Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach

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dc.contributor.authorBachli, M. Belen
dc.contributor.authorSedeno, Lucas
dc.contributor.authorOchab, Jeremi K.
dc.contributor.authorPiguet, Olivier
dc.contributor.authorKumfor, Fiona
dc.contributor.authorReyes, Pablo
dc.contributor.authorTorralva, Teresa
dc.contributor.authorRoca, María
dc.contributor.authorCardona, Juan Felipe
dc.contributor.authorGonzalez Campo, Cecilia
dc.contributor.authorHerrera, Eduar
dc.contributor.authorSlachevsky, Andrea
dc.contributor.authorMatallana, Diana
dc.contributor.authorManes, Facundo
dc.contributor.authorGarcía, Adolfo M.
dc.contributor.authorIbanez, Agustín
dc.contributor.authorChialvo, Dante R.
dc.date.accessioned2021-10-27T14:57:56Z
dc.date.available2021-10-27T14:57:56Z
dc.date.issued2020
dc.description.abstractAccurate 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 reproducibilityes
dc.identifier.citationNeuroImage Volume 208, March 2020, 116456es
dc.identifier.urihttps://doi.org/10.1016/j.neuroimage.2019.116456es
dc.identifier.urihttp://hdl.handle.net/11447/4953
dc.language.isoen_USes
dc.subjectAlzheimer’s diseasees
dc.subjectFrontotemporal dementiaes
dc.subjectMachine-learninges
dc.subjectExecutive functionses
dc.subjectVoxel-based morphometryes
dc.subjectClassificationes
dc.titleEvaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approaches
dc.typeArticlees

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