Real-time estimation of dynamic functional connectivity networks

Ricardo Pio Monti, Romy Lorenz, Rodrigo M. Braga, Christoforos Anagnostopoulos, Robert Leech, Giovanni Montana*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

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 languageEnglish (US)
Pages (from-to)202-220
Number of pages19
JournalHuman Brain Mapping
Volume38
Issue number1
DOIs
StatePublished - Jan 1 2017
Externally publishedYes

Keywords

  • dynamic networks
  • functional connectivity
  • neurofeedback
  • real-time
  • streaming penalized optimization

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology

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