Open AccessRobust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging
Author(s) -
DonnellyKehoe Patricio Andres,
Pascariello Guido Orlando,
García Adolfo M.,
Hodges John R.,
Miller Bruce,
Rosen Howie,
Manes Facundo,
LandinRomero Ramon,
Matallana Diana,
Serrano Cecilia,
Herrera Eduar,
Reyes Pablo,
SantamariaGarcia Hernando,
Kumfor Fiona,
Piguet Olivier,
Ibanez Agustin,
Sedeño Lucas
Publication year - 2019
Publication title -
alzheimer's and dementia: diagnosis, assessment and disease monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.497
H-Index - 37
ISSN - 2352-8729
DOI - 10.1016/j.dadm.2019.06.002
Subject(s) - neuroimaging , frontotemporal dementia , artificial intelligence , computer science , random forest , machine learning , dementia , medicine , neuroscience , psychology , pathology , disease
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.