Fast-scale network dynamics in human cortex have specific spectral covariance patterns

Zachary V. Freudenburg, Charles M. Gaona, Mohit Sharma, David T. Bundy, Jonathan D. Breshears, Robert B. Pless, Eric C. Leuthardt

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Whether measured by MRI or direct cortical physiology, infraslow rhythms have defined state invariant cortical networks. The time scales of this functional architecture, however, are unlikely to be able to accommodate the more rapid cortical dynamics necessary for an active cognitive task. Using invasively monitored epileptic patients as a research model, we tested the hypothesis that faster frequencies would spectrally bind regions of cortex as a transient mechanism to enable fast network interactions during the performance of a simple hear-and-repeat speech task. We term these short-lived spectrally covariant networks functional spectral networks (FSNs). We evaluated whether spectrally covariant regions of cortex, which were unique in their spectral signatures, provided a higher degree of task-related information than any single site showing more classic physiologic responses (i.e., single-site amplitude modulation). Taken together, our results showing that FSNs are a more sensitive measure of task-related brain activation and are better able to discern phonemic content strongly support the concept of spectrally encoded interactions in cortex. Moreover, these findings that specific linguistic information is represented in FSNs that have broad anatomic topographies support a more distributed model of cortical processing.

Original languageEnglish
Pages (from-to)4602-4607
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume111
Issue number12
DOIs
StatePublished - Mar 25 2014

Keywords

  • Covariant amplitude response
  • Electrocorticography
  • Oscillating electrical potential

Fingerprint Dive into the research topics of 'Fast-scale network dynamics in human cortex have specific spectral covariance patterns'. Together they form a unique fingerprint.

  • Cite this