Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach
Author
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Bachli, M. Belén
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Sedeño, Lucas
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Ochab, Jeremi K
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Piguet, Olivier
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Kumfor, Fiona
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Reyes, Pablo
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Torralva, Teresa
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Roca, María
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Cardona, Juan Felipe
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González Campo, Cecilia
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Herrera, Eduar
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Slachevsky Chonchol, Andrea
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Matallana, Diana
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Manes, Facundo
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García, Adolfo M.
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Ibáñez, Agustín
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Chialvo, Dante R.
Admission date
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2020-05-07T22:40:53Z
Available date
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2020-05-07T22:40:53Z
Publication date
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2020
Cita de ítem
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NeuroImage 208 (2020) 116456
es_ES
Identifier
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10.1016/j.neuroimage.2019.116456
Identifier
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https://repositorio.uchile.cl/handle/2250/174532
Abstract
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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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Patrocinador
dc.description.sponsorship
Jagellonian University-UNSAM Cooperation Agreement
CEUNIM-INCYT-CEMSC3 Collaboration Agreement
National Science Centre (Poland) DEC-2015/17/D/ST2/03492
Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET)
Escuela de Ciencia y Tecnología, UNSAM
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)
Comisión Nacional de Investigación Científica y Tecnológica (CONICYT) CONICYT FONDECYT 1170010
FONDAP 15150012
InterAmerican Development Bank (IDB)
ANPCyT 20171818 /2017-1820
INECO Foundation
United States Department of Health & Human Services R01AG057234
National Institutes of Health (NIH) - USA
NIH National Institute on Aging (NIA)
GBHI ALZ UK-20-639295
Departamento Administrativo de Ciencia, Tecnología e Innovación Colciencias 697-2014 110674455314
National Health and Medical Research Council of Australia APP1037746
Australian Research Council CE110001021
Australian Research Council APP1097026
National Health and Medical Research Council of Australia
National Health and Medical Research Council of Australia APP1103258
Comisión Nacional de Investigación Científica y Tecnológica (CONICYT) CONICYT FONDAP 15150012