Reconfigurations within resonating communities of brain regions following tms reveal different scales of processing

Javier Omar Garcia, Arian Ashourvan, Steven M. Thurman, Ramesh Srinivasan, Danielle S. Bassett, Jean M. Vettel

Research output: Contribution to journalArticlepeer-review

Abstract

An overarching goal of neuroscience research is to understand how heterogeneous neuronal ensembles cohere into networks of coordinated activity to support cognition. To investigate how local activity harmonizes with global signals, we measured electroencephalography (EEG) while single pulses of transcranial magnetic stimulation (TMS) perturbed occipital and parietal cortices. We estimate the rapid network reconfigurations in dynamic network communities within specific frequency bands of the EEG, and characterize two distinct features of network reconfiguration, flexibility and allegiance, among spatially distributed neural sources following TMS. Using distance from the stimulation site to infer local and global effects, we find that alpha activity (8–12 Hz) reflects concurrent local and global effects on network dynamics. Pairwise allegiance of brain regions to communities on average increased near the stimulation site, whereas TMS-induced changes to flexibility were generally invariant to distance and stimulation site. In contrast, communities within the beta (13–20 Hz) band demonstrated a high level of spatial specificity, particularly within a cluster comprising paracentral areas. Together, these results suggest that focal magnetic neurostimulation to distinct cortical sites can help identify both local and global effects on brain network dynamics, and highlight fundamental differences in the manifestation of network reconfigurations within alpha and beta frequency bands.

Original languageEnglish (US)
Pages (from-to)611-636
Number of pages26
JournalNetwork Neuroscience
Volume4
Issue number3
DOIs
StatePublished - 2020

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)
  • Computer Science Applications
  • Artificial Intelligence
  • Applied Mathematics

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