Trends in Neurosciences
OpinionNew vistas for α-frequency band oscillations
Introduction
Hans Berger was among the first to witness electroencephalogram (EEG) rhythms in the α- (8–14 Hz) and β- (14–30 Hz) frequency bands [1]. During the following four decades, the parieto-occipital α rhythm was found to be attenuated by eye opening, visual stimuli and by increased attentiveness. These findings inspired the idea of α oscillations functioning as an ‘idling’ rhythm that characterizes an alert-but-still brain state [2]. Today, the idling hypothesis has been largely overtaken by a framework where the amplitude of α oscillations reflects a level of cortical inhibition 3, 4, 5, 6, 7. Accumulating data on α phase dynamics 8, 9, 10, 11, 12, however, add a twist to the story; endogenous as well as stimulus-locked α-band phase correlations seem to have a direct role in the neuronal machinery underlying behavioral-level phenomena, such as attention, STM and sensory awareness. These findings challenge the inhibition hypothesis, as well as the prevailing interpretation of α amplitude dynamics.
Section snippets
Do large-amplitude α oscillations reflect cortical inhibition?
Early on, the α-band oscillations were observed to be strengthened during internal tasks, such as mental arithmetic and visual imagery, which was interpreted by Ray and Cole [3] to reflect rejection of sensory information intake. This idea was advanced into an α-inhibition hypothesis by Klimesch [4] and Pfurtscheller 5, 6, who proposed that small α amplitudes are a signature of regions of active neuronal processing, whereas large-amplitude α oscillations reflect the inhibition and disengagement
Alpha-frequency band phase correlations
Compared with the large body of data on α-amplitude dynamics, investigations on α-phase dynamics have remained infrequent, although this line of research is now rapidly gaining popularity. Phase synchrony is essential in the formation of transient neuronal assemblies [24], in communication therein [25] and, consequently, in large-scale integration [26]. Phase interactions thus define functional networks in the cerebral cortex. Therefore, we propose here that it is the phase (Box 1), not
Thalamic burst discharges during α-frequency band oscillations
At the cellular level, the concept of α-frequency band inhibition has been largely based on an association between sleep-state α spindles and burst discharges of thalamocortical neurons [6]. Thalamocortical relay neurons have long been known to operate in two distinct modes: in a depolarized state they tonically fire single spikes, whereas following a period of hyperpolarization (∼−70 mV), long enough to de-inactivate T-type calcium channels, they discharge spike bursts (interspike intervals 2–5
Phase reset of ongoing α oscillations
The classical event-related potentials (ERPs) are influenced by both peristimulus amplitude and phase dynamics (Box 1). In contrast with the classical view of ERPs revealing stimulus-evoked components from ongoing ‘noise’, several human EEG studies suggest that some early ERP components, N1 in particular, at least partly emerge from a stimulus-caused phase reset of ongoing θ 44, 45 and α oscillations 44, 45, 46 (Box 1, Figure Ib, c). These findings place a large body of data on the N1 component
Phase synchrony among α, β and γ oscillations
In recent years, β- and γ-frequency band oscillations have attracted widespread interest. Transient synchronization of neuronal activity seems to be a key mechanism in the binding of anatomically distributed feature processing into coherent perceptual objects, where it is often associated with β or γ oscillations [24]. Also, the phenomenology of γ oscillations in the human EEG is in line with a role in the formation of neuronal object representations [51], and, accordingly, γ oscillations are
Discrete perception and action
When visual objects are presented serially at fixation, humans can recognize and categorize them at rates up to 8–12 Hz 67, 68. In a continuous wagon wheel illusion experiment, illusory reversals are most probable at wheel-motion frequencies of ∼10 Hz, which is suggestive of discrete perceptual sampling [69] (Figure 2c). Similarly, rats sample and discriminate odors at a rate of 8 Hz [70] and use active whisking at 7–14 Hz for tactile perception [34]. Hence, perception seems to operate in discrete
Discrete cognition: α oscillations in the ‘global neuronal workspace’
According to the current dogma of neuronal network dynamics, synchronous γ-frequency band assemblies account for ‘active’ neuronal processing, whereas the roles of α oscillations are in the inhibition and ‘inactivation’ of task-irrelevant cortical regions. However, as discussed here, an accumulating body of evidence emphasizes a direct involvement of α oscillations in the mechanisms of top-down modulation, attention and consciousness.
Neural correlates of consciousness (NCC) are widely
Acknowledgements
This work was supported by the Academy of Finland and by the Ella and Georg Ehrnrooth Foundation.
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