Distinct Superficial and deep laminar domains of activity in the visual cortex during rest and stimulation

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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...

Section: Significancementioning

confidence: 50%

“…In contrast, γ-power was stronger in layer 3 than in the other layers (Fig. 2 F and G) (t-test, all Ps < 0.001) (39,40). The laminar pattern of LFP power was similar, irrespective of whether the RF fell on figure or ground (Fig.…”

Section: Significancementioning

confidence: 95%

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

Kerkoerle

Self

Dagnino

et al. 2014

875

138

776

“…The cat visual cortex also contains a class of pyramidal and stellate cells termed "chattering cells," which are localized to layers 2/3 and fire with high-frequency bursts in the gamma-frequency range (32,33). A recent study in awake monkeys using translaminar electrodes also reported a predominance of gamma activity in the LFP of superficial layers in area V1 (34).…”

Section: Discussionmentioning

confidence: 95%

Laminar differences in gamma and alpha coherence in the ventral stream

Buffalo

Fries

Landman

et al. 2011

583

528

Attention to a stimulus enhances both neuronal responses and gamma frequency synchrony in visual area V4, both of which should increase the impact of attended information on downstream neurons. To determine whether gamma synchrony is common throughout the ventral stream, we recorded from neurons in the superficial and deep layers of V1, V2, and V4 in two rhesus monkeys. We found an unexpected striking difference in gamma synchrony in the superficial vs. deep layers. In all three areas, spike-field coherence in the gamma (40-60 Hz) frequency range was largely confined to the superficial layers, whereas the deep layers showed maximal coherence at low frequencies (6-16 Hz), which included the alpha range. In the superficial layers of V2 and V4, gamma synchrony was enhanced by attention, whereas in the deep layers, alpha synchrony was reduced by attention. Unlike these major differences in synchrony, attentional effects on firing rates and noise correlation did not differ substantially between the superficial and deep layers. The results suggest that synchrony plays very different roles in feedback and feedforward projections. electrophysiology | macaque | oscillation A natomical and physiological studies have characterized the afferent inputs to and efferent inputs from neurons in different layers of visual cortical areas. However, physiological distinctions across layers, such as synchronous interactions, have not been fully identified. We first came across laminar differences in synchrony serendipitously. Gamma-band synchrony, measured either by spike-field or spike-spike interactions across multiple electrodes, is a prominent feature in visual cortex, and several studies have shown that attention enhances gamma-band synchrony in area V4 (1-5). In our first recordings in area V1, we also found prominent gamma-band synchrony, although the effects of attention, if any, were much smaller than what we previously found in V4 (1). However, in our first recordings in area V2 in the lunate sulcus, we were surprised to find hardly any gamma-band synchrony. We initially had no explanation for why V2 should be so different from V1 and V4. Probing at greater electrode depths led to the discovery that V2 cells do show gamma-band synchrony but only at those deeper electrode depths. Because V2 in the lunate sulcus bends under V1, layer 6 cells are closer to V1 on the occipital surface than are layer 1 cells. Thus, our deeper electrode recordings were actually located in the more superficial layers of V2. Because we typically studied the first responsive cells found in any penetration, this must have strongly biased our first recordings in V2 to the deep layers, and these deep layers apparently had little gamma-band synchrony. Conversely, the same tendency to sample the first responsive cells on a penetration would have resulted in a strong bias to record cells in the superficial layers of V1 and V4, from which we recorded directly on the cortical surface. This possibility led us to test whether the deep layers of V1 and V4 were...

“…The laminar structure of the visual cortex has been known for a long time, yet whether there are differences in the way in which neuronal populations across cortical layers encode information is just beginning to be understood (3,4,43). Aside from a study in anesthetized cat V1 (32) reporting pronounced adaptive effects irrespective of cortical depth, whether adaptation influences sensory coding in a layer-specific manner has never been investigated.…”

Section: Discussionmentioning

confidence: 99%

“…Cortical layers are ubiquitous structures throughout neocortex (1, 2) that consist of highly recurrent local networks that communicate among each other to possibly influence the information encoded in population activity. In recent years, significant progress has been made in our understanding of the differences in response properties of neurons across cortical layers (3,4), yet there is still a great deal to learn about whether and how neuronal populations encode information in a layerspecific manner. A measure of the activity of a local population (or ensemble) of neurons (5) is captured by local field potentials (LFPs), which are composed of low-frequency extracellular voltage fluctuations, including local excitatory and inhibitory intracortical inputs (6) believed to originate from within 250-500 μm of the recording site (7,8).…”

mentioning

confidence: 99%

Adaptation-induced synchronization in laminar cortical circuits

Hansen¹,

Dragoi

2011

A fundamental feature of information processing in neocortex is the ability of individual neurons to adapt to changes in incoming stimuli. It is increasingly being understood that cortical adaptation is a phenomenon that requires network interactions. The fact that the structure of local networks depends critically on cortical layer raises the possibility that adaptation could induce specific effects in different layers. Here we show that brief exposure (300 ms) to a stimulus of fixed orientation modulates the strength of synchronization between individual neurons and local population activity in the gamma-band frequency (30-80 Hz) in macaque primary visual cortex (V1) and influences the ability of individual neurons to encode stimulus orientation. Using laminar probes, we found that although stimulus presentation elicits a large increase in the gamma synchronization of rhythmic neuronal activity in the input (granular) layers of V1, adaptation caused a pronounced increase in synchronization in the cortical output (supragranular) layers. The increase in gamma synchronization after adaptation was significantly correlated with an improvement in neuronal orientation discrimination performance only in the supragranular layers. Thus, synchronization between the spiking activity of individual neurons and their local population may enhance sensory coding to optimize network processing across laminar circuits.A fundamental issue in our understanding of brain circuits is how networks in different layers of the cerebral cortex encode information. Cortical layers are ubiquitous structures throughout neocortex (1, 2) that consist of highly recurrent local networks that communicate among each other to possibly influence the information encoded in population activity. In recent years, significant progress has been made in our understanding of the differences in response properties of neurons across cortical layers (3, 4), yet there is still a great deal to learn about whether and how neuronal populations encode information in a layerspecific manner. A measure of the activity of a local population (or ensemble) of neurons (5) is captured by local field potentials (LFPs), which are composed of low-frequency extracellular voltage fluctuations, including local excitatory and inhibitory intracortical inputs (6) believed to originate from within 250-500 μm of the recording site (7,8). In visual cortex, it has been found that neuronal groups exhibit strong responses in the gammaband frequency (30-80 Hz) (9-11) and that single neurons synchronize their responses with the local population activity (12, 13). Synchronization in visual cortex, particularly in the gammaband, has been found to be critically involved in sensory processing (9,11,14), grouping (9, 11; but see refs. 15-17), attention (10,(18)(19)(20), working memory (5), and behavioral reaction times (20). The results of these studies support the hypothesis that efficient information transmission would occur whenever two networks are synchronous in their excitability peaks, wh...