Spatiotemporal Integrity and Spontaneous Nonlinear Dynamic Properties of the Salience Network Revealed by Human Intracranial Electrophysiology: A Multicohort Replication

Cereb Cortex. 2020 Sep 3;30(10):5309-5321. doi: 10.1093/cercor/bhaa111.

Abstract

The salience network (SN) plays a critical role in cognitive control and adaptive human behaviors, but its electrophysiological foundations and millisecond timescale dynamic temporal properties are poorly understood. Here, we use invasive intracranial EEG (iEEG) from multiple cohorts to investigate the neurophysiological underpinnings of the SN and identify dynamic temporal properties that distinguish it from the default mode network (DMN) and dorsolateral frontal-parietal network (FPN), two other large-scale brain networks that play important roles in human cognition. iEEG analysis of network interactions revealed that the anterior insula and anterior cingulate cortex, which together anchor the SN, had stronger intranetwork interactions with each other than cross-network interactions with the DMN and FPN. Analysis of directionality of information flow between the SN, DMN, and FPN revealed causal outflow hubs in the SN consistent with its role in fast temporal switching of network interactions. Analysis of regional iEEG temporal fluctuations revealed faster temporal dynamics and higher entropy of neural activity within the SN, compared to the DMN and FPN. Critically, these results were replicated across multiple cohorts. Our findings provide new insights into the neurophysiological basis of the SN, and more broadly, foundational mechanisms underlying the large-scale functional organization of the human brain.

Keywords: causality; human intracranial EEG; multicohort replication; nonlinear dynamics; salience network.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Brain / physiology*
  • Cognition / physiology
  • Cohort Studies
  • Default Mode Network / physiology
  • Electroencephalography
  • Humans
  • Neural Pathways / physiology
  • Nonlinear Dynamics
  • Reproducibility of Results
  • Signal Processing, Computer-Assisted