Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging

Patricio Andres Donnelly-Kehoe, Guido Orlando Pascariello, Adolfo M. García, John R. Hodges, Bruce Miller, Howie Rosen, Facundo Manes, Ramon Landin-Romero, Diana Matallana, Cecilia Serrano, Eduar Herrera, Pablo Reyes, Hernando Santamaria-Garcia, Fiona Kumfor, Olivier Piguet, Agustin Ibanez, Lucas Sedeño

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

37 Citas (Scopus)

Resumen

Introduction: Timely diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging because it depends on clinical expertise and potentially ambiguous diagnostic guidelines. Recent recommendations highlight the role of multimodal neuroimaging and machine learning methods as complementary tools to address this problem. Methods: We developed an automatic, cross-center, multimodal computational approach for robust classification of patients with bvFTD and healthy controls. We analyzed structural magnetic resonance imaging and resting-state functional connectivity from 44 patients with bvFTD and 60 healthy controls (across three imaging centers with different acquisition protocols) using a fully automated processing pipeline, including site normalization, native space feature extraction, and a random forest classifier. Results: Our method successfully combined multimodal imaging information with high accuracy (91%), sensitivity (83.7%), and specificity (96.6%). Discussion: This multimodal approach enhanced the system's performance and provided a clinically informative method for neuroimaging analysis. This underscores the relevance of combining multimodal imaging and machine learning as a gold standard for dementia diagnosis.

Idioma originalInglés
Páginas (desde-hasta)588-598
Número de páginas11
PublicaciónAlzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
Volumen11
DOI
EstadoPublicada - dic. 2019
Publicado de forma externa

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