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  • 标题:Fast-scale network dynamics in human cortex have specific spectral covariance patterns
  • 本地全文:下载
  • 作者:Zachary V. Freudenburg ; Charles M. Gaona ; Mohit Sharma
  • 期刊名称:Proceedings of the National Academy of Sciences
  • 印刷版ISSN:0027-8424
  • 电子版ISSN:1091-6490
  • 出版年度:2014
  • 卷号:111
  • 期号:12
  • 页码:4602-4607
  • DOI:10.1073/pnas.1311716111
  • 语种:English
  • 出版社:The National Academy of Sciences of the United States of America
  • 摘要: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.
  • 关键词:electrocorticography ; oscillating electrical potential ; covariant amplitude response
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