info:eu-repo/semantics/article
Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging
Fecha
2019-09Registro en:
Donnelly Kehoe, Patricio Andres; Pascariello, Guido Orlando; García, Adolfo Martín; Hodges, John R.; Miller, Bruce; et al.; Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging; Elsevier; Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring; 11; 9-2019; 588-598
2352-8729
CONICET Digital
CONICET
Autor
Donnelly Kehoe, Patricio Andres
Pascariello, Guido Orlando
García, Adolfo Martín
Hodges, John R.
Miller, Bruce
Rosen, Howie
Manes, Facundo Francisco
Landin Romero, Ramon
Matallana, Diana
Serrano, Cecilia Mariela
Herrera, Eduar
Reyes, Pablo
Santamaria-Garcia, Hernando
Kumfor, Fiona
Piguet, Olivier
Ibañez, Agustin Mariano
Sedeño, Lucas
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.