Laminar microcircuitry of visual cortex producing attention-associated electric fields

  1. Jacob A Westerberg
  2. Michelle S Schall
  3. Alexander Maier
  4. Geoffrey F Woodman
  5. Jeffrey D Schall

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    Evaluation Summary:

    This study recorded brain activity in monkeys to identify the neural mechanisms underlying an attention-related scalp ERP component that is similar to the human N2pc component. Intriguing evidence was provided that the surface potential was at least partly a result of current flows in the feedback-receiving supragranular and infragranular layers of area V4, not the granular layer that receives feedforward inputs. However, it is not entirely clear if these very interesting intracortical effects are the source of the scalp ERP effects.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 and Reviewer #3 agreed to share their names with the authors.)

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Abstract

Cognitive operations are widely studied by measuring electric fields through EEG and ECoG. However, despite their widespread use, the neural circuitry giving rise to these signals remains unknown because the functional architecture of cortical columns producing attention-associated electric fields has not been explored. Here, we detail the laminar cortical circuitry underlying an attention-associated electric field measured over posterior regions of the brain in humans and monkeys. First, we identified visual cortical area V4 as one plausible contributor to this attention-associated electric field through inverse modeling of cranial EEG in macaque monkeys performing a visual attention task. Next, we performed laminar neurophysiological recordings on the prelunate gyrus and identified the electric-field-producing dipoles as synaptic activity in distinct cortical layers of area V4. Specifically, activation in the extragranular layers of cortex resulted in the generation of the attention-associated dipole. Feature selectivity of a given cortical column determined the overall contribution to this electric field. Columns selective for the attended feature contributed more to the electric field than columns selective for a different feature. Last, the laminar profile of synaptic activity generated by V4 was sufficient to produce an attention-associated signal measurable outside of the column. These findings suggest that the top-down recipient cortical layers produce an attention-associated electric field that can be measured extracortically with the relative contribution of each column depending upon the underlying functional architecture.

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  1. Version published to 10.7554/elife.72139 on eLife
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    Author Response

    Reviewer #1 (Public Review):

    The recordings done by the authors are impressive and rare, and I appreciate the efforts of the authors to bridge very different types of signals that are generally recorded in different paradigms. However, the analysis at many places is quite nuanced and high-level, making it difficult to directly compare these findings with previous results. I think several additional analyses are needed to properly place these findings with previous results.

    1. Effects of attention in V4 generally start earlier (~100 ms). It is unclear why no effect is observed during earlier time periods in these data. To make better comparison with previous studies (such as Nandy et al., 2017), the authors should show the average PSTHs in supragranular, granular and infragranular layers during both target-out versus target-in conditions. Interestingly, Nandy and colleagues found largest changes in firing rates in the granular layer. To better understand the ERP outside the cortex, the authors should also show the average LFPs in the three layers, for target-in and target-out conditions. It is surprising that MI analysis reveals no significant information about the target in granular layer - given that some attentional effects are seen in upstream areas such as V1 and V2.

    We have created a new figure showing multiunit activity and LFP across the layers in both attention conditions. It is included here for convenience. Accompanying text has been added to the Results and Discussion sections to address the reviewers’ comments.

    The timing of differentiation between attended and unattended in the population spiking activity is evident in both MUA and LFP. We note that the largest magnitude difference in population spiking between attention conditions was observed in the middle layers, consistent with Nandy et al., 2017. We wish to highlight two observations.

    First, with respect to the timing of attentional modulation, it should be noted that the attention task used in our study (pop-out visual search) is different from that used by Nandy et al., 2017, Neuron (cued change detection). The timing of “effects of attention” vary according to stimulus properties and task demands (the number of publications demonstrating this is too long to list). Hence, we do not expect equivalence between the times we measure and times Nandy et al. measure. Nonetheless we are happy to include the requested supplementary figure with that caveat in mind.

    Second, with respect to the surprising observation of a relationship between activity in the granular layer and the extracortical signal, we think it is important to remember that these information theoretic analyses are not simply correlational. That is, attentional modulation might be observed in both signals, but if the covariation of these signals trial-to-trial does not exist, then we would not expect a relationship in the mutual information analysis.

    1. Eye position analysis: my understanding is that the animals could make a saccade as soon as the arrays were displayed. Given that the main effect of attention is observed after ~150-200 ms, the potential effect of saccade preparation could be important. There could also be small eye movements before the saccade. Given that the RFs were quite fovial for one monkey and not too far from the fixation window, and the effect of attention appears to be quite late, detailed analysis of eye position and microsaccades is needed to rule out the possibility of differences in eye movements between target in and target-out conditions influencing the results. A timeline and some analysis of eye movement patterns would be appropriate. The authors should also clearly mention the mean and SD of the saccade onset.

    The reviewer makes a valuable observation. Saccades will influence the electrical signals, something we are quite familiar with (e.g., Godlove et al., 2011, J Neurophysiol). In an effort to combat this, we have two points worth noting. First, as was the case in the initial submission (which remains the same in the revision), we have clipped signals on a trial-by-trial basis prior to eye movements. By doing so, we cannot have an influence of the motor-related polarization of the task-demanded eye movement on the data.

    Second, we have prepared a microsaccade analysis – and accompanying newly added supplementary figure included here for convenience – to determine whether they might be driving the results. To do this, we identified trials where microsaccades occurred using a well-regarded microsaccade detection algorithm (Otero-Millan et al., 2014, J Vis). We then reperformed the information theoretic analysis across sessions after removing trials where microsaccades were detected. Briefly, we found that the information theoretic relationship persists in the absence of trials where microsaccades occurred. We believe this serves as evidence that microsaccades are not responsible for the information theoretic findings.

    To address the reviewer’s last point, we have included response time data (defined as the saccade onset latency) in the Results.

    1. Attention studies typically keep the stimulus in the RF the same to tease out the effect of attention from stimulus selectivity. Ideally, the comparison should be between the two green (or red) in RF conditions as shown in Figure 4A. However, these results are shown only after pooling across all color selective columns. This comparison should be shown from Figure 2 itself (i.e., Figure 2C should have green in the RF and red target outside).

    We have clarified prior to Figure 4 that we used a all trials including both colors in each of the attention conditions. That is, while the cartoon in Figure 2 shows only green-attended and red-unattended conditions, green-unattended and red-attended conditions were also included in this analysis. As the proportion of red-target and green-target trials was matched, this first analysis was designed in such a way that the influence of stimulus color should be minimized, yet all trials could still contribute to the calculation. We have included a new supplementary figure (included here for convenience) which is what we believe the reviewer requests. In this addition, we perform the information theoretic computation on only stimulus matched conditions. Briefly, we find that this approach does not seem to alter the temporal profile of information theoretic findings.

    1. Information has been well characterized in a large number of previous studies (generally yielding values between a few bits/s, see for example, Reich et. al, 2001, JNP). Here, the absolute value of mutual information seems rather low. This may be due to the way the information is computed. A discussion about these reasons would be useful for scientists interested in information-theoretic measures.

    We agree that the exact magnitude of our information theoretic analyses in curious. And while these methods have been widely characterized – they have not been characterized, to our knowledge, in relating intracortical laminar currents to extracortical field potentials. As such, we do not have a strong prior as to what we should expect magnitude-wise. We have expanded the discussion to note this observation and provide potential reasons as to why this might be the case. The conclusion being that further application of these methods to these datatypes is necessary to really gain a fuller sense of what should and shouldn’t be expected.

    1. Dependence on feature preference: The effect of spatial and feature attention is well studied. A multiplicative gain model of spatial attention would predict a larger increase in firing rates )and perhaps other signals such as CSD) for preferred versus non-preferred signals. Feature similarity gain model would predict the red preferring columns to increase their activity and green preferring columns to reduce their activity when the animal is attending to the feature red, irrespective of which stimulus is in the receptive field. Here, the task is a pop-out task which likely has both a spatial and feature attention component. The authors should discuss their findings in these contexts. Further, the authors should discuss whether their findings could just be a reflection of the magnitude of the change (which could be larger for preferred versus non-preferred stimulus). The information-theoretic measure should ideally not depend on the absolute magnitude, but these quantities often get biased in non-trivial ways based on the magnitude. Does information transmission depend on the magnitudes of firing rates/CSDs?

    The relationship of these findings to the specificities of attentional mechanisms and models is indeed intriguing. As the reviewer suggested, this task likely engages both spatial and feature attention – however, the design was not such that they can be disentangled wholly. We have added text to the Discussion to reflect this consideration. As for the potential influence of response magnitude changes on the information theoretic analyses – the exact parameters were chosen to mitigate concerns about magnitude. That is, we chose a uniform count binning procedure on the data which eliminates potential issues such as outliers driving relationships as well as the changes in variability associated with increases in magnitude. Moreover, the uniform count binning procedure results with states rather than magnitudes which again mitigates response-magnitude-driven effects.

    1. For columns that were not feature selective, is there an effect of attention? Does the magnitude of N2pc change depend on color selectivity? I think that should be the case based on Figure 4H and 4I, but a plot and/or some quantification would be useful.

    These questions have been addressed in a newly added supplementary figure as well as quantification in the Results. Briefly, we did find an effect of attention non-selective columns. Also, we found the magnitude of N2pc did not depend on color-selectivity of the intracortical recording. The results were reported as:

    “We also tested whether feature selective columns, on average, transmitted more information than their non-feature-selective counterparts. We found that feature selective columns, in all laminar compartments, transmitted significantly more information (Figure 4I) (two-sample t test: L2/3, p = 0.044; L4, p = 0.023; L5/6, p = 0.009). As such, we wanted to determine if this was due to a lack of attentional modulation in the non-selective columns. This was not the case, we observed that non-selective columns were modulated with attention. Attentional modulation was observed in both the CSD in L2/3 and L5/6 (one-sample t test: L2/3: t(64) = -6.01, p = 9.8e-8; L4: t(64) = -0.18, p = 0.86; L5/6: t(64) = 5.24, p = 1.9e-6) as well as across all layers in the population spiking activity (one-sample t test: L2/3: t(64) = 8.00, p = 3.7e-11; L4: t(64) = 9.66, p = 4.1e-14; L5/6: t(64) = 7.58, p = 1.8e-10) during the N2pc interval (averaged 150-190 ms following array onset) (Figure S6).

    Importantly, we tested whether the N2pc varied across sessions with or without color-selective columns sampled. We found no difference between N2pc polarization (150-190 ms after the array) between sessions with (n = 17) or without (n = 13) sampling of color selective columns (two sample t test: t(28) = -0.75, p = 0.46). This invariance is expected because extracortical EEG spatially integrates signals from multiple cortical columns.”

    Reviewer #2 (Public Review):

    Scalp ERPs are widely used in human neuroscience research to understand basic mechanisms of neural and cognitive function and to understand the nature of neurological and psychiatric research. However, this research is hampered by a surprising lack of research in animal models exploring the neural mechanisms that produce specific ERP components.

    Previous research by this research group identified a potential monkey homologue of the N2pc component, a neural correlate of the focusing of attention onto visual objects embedded in arrays of distractors. The present study took a giant leap forward by recording extracellular potentials from densely spaced arrays of electrodes (.1 mm spacing) on probes that extended perpendicular to the cortical surface. These electrode arrays made it possible to simultaneously record voltages throughout the different layers of a cortical column and convert these voltages into current source density (CSD, which isolates local synaptic current flow and minimize volume-conducted activity from other brain regions). In addition, simultaneously recorded voltage from an electrode just above the cortical surface was used as a proxy for scalp potentials. Scalp ERP recordings were also obtained from separate monkeys to measure the actual scalp ERPs and verify that an N2pc-like ERP was elicited by the task (a simple visual search task in which the monkey made an eye movement to the location of a color popout item).

    Very clear CSD was observed in V4 in both supragranular and infragranular layers that was stronger when attention was directed to the contralateral visual field than when attention was directed to the ipsilateral visual field, which is the hallmark of the N2pc component. Little or no such activity was observed in the granular layer (the primary recipient of feedforward projections). In addition, the effects were observed primarily when the column was selective for the target's color. An information theory analysis showed that these intracortical current flows contained significant information about the voltage measured on the cortical surface and the location of the target object.

    All of these results were clear and convincing. Moreover, the laminar and columnar analyses provide interesting new evidence about attention-related neural activity independent of any considerations about ERPs. The most challenging aspect of the study is to provide a solid link from the intracortical activity to the voltage on the cortical surface, and then to the monkey scalp ERPs, and finally to human ERPs. Toward that end, the present study relied entirely on correlational evidence, rather than experimental manipulations. That's quite appropriate for a first step, but it must be considered an important limitation on the conclusions that can be drawn. It would be wonderful if future research took the next step of providing experimental evidence.

    We appreciate the reviewer noting that this manuscript is a valuable step in linking attention-associated electrophysiological signals across species. We also recognize that there is much work to be done in this domain. As requested, we have added to the Discussion the limitation of this type of study as well as what should be considered valuable next steps in this program of research.

    There are also some troubling aspects of the existing evidence. The scalp ERP effect in this study and the prior work from this groups is a positive voltage over the contralateral hemisphere, whereas in humans the voltage is negative. This may well reflect the orientation of the relevant cortical surface in monkeys versus humans. However, the voltage on the cortical surface in the present study was negative contralateral to the target, not positive. Unless this opposite voltage on the cortical surface relative to the scalp reflects something about the reference site for the cortical surface electrode, then this makes it difficult to link the intracortical effects and cortical surface effects to the scalp ERP effects. Also, the CSD was negative in the upper layers and positive in the lower layers, again suggesting that the voltage should be negative contralateral to the target on the surface. Ironically, this polarity is what would be expected from the human brain, where a contralateral negativity is observed. The oddity seems to be the contralateral positivity in the monkey scalp data. Also, the cortical surface voltage exhibits a polarity reversal at approximately 180 ms, which is not seen in the intracortical CSD.

    One possible explanation for the discrepancy is that the scalp voltage likely comes from multiple brain areas besides V4. If, for example, areas on the ventral surface of the occipital and temporal lobes produce stronger scalp voltages than V4 under the present conditions, the opposite orientation of these areas relative to the cortical surface would be expected to produce a positive voltage at the scalp electrodes.

    The manuscript notes that multiple areas probably contribute to the scalp ERPs and argues that the pattern of intracortical CSD results obtained in V4 will likely generalize to those areas. That seems quite plausible. Moreover, the results are interesting independent of their link to scalp ERPs. Thus, the present results are important even if the scalp polarity issue cannot be definitively resolved at this time.

    We thank the reviewer for expressing that the results are important whether this polarity difference can be resolved. This is an interesting observation and quite important to consider carefully. First, it is worth reiterating that the referencing setup in our ‘10/20’ monkeys was different than that for the monkeys where intracranial recordings took place. Specifically, the 10/20 recordings were more similar to our previous reports of monkey EEG (e.g., Woodman et al., 2007, PNAS; Cohen et al., 2009, J Neurophysiol; Purcell et al., 2013, J Neurophysiol). Recordings from these monkeys used either a frontal EEG electrode (approximately FpFz) or linked ears for referencing. These yielded the positive-going N2pc and contrast the negative-going N2pc found in humans. The V4 laminar recordings – and their accompanying extracortical signal – used a different referencing setup that we believe is the most likely candidate for the observed difference. Specifically, these recordings used a tied ground-reference setup which incorporated the support rod of the linear multielectrode array. This support rod extended into the brain meaning we had a neural tissue grounded signal and that the reference spanned the neural generator. Therefore, if we are not measuring both sides of the electric field across the generator equally, we might observe an inverted signal. Unfortunately, we cannot observe the 10/20 EEG distribution with an intracranial reference. Ideally, this could be resolved by an experiment where referencing setups are tested before and after performing craniotomy with a series of reference locations used to understand where exactly this flipping of polarization takes place. We have added this consideration to the Discussion and more thoroughly detailed the referencing setups in the Methods.

    There are also some significant concerns about the filters. The high-pass cutoff was high enough that it could have produced artifactual opposite polarity deflections in the data. If causal filters were applied (e.g., in hardware during the recordings), these artifactual deflections would have been after rather than before the initial deflection, possibly explaining the polarity reversal at 180 ms. If noncausal filters were applied in software, this would be a larger problem and could produce artifacts at both the beginning and end of the waveform. Moreover, the filters were different for the CSD data and the extracortical voltages, which is somewhat problematic for the information theoretic comparisons of these two data sources (but is likely to reduce rather than inflate the effects).

    In revisiting the description of the recording system and filters, we see how some information was conveyed poorly. The language describing the recording in the original submission suggested that online filters were applied to the data as it was being recorded. This was not the case. We have changed that language so that it reads as the data was being collected at a sampling frequency sufficient to observe data between 0.1 Hz and 12 kHz rather than the data being filtered between 0.1 Hz and 12 kHz. Also, it appears that the description of the processing sequence regarding CSD was ambiguous in the original submission. The CSD underwent the same offline, bandpass filtering procedure (1-100 Hz) as the extracortical signal. We have clarified the Methods accordingly.

    Reviewer #3 (Public Review):

    In this study, Westerberg et al., investigate the cortical origins of the N2pc, an ERP for selective attention. By using a combination of indefinite inverse models of cranial EEG and translaminar electrophysiology, the authors demonstrate that dipoles in V4 are the source of the N2pc.

    The study is well conducted and the manuscript is well written.

    We are pleased that the reviewer recognized the contribution of our efforts.

    I have a few comments about the CSD, RF alignment profiles, and LFP based analyses:

    (A) The method section states correctly that "current sinks following visual stimulation first appear in the granular input layer of the cortex, then ascend and descend to extra granular compartments". However in the example CSDs shown in Fig 2, Fig 3, Fig S3 there is no visible current sink in the infra-granular layers. Instead, the identified infra-granular layers show a prolonged current source (e.g. Fig S5B,C), which is unexpected.

    We have clarified the Methods to reflect the observations of our data and why they may differ from previous reports. We believe the discrepancy is likely due to the stimulus conditions used to evoke the CSD profile. Specifically, the descending infragranular sink in visual cortical columns has most commonly been described when CSD was computed while monkeys view briefly presented flashes or stimuli (e.g., Schroeder et al., 1998, Cereb Cortex). However, our study uses task evoked CSD to perform the alignment. Importantly, this means there is a persistent stimulus in the receptive field. We believe this persistent stimulus, rather than a flashed stimulus, leads to a persistent, strong sink in the superficial layers of cortex which would mask any current sink present in the infragranular layers (Mitzdorf, 1985, Physiol Rev). This is an observation we made in previous reports (Task evoked CSD: Westerberg et al., 2019, J Neurophysiol vs. Flash evoked CSD: Maier et al., 2010, Front Syst Neurosci), albeit in V1 instead of V4. Given the latency offset between putative granular and supragranular sinks, that we observe receptive fields below the putative granular input sink, and the demonstrable multiunit activation as indicated by the newly included Figure S2, we have no reservations in our assessment of the position of the electrode relative to the layers across sessions.

    (B) The example RF profile shown in Fig S5A, although aligned, looks a little strange in that the RFs taper off rapidly in the infra-granular layer. Is this the best representative example? It will be important to see other examples of RF alignment.

    The attenuation observed in the lower layers is largely due to overall decreased gamma power in the lower layers of cortex as compared to upper and middle layers (Maier et al., 2010, Front Syst Neurosci). At the reviewer’s request, we have added an additional panel to the noted supplementary figure which shows additional laminar receptive field profiles using the evoked LFP so that they are more directly comparable to those shown in Nandy et al., 2017, Neuron.

    (C) The study used LFP power in the gamma range to compute the response ratio between red and green stimuli. LFPs measured across the cortical depth are highly correlated, and so would gamma power estimated from the LFPs. Given this, how meaningful is the laminar analysis shown in Fig 4B? How confidently can it be established that the LFP derived gamma power estimates have laminar specificity?

    An astute observation – there are two aspects to consider. The existence of color-feature columns has been well-documented in V4 (e.g., Zeki, 1973, Brain Res; Zeki, 1980, Nature; Tootell et al., 2004, Cereb Cortex; Conway and Tsao, 2009, PNAS; Kotake et al., 2009, J Neurophysiol; Westerberg et al., 2021, PNAS). This manuscript did not need the evaluation of interlaminar differences in color selectivity to address the question at hand – the top of Figure 4B only serves as a step to the bottom of Figure 4B which provides the measurements used for the subsequent analyses. Thus, the estimation of color selectivity from gamma was sufficient to capture a general sense of the color selectivity of the column. Second, we recently published a manuscript which directly addresses the laminar specificity of gamma with respect to feature selectivity. Westerberg et al., 2021, PNAS uses a spatially localized form of gamma to evaluate color-feature selectivity along V4 columns. In that manuscript, we find a high degree of consistency along the layers of cortex using the gamma signal. Notably, we compared the gamma signal to the population spiking and found a high degree of coherence between selectivity in those two measures as a function of cortical depth. Given the secondary nature of the interlaminar feature selectivity to this submitted manuscript and the detailed report of laminar feature selectivity using the same dataset in another manuscript, we are inclined to leave the analysis reported here as is with adjustments to the text that note these considerations now included in the Results.

  3. eLife

    Evaluation Summary:

    This study recorded brain activity in monkeys to identify the neural mechanisms underlying an attention-related scalp ERP component that is similar to the human N2pc component. Intriguing evidence was provided that the surface potential was at least partly a result of current flows in the feedback-receiving supragranular and infragranular layers of area V4, not the granular layer that receives feedforward inputs. However, it is not entirely clear if these very interesting intracortical effects are the source of the scalp ERP effects.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 and Reviewer #3 agreed to share their names with the authors.)

  4. eLife

    Reviewer #1 (Public Review):

    The recordings done by the authors are impressive and rare, and I appreciate the efforts of the authors to bridge very different types of signals that are generally recorded in different paradigms. However, the analysis at many places is quite nuanced and high-level, making it difficult to directly compare these findings with previous results. I think several additional analyses are needed to properly place these findings with previous results.

    1. Effects of attention in V4 generally start earlier (~100 ms). It is unclear why no effect is observed during earlier time periods in these data. To make better comparison with previous studies (such as Nandy et al., 2017), the authors should show the average PSTHs in supragranular, granular and infragranular layers during both target-out versus target-in conditions. Interestingly, Nandy and colleagues found largest changes in firing rates in the granular layer. To better understand the ERP outside the cortex, the authors should also show the average LFPs in the three layers, for target-in and target-out conditions. It is surprising that MI analysis reveals no significant information about the target in granular layer - given that some attentional effects are seen in upstream areas such as V1 and V2.

    2. Eye position analysis: my understanding is that the animals could make a saccade as soon as the arrays were displayed. Given that the main effect of attention is observed after ~150-200 ms, the potential effect of saccade preparation could be important. There could also be small eye movements before the saccade. Given that the RFs were quite fovial for one monkey and not too far from the fixation window, and the effect of attention appears to be quite late, detailed analysis of eye position and microsaccades is needed to rule out the possibility of differences in eye movements between target in and target-out conditions influencing the results. A timeline and some analysis of eye movement patterns would be appropriate. The authors should also clearly mention the mean and SD of the saccade onset.

    3. Attention studies typically keep the stimulus in the RF the same to tease out the effect of attention from stimulus selectivity. Ideally, the comparison should be between the two green (or red) in RF conditions as shown in Figure 4A. However, these results are shown only after pooling across all color selective columns. This comparison should be shown from Figure 2 itself (i.e., Figure 2C should have green in the RF and red target outside).

    4. Information has been well characterized in a large number of previous studies (generally yielding values between a few bits/s, see for example, Reich et. al, 2001, JNP). Here, the absolute value of mutual information seems rather low. This may be due to the way the information is computed. A discussion about these reasons would be useful for scientists interested in information-theoretic measures.

    5. Dependence on feature preference: The effect of spatial and feature attention is well studied. A multiplicative gain model of spatial attention would predict a larger increase in firing rates )and perhaps other signals such as CSD) for preferred versus non-preferred signals. Feature similarity gain model would predict the red preferring columns to increase their activity and green preferring columns to reduce their activity when the animal is attending to the feature red, irrespective of which stimulus is in the receptive field. Here, the task is a pop-out task which likely has both a spatial and feature attention component. The authors should discuss their findings in these contexts. Further, the authors should discuss whether their findings could just be a reflection of the magnitude of the change (which could be larger for preferred versus non-preferred stimulus). The information-theoretic measure should ideally not depend on the absolute magnitude, but these quantities often get biased in non-trivial ways based on the magnitude. Does information transmission depend on the magnitudes of firing rates/CSDs?

    6. For columns that were not feature selective, is there an effect of attention? Does the magnitude of N2pc change depend on color selectivity? I think that should be the case based on Figure 4H and 4I, but a plot and/or some quantification would be useful.

  5. eLife

    Reviewer #2 (Public Review):

    Scalp ERPs are widely used in human neuroscience research to understand basic mechanisms of neural and cognitive function and to understand the nature of neurological and psychiatric research. However, this research is hampered by a surprising lack of research in animal models exploring the neural mechanisms that produce specific ERP components.

    Previous research by this research group identified a potential monkey homologue of the N2pc component, a neural correlate of the focusing of attention onto visual objects embedded in arrays of distractors. The present study took a giant leap forward by recording extracellular potentials from densely spaced arrays of electrodes (.1 mm spacing) on probes that extended perpendicular to the cortical surface. These electrode arrays made it possible to simultaneously record voltages throughout the different layers of a cortical column and convert these voltages into current source density (CSD, which isolates local synaptic current flow and minimize volume-conducted activity from other brain regions). In addition, simultaneously recorded voltage from an electrode just above the cortical surface was used as a proxy for scalp potentials. Scalp ERP recordings were also obtained from separate monkeys to measure the actual scalp ERPs and verify that an N2pc-like ERP was elicited by the task (a simple visual search task in which the monkey made an eye movement to the location of a color popout item).

    Very clear CSD was observed in V4 in both supragranular and infragranular layers that was stronger when attention was directed to the contralateral visual field than when attention was directed to the ipsilateral visual field, which is the hallmark of the N2pc component. Little or no such activity was observed in the granular layer (the primary recipient of feedforward projections). In addition, the effects were observed primarily when the column was selective for the target's color. An information theory analysis showed that these intracortical current flows contained significant information about the voltage measured on the cortical surface and the location of the target object.

    All of these results were clear and convincing. Moreover, the laminar and columnar analyses provide interesting new evidence about attention-related neural activity independent of any considerations about ERPs. The most challenging aspect of the study is to provide a solid link from the intracortical activity to the voltage on the cortical surface, and then to the monkey scalp ERPs, and finally to human ERPs. Toward that end, the present study relied entirely on correlational evidence, rather than experimental manipulations. That's quite appropriate for a first step, but it must be considered an important limitation on the conclusions that can be drawn. It would be wonderful if future research took the next step of providing experimental evidence.

    There are also some troubling aspects of the existing evidence. The scalp ERP effect in this study and the prior work from this groups is a positive voltage over the contralateral hemisphere, whereas in humans the voltage is negative. This may well reflect the orientation of the relevant cortical surface in monkeys versus humans. However, the voltage on the cortical surface in the present study was negative contralateral to the target, not positive. Unless this opposite voltage on the cortical surface relative to the scalp reflects something about the reference site for the cortical surface electrode, then this makes it difficult to link the intracortical effects and cortical surface effects to the scalp ERP effects. Also, the CSD was negative in the upper layers and positive in the lower layers, again suggesting that the voltage should be negative contralateral to the target on the surface. Ironically, this polarity is what would be expected from the human brain, where a contralateral negativity is observed. The oddity seems to be the contralateral positivity in the monkey scalp data. Also, the cortical surface voltage exhibits a polarity reversal at approximately 180 ms, which is not seen in the intracortical CSD.

    One possible explanation for the discrepancy is that the scalp voltage likely comes from multiple brain areas besides V4. If, for example, areas on the ventral surface of the occipital and temporal lobes produce stronger scalp voltages than V4 under the present conditions, the opposite orientation of these areas relative to the cortical surface would be expected to produce a positive voltage at the scalp electrodes.

    The manuscript notes that multiple areas probably contribute to the scalp ERPs and argues that the pattern of intracortical CSD results obtained in V4 will likely generalize to those areas. That seems quite plausible. Moreover, the results are interesting independent of their link to scalp ERPs. Thus, the present results are important even if the scalp polarity issue cannot be definitively resolved at this time.

    There are also some significant concerns about the filters. The high-pass cutoff was high enough that it could have produced artifactual opposite polarity deflections in the data. If causal filters were applied (e.g., in hardware during the recordings), these artifactual deflections would have been after rather than before the initial deflection, possibly explaining the polarity reversal at 180 ms. If noncausal filters were applied in software, this would be a larger problem and could produce artifacts at both the beginning and end of the waveform. Moreover, the filters were different for the CSD data and the extracortical voltages, which is somewhat problematic for the information theoretic comparisons of these two data sources (but is likely to reduce rather than inflate the effects).

  6. eLife

    Reviewer #3 (Public Review):

    In this study, Westerberg et al., investigate the cortical origins of the N2pc, an ERP for selective attention. By using a combination of indefinite inverse models of cranial EEG and translaminar electrophysiology, the authors demonstrate that dipoles in V4 are the source of the N2pc.

    The study is well conducted and the manuscript is well written.

    I have a few comments about the CSD, RF alignment profiles, and LFP based analyses:

    (A) The method section states correctly that "current sinks following visual stimulation first appear in the granular input layer of the cortex, then ascend and descend to extra granular compartments". However in the example CSDs shown in Fig 2, Fig 3, Fig S3 there is no visible current sink in the infra-granular layers. Instead, the identified infra-granular layers show a prolonged current source (e.g. Fig S5B,C), which is unexpected.

    (B) The example RF profile shown in Fig S5A, although aligned, looks a little strange in that the RFs taper off rapidly in the infra-granular layer. Is this the best representative example? It will be important to see other examples of RF alignment.

    (C) The study used LFP power in the gamma range to compute the response ratio between red and green stimuli. LFPs measured across the cortical depth are highly correlated, and so would gamma power estimated from the LFPs. Given this, how meaningful is the laminar analysis shown in Fig 4B? How confidently can it be established that the LFP derived gamma power estimates have laminar specificity?

  7. Version published to 10.1101/2021.09.09.459639v1 on bioRxiv