Abstract
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.
Original language | English (US) |
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Pages (from-to) | 202-220 |
Number of pages | 19 |
Journal | Human Brain Mapping |
Volume | 38 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2017 |
Keywords
- dynamic networks
- functional connectivity
- neurofeedback
- real-time
- streaming penalized optimization
ASJC Scopus subject areas
- Clinical Neurology
- Neurology
- Radiological and Ultrasound Technology
- Radiology Nuclear Medicine and imaging
- Anatomy