Open AccessReal‐time estimation of dynamic functional connectivity networks
Author(s) -
Monti Ricardo Pio,
Lorenz Romy,
Braga Rodrigo M.,
Anagnostopoulos Christoforos,
Leech Robert,
Montana Giovanni
Publication year - 2017
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.23355
Subject(s) - dynamic functional connectivity , human connectome project , computer science , task (project management) , hum , functional connectivity , artificial intelligence , connectome , resting state fmri , machine learning , neuroscience , psychology , art , management , performance art , economics , art history
Two novel and exciting avenues of neuroscientific research involve the study of task‐driven dynamic reconfigurations of functional connectivity networks and the study of functional connectivity in real‐time. While the former is a well‐established field within neuroscience and has received considerable attention in recent years, the latter remains in its infancy. To date, the vast majority of real‐time fMRI studies have focused on a single brain region at a time. This is due in part to the many challenges faced when estimating dynamic functional connectivity networks in real‐time. In this work, we propose a novel methodology with which to accurately track changes in time‐varying functional connectivity networks in real‐time. The proposed method is shown to perform competitively when compared to state‐of‐the‐art offline algorithms using both synthetic as well as real‐time fMRI data. The proposed method is applied to motor task data from the Human Connectome Project as well as to data obtained from a visuospatial attention task. We demonstrate that the algorithm is able to accurately estimate task‐related changes in network structure in real‐time. Hum Brain Mapp 38:202–220, 2017 . © 2016 Wiley Periodicals, Inc.