Real-time estimation of dynamic functional connectivity networks.
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.
Department of Mathematics, Imperial College London, London, United Kingdom.
Ricardo Pio
RP
Monti
The Computational, Cognitive and Clinical Neuroimaging Laboratory, the Division of Brain Sciences, Imperial College London, London, United Kingdom.|Department of Bioengineering, Imperial College London, London, United Kingdom.
Romy
R
Lorenz
Department of Mathematics, Imperial College London, London, United Kingdom.|Center for Brain Science, Harvard University, Cambridge, Massachusetts.
Rodrigo M
RM
Braga
Department of Mathematics, Imperial College London, London, United Kingdom.
Christoforos
C
Anagnostopoulos
The Computational, Cognitive and Clinical Neuroimaging Laboratory, the Division of Brain Sciences, Imperial College London, London, United Kingdom.
Robert
R
Leech
Department of Mathematics, Imperial College London, London, United Kingdom.|Department of Biomedical Engineering, King's College London, London, United Kingdom.
Giovanni
G
Montana
D001288:Attention / Q000502:physiology; D001921:Brain / Q000000981:diagnostic imaging / Q000502:physiology*; D001931:Brain Mapping; D003198:Computer Simulation; D003463:Cues; D005260:Female; D007839:Functional Laterality / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008959:Models, Neurological; D009043:Motor Activity / Q000502:physiology; D009434:Neural Pathways / Q000000981:diagnostic imaging / Q000502:physiology*; D010100:Oxygen / Q000097:blood; D010775:Photic Stimulation; D013028:Space Perception / Q000502:physiology; D018709:Statistics, Nonparametric; D013997:Time Factors
D010100:Oxygen
dynamic networks; functional connectivity; neurofeedback; real-time; streaming penalized optimization