Publication | Open Access
Shaping Functional Architecture by Oscillatory Alpha Activity: Gating by Inhibition
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79
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2010
Year
Brain MechanismInhibitory ProcessBrain OrganizationAttentionSynaptic SignalingSocial SciencesOscillatory Alpha ActivityNeural MechanismNeurodynamicsOptimal Task PerformanceCognitive ElectrophysiologyCognitive NeuroscienceNetwork NeuroscienceSensorimotor ControlCognitive ScienceBehavioral SciencesAlpha ActivityNeuroimagingNervous SystemBrain CircuitryNeurobiological MechanismNeurophysiologyComputational NeuroscienceEeg Signal ProcessingPhysiologyNeuroscienceBrain ElectrophysiologyMedicineAlpha Band
The brain’s functional inhibition is reflected in alpha‑band oscillations that pulse inhibition of task‑irrelevant regions, while engaged areas show gamma synchronization and reduced alpha activity, underscoring the need to identify gating mechanisms in neural networks. The authors propose that information is gated by inhibiting task‑irrelevant regions, enabling routing to task‑relevant areas, and suggest studying cross‑frequency interactions between gamma and alpha activity to test this framework. They argue that alpha activity, the strongest EEG/MEG signal, reflects gating by inhibition, making a major portion of electrophysiological recordings indicative of this mechanism. The framework predicts that optimal task performance will correlate with increased alpha activity in task‑irrelevant regions.
In order to understand the working brain as a network, it is essential to identify the mechanisms by which information is gated between regions. We here propose that information is gated by inhibiting task-irrelevant regions, thus routing information to task-relevant regions. The functional inhibition is reflected in oscillatory activity in the alpha band (8-13 Hz). From a physiological perspective the alpha activity provides pulsed inhibition reducing the processing capabilities of a given area. Active processing in the engaged areas is reflected by neuronal synchronization in the gamma band (30-100 Hz) accompanied by an alpha band decrease. According to this framework the brain could be studied as a network by investigating cross-frequency interactions between gamma and alpha activity. Specifically the framework predicts that optimal task performance will correlate with alpha activity in task-irrelevant areas. In this review we will discuss the empirical support for this framework. Given that alpha activity is by far the strongest signal recorded by EEG and MEG, we propose that a major part of the electrophysiological activity detected from the working brain reflects gating by inhibition.
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