Theta, but Not Gamma Oscillations in Area V4 Depend on Input from Primary Visual Cortex

Theta (3–9 Hz) and gamma (30–100 Hz) oscillations have been observed at different levels along the hierarchy of cortical areas and across a wide set of cognitive tasks. In the visual system, the emergence of both rhythms in primary visual cortex (V1) and mid-level in in (V1) and Data in

In Brief Kienitz et al. show that, upon visual stimulation, V1 and V4 show theta and gamma oscillations, which interacted in terms of phase-to-amplitude coupling. Lesion of V1, the major input source to V4, eliminated V4 theta oscillations. In contrast, V4 gamma oscillations were less affected and still contained stimulus information but emerged delayed (>100 ms). SUMMARY Theta (3-9 Hz) and gamma (30-100 Hz) oscillations have been observed at different levels along the hierarchy of cortical areas and across a wide set of cognitive tasks. In the visual system, the emergence of both rhythms in primary visual cortex (V1) and mid-level cortical areas V4 has been linked with variations in perceptual reaction times. [1][2][3][4][5] Based on analytical methods to infer causality in neural activation patterns, it was concluded that gamma and theta oscillations might both reflect feedforward sensory processing from V1 to V4. [6][7][8][9][10] Here, we report on experiments in macaque monkeys in which we experimentally assessed the presence of both oscillations in the neural activity recorded from multi-electrode arrays in V1 and V4 before and after a permanent V1 lesion. With intact cortex, theta and gamma oscillations could be reliably elicited in V1 and V4 when monkeys viewed a visual contour illusion and showed phase-to-amplitude coupling. Laminar analysis in V1 revealed that both theta and gamma oscillations occurred primarily in the supragranular layers, the cortical output compartment of V1. However, there was a clear dissociation between the two rhythms in V4 that became apparent when the major feedforward input to V4 was removed by lesioning V1: although V1 lesioning eliminated V4 theta, it had little effect on V4 gamma power except for delaying its emergence by >100 ms. These findings suggest that theta is more tightly associated with feedforward processing than gamma and pose limits on the proposed role of gamma as a feedforward mechanism.

Visual Stimulation Elicits Theta and Gamma Activity in V1 and V4
To assess theta and gamma oscillations across two different levels of the cortical hierarchy, we recorded multi-unit activity (MUA) and local field potential (LFP) in visual areas primary visual cortex (V1) and V4 in monkeys that passively viewed a visual contour (''Kanizsa'') illusion and its non-illusory control (Figure 1A). This visual stimulation elicited robust increases in MUA both in V1 and V4, whereas stimulus-specific effects across channels were only seen in V4 ( Figures 1B, 1C, and S1A; Table S1; see also Figures 4E and 4F, left panels, left wings). In addition to this increase in firing rates after stimulus onset, spectral analyses of MUA responses focusing on the sustained response period after stimulus onset (0.3-1 s) revealed significant theta oscillations both in V1 and V4 and gamma oscillations in V1 ( Figures 1B, 1C, and S1A; see Table S2 for detailed statistics). In contrast to the non-rhythmic MUA in V1, both the theta and gamma modulation of V1 MUA showed significantly stronger increases for the illusion compared to the control (Figure 1B; Table S2). Similarly, V4 exhibited strong theta oscillations associated with the Kanizsa illusion ( Figure 1C Table  S2).
Analysis of V1 LFP revealed that all channels showed theta and gamma power increases following visual stimulation. However, in contrast to the rhythmic MUA, these power changes were not significantly modulated by the presence of the illusion (Figures 1D and S1B; Table S3; theta: p = 0.99; gamma: p = 0.97; n = 61; Wilcoxon rank sum test). V4 LFP exhibited similar theta oscillations as V4 MUA. Yet, compared to V4 MUA, it also showed significant power increases in the gamma range (Figures 1E, S1C, and S1D; Table S3; see also Figure 4G, left and right panel, left wings). Both the theta and gamma activity in V4 proved sensitive to the illusion (see also Figure 4H, left and right panel, left wings). As spiking and gamma oscillations can sometimes be linked to each other, 11,12 we correlated MUA and LFP gamma power and found close to zero correlations between MUA and LFP gamma power across trials in both monkeys (average r = 0.01, n = 60 in monkey B and average r = 0.02, n = 57 in monkey F). No channel showed significant correlations after correcting for multiple comparisons.
In addition to their mere presence in both areas, LFP theta and gamma oscillations showed interactions in time ( Figure 2A).
Such phase-to-amplitude coupling (PAC) has been proposed as a link between large-scale and local neuronal computation 13 and has been shown to decrease with attention-related modulation in electrocorticogram (ECoG) recordings of V1 and V4. 7 To examine this PAC further in our data, we computed a modulation index (MI) 14 in V1 and V4 (STAR Methods). We found that both V1 and V4 showed significant phase-to-amplitude coupling in the majority of channels, whereas illusion-related modulation was stronger in V4 (Figures 2B and 2C; see Table S4 for detailed  statistics).
Taken together, visual stimulation elicited theta-and gammarhythmic MUA in V1 and theta-rhythmic MUA in V4, all of which carried information about stimulus identity. In the LFP, theta and   Figure S1 and Tables S1-S3. gamma oscillations were present both in V1 and V4. However, stimulus-specific modulation of rhythmic LFP was more prominent in V4. In addition, gamma amplitude was modulated by theta phase, with stronger illusion-specific effects in V4 and close to no illusion effects in V1.

Theta and Gamma Activity Predominantly Emerges in V1 Supragranular Layers
Having verified the presence of both theta and gamma rhythms in V1 and V4, our next aim was to assess whether their laminar cortical distribution within V1 as recorded by linear multi-contact electrodes (monkey Br; STAR Methods) is consistent with their proposed role in feedforward processing. Although feedforward projections tend to originate from supragranular layers, feedback connections preferentially target extragranular layers. 15,16 Using the laminar designation from the current source density (CSD) profile in response to visual stimulation to identify cortical layers ( Figure 3A; STAR Methods), 17 we analyzed the theta-MUA as well as theta and gamma LFP as a function of V1's cortical depth ( Figure 3B). This revealed that peak MUA theta power is specific to the superficial layers. For theta-range LFP, an additional peak emerged in V1's infragranular layers. Gamma-range power of the LFP peaked (supra-)granularly, in line with previous findings. 18 In summary, the predominant supragranular localization of theta and gamma oscillations supports the proposed engagement of both rhythms in feedforward processing, as V1 projections to V4 originate in supragranular layers. 15,16 V1 Lesion Diminishes Spiking and Eliminates Theta Activity in V4 Our next aim was to experimentally test the proposed feedforward hypothesis of theta and gamma oscillations by longitudinally comparing V4 activity before and after a focal V1 lesion that causes persistent cortical blindness 19,20 and removes the major feedforward sensory input source to V4. Stimulus-specific changes were assessed by a sensitivity measure d', where positive values indicate stronger responses to the illusionary stimulus compared to the control (STAR Methods). We first examined  non-rhythmic components of the recorded signals. Although lesioning V1 removed the largest part of V4 activity ( Figure 4A), residual visually evoked MUA responses were still significantly present in almost all electrodes (p < 0.05; Wilcoxon signed rank test; Figure 4E, left panel), likely due to residual input from the V1 lesion boundary or V1-bypassing geniculate input to V4. 19,[21][22][23] Analysis of MUA onset latencies after the lesion showed a slight delay relative to pre-lesion conditions of 13.0 ± 2 ms in monkey B (p = 1.9 3 10 À10 ; n = 59) and 7.7 ± 0.5 ms in monkey F (p = 3.8 3 10 À4 ; n = 54). However, the stimulus selectivity related to the visual illusion was greatly diminished in monkey B or even lost in monkey F after the lesion (d' of Kanizsa-modulated channels; p(pre > post): p = 1 3 10 À6 , n = 37 in monkey B; p = 2 3 10 À5 , n = 24 in monkey F; Wilcoxon signed rank test; Figure 4F, left panel). The stimulus selectivity related to the visual illusion was thus virtually lost after the V1 lesion. We then used spectral analysis to assess the rhythmic nature of the recorded signals. We first describe the results for theta and in the following section for gamma, as the results differed dramatically for these two rhythms.
Following the V1 lesion, V4 theta rhythmicity disappeared both for MUA and LFP (see Figures 4B, 4C, and S2A; see Table S5 for further statistics). This loss of theta activity was seen throughout our sampled population. The average theta power for the MUA channels that were visually responsive in the theta range dropped from an average of 167% ± 21% in monkey B and 79% ± 16% in monkey F to the non-significant noise level with residual values of 24% ± 12% and 33% ± 9% in monkeys B and F, respectively ( Figure 4E, right panel; Table S5). Not surprisingly, the corresponding Kanizsa MUA theta d' values dropped from 0.37 ± 0.03 and 0.27 ± 0.02 to non-significant values close to zero in monkey B and F, respectively ( Figure  Report 27 in monkey F; Wilcoxon signed rank test). Thus, although nonrhythmic residual visual activation was present in both monkeys, the neural theta rhythm and its illusion-related modulation completely vanished when V1 input was removed.

V4 Gamma Oscillations Survive V1 Lesion
The observed dependence of V4 theta rhythms on V1 input did not hold for LFP gamma, for which lesioning V1 appeared to have little effect. In fact, visually elicited activity in the low gamma band remained clearly present, despite the severed V1 input ( Figures  4C, 4D, and S2B). V4 LFP gamma power responses, averaged across trial time ( Figure 4G, right panel), decreased after the V1 lesion (À16.0% ± 5.13%, n = 60, in monkey B and À31.9% ± 2.22%, n = 57 in monkey F) but remained overall positive (SNR > 0: p = 3.9 3 10 À11 , n = 60 in monkey B; p = 0.001, n = 57 in monkey F; Wilcoxon signed rank test). Even more surprisingly, gamma power was still significantly modulated by the Kanizsa illusion compared to its control condition (d' > 0: p = 7.4 3 10 À7 , n = 36   in monkey B and p = 1.9 3 10 À7 , n = 47 in monkey F; Wilcoxon signed rank test; Figure 4H, right panel). We performed again a correlation between post-lesion LFP gamma power and residual MUA across trials per channel and found only very weak correlation in both monkeys (r = 0.04, n = 60 in monkey B and r = 0.02, n = 57 in monkey F). Only 5 channels in monkey B and 0 channels in monkey F showed significant correlations across trials (Student's t test, corrected for multiple comparisons). Thus, in our data, there seemed to be very little relation between V4 MUA and LFP gamma oscillations. This pattern was present under intact conditions and did not change when V1 was lesioned.
Interestingly, however, the onset of gamma activity in V4 postlesion increased by >100 ms compared to pre-lesion conditions ( Figures 4D and S2B). On average, following the V1 lesion, gamma power responses significantly exceeded pre-stimulus baseline levels 161.12 ± 31.04 ms and 121.01 ± 43.69 ms after they did so with intact V1 in monkey B (p = 5.2 3 10 À6 ; n = 50; Wilcoxon signed rank test) and F (p = 0.0058; n = 31; Wilcoxon signed rank test), respectively ( Figure 4I; see Figure S2C for absolute latencies). This marked delay by >100 ms in gamma oscillation onset is in contrast with the finding for MUA, for which the onset latency increased by <15 ms post-lesion. Further analysis of the gamma onset latency as function of recording sessions after the V1 lesion showed that significant gamma power was present in each recording session and that there was no consistent effect regarding changes over time across monkeys (which might have been due to post-lesional plasticity; Figure S2E).
In addition, we found a decrease in peak gamma frequency without V1 in one monkey ( Figure S2B). Specifically, gamma peak frequencies changed on average by À5.73 ± 0.86 Hz in monkey B (p = 1.29 3 10 À6 ; n = 49; Wilcoxon signed rank test) and 0.51 ± 0.64 Hz in monkey F (p = 0.51; n = 60; Wilcoxon signed rank test), respectively, compared to pre-lesion conditions ( Figure 4J; see Figure S2D for absolute frequencies).
In summary, although residual MUA responses could still be visually elicited in V4 following V1 lesions, theta activity and Kanizsa-associated modulations of spiking activity were lost. In contrast, gamma activity was well preserved and even contained stimulus-related information that emerged with a significant time delay compared to pre-lesion conditions.

Theta Rhythms across the Cortical Hierarchy
In primates, spiking and LFP theta oscillations have been observed in various cortical as well as subcortical structures during a variety of cognitive tasks. [24][25][26][27][28][29][30][31][32][33][34][35] Our results show that theta oscillations are present in the spiking of neurons both in V4 and V1. However, whether theta emerges across these cortical areas (like V1 and V4) in parallel via independent local processes 2 or whether it is coordinated to enhance long-range interareal communication 32,36,37 remains to be solved. In our data, theta organized gamma oscillations 38 and might be a candidate mechanism for long-distance integration or transfer of information to high-level association areas. 32,39,40 The predominant supragranular occurrence of MUA theta in V1 is in line with it being a feedforward signal, 15,16,41 given that supragranular layers project to downstream visual areas. In addition, we found an infragranular peak of LFP theta oscillations.
This finding might reflect the LFP's sensitivity to synaptic signals, 42,43 in principle arising from either local or remote sources, 15,16,41 and therefore does not contradict the feedforward hypothesis of theta. The infragranular LFP theta peak could point toward an integrative role of theta oscillations: although the supragranular MUA theta arguably reflects oscillatory spiking of feedforward projection units, infragranular LFP theta oscillations could reflect local postsynaptic oscillations that might serve to integrate incoming feedback signals to the local computations (e.g., via PAC to gamma oscillations) or to the thetarhythmic feedforward output. In that sense, theta might also help to integrate feedback signals.
In a direct test of the hypothesis that theta spiking represents a feedforward signal 6 by lesioning V1 and recording from V4, our data provide first causal evidence that this may indeed be the case. A theta rhythm that emerges first in early visual cortex and is then transmitted into higher cognitive and motor areas appears as an attractive mechanism for long-range coordination of local activity. This could help explain the widespread observations of theta oscillations across a wide set of visuo-motor tasks in various areas, including attentive sampling, saccadic exploration, and motor tracking. 3,26,27,[44][45][46] A loss of theta oscillations and stimulus-related information in spiking, as seen here under conditions of cortical blindness from V1 injury, might be indicative of a disrupted cortical information transfer and neurological dysfunction.

Unlikely Role of Gamma as Feedforward Signal
Perhaps the most surprising finding of our study is that, compared to theta oscillations, gamma oscillations in V4 remained less affected by the lesion in V1, which is at odds with the proposal of gamma as a feedforward signal. 6,10,47 What might then be the source of this V1-independent gamma in V4? One possibility is that it may reflect weak preserved gamma-rhythmic V1 input from the border of the lesion zone. Although we cannot entirely rule out this scenario, it seems unlikely, as it would involve an intact V1-V4 transmission circuit that cannot easily explain the >100 ms delayed emergence of the gamma response with little change in amplitude. A similar interpretation of post-lesional gamma activity as a mere reflection of (non-rhythmic) residual MUA appears also unlikely: for one, under our testing conditions, there was no significant correlation between MUA and LFP gamma power before as well as after the V1 lesion. Whereas the lesion delayed MUA onset in V4 by <15 ms relative to prelesion conditions, LFP gamma power was delayed by >100 ms after the lesion. Though it is tempting to compare residual V1 input to low-contrast stimulation conditions, the latency delay accounted for stimulation at low contrast [48][49][50] would only explain the delay in MUA, but not the more pronounced effect in the LFP. A third possibility, that this gamma oscillation is a result of microsaccades, 51 also appears unlikely, given the narrowband frequency range of the oscillation and its sustained time course. A fourth possibility builds up on the thalamic, V1-bypassing inputs to V4, which can account for at least part of the residual activity in V4. Yet that gamma in V4 is inherited from direct input from the lateral geniculate nucleus (LGN) or pulvinar appears unlikely, again due to latency considerations and also as gamma has so far not been reported for these brain structures. Irrespective of the actual source of the input source to V4 in our experiments, Report these inputs are able to induce sufficiently strong interactions between local excitatory and inhibitory networks within V4 to generate gamma. Our results thus hint at a very local origin of gamma oscillations within the microcircuit of an area. According to this view, a visual stimulus will drive a sweep of excitation across cortical areas that is associated with subsequent increases in gamma response in each area, simply due to the repeated gamma-generating microarchitecture in each area. However, secondary synchronization of local excitatory activity might be a very useful marker of ongoing interareal communication.
In this study, we tested the hypothesis that theta and gamma rhythms act as sensory feedforward signals from V1 to V4 when monkeys viewed a visual contour illusion. With intact cortex, both oscillations were present in both areas, interacted in time, and showed stronger illusory contour-related activity in V4. Although their predominant occurrence in V1 supragranular layers is consistent with a feedforward circuit, a direct causal test revealed a clear difference for the two rhythms: although lesioning V1 eliminated the theta rhythm of V4, gamma rhythms were less affected. This result supports the proposed function for feedforward processing from V1 to V4 of theta, but not gamma rhythms and poses, together with the increasing literature body of the stimulus dependency of gamma, 52-55 limits on the proposed role of gamma as a feedforward mechanism.

STAR+METHODS
Detailed methods are provided in the online version of this paper and include the following:

DECLARATION OF INTERESTS
The authors declare no competing interests.

Materials Availability
This study did not generate new unique items, such as animal lines or reagents.

Data and Code Availability
The data underlying the figures were deposited on a public repository (https://doi.gin.g-node.org/10.12751/g-node.nb4nnp).

EXPERIMENTAL MODEL AND SUBJECT DETAILS
Two healthy adult female and two male rhesus monkeys (Macaca mulatta, monkey B, F, K and Br) were used in the study (Table S6). All procedures were in accordance with the Institute for Laboratory Animal Research Guide for the Care and Use of Laboratory Animals and approved by the Animal Care and Use Committees of the National Institute of Mental Health and Vanderbilt University or by the Regierungspr€ asidium Darmstadt in accordance with EU directive 2010/63. All surgeries were carried out aseptically under general anesthesia using standard techniques including peri-surgical analgesia and monitoring. Each monkey received a head-immobilization implant and an implant to record neural data (see section below on Neurophysiology). Throughout the study animal welfare was monitored by veterinarians, technicians and scientists.

METHOD DETAILS
Parts of the dataset underlying this study (V4 data from monkey B and F) have been analyzed and published with regard to the dependence of a Kanizsa-specific increase in single (and multi-) unit spiking on the receptive field focus 57 . The respective study did not study oscillatory signals. REAGENT  Report Behavioral task and visual stimulation Each monkey was trained to maintain fixation within a 1-1.5 diameter window centered on a small red spot (0.2 diameter, white for monkey K) during the presentation of various visual stimuli. To map receptive fields, white random dot kinematograms (1.5 diameter, see 57 for details) were shown on a black background at 64 different positions in the lower right visual hemifield. The Kanizsa illusion and the control stimulus consisted of four inducers ($1 diameter) located at (1 , À1 ), (3 , À1 ), (1 , À3 ) and (3 , À3 ), presented for 1 s (1.5 s in V1 recordings) after 1 s of fixation baseline. Each inducer consisted of a white disk with one quarter of the circle colored in red, giving them a ''pacman-like'' appearance. For the illusory stimulus, the red quarter faced the inner illusory surface (IF1 in 57 ) creating an illusory rectangle. The control stimulus consisted of inducers that were rotated by 180 such that the red cutouts were facing outward (CF1 in 57 ). For monkey K, the stimulus position was adapted to the V1 receptive fields (center of stimulus: x = 1 , y = À4.2 ). V1 receptive field centers ranged from 0.06 to 4.7 and from À6.7 to À0.9 along the horizontal and vertical meridian respectively. For the laminar V1 recordings, the Kanizsa stimulus and its control were positioned such that the receptive field focus (RFF, see 57 ) of the recording site was centered on the illusory parts of the stimulus.
Neurophysiology and chronic cortical lesioning Neurophysiological data was recorded via chronically implanted multi-microelectrode (''Utah'') arrays that were located in area V4 (monkeys B and F) or V1 (monkey K) (see 19 for details regarding surgery and implantation). Each electrode was spaced 400 mm from its neighboring electrodes, and 1.5 mm (0.6 and 1.5 mm for monkey K) long. Neural data from monkeys B and F was recorded at a sampling rate of 24414.1 Hz using a Tucker Davis Technology system and at 30 kHz for monkeys K and Br on a Blackrock Microsystems Cerebus System. Following 13 sessions in monkey B and 6 sessions in monkey F, permanent focal aspiration lesions of isohemispheric primary visual cortex (V1) were performed (see 23 for details). After the lesion, post-lesion data were recorded in 15 and 6 sessions for monkey B and monkey F, respectively. To confirm the visual deficit (scotoma) following the V1 lesion, monkeys performed a perimetry task covering the lower right quadrant (see 20 for details). Data from monkey K was collected in two sessions. Layer-resolved V1 data was recorded from monkey Br using a linear microelectrode array, consisting of 22-24 active microelectrodes, linearly spaced 0.1 mm apart, with impedances ranging 0.2-0.8 MU at 1kHz (UProbe, Plexon). Electrical reference for data from the UProbe was the probe shaft.

Data preprocessing
All neurophysiological data were processed and analyzed using custom-written code for MATLAB (MathWorks, Inc.) and the Field-Trip MATLAB toolbox 56 . The continuous recordings were separated into individual stimulus presentations (trials) using digital event markers aligned on stimulus onset. We focused our analyses on the sustained response period 300-1000 ms after stimulus onset, excluding the transient onset response. Trials containing motion artifacts were excluded by visual inspection without knowledge of trial type. Four dysfunctional recording channels in monkey B, four in monkey F and two in monkey K were excluded from the analysis. Details on receptive field mapping can be found in Cox et al. 57 An estimate for multi-unit activity (MUA) was obtained from the high frequency envelope: MUA was extracted by high-pass filtering (300-12000 Hz), followed by rectification, and low-pass filtering (120 Hz) of the broadband data (see 19 for further details). The local field potential (LFP) was obtained by low-pass filtering the signal at 256 Hz. Data from microelectrode arrays was pooled across sessions. In order to assess the stimulus-specific effects of the Kanizsa illusion, data were normalized using the average baseline value (À0.7 -0 s of prestimulus fixation period). MUA and powerspectra are expressed as percent change from this baseline.

Spectral analysis
To obtain the spectral profile of MUA and LFP responses, we used a Hanning-tapered Fourier transformation. Visual inspection of the spectra revealed peaks in the 3-6 Hz and 25-70 Hz bands (monkey B: 25-40, monkey F: 30-60 Hz, monkey K: 40-70 Hz), which are referred to as theta and (low) gamma, respectively. To obtain time-frequency representations (Figure 1), we performed a wavelet transform based on Morlets. To optimally assess low and high frequency components, we separately analyzed frequencies from 1-20 Hz (''low frequencies,'' width 3 cycles, 1.3 Hz bandwidth at 4 Hz, 0.01 Hz steps) and > 20 Hz (''high frequencies,'' width 7 cycles, 15.7 Hz bandwidth at 40 Hz). As described above, analyses including spectral assessment focused on the sustained response period 300-1000 ms after stimulus onset.

Cross-frequency coupling
In order to assess phase-to-amplitude coupling between theta and gamma oscillations in the LFP, we computed a modulation index MI as follows 14 : The original LFP Signal SðtÞ was bandpass-filtered into the theta and gamma ranges, respectively, using a two-pass filter (fourth order Butterworth) to avoid frequency-dependent phase shifts: S q ðtÞ and S g ðtÞ. As a next step, the Hilbert transform h of both signals was computed, producing complex values whose real components represent the amplitude of the signal and the imaginary part represent phase: hðS q ðtÞÞ and hðS g ðtÞÞ. From the Hilbert-transformed signals we extracted the phases of the theta signal We tested for significant modulation using a Monte Carlo technique, where we randomly permuted the amplitude-signal trial-wise against the phase-signal 500 times. To test for significant differences between Kanizsa illusion and control conditions, we performed a matched non-parametric test (Wilcoxon signed rank test) across channels. MI comes with several caveats (see 14 for a discussion).
To avoid an overestimation of phase-amplitude coupling we verified that (1) there were clear peaks in the TFR and powerspectrum at the frequencies of interest (theta and gamma) and (2) that frequency band used for amplitude (gamma, 25-50 Hz) was at least double the frequency we used for the phase signal (theta, 3-6 Hz). To compute the Comodulogram (Figure 2A, middle panel) phase frequencies ranged from 5 to 10 Hz (±2 Hz) and amplitude frequencies from 15 to 80 Hz (±10 Hz). To compute the theta-phase triggered spectrogram (Figure 2A, right panel), the amplitude of bandpass-filtered high frequency (ranging from 10 to 80 Hz) was triggered on theta oscillation troughs and averaged across trials.

Analysis of laminar V1 data
To obtain a more localized measure of neural activity based on the LFP, and to locate electrodes on the U-Probe across cortical layers, we computed the laminar current source density (CSD) by approximating the second spatial derivative of the LFP 58 . The CSD constitutes a measure of localized current flow, which can be used to delineate upper from middle and lower cortical layers 17 . The transition from granular to infragranular layers was visually identified by selecting the bottom of the initial response sink of the CSD profile of the respective recording session 17 . We computed the laminar theta power based on MUA 43 and the laminar theta and gamma power based on LFP. The average laminar profiles were smoothed by fitting a spline for display purpose. Significance was assessed using a one-sided Wilcoxon signed rank test against baseline, n = 9).