Multi-feature computational framework for combined signatures of dementia in underrepresented settings
Reyes Gavilan, Pablo Alexander
Parra, Mario A.
Farías, Gonzalo A.
Eyre, Harris A.
La Joie, Renaud
Pontificia Universidad Javeriana. Facultad de Medicina. Instituto de Envejecimiento
Pontificia Universidad Javeriana. Facultad de Medicina. Departamento de Psiquiatría y Salud Mental
Artículo de revista
1741-2560 / 1741-2552 (Electrónico)
COARArtículo de revista
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Objective: 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.
Spatial coverageAmérica Latina
Link to the resourcehttps://iopscience.iop.org/article/10.1088/1741-2552/ac87d0
SourceJournal of Neural Engineering; Volumen 19 Número 4 , Páginas 1 - 17 (2022)
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