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

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, Agustín Ibáñez

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish
Article number046048
JournalJournal of neural engineering
Volume19
Issue number4
DOIs
StatePublished - 1 Aug 2022
Externally publishedYes

Keywords

  • feature selection
  • harmonization
  • machine learning
  • multimodal neuroimaging
  • neurodegeneration

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