Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex

Abstract

This Feature Article is part of a series identified by the Editorial Board as reporting findings of exceptional significance.Edited by Terrence J. Sejnowski, Salk Institute for Biological Studies, La Jolla, CA, and approved August 8, 2014 (received for review February 22, 2014) Cognitive functions rely on the coordinated activity of neurons in many brain regions, but the interactions between cortical areas are not yet well understood. Here we investigated whether lowfrequency (α) and high-frequency (γ) oscillations characterize different directions of information flow in monkey visual cortex. We recorded from all layers of the primary visual cortex (V1) and found that γ-waves are initiated in input layer 4 and propagate to the deep and superficial layers of cortex, whereas α-waves propagate in the opposite direction. Simultaneous recordings from V1 and downstream area V4 confirmed that γ-and α-waves propagate in the feedforward and feedback direction, respectively. Microstimulation in V1 elicited γ-oscillations in V4, whereas microstimulation in V4 elicited α-oscillations in V1, thus providing causal evidence for the opposite propagation of these rhythms. Furthermore, blocking NMDA receptors, thought to be involved in feedback processing, suppressed α while boosting γ. These results provide new insights into the relation between brain rhythms and cognition.neuronal synchronization | attention | perceptual organization | phase coherence | Granger causality A reas of the visual cortex are arranged hierarchically, with low-level areas representing simple features and higher areas representing the more complex aspects of the visual world (1, 2). Neurons in many visual areas are coactive during the perception of a visual stimulus and it is difficult to disentangle the influences of lower areas onto higher areas from the effects that go in the opposite direction (3). Studies of visual cognition could benefit enormously from markers of cortical activity that distinguish between feedforward and feedback effects. One such putative marker is cortical oscillatory activity, because oscillations of different frequencies have been proposed to propagate either in feedforward or in the feedback direction (4, 5), but experimental evidence for this view is sparse (6).Low-frequency rhythms, like the α-rhythm-which is particularly pronounced in the visual cortex-have been proposed to characterize spontaneous activity (7,8) as the α-rhythm increases when the subject closes the eyes (9). More recent observations have also implicated α-oscillations in the active suppression of irrelevant, unattended information (10, 11). In contrast, the high-frequency γ-rhythm increases if visual stimuli are presented, and in particular if they are task-relevant (12, 13). One influential hypothesis has been that γ-oscillations play a role in feature binding (14), but later studies cast doubt on this proposal (15,16). A more recent hypothesis holds that γ-oscillations facilitate the communication between cortical areas (17), but both evidence in fa...

The relationship between oscillatory EEG activity and the laminar-specific BOLD signal

“…Intracranial electrophysiological recordings in animals have linked neuronal oscillations in different frequency bands to the cortical feedforward and feedback information flow and to cortico-subcortical interactions (2)(3)(4)(5). In line with these findings, electrophysiological recordings in animals have also demonstrated that changes in α-, β-, and γ-bands can be localized to specific cortical layers (6)(7)(8)(9)(10)(11)(12).…”

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

“…From this perspective, the phase locking of scalp EEG data as accounted for by microstates and related models may probably better be considered as signatures of transient large-scale processes that organize neuronal excitability rather than the signatures of neuronal communication within these cycles of excitability itself. One may further argue that these latter processes that implement communication itself may take place on spatial scales that are mostly below the resolution of scalp EEG data and typically need to be resolved by recording local field potentials (van Kerkoerle et al, 2014). The fact that combined EEG-fMRI data has shown that the topographic appearance of specific transient states of EEG synchronization (that are assumingly cortical) covaried with the spatial distribution of thalamic activity (Schwab et al, 2015) may further support this view, since the thalamus is a well-known pace-maker for cortical cycles of M A N U S C R I P T…”

“…interactions to assess brain connectivity (Pascual-Marqui et al, 2011), and there is good electrophysiological evidence that these lags exist and play a functional role (van Kerkoerle et al, 2014). However, the microstate model contains a strong and a priori constraint on simultaneity, which excludes the existence of significant lags within a microstate.…”

MEG-measured visually induced gamma-band oscillations in chronic schizophrenia: Evidence for impaired generation of rhythmic activity in ventral stream regions

“…More specifically, high frequency oscillations occur predominantly in granular layers 3 and 4, while alpha (8-12 Hz)/beta (13-30 Hz) oscillations occur predominantly in infra-granular layers 5 and 6 (Bastos et al, 2015;van Kerkoerle et al, 2014). In addition to stimulus parameters, the amplitude and frequency of high-frequency oscillations in visual cortices can also be influenced by central states, such as attention.…”

“…Alpha generators have been found in different layers of both striate and extra-striate visual areas (Bollimunta et al, 2008(Bollimunta et al, , 2011Mo et al, 2011;Haegens et al, 2015;van Kerkoerle et al, 2014), and are known to be modulated in activity by both cortico-cortical and recurrent thalamo-cortical (TC) communication (Steriade et al, 1990), influenced by thalamic nuclei such as the lateral geniculate nucleus (LGN), the pulvinar nucleus, and the thalamic reticular nucleus (TRN). Evidence is growing that these different thalamic nuclei play different roles in sensory information processing, both spatially (i.e., across the visual hierarchy) and temporally (i.e., during early feedforward and later feedback processing), raising the possibility that aberrant alpha-band activity in visual cortices may in part reflect aberrant TC-interactions.…”