TY - JOUR
T1 - Multi-feature computational framework for combined signatures of dementia in underrepresented settings
AU - Moguilner, Sebastian
AU - Birba, Agustina
AU - Fittipaldi, Sol
AU - Gonzalez-Campo, Cecilia
AU - Tagliazucchi, Enzo
AU - Reyes, Pablo
AU - Matallana, Diana
AU - Parra, Mario A.
AU - Slachevsky, Andrea
AU - Farías, Gonzalo
AU - Cruzat, Josefina
AU - García, Adolfo
AU - Eyre, Harris A.
AU - La Joie, Renaud
AU - Rabinovici, Gil
AU - Whelan, Robert
AU - Ibáñez, Agustín
N1 - Publisher Copyright:
© 2022 The Author(s). Published by IOP Publishing Ltd.
PY - 2022/8/1
Y1 - 2022/8/1
N2 - 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.
AB - 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.
KW - feature selection
KW - harmonization
KW - machine learning
KW - multimodal neuroimaging
KW - neurodegeneration
UR - http://www.scopus.com/inward/record.url?scp=85137127365&partnerID=8YFLogxK
U2 - 10.1088/1741-2552/ac87d0
DO - 10.1088/1741-2552/ac87d0
M3 - Article
C2 - 35940105
AN - SCOPUS:85137127365
SN - 1741-2560
VL - 19
JO - Journal of Neural Engineering
JF - Journal of Neural Engineering
IS - 4
M1 - 046048
ER -