INCYT 25562
INSTITUTO DE NEUROCIENCIA COGNITIVA Y TRASLACIONAL
Unidad Ejecutora - UE
artículos
Título:
Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging
Autor/es:
DONNELLY-KEHOE, PATRICIO; HODGES, JOHN; MANES, FACUNDO; SERRANO, CECILIA; SANTAMARÍA-GARCÍA, HERNANDO; GARCÍA, ADOLFO M.; PASCARIELLO, GUIDO; IBÁÑEZ, AGUSTÍN; ROSEN, HOWIE; PASCARIELLO, GUIDO; MILLER, BRUCE; MATALLANA, DIANA; MILLER, BRUCE; LANDIN-ROMERO, RAMÓN; REYES, PABLO; LANDIN-ROMERO, RAMÓN; HERRERA, EDUAR; PIGUET, OLIVER; KUMFOR, DIANA; HERRERA, EDUAR; DONNELLY-KEHOE, PATRICIO; SEDEÑO, LUCAS; KUMFOR, DIANA; HODGES, JOHN; GARCÍA, ADOLFO M.; SEDEÑO, LUCAS; MANES, FACUNDO; ROSEN, HOWIE; SERRANO, CECILIA; SANTAMARÍA-GARCÍA, HERNANDO; MATALLANA, DIANA; IBÁÑEZ, AGUSTÍN; REYES, PABLO; PIGUET, OLIVER
Revista:
ALZHEIMERS & DEMENTIA
Editorial:
ELSEVIER SCIENCE INC
Referencias:
Lugar: Amsterdam; Año: 2019 p. 588 - 598
ISSN:
1552-5260
Resumen:
Introduction: Timely diagnosis of behavioral-variant frontotemporal dementia (bvFTD) remains challenging as 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 bvFTD patients and healthy controls (HCs). We analyzed structural magnetic resonance imaging and resting‐state functional connectivity from 44 bvFTD patients and 60 HCs (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.
