ArticleNeuroImage2020
Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 4 of them syntheses that pooled it.
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The trial behind it
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Who cites it
32 citing papers in PubMed, 4 syntheses or guidelines pooled it, 54 citations in OpenAlex.
- Frontotemporal dementia: a systematic review of artificial intelligence approaches in differential diagnosis.Frontiers in aging neuroscience · 2025Pooled it
- Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease.Alzheimer's research & therapy · 2023Pooled it
- Systematic Review: Genetic, Neuroimaging, and Fluids Biomarkers for Frontotemporal Dementia Across Latin America Countries.Frontiers in neurology · 2021Pooled it
- Frontotemporal Dementias in Latin America: History, Epidemiology, Genetics, and Clinical Research.Frontiers in neurology · 2021Pooled it
- The impact of balance exercise on brain age and brain morphometry: insights from MRI analysis.Aging clinical and experimental research · 2026Observational
- Frontotemporal dementia characterization using neurite orientation dispersion and density imaging.Brain communications · 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
- Article
- Atrophy of the cholinergic regions advances from early to late mild cognitive impairment.Neuroradiology · 2024Article
- Multivariate word properties in fluency tasks reveal markers of Alzheimer's dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Synthetic data analysis for early detection of Alzheimer progression through machine learning algorithms.PeerJ. Computer science · 2024Article
- The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds.Scientific data · 2023Article
- Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data.Human brain mapping · 2023Article
- Neuroprotective effects of a lead compound from coral via modulation of the orphan nuclear receptor Nurr1.CNS neuroscience & therapeutics · 2023Article
- Syndrome Pattern Recognition Method Using Sensed Patient Data for Neurodegenerative Disease Progression Identification.Diagnostics (Basel, Switzerland) · 2023Article
- Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression.Diagnostics (Basel, Switzerland) · 2023Review
- Biomarkers for dementia in Latin American countries: Gaps and opportunities.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference.Network neuroscience (Cambridge, Mass.) · 2023Article
- Multidimensional inhibitory signatures of sentential negation in behavioral variant frontotemporal dementia.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Multi-feature computational framework for combined signatures of dementia in underrepresented settings.Journal of neural engineering · 2022Article
Corrections and comments
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Authors and funding
17 authors at 12 institutions in 5 countries.
Funding
Abstract
Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be complemented by computational decision-support methods to objectively analyze multidimensional measures and increase diagnostic confidence. Yet, widespread application of these tools cannot be recommended unless they are proven to perform consistently and reproducibly across samples from different countries. We implemented machine-learning algorithms to evaluate the prediction power of neurocognitive biomarkers (behavioral and imaging measures) for classifying two neurodegenerative conditions -Alzheimer Disease (AD) and behavioral variant frontotemporal dementia (bvFTD)- across three different countries (>200 participants). We use machine-learning tools integrating multimodal measures such as cognitive scores (executive functions and cognitive screening) and brain atrophy volume (voxel based morphometry from fronto-temporo-insular regions in bvFTD, and temporo-parietal regions in AD) to identify the most relevant features in predicting the incidence of the diseases. In the Country-1 cohort, predictions of AD and bvFTD became maximally improved upon inclusion of cognitive screenings outcomes combined with atrophy levels. Multimodal training data from this cohort allowed predicting both AD and bvFTD in the other two novel datasets from other countries with high accuracy (>90%), demonstrating the robustness of the approach as well as the differential specificity and reliability of behavioral and neural markers for each condition. In sum, this is the first study, across centers and countries, to validate the predictive power of cognitive signatures combined with atrophy levels for contrastive neurodegenerative conditions, validating a benchmark for future assessments of reliability and reproducibility.
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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.