The multiple layers of the neocortex show an elaborate connectivity pattern, which predicts a specific directionality of interlaminar communication (Callaway, 1998; Douglas and Martin, 2004; Douglas et al., 1989). Feedforward inputs predominantly terminate in layer 4 of sensory cortical areas, before being passed to layers 2/3 and then onwards to layers 5/6, where recurrent inputs to layer 2/3 arise (Callaway, 1998; Callaway, 2004). Conversely, feedback connections from downstream areas predominantly terminate in layer 1 and 5 (Rockland and Pandya, 1979), from where the information is passed to layers 2/3. Local field potential (LFP) recordings, using different stimulus and task conditions, have provided physiological support for these anatomical pathways. Current source density analysis of the LFP, as well as latency of spiking activity has demonstrated that the earliest activity elicited by stimuli centered on the classical receptive field occurs in layer 4c (Bijanzadeh et al., 2018). Stimuli restricted to the far receptive field surround, which are hypothesized to affect processing in V1 primarily through feedback projections (Bair et al., 2003), initially activate V1 in upper superficial layers and in layer 5 (Bijanzadeh et al., 2018). In addition to this anatomical segregation of feedforward and feedback signal input to V1, it has been proposed that feedforward and feedback signals have separable spectral signatures of the LFP. Feedback processing has been aligned with low frequency (alpha [α] band) oscillatory activity, dominating activity in infragranular layers (Buffalo et al., 2011; Lopes Da Silva and Storm Van Leeuwen, 1977; van Kerkoerle et al., 2014), and with beta (β) band oscillations (Bastos et al., 2015a; Xing et al., 2012). Conversely, feedforward processing has been associated with low frequency theta [θ], and higher frequency oscillatory activity (gamma [γ] band), originating in layer 4, dominating in (or even being largely restricted to) supragranular layers (Bastos et al., 2015a; Bollimunta et al., 2011; Buffalo et al., 2004; Buffalo et al., 2011; Maier et al., 2010; Spaak et al., 2012; van Kerkoerle et al., 2014; Xing et al., 2012). However, the generality of this scenario has recently been questioned (Haegens et al., 2015), whereby the strongest low-frequency (α) oscillations generators were found in supragranular layers of sensory cortex. Moreover, while most studies failed to find prominent γ oscillations outside supragranular layers (Buffalo et al., 2011; Maier et al., 2010; Xing et al., 2012), exceptions do exist (Maier et al., 2011).

To address these controversies and investigate the origin and spectral signature of feedforward and feedback signals in detail, we recorded across cortical layers of V1 of awake macaque monkeys, while manipulating stimulus size systematically. Increasing the stimulus size invokes center surround interactions, which result in specific input patterns across a cortical column (Bijanzadeh et al., 2018), and which strongly alter the spectral signatures LFPs. Increasing the size of a patch of an orientated grating centered on a receptive field (RF) increases γ power in conjunction with a general decrease of neurons firing rate (Bauer et al., 1995; Gieselmann and Thiele, 2008; Jia et al., 2011; Ray and Maunsell, 2010). If this increased γ power was a signature of a feedforward signal, then we would expect it to arise in either layer 4, or layer 2/3 of V1. Conversely, the feedback signals should show spectral signatures in lower frequency bands, such as α (van Kerkoerle et al., 2014), which should arise and dominate in layers 1 and 5 (Bastos et al., 2015a; van Kerkoerle et al., 2014).

While our results support some of the features outlined above, they violate them in multiple aspects, and show that feedforward and feedback signals attain stimulus dependent oscillatory frequencies across cortical layers on V1.

We recorded neuronal activity from area V1 from 16 contact laminar electrodes in two passively viewing male macaque monkeys. Electrodes were inserted perpendicular to the cortical surface, aiming to record from within a single V1 cortical column. To ensure consistency of our analysis we rejected experiments (total n = 25/9, monkey 1/2) from further analysis if the current-source-density (CSD, see Materials and methods) profiles did not meet any of the following three conditions: (1) An identifiable current source as early as 50ms after the stimulus onset (n = 1/1, monkey 1/2). (2) The ‘early’ current source is identified on a channel between the seventh and twelfth contact (from the top of the electrode array, to ensure a minimum coverage of cortical layers 2–6; n = 3/1, monkey 1/2). (3) A pattern of the CSD-profile that is consistent with the majority of experiments (n = 1/1, monkey 4/0). After applying the rejection criteria, we were left with 24 experiments (monkey 1: n = 17, monkey 2: n = 7) from three hemispheres which were included into this study.

Figure 1 shows the alignment of neuronal data from an example experiment. The data were obtained during receptive field (RF) mapping (Figure 1B) and during presentation of gratings (Figure 1C and D). In this recording the eighth channel from the top was defined as the alignment channel indicating putative layer 4c at zero depth (Figure 1A). Correspondingly, the RF maps of the envelope of multi-unit-activity (MUAe, see Materials and methods) showed the early emergence of a visual RF in the same channel between 30 and 50ms (Figure 1B left), demonstrating that the early current sink coincides with the earliest activation from thalamo-cortical afferents. This electrode placement resulted in contacts in supragranular layers (SG), alignment layer 4c, and infragranular layers (IG) (Figure 1A). The gray matter region (from third channel from top to sixths from the bottom in Figure 1A) is flanked by two large current sources at the top and at the bottom (Figure 1D). The perpendicularity of probe orientation relative to the cortical surface is indicated by the good spatial alignment of RF-centers (Figure 1B). The LFP responses in the gray matter region show strong evoked potentials, and sustained oscillations (Figure 1C). The lower five channels in Figure 1 were located in the white matter, and possibly in area V2, as evident by a sudden shift in visual RF’s (Figure 1B, lowest contacts). While the strength of the LFP-signal seems to be unchanged (Figure 1C) for these contacts, current sources and sinks were weaker and less structured than in the gray matter. A detailed analysis of RF sizes for different layers is presented in Figure 1—figure supplement 1.

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We next focus our analysis on basic phenomena of spiking activity and current source density changes with different sized stimuli, as these provide insight into the underlying pathways that mediate changes (feedforward, lateral intra-areal, intracolumnar feedback and feedback from downstream areas), as well as providing a benchmark against previously published results.

Response latency as a function of cortical depth and stimulus size

We calculated spiking latencies by using the MUAe signal using a curve-fitting procedure (Figure 2A, see Materials and methods and Self et al., 2013). The shortest latencies occurred in granular layers, followed by infragranular layers, with supragranular layers generally lagging behind (Figure 2B). This overall pattern was independent of stimulus size used. However, stimulus size significantly altered response latencies in granular (p < 0.01, one-factor-ANOVA, Figure 2C) and in infragranular layers (p < 0.001, one-factor-ANOVA, Figure 2C). In granular layers larger stimuli resulted in up to ~3ms longer latencies, while in infragranular layers they caused up to ~6ms shorter latencies. Thus, when large stimuli were presented the lag between granular and infragranular layers was reduced to ~2ms (from ~7ms for small stimuli, Figure 2C). No significant changes were found for supragranular layers (p = 0.98, one-factor-ANOVA, Figure 2C). Latency changes also appeared to occur in the top-most layer, although this was not significant, possibly because of relatively small sample sizes (not every recording included that layer/contact due to electrode placement).

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The CSD profile equally changed with stimulus size. Figure 2D and E show the average CSD profile across all experiments for a small (0.5°) and a large (15.0°) stimulus. The CSD-profile of each experiment was normalized to the peak of the early onset current sink in the alignment layer (0 µm). Figure 2E shows a surface plot of time depth CSD differences plot (difference between CSD profiles in C and D), where only significant differences are displayed (p < 0.05, cluster-mass-test). In granular and supragranular layers we observed moderate decreases in inward current amplitudes with larger stimuli, which occurred as early as 50ms in granular layers. Most of the changes in and above layer 0 can be described as a general attenuation of current amplitudes. In other words, the overall laminar pattern of current sinks and sources in granular and supragranular layers was similar in the CSD profile for small and large stimuli, but it was overall slightly attenuated for large stimuli, probably due to surround suppression effects at the level of the lateral-geniculate-nucleus (LGN). The currents (sinks) in infragranular layers were also mostly reduced, but in addition changes in the pattern of sinks and sources occurred. Specifically, a current sink emerged ~75ms after stimulus onset at –600 µm (Figure 2D and E), where a source was present when small stimuli were presented. This indicates that stimulating the surround of a V1 RF results in additional input to infragranular layers. Given that RF sizes differed somewhat between layers (Figure 1—figure supplement 1) it could be argued that the analysis should be repeated for different reference sizes (other than e.g. 0.5°). Doing so yielded qualitatively identical results (Figure 2—figure supplement 1; Figure 2—figure supplement 2).

We next focused on surround suppression/enhancement effects on MUAe, which are most profound during later parts of the response. Here we analyzed the activity from 200 to 500ms after stimulus onset. Surround suppression affected all cortical layers. However, it was more pronounced in supragranular and granular than in infragranular layers (Figure 3A and B). Moreover, the stimulus size that resulted in maximal activity equally varied between layers, with slightly larger stimuli (0.75°, 1.5°) eliciting maximal activity in infragranular layers, while the smallest stimulus (0.5°) elicited maximal activity in supragranular layers (Figure 3C).

Figure 3

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Together the results from Figures 2 and 3 are compatible with recent reports suggesting that stimuli extending into the far surround of neurons invoke center surround interactions through feedback connections from higher cortical areas that terminate predominantly in layers 1 and 5 (Bijanzadeh et al., 2018; Nassi et al., 2013; Nurminen et al., 2018; van Kerkoerle et al., 2014).

LFP spectral power and phase relationship analysis

We now turn to the question how feedback signals impose their effects on local cortical processing in area V1. It has been argued that feedback signals can be characterized in terms of low frequency (α, β) oscillatory activity, while feedforward signals can be characterized in terms of θ- and γ-frequency oscillations (Bastos et al., 2015a; Bosman et al., 2012; Buschman and Miller, 2009; Fries, 2015). A simple prediction would thus be that all stimulus sizes elicit γ frequency oscillations in layers 2/3, as all of the stimuli will invoke feedforward drive. Within this context, two scenarios are possible. Firstly, γ frequency oscillations in L2/3 are slightly reduced when larger stimuli are presented, due to the effects of surround suppression that are inherited from the LGN. Alternatively, their spectral power increases as stimulus size increases, because large stimuli invoke stronger inhibitory surround interactions, which through pyramidal-inhibitory interactions drive γ frequency oscillations (Tiesinga and Sejnowski, 2009). Additionally, infragranular layers should show limited γ frequency power (Dougherty et al., 2017; Maier et al., 2011; Spaak et al., 2012; Xing et al., 2012), but they should show increased low frequency (α/β) oscillations with increasing stimulus size as the level of feedback input increases (Bastos et al., 2015a; Bosman et al., 2012; Buffalo et al., 2011; van Kerkoerle et al., 2014).

To answer these questions, we transformed the raw LFP signal into the bipolar referenced LFP (LFPbp, see Materials and methods). This was necessary, as the raw LFP is not a sufficiently local signal to allow for meaningful layer specific analysis (Trongnetrpunya et al., 2016). The single trial LFPbp of the sustained part of the response (200–500ms after stimulus onset) was then subjected to standard spectral analysis (see Materials and methods). We focused on the sustained part of the neuronal response, as endogenous γ oscillations are most pronounced during this period (Gieselmann and Thiele, 2008). The results are shown in Figure 4. It shows a CSD profile (Figure 4A) and the layer dependent induced spectral power (iSP, z-score relative to spontaneous activity 300ms before stimulus onset) as a function of stimulus size for an example experiment (Figure 4B, for cross-cuts at selected depths see Figure 4—figure supplement 3). When small stimuli were presented, the supragranular layers showed relatively strong iSP across multiple γ frequencies (20–100 Hz). In addition, there was weaker, but nevertheless pronounced broad band iSP (20–100 Hz) in infragranular layers. As stimulus size increased the iSP across all layers became more focused with clear peaks emerging in the low γ frequency range (~40 Hz). This pattern became even more pronounced when analyzing iSP across different laminae averaged across all experiments (Figure 4C–E, Figure 4—figure supplement 3). Small stimuli (0.5°) elicit broad band power changes ranging from 30 to 100 Hz, with increasing iSP toward higher frequencies in supragranular layers (Figure 4D). Infragranular layers showed relatively small, but~ homogenous broad band β/γ iSP in the range from 20 to 80 Hz (Figure 4D). When large stimuli were shown, iSP in the supragranular layers concentrated in more narrow band regions of ~40 Hz. A smaller peak in that frequency range also occurred in granular layers. A surprisingly large γ frequency iSP increase occurred in infragranular layers 5 and 6, at ~40–50 Hz (compare to Xing et al., 2012). Also, the peak frequency of the evoked gamma oscillation at around 40 Hz showed the same inverse relationship to stimulus size (Figure 4—figure supplement 3 and Gieselmann and Thiele, 2008), and is in line with other previous publications using grating stimuli (Bauer et al., 1995; Gray et al., 1989; Jia et al., 2011). Calculating power changes relative to different reference stimuli resulted in similar outcomes.

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We quantified the effects of stimulus size on spectral power by calculating the mean iSP of the LFPbp-signal in discrete frequency bands. This was done for the θ (4–8 Hz, Figure 5A–C), α (8–13 Hz, Figure 5D–F), β (14–25 Hz, Figure 5G–I) and low γ (35–55 Hz, Figure 5J–L) range. Figure 5A, D, G and J show the average depth profiles of band power in response to a small (0.5°, blue) and a large (15°, red) stimulus. For all frequency bands, stimulus size modulated the band, but these modulations differed between layers and frequency bands. For the θ-band, α-band, and β-band, stimulus-size-induced changes are most pronounced for granular and lower supragranular layers (Figure 5C, F 1). For infragranular layers, larger stimuli caused a reduction in θ-band (Figure 5B), no change in α-band or β-band, and strong increases in γ-band. Overall, larger stimuli caused significantly more γ-band across all contacts (Figure 5J), but relative changes are strongest in supragranular and infragranular layers (Figure 5L).

Figure 5

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Gamma-connectivity across cortical laminae

Previous studies have argued that phase-locked oscillatory activity can provide insight into the directionality of information flow for a given frequency band. Specifically, it was shown that α-oscillations locked to the α-phase in granular layers appeared to originate in infragranular and upper supragranular layers, progressing toward middle supragranular layers (van Kerkoerle et al., 2014). Conversely, γ-oscillations locked to the γ -phase in granular layers originated in granular or lower supragranular layer progressing toward upper supragranular and infragranular layers respectively. To analyze this for different stimulus sizes, we computed the phase-triggered average (PTA) to estimate the phase-locked oscillations between electrode contacts for different frequency bands. Phase-triggered-averaging describes the alignment of LFPbp signals of a given electrode contact to the phase of an oscillation at a defined frequency at the reference-channel. The aligned LFPbp is then averaged across the number of alignments. Temporal structure in the resulting histogram can be interpreted as a phase-coupled oscillation between the two signals. Figure 6 shows the depth profiles of the four main frequency band PTA-histograms for a reference channel at –150 µm, averaged across all experiments. As expected, the depth used as the reference phase of the oscillatory cycle always showed strong phase coupling, with close to zero phase delays (Figure 6, gray triangles indicate the reference channel). Many other contacts showed coupling to the reference channel for all frequency bands. However, the exact phase coupling varied between frequency bands and between stimulus sizes. Low frequency (θ, α) showed a phase progression from layer 5 to layer 6, and then to granular and supragranular layers for small stimuli. This pattern changed radically for larger stimuli, where phase progression was seen bidirectionally from granular to supragranular and to infragranular layer 5, but also from infragranular layer 6 to layer 5. For γ frequencies, layer 4 and 6 were largely in phase for small stimuli, but almost 90° out of phase with layer 5. Some continuous phase progression could be seen from granular toward upper supragranular layers. However, for large stimuli phase progression occurred from layer 5 to layer 6, onwards to granular layers, and then to supragranular layers. These patterns were not artefacts of selecting a specific reference channel (Figure 6—figure supplements 1–4). These patterns were less pronounced when using the iCSD signal instead of the bpLFP signal but the globally described pattern largely prevailed (Figure 6—figure supplements 1–4).

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Isolated phase relationships per se do not yield insight into causal interactions, as phase lags can equally be interpreted as phase advances due to the periodicity of the signal. Moreover, causal interaction assignment based on phase relationships can be inconsistent with Granger causality analysis (Brovelli et al., 2004). To gain insight into causality, we performed spectral Granger-causality (GC) analysis (Figure 7—figure supplement 1). We then integrated the GC-Index in the four different frequency bands analyzed above (θ, α, β, and γ frequency) and generated connectivity plots using the net GC (difference between directions) for each connection (Figure 7). Hence, Figure 7 shows the direction and associated strength of granger-causal influence of the four different frequency LFP bands between different layers. We only plotted those connections that passed the reverse-granger-test (RGT) to protect against spurious results induced by linear mixed noise (Haufe et al., 2013; Vinck et al., 2015). In Figure 7—figure supplements 2 and 3 we show the same data including the connections which failed RGT and the results of the time reversed GC analysis, respectively. Figure 7 shows the analysis of non-conditional GC. Conditional GC produced qualitatively equivalent results, albeit with overall smaller granger causality indices. A striking feature of the directionality of all GCs (irrespective of the frequency bands analyzed) was its dominance in the 'upward' direction, namely toward supragranular layers. This indicates that communication was directed toward the cortico-cortical feedforward channels in V1 (for related results see Ferro et al., 2021). Sizeable GCs in the downward direction were usually restricted to granular to infragranular interactions, and to layer 5 to layer 6 interactions. Downward GCs were generally more pronounced with larger stimuli.

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In the upward direction strong θ-GCs originated in layer 6 (lower contacts) affecting predominantly supragranular layers, irrespective of stimulus size. With increasing stimulus size GCs increased in an upward direction between granular and supragranular, and between supragranular-supragranular contacts. Overall, a very similar pattern was present for GCs in the α- frequency band. In the β- frequency band, GCs were fairly weak in the downward direction, when small stimuli were presented, but increased between layer 5 and layer 6 for large stimuli. Upward GCs were pronounced between granular and supragranular, and between supra-supragranular contacts for all stimulus sizes. With increase in stimulus size a pronounced infragranular-supragranular GC occurred. An overall similar pattern was present for γ-frequency GCs, with an even stronger increase in upwards directed supra-supragranular GCs with stimulus size. The results from Figure 7 show that surround modulation results in strongly increased low, mid and high frequency band communication between granular and supragranular compartments, and it results in strongly increased γ-band communication originating from layer 5, which drives γ frequency changes in layer 6, and which in turn changes γ frequency oscillations in upper layer four and in layers 2/3. Performing a similar analysis using MUAe signals instead of LFP signals results in overall similar pattern, with an additional component of γ frequency GC originating in layer 4 (Figure 7—figure supplement 4).

Using stimuli of different sizes in combination with laminar recordings in primary visual cortex of the macaque revealed that narrow band γ frequency oscillations occur across cortical layers and increase across cortical layers with increasing stimulus size. When stimuli encroached on the far surround of the receptive field, the LFP γ oscillations in layer 5, the feedback recipient layer, exerted pronounced granger causal influences on γ oscillations in layer 6, which in turn granger causal influenced γ oscillations in supragranular layers. This pattern of γ frequency granger causal interactions is compatible with the known anatomical termination sites of feedback connections into V1 (Rockland and Pandya, 1979). The flow of information (granger causal interactions in the γ frequency range) occurred in a manner compatible with the canonical microcircuit, but in a largely reverse direction. Here, information terminating in layer 5, is passed on to layers 6 and 4, who in turn send the information to layers 2/3. This demonstrates that γ oscillations are neither restricted to superficial layers, nor are they invariably signatures of feedforward communication. Moreover, low-frequency oscillation GC interactions did not strictly adhere to their proposed role in feedback communication, showing most profound increases in granular to supragranular and supra- to supragranular interactions with stimulus size. Feedback influence terminating in layer five was not dominated by low-frequency oscillations as predicted by recent publications (Bastos et al., 2015a; Bastos et al., 2015b; Bosman et al., 2012; Buffalo et al., 2011; Buschman and Miller; van Kerkoerle et al., 2014), even though GC interactions in low-frequency bands in the intracolumnar feedback direction (infragranular to granular/supragranular layers) were profound (Figure 7—figure supplement 1). This demonstrates that low-frequency oscillations are not restricted (or dominant) to infragranular layer and can attain feedforward roles in V1. As described in Materials and methods, the stimuli were optimized in relation to orientation tuning in one of the monkeys, but not the other. However, the key results reported in the paper did not differ between animals, which suggests that stimulus optimization is not critical for these results to be obtained.

The differences to previous publications could be due to the use of cognitively demanding task engagement (Bastos et al., 2015a; Bastos et al., 2015b; Bosman et al., 2012; Buffalo et al., 2011; Buschman and Miller, 2007; van Kerkoerle et al., 2014) vs. passive fixation as used in our task. Additionally, the use of large grating stimuli might be a contributing factor, as our phase lag and GC results using small stimuli are more similar to those reported in other publications (van Kerkoerle et al., 2014), even though their stimuli are not directly comparable to our small grating stimuli.

Two awake male rhesus monkeys were used for the electrophysiological recordings reported in this study. After initial training, monkeys were implanted with a head holder and recording chambers above V1 under general anesthesia and sterile conditions (for details of surgical procedures, postsurgical analgesics, and general postsurgical treatment, see Thiele et al., 2006). All procedures complied with the European Communities Council Directive RL 2010/63/EC, the U.S. National Institutes of Health Guidelines for the Care and Use of Animals for Experimental Procedures, and the UK Animals Scientific Procedures Act. Animals were motivated to engage in the task through fluid control at levels that do not affect animal physiology and have minimal impact on psychological wellbeing (Gray et al., 2016).

Original source data from all relevant data sets are available at DOI: https://doi.org/10.25405/data.ncl.17136182.v1 in the data depository of Newcastle University (https://data.ncl.ac.uk/ managed by figshare). Data are in binary format (Neuralynx) and are in the form of original recordings, along with relevant event files.

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Author details

1. Marc Alwin Gieselmann

   Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8034-5134

2. Alexander Thiele

   Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom

   Contribution

   Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing - original draft, Writing – review and editing

   For correspondence

   alex.thiele@newcastle.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4894-0213

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