Dynamics of Dynamics within a Single Data Acquisition Session: Variation in Neocortical Alpha Oscillations in Human MEG
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Wan-2011-Dynamics of Dynamics.pdf
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Author(s)
Kerr, Catherine E.
Hamalainen, Matti S.
Jones, Stephanie R.
Wan, Qian
Pritchett, Dominique Leon
Moore, Christopher I.
Date Issued
September 2011
Journal
PLoS ONE
Publisher
Public Library of Science
Citation
Wan, Qian et al. “Dynamics of Dynamics Within a Single Data Acquisition Session: Variation in Neocortical Alpha Oscillations in Human MEG.” Ed. Joseph Najbauer. PLoS ONE 6.9 (2011): e24941. Web. 15 Feb. 2012.
Version
Final published version
Abstract
Background
Behavioral paradigms applied during human recordings in electro- and magneto- encephalography (EEG and MEG) typically require 1–2 hours of data collection. Over this time scale, the natural fluctuations in brain state or rapid learning effects could impact measured signals, but are seldom analyzed.
Methods and Findings
We investigated within-session dynamics of neocortical alpha (7–14 Hz) rhythms and their allocation with cued-attention using MEG recorded from primary somatosensory neocortex (SI) in humans. We found that there were significant and systematic changes across a single ~1 hour recording session in several dimensions, including increased alpha power, increased differentiation in attention-induced alpha allocation, increased distinction in immediate time-locked post-cue evoked responses in SI to different visual cues, and enhanced power in the immediate cue-locked alpha band frequency response. Further, comparison of two commonly used baseline methods showed that conclusions on the evolution of alpha dynamics across a session were dependent on the normalization method used.
Conclusions
These findings are important not only as they relate to studies of oscillations in SI, they also provide a robust example of the type of dynamic changes in brain measures within a single session that are overlooked in most human brain imaging/recording studies.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
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DOI of Published Version
https://doi.org/10.1371/journal.pone.0024941
