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Multi-feature computational framework for combined signatures of dementia in underrepresented settings

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dc.contributor.authorMoguilner, Sebastián
dc.contributor.authorBirba, Agustina
dc.contributor.authorFittipaldi, Sol
dc.contributor.authorGonzalez, Cecilia
dc.contributor.authorTagliazucchi, Enzo
dc.contributor.authorReyes, Pablo
dc.contributor.authorMatallana, Diana
dc.contributor.authorParra, Mario
dc.contributor.authorSlachevsky Chonchol, Andrea
dc.contributor.authorFarías, Gonzalo
dc.contributor.authorCruzat, Josefina
dc.contributor.authorGarcía, Adolfo
dc.contributor.authorEyre, Harris
dc.contributor.authorLa Joie, Renaud
dc.contributor.authorRabinovici, Gil
dc.contributor.authorWhelan, Robert
dc.contributor.authorIbáñez, Agustín
dc.date.accessioned2023-12-14T19:07:48Z
dc.date.available2023-12-14T19:07:48Z
dc.date.issued2022
dc.description.abstractObjective.The differential diagnosis of behavioral variant frontotemporal dementia (bvFTD) and Alzheimer's disease (AD) remains challenging in underrepresented, underdiagnosed groups, including Latinos, as advanced biomarkers are rarely available. Recent guidelines for the study of dementia highlight the critical role of biomarkers. Thus, novel cost-effective complementary approaches are required in clinical settings.Approach. We developed a novel framework based on a gradient boosting machine learning classifier, tuned by Bayesian optimization, on a multi-feature multimodal approach (combining demographic, neuropsychological, magnetic resonance imaging (MRI), and electroencephalography/functional MRI connectivity data) to characterize neurodegeneration using site harmonization and sequential feature selection. We assessed 54 bvFTD and 76 AD patients and 152 healthy controls (HCs) from a Latin American consortium (ReDLat).Main results. The multimodal model yielded high area under the curve classification values (bvFTD patients vs HCs: 0.93 (±0.01); AD patients vs HCs: 0.95 (±0.01); bvFTD vs AD patients: 0.92 (±0.01)). The feature selection approach successfully filtered non-informative multimodal markers (from thousands to dozens).Results. Proved robust against multimodal heterogeneity, sociodemographic variability, and missing data.Significance. The model accurately identified dementia subtypes using measures readily available in underrepresented settings, with a similar performance than advanced biomarkers. This approach, if confirmed and replicated, may potentially complement clinical assessments in developing countries
dc.description.versionVersión Publicada
dc.identifier.citationMoguilner S, Birba A, Fittipaldi S, Gonzalez-Campo C, Tagliazucchi E, Reyes P, Matallana D, Parra MA, Slachevsky A, Farías G, Cruzat J, García A, Eyre HA, La Joie R, Rabinovici G, Whelan R, Ibáñez A. Multi-feature computational framework for combined signatures of dementia in underrepresented settings. J Neural Eng. 2022 Aug 25;19(4). doi: 10.1088/1741-2552/ac87d0.
dc.identifier.doihttps://doi.org/10.1088/1741-2552/ac87d0
dc.identifier.urihttps://repositorio.udd.cl/handle/11447/8194
dc.language.isoen
dc.subjectFeature selection
dc.subjectHarmonization
dc.subjectMultimodal neuroimaging
dc.subjectMachine learning
dc.subjectNeurodegeneration
dc.titleMulti-feature computational framework for combined signatures of dementia in underrepresented settings
dc.typeArticle
dcterms.accessRightsAcceso Abierto
dcterms.sourceJournal of neural engineering
dspace.entity.typePublication
relation.isAuthorOfPublicatione25c3d3e-63b5-4e04-951a-12a4989aa772
relation.isAuthorOfPublication.latestForDiscoverye25c3d3e-63b5-4e04-951a-12a4989aa772

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