Midfrontal conflict-related theta-band power reflects neural oscillations that predict behavior.
نویسندگان
چکیده
Action monitoring and conflict resolution require the rapid and flexible coordination of activity in multiple brain regions. Oscillatory neural population activity may be a key physiological mechanism underlying such rapid and flexible network coordination. EEG power modulations of theta-band (4-8 Hz) activity over the human midfrontal cortex during response conflict have been proposed to reflect neural oscillations that support conflict detection and resolution processes. However, it has remained unclear whether this frequency-band-specific activity reflects neural oscillations or nonoscillatory responses (i.e., event-related potentials). Here, we show that removing the phase-locked component of the EEG did not reduce the strength of the conflict-related modulation of the residual (i.e., non-phase-locked) theta power over midfrontal cortex. Furthermore, within-subject regression analyses revealed that the non-phase-locked theta power was a significantly better predictor of the conflict condition than was the time-domain phase-locked EEG component. Finally, non-phase-locked theta power showed robust and condition-specific (high- vs. low-conflict) cross-trial correlations with reaction time, whereas the phase-locked component did not. Taken together, our results indicate that most of the conflict-related and behaviorally relevant midfrontal EEG signal reflects a modulation of ongoing theta-band oscillations that occurs during the decision process but is not phase-locked to the stimulus or to the response.
منابع مشابه
Midfrontal conflict - related theta - band power reflects 4 neural oscillations that predict behavior 5 6 7 8 9
neural oscillations that predict behavior 5 6 7 8 9 Michael X Cohen1,2,3,* and Tobias H. Donner1,2,4,* 10 11 1 Department of Psychology, University of Amsterdam, 12 2 Cognitive Science Center Amsterdam, University of Amsterdam, 13 3 Department of Physiology, University of Arizona 14 4Bernstein Center for Computational Neuroscience, Charité Universitätsmedizin, Berlin, Germany 15 16 * Shared aut...
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ورودعنوان ژورنال:
- Journal of neurophysiology
دوره 110 12 شماره
صفحات -
تاریخ انتشار 2013