Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/136585
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Type: | Journal article |
Title: | Multi-feature computational framework for combined signatures of dementia in underrepresented settings |
Author: | Moguilner, S. Birba, A. Fittipaldi, S. Gonzalez-Campo, C. Tagliazucchi, E. Reyes, P. Matallana, D. Parra, M.A. Slachevsky, A. Farías, G. Cruzat, J. García, A. Eyre, H.A. La Joie, R. Rabinovici, G. Whelan, R. Ibáñez, A. |
Citation: | Journal of Neural Engineering, 2022; 19(4):046048-1-046048-17 |
Publisher: | IOP Publishing |
Issue Date: | 2022 |
ISSN: | 1741-2560 1741-2552 |
Statement of Responsibility: | Sebastian Moguilner, Agustina Birba, Sol Fittipaldi, Cecilia Gonzalez-Campo, Enzo Tagliazucchi, Pablo Reyes, Diana Matallana, Mario A Parra, Andrea Slachevsky, Gonzalo Farías, Josefina Cruzat, Adolfo García, Harris A Eyre, Renaud La Joie, Gil Rabinovici, Robert Whelan and Agustín Ibáñez |
Abstract: | 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. |
Keywords: | multimodal neuroimaging; neurodegeneration; harmonization; feature selection; machine learning |
Description: | PUBLISHED 25 August 2022 |
Rights: | © 2022 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
DOI: | 10.1088/1741-2552/ac87d0 |
Published version: | http://dx.doi.org/10.1088/1741-2552/ac87d0 |
Appears in Collections: | Psychiatry publications |
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File | Description | Size | Format | |
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hdl_136585.pdf | Published version | 2.23 MB | Adobe PDF | View/Open |
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