Detection of functional brain network reconfiguration during task-driven cognitive states.
Network science offers computational tools to elucidate the complex patterns of interactions evident in neuroimaging data. Recently, these tools have been used to detect dynamic changes in network connectivity that may occur at short time scales. The dynamics of fMRI connectivity, and how they differ across time scales, are far from understood. A simple way to interrogate dynamics at different time scales is to alter the size of the time window used to extract sequential (or rolling) measures of functional connectivity. Here, in n=82 participants performing three distinct cognitive visual tasks in recognition memory and strategic attention, we subdivided regional BOLD time series into variable sized time windows and determined the impact of time window size on observed dynamics. Specifically, we applied a multilayer community detection algorithm to identify temporal communities and we calculated network flexibility to quantify changes in these communities over time. Within our frequency band of interest, large and small windows were associated with a narrow range of network flexibility values across the brain, while medium time windows were associated with a broad range of network flexibility values. Using medium time windows of size 75-100s, we uncovered brain regions with low flexibility (considered core regions, and observed in visual and attention areas) and brain regions with high flexibility (considered periphery regions, and observed in subcortical and temporal lobe regions) via comparison to appropriate dynamic network null models. Generally, this work demonstrates the impact of time window length on observed network dynamics during task performance, offering pragmatic considerations in the choice of time window in dynamic network analysis. More broadly, this work reveals organizational principles of brain functional connectivity that are not accessible with static network approaches.
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Army Research Laboratory, Aberdeen Proving Ground, MD 21001, USA.
Qawi K
QK
Telesford
Department of Psychiatry, University of Cambridge, Cambridge, UK; Department Psychological and Brain Science, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.
Mary-Ellen
ME
Lynall
Army Research Laboratory, Aberdeen Proving Ground, MD 21001, USA; Department Psychological and Brain Science, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.
Jean
J
Vettel
Department Psychological and Brain Science, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.
Michael B
MB
Miller
Department Psychological and Brain Science, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.
Scott T
ST
Grafton
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: dsb@seas.upenn.edu.
Danielle S
DS
Bassett
D000328:Adult; D001288:Attention / Q000502:physiology; D001921:Brain / Q000000981:diagnostic imaging / Q000502:physiology*; D063132:Connectome / Q000379:methods*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010364:Pattern Recognition, Visual / Q000502:physiology; D011597:Psychomotor Performance / Q000502:physiology*; D021641:Recognition, Psychology / Q000502:physiology; D013997:Time Factors