked to brain volume and network dynamics in aging and dementia across the Americas.Nature aging · 2025Article
- Biophysical models applied to dementia patients reveal links between geographical origin, gender, disease duration, and loss of neural inhibition.Alzheimer's research & therapy · 2024Article
- The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds.Scientific data · 2023Article
- Frontotemporal dementia presentation in patients with heterozygous p.H157Y variant ofJournal of medical genetics · 2023Article
- Factors associated with healthy aging in Latin American populations.Nature medicine · 2023Article
- The impacts of social determinants of health and cardiometabolic factors on cognitive and functional aging in Colombian underserved populations.GeroScience · 2023Article
- Social and non-social working memory in neurodegeneration.Neurobiology of disease · 2023Article
- Article
- Temporal Irreversibility of Large-Scale Brain Dynamics in Alzheimer's Disease.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: A cross sectional observational study.Lancet regional health. Americas · 2023Article
- The impact of regional heterogeneity in whole-brain dynamics in the presence of oscillations.Network neuroscience (Cambridge, Mass.) · 2023Article
- Understanding mental health through computers: An introduction to computational psychiatry.Frontiers in psychiatry · 2023Review
- Multidimensional inhibitory signatures of sentential negation in behavioral variant frontotemporal dementia.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Functional connectivity in behavioral variant frontotemporal dementia.Brain and behavior · 2022Review
- Multi-feature computational framework for combined signatures of dementia in underrepresented settings.Journal of neural engineering · 2022Article
- Multimodal Neurocognitive Markers of Naturalistic Discourse Typify Diverse Neurodegenerative Diseases.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
Corrections and comments
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Authors and funding
17 authors at 12 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionTimely diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging because it depends on clinical expertise and potentially ambiguous diagnostic guidelines. Recent recommendations highlight the role of multimodal neuroimaging and machine learning methods as complementary tools to address this problem.
methodsWe developed an automatic, cross-center, multimodal computational approach for robust classification of patients with bvFTD and healthy controls. We analyzed structural magnetic resonance imaging and resting-state functional connectivity from 44 patients with bvFTD and 60 healthy controls (across three imaging centers with different acquisition protocols) using a fully automated processing pipeline, including site normalization, native space feature extraction, and a random forest classifier.
resultsOur method successfully combined multimodal imaging information with high accuracy (91%), sensitivity (83.7%), and specificity (96.6%). DISCUSSION: This multimodal approach enhanced the system's performance and provided a clinically informative method for neuroimaging analysis. This underscores the relevance of combining multimodal imaging and machine learning as a gold standard for dementia diagnosis.
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Registered trials
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.