Similar to previous studies (Hasson et al., 2004; Honey et al., 2012a), we assessed the correlations of the fMRI signals during stimulus presentation (length: 390 s in Experiment 1; 340 s in Experiment 2) across subjects at each voxel. The first 5 frames of each fMRI run corresponded to the blank period and were not included in the correlation. Thus, scanner magnetic stabilization was already reached by the beginning of stimulus presentation. In Experiment 1, for within-condition reliability assessment, Groups 1 and 2 (see **Figure 1A**) were analyzed separately. fMRI signal time course from each subject was correlated with the mean time course across the remaining subjects in the same group. The Pearson correlation r values were transformed into Fisher *z*-values, which are approximately normally distributed. The Fisher z maps were then averaged across all subjects in each group (*N* = 9) to yield the population average. For across-condition reliability assessment, fMRI signal correlations were evaluated between Groups 1 and 2 that followed the same story presented via visual and auditory modality, respectively (**Figure 1A**). Each subject's fMRI signal time course was correlated with the average time course from the other group. The Pearson correlation r values were transformed into Fisher *z*-values, which were then averaged across all subjects in both groups (*N* = 18) to yield the population average.

In Experiment 2, the correlations were carried out across subjects presented with identical physical stimuli (i.e., between Groups 1A and 1B, and between Groups 2A and 2B, see **Figure 1B**). Two analyses were carried out. In the first, we correlated fMRI runs in which one subgroup attended to the visual stream and the other subgroup attended to the auditory stream (e.g., Run 2 of both Groups 1A and 1B). To avoid repetition suppression effect (Grill-Spector and Malach, 2001), only the fMRI runs in which the stimulus was presented for the first time were used (Run 2 in **Figure 1B**). Because the correlation was carried out between subjects presented with identical physical stimulus but attending to different sensory modalities and thus different stories, the brain regions showing reliable responses should be those involved in low-level sensory processing. In the second analysis, we correlated fMRI runs in which the two subgroups of subjects were presented with identical stimulus and attended to the same sensory stream (e.g., Run 2 from Group 1A and Run 3 from Group 1B), which assessed which brain regions were consistently modulated by each task condition. In both analyses, the fMRI signal time course from each subject was correlated with the average time course from the other subgroup. The Fisher-ztransformed correlation maps were averaged across all subjects in each group (*N* = 9, since Groups 1 and 2 were analyzed separately).

#### **BOOTSTRAPPING BY PHASE-RANDOMIZATION TO ASSESS SIGNIFICANCE**

We assessed statistical significance using a bootstrapping procedure based on phase-randomization. For each voxel, we applied Fourier transform on the time series, randomized the phase component, and inverted the Fourier transform to obtain the shuffled time series. For Experiment 1, each subject's time series was phase-shuffled and correlated with the original average of the other group; this was done 50 times per subject to create a distribution of 900 bootstrapped correlations. For Experiment 2, bootstrapping was performed 100 times per subject, again to yield a distribution of 900 bootstrapped correlations for each analysis (Groups 1 and 2 were analyzed separately). All of the bootstrap correlations were transformed into Fisher *z*-values. We then calculated the mean and standard deviation (SD) across the distribution of 900 bootstrap iterations. Because in the original analysis the correlations were averaged across subjects, the SD of the bootstrap distribution was corrected by a factor of √*N*, where *N* = 18 in Experiment 1 and *N* = 9 in Experiment 2. The mean of the bootstrap distribution and the corrected SD were then used to convert the original population-average Fisher-z maps into Z-scores, from which statistical significance was determined. To correct for multiple comparisons, we adopted the Monte Carlo method for family-wise error (FWE) correction (McAvoy et al., 2001) and applied a threshold of Z score >3 and cluster size >17 voxels, yielding clusters that survived *p* < 0.05.

## **RESULTS**

To investigate ongoing cognition using controlled semantic content, we presented stories as auditory speech and/or visual text to subjects and correlated the fMRI time series across subjects to map brain areas that responded reliably to a task condition or across different conditions. We applied this correlational approach to identify brain regions underlying sensory processing vis-à-vis ongoing cognition.

#### **BEHAVIORAL RESULTS**

For the "Cage," "King," and "Fish" stories, subjects correctly answered an average of 4.4 ± 0.17 (mean ± s.e.m. across 18 subjects), 4.2 ± 0.23 and 4.3 ± 0.16 comprehension questions, respectively, and there was no significant difference between stories (*p* = 0.73, Kruskal-Wallis test). In Experiment 1, the level of comprehension was not significantly different between subjects who heard the story and those who read the story (*p* = 0.09, Wilcoxon rank-sum test). In Experiment 2, there was no significant effect of the attended modality (*p* = 0.26, Wilcoxon signedrank test) or run order (*p* = 0.97) on the level of comprehension of the attended story.

In Experiment 2, we asked an additional question concerning the level of intrusion by the unattended story. The intrusion level averaged across all runs from all subjects was 2.1 ± 0.13. It was not significantly different between the AV and AA conditions (*p* = 0.49, Wilcoxon signed-rank test), or between the first and second fMRI runs (*p* = 0.38, Wilcoxon signed-rank test). Interestingly, when attending to the "King" and "Fish" stories, subjects reported an average intrusion level of 2.5 ± 0.15 and 1.8 ± 0.19, respectively (*p* = 0.01, Wilcoxon signed-rank test), suggesting that the "Fish" story was more intrusive. This is likely due to the fact that the "Fish" story was told in first-person perspective and had more emotional and personal content while the "King" story was a fable told in third-person perspective.

### **EXPERIMENT 1—"CAGE" STORY PRESENTED ALONE VIA VISUAL OR AUDITORY MODALITY**

In Experiment 1, one group of subjects (*N* = 9) listened to the "Cage" story through headphones, and another group of subjects (*N* = 9) read the "Cage" story presented visually at the center of the screen. The auditory and visual versions of the story were presented at roughly the same speed. First, we identified brain regions that were reliably modulated across subjects within each condition alone. To this end, we computed inter-subject correlations for each voxel within the first group of subjects that read the "Cage" story (**Figure 2A**, top row), and within the second group of subjects that listened to the "Cage" story (**Figure 2A**, bottom row). Unsurprisingly, in the reading ("V") condition, the occipital visual cortices, as well as the intraparietal sulci (IPS) involved in visuospatial attention, are reliably modulated. By contrast, in the listening ("A") condition, there was extensive reliable modulation of the early and higher-order auditory cortices along the superior temporal gyrus (STG). Both the reading and listening conditions consistently modulated the inferior frontal gyrus (IFG), temporal pole (TP), the superior temporal sulcus (STS), anterior cingulate cortex (ACC), and the thalami. Interestingly, the posterior cingulate cortex (PCC) was substantially more involved in the listening condition than the reading condition.

The above results reveal a common set of brain regions that are reliably modulated in both reading and listening conditions. Nonetheless, it remains unknown whether their temporal response profiles are similar across these two conditions. To address this question, we correlated the fMRI time series across these two groups of subjects to extract brain regions reliably modulated by the "Cage" story regardless of the sensory modality used for stimulus input. The results are shown in **Figure 2B** (*p* < 0.05,

**FIGURE 2 | Results of Experiment 1. (A)** Within-condition reliability map. fMRI time series were correlated between subjects who read the "Cage" story (top row) and between those who listened to the "Cage" story (bottom row). The Pearson correlation coefficients were z-transformed and averaged across all subjects in each group (*N* = 9). **(B)** Across-condition reliability map. fMRI time series were correlated between subjects who listened to the "Cage" story and those who read the same story (V × A, see **Figure 1C**, Experiment 1). The population-average z-transformed correlation values were compared to phase-shuffled data to determine the *Z*-score (thresholded at *Z* > 3, cluster size >17 voxels, corresponding to *p* < 0.05 after correction for multiple comparisons).

FWE corrected), which included bilateral STS/STG, TP, and IFG, the left temporal parietal junction (TPJ), the dorsal medial prefrontal cortex (dmPFC) and the thalamus. There was a slight left asymmetry in the response pattern.

#### **EXPERIMENT 2—"FISH" AND "KING" STORIES PRESENTED SIMULTANEOUSLY VIA AUDITORY AND VISUAL MODALITIES**

In Experiment 2, one group of subjects (*N* = 9) were simultaneously presented with the "Fish" story as visual text and the "King" story as auditory speech (FishV+KingA), and instructed to attend to the two sensory streams in alternate runs in a counterbalanced manner (Group 1 in **Figure 1B**). A second group of subjects (*N* = 9) were presented with the "King" story as visual text and the "Fish" story as auditory speech (KingV+FishA) and also instructed to attend to different sensory streams in alternate runs (Group 2 in **Figure 1B**).

We first examined which brain regions were consistently modulated by this task. To this end, we correlated fMRI time series across subjects under an identical task condition. Given two stimulus conditions ("FishV+KingA" and "KingV+FishA") and two attentional states (AV and AA), there were four task conditions in total. Thus, four correlational analyses were carried out between subjects exposed to identical task stimuli and instructions (e.g., between Run 2 from Group 1A and Run 3 from Group 1B). The results from this analysis are shown in **Figure 3** (top and

in the top row, AV × AV; attending the auditory stream in the middle row, AA × AA). In the bottom row, fMRI time series were correlated across subjects

*Z*-scores (*p* < 0.05, corrected for multiple comparisons).

middle rows). Widespread brain regions were consistently modulated by this task, with the strongest activities residing in the lateral occipital cortex (LOC), STG, PCC, TP, IFG, and the TPJ.

To locate brain areas involved in the processing of sensory stimulus regardless of attentional focus and the story followed, we correlated fMRI time series between subjects presented with identical physical stimulus but instructed to attend to opposite sensory streams, such that the attended sensory modality (visual vs. auditory) and the story followed ("Fish" vs. "King") differed between the correlated runs (i.e., Run 2 was correlated between Groups 1A and 1B, and between Groups 2A and 2B). The results of this analysis are shown in the bottom row of **Figure 3**. Unsurprisingly, auditory cortex along the STG showed reliable responses. In addition, ventral visual areas, thalamus, dmPFC and part of the angular gyrus (AG) were involved. Interestingly, there was limited recruitment of the primary visual cortex (V1), likely because the eye movement pattern differed between the AV and AA conditions, under which the subjects performed active reading and passive fixation, respectively.

The above two analyses respectively extracted brain regions reliably modulated by performing this task (i.e., being exposed to simultaneous auditory and visual streams and attending to one of them) and those reliably modulated by the physical sensory stimuli regardless of the required attentional focus and the story content followed. Hence, contrasting them should reveal brain areas involved in "ongoing cognition" beyond sensory inputs—that is, from attentional fluctuations (if they were similar across subjects) and the understanding of the evolving story content to post-semantic processes such as imagery and emotional response. In **Figure 4**, we overlaid the results from the first analysis showing regions consistently modulated by performing this task (shown in yellow, from the top two rows in **Figure 3**) and those from the second analysis showing regions consistently modulated by the physical stimulus alone (shown in blue, from the bottom row in **Figure 3**), with their overlaps shown in orange. For example, in the top-left panels, yellow/orange regions are those reliably modulated when subjects were presented with the KingV+FishA stimulus and attended to the visual stream; the blue/orange regions were those consistently modulated between the AV and AA conditions under the KingV+FishA stimulus. Thus, regions in yellow represent those contributing to ongoing cognition beyond the processing of physical sensory inputs. Next, we extracted these regions and investigated whether, and if so how, their patterns depended on the attended sensory modality.

To compare the putative brain areas involved in ongoing cognition between the AV and AA conditions, we combined the yellow regions in **Figure 4** across the "King" and "Fish" stories for the AV (**Figure 4**, left column) and AA (right column) conditions, respectively. The results are shown in **Figure 5**. As expected, low-level visual areas (Brodmann areas 17 and 18) were more reliably modulated in the AV condition, and the primary auditory cortex (Brodmann areas 41 and 42) was more reliably modulated in the AA condition. These results suggest that primary sensory areas may also contribute to ongoing cognition, depending on the attentional focus.

#### **DISCUSSION**

In summary, we used an audiovisual cross-modal approach to map brain activity patterns underlying ongoing cognition elicited by hearing or reading a story, and to dissociate these activity patterns from modality-specific sensory processing. The brain areas we identified bear significant similarities to previously described semantic network (Martin, 2007; Patterson et al., 2007; Binder et al., 2009; Schwartz et al., 2011; Honey et al., 2012a; Wei et al., 2012; Fairhall and Caramazza, 2013) and the default-mode network (DMN) (Raichle et al., 2001; Buckner et al., 2008), adding further support for the anatomical convergence of these different brain functions.

In the first experiment, we presented a factual story about the composition of *4*- *33*- by John Cage to subjects via either the auditory or visual modality. The visual presentation of the story was synchronized phrase by phrase with the auditory version, which comprised of natural reading by a native English speaker. By correlating brain activity fluctuations between subjects who listened to the story and those who read the story, we were able to extract brain regions consistently modulated by this story regardless of the sensory modality used for stimulus delivery. The most consistently modulated brain areas included the STS/STG, the IFG, and the TP (**Figure 2**), all of which are well-known nodes within the semantic network (Patterson et al., 2007; Binder et al., 2009). Our results from Experiment 1 are largely similar to a previous study that used very similar experimental design (Regev et al., 2013). The main difference between our results and theirs is that we did not find robust cross-modality reliable modulation in PCC (**Figure 2B**), which is consistent with its lack of reliable modulation within the visual condition alone (**Figure 2A**, top). This difference might result from the fact that Regev et al. used a real-life story with autobiographical content, which is known to activate the PCC (Buckner et al., 2008), while the "Cage" story we used in Experiment 1 is an encyclopedic entry about a piece of controversial musical history.

In the second experiment, by presenting two stories simultaneously to the subjects via visual and auditory modalities, and

cueing them to attend to one or the other in different runs, we were able to manipulate the content of ongoing cognition under identical physical stimulus. This attentional modulation was successful, as demonstrated by the low level of intrusion from the unattended story (rated ∼2 on a scale from 1 to 4) and the satisfactory comprehension of the attended story (on average, subjects answered 4.2 and 4.3 out of 5 questions correctly for "King" and "Fish," respectively). We extracted brain regions consistently modulated by performing this task (by correlating fMRI signals across subjects under an identical task condition), and those modulated by the physical stimuli alone (by correlating fMRI signals between task conditions with identical stimulus but opposite attentional focus). Contrasting these results, we found an extensive brain network consistently modulated by ongoing cognition beyond physical sensory inputs, which had very similar spatial patterns for the two stories (**Figure 4**). The main regions involved included the TPJ, IFG, TP, PCC, and the posterior middle temporal gyrus (pMTG), consistent with previous studies on semantic processing (Binder et al., 2009; Honey et al., 2012a; Wei et al., 2012; Fairhall and Caramazza, 2013). Nonetheless, there were some subtle differences between the activation patterns in response to the two stories. First, the "Fish" story elicited stronger response in the retrosplenial cortex, potentially due to the strong autobiographical nature of this story. Second, in both hemispheres, the activation pattern around the TPJ was continuous for the "King" story, while it comprised of three disjoint regions in the SMG, AG, and pMTG in response to the "Fish" story. At present the origin of this difference is unclear. One potential contributor is the different levels of theory-of-mind processing involved (Buckner et al., 2008; Corbetta et al., 2008; Carter and

Huettel, 2013): In particular, the "King" story, which involves a substantial amount of conjecturing of the character's mental state, evoked an activation pattern around the TPJ that is very similar to previous findings on theory-of-mind (see Figure 12C in Buckner et al., 2008).

Interestingly, the regions identified in Experiment 2 were more extensive than those found in Experiment 1. Two factors may have contributed to this difference: First, the comparative analysis illustrated in **Figure 4** may not have completely removed brain regions involved in modality-specific processing. Specifically, since we used conservative whole-brain multiple comparisons correction (*p* < 0.05, FEW corrected) to identify brain areas modulated by the physical stimuli (**Figure 3** bottom row, show as blue/orange in **Figure 4**), there may well be brain areas that we did not have power to detect with this statistical threshold. Second, the difference in story content may have contributed to the more extensive response patterns in Experiment 2: While "Cage" is an encyclopedic entry, "King" and "Fish" are vivid fable and personal stories that are likely to elicit stronger imagery, theory of mind, episodic, and emotional processing.

Many of the brain areas identified in this study as underlying ongoing cognition are well-known components of the DMN, including the PCC, dmPFC, the inferior parietal lobule, and the MTG (Raichle et al., 2001; Fox et al., 2005; Buckner et al., 2008). Previous studies have established that the DMN is activated during "task-independent thought," suggesting that it may be involved in spontaneous cognition (McGuire et al., 1996; Mason et al., 2007; Christoff et al., 2009). Our results extend these previous findings by showing that the DMN time courses are similarly modulated over time across different individuals by the ongoing cognition evoked by a story regardless of the sensory modality used for story presentation, supporting the idea that the *continuous* activity fluctuations in the DMN are reliably modulated by the evolving flow of conscious content (He and Raichle, 2009).

The similarity between the "semantic network" and the DMN has long been noted (Binder et al., 1999, 2009; Wei et al., 2012; Fairhall and Caramazza, 2013). To account for this observation, Binder et al. (1999, 2009) proposed that semantic processing constitutes a large component of spontaneous thoughts under the resting state, during which the DMN is typically more active. The present study identified brain areas consistently modulated by the evolving content of a story, which relies on semantic processing. Thus, the similarity between the present results and the semantic network as well as the DMN is not surprising. Nonetheless, we believe that potential differences in the spatial patterns among the DMN, the semantic network and the brain areas involved in ongoing cognition should be an interesting and important topic for future research. For example, previous studies found that in addition to the DMN, the executive network including the dorsolateral prefrontal cortex (DLPFC) and the dorsal anterior cingulate cortex (dACC) were also involved in task-independent thoughts (Christoff et al., 2009; Spreng et al., 2010). This is similar to our results (**Figures 2**, **4**). Future studies employing detailed dissection of how the specific content of ongoing cognition or semantic processing relates to specific brain activity patterns would be needed to shed light on the intricate functional/anatomical brain architecture supporting these functions, and how these structures intertwine with the subsystems of the DMN (Andrews-Hanna et al., 2010). A promising approach to this end is demonstrated by a recent study using a data-driven generative model applied to fMRI data obtained under natural movie viewing, which revealed a continuous semantic space across the cortical surface (Huth et al., 2012). As the authors noted in that paper, because only visual stimuli were used in their study, visual and conceptual features were likely mixed in their results. Combining the generative model used therein and the present cross-modal approach in the context of natural stimuli should allow future studies to map the hetero-modal semantic space in the human brain.

As mentioned in Introduction, consistent temporal modulation in the presence of similar conscious stream constitutes a necessary albeit insufficient condition for identifying brain activities underlying the conscious flow. An important future direction is to dissociate brain activities directly contributing to the conscious flow and those consistently modulated by the unconscious processes related to the conscious flow, in line with the recently proposed framework of the tripartite process including prerequisites for the neural correlate of consciousness (NCC-pr), neural correlate of consciousness proper (NCC), and consequences of the neural correlate of consciousness (NCC-co) (Bachmann, 2009; Aru et al., 2012; de Graaf et al., 2012; Li et al., 2014). We anticipate that novel cognitive paradigms and/or analytical approaches will need to be developed in order to separate these processes in the context of ongoing cognition. Lastly, the use of electrophysiological recordings (e.g., Honey et al., 2012b) in the context of these paradigms should shed additional light on the underlying neural mechanisms.

## **ACKNOWLEDGMENTS**

This research was supported by the Intramural Research Program of the NIH/NINDS. We are grateful to Dr. Alex Martin for helpful discussions and feedback on a previous draft, as well as Dr. John Ingeholm for assistance with the eye-tracking system.

## **SUPPLEMENTARY MATERIAL**

The Supplementary Material for this article can be found online at: http://www.frontiersin.org/journal/10.3389/fpsyg.2014. 00945/abstract

## **REFERENCES**

He, B. J., and Raichle, M. E. (2009). The fMRI signal, slow cortical potential and consciousness. *Trends Cogn. Sci.* 13, 302–309. doi: 10.1016/j.tics.2009.04.004

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 08 June 2014; accepted: 07 August 2014; published online: 26 August 2014. Citation: Wang M and He BJ (2014) A cross-modal investigation of the neural substrates for ongoing cognition. Front. Psychol. 5:945. doi: 10.3389/fpsyg.2014.00945 This article was submitted to Consciousness Research, a section of the journal Frontiers in Psychology.*

*Copyright © 2014 Wang and He. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Perceptual and contextual awareness: methodological considerations in the search for the neural correlates of consciousness

## *Joaquin Navajas, Hernan G. Rey and Rodrigo Quian Quiroga\**

*Centre for Systems Neuroscience, University of Leicester, Leicester, UK*

#### *Edited by:*

*Jaan Aru, University of Tartu, Estonia*

#### *Reviewed by:*

*Ned Block, New York University, USA Jacobo Diego Sitt, Commissariat à l'énergie atomique et aux énergies alternatives, France*

#### *\*Correspondence:*

*Rodrigo Quian Quiroga, Centre for Systems Neuroscience, 9 Salisbury Road, Leicester, LE1 7QR, UK e-mail<EMAIL_ADDRESS>
In the last decades, the neural correlates of consciousness (NCCs) have been explored using both invasive and non-invasive recordings by comparing the brain activity elicited by seen versus unseen visual stimuli (i.e., the contrastive analysis). Here, we review a selection of these studies and discuss a set of considerations to improve the search for the NCCs using the contrastive analysis. In particular, we first argue in favor of implementing paradigms where different perceptual outputs are obtained using identical visual inputs. Second, we propose that the large disagreement in the field -in terms of the dissimilar neural patterns proposed as NCCs- is partially explained by the fact that different studies report the neural correlates of different conscious processes in the brain. More specifically, we distinguish between the *perceptual awareness* of a visual stimulus, associated to a boost in object-selective neural assemblies, and a more elaborate process (*contextual awareness*) that we argue is reflected in the firing of concept neurons in the medial temporal lobe, triggering a rich representation of the context, associations, and memories linked to the specific stimulus.

**Keywords: consciousness, NCCs, contrastive analyses, perceptual awareness, contextual awareness, physical confounds**

#### **INTRODUCTION**

When we see a picture of a person, our retinal cells transduce light into electrical signals propagated through the brain, triggering a cascade of neural processes that leads to the conscious percept of the specific person we are looking at. The minimal neuronal mechanisms that are jointly sufficient to elicit a specific conscious percept are known in the literature as the neural correlates of consciousness (NCCs; Crick and Koch, 1990).

In order to empirically manipulate awareness, different methods were developed in the past to render a stimulus invisible despite retinal stimulation (Kim and Blake, 2005). For example, a brief stimulus that is normally visible can become invisible if it is preceded or followed by a second one; a phenomenon called "visual masking" (Enns and Di Lollo, 2000). With "Attentional Blink" (AB), the perception of a salient target presented in rapid visual serial presentation (RSVP) is impaired by the detection of a previous stimulus (Raymond et al., 1992). Similarly, when two clearly different images are sequentially shown separated by a brief blank interval, observers typically fail to detect the change in the images, leading to a manipulation called "Change Blindness" (CB; Simons and Rensink, 2005). These experimental manipulations have in common that the stimulus is transient, i.e., it is presented for a short period of time. However, other techniques allow inducing lack of awareness even with prolonged retinal stimulation. For example, during "Binocular Rivalry" (BR) two disparate images are presented to each eye, causing a sequence of subjective perceptual switches experienced by the observer, suppressing one or the other image despite constant visual stimulation (Blake and Logothetis, 2002). The main downside of BR is that the number of

subjective alternations, along with their duration and latencies, are not under experimental control. This issue is absent in a similar technique called "Flash Suppression" (FS) in which one image is presented to one eye, and then is removed from visual awareness by suddenly presenting another image to the other eye (Lansing, 1964; Wolfe, 1984). In the same line, Tsuchiya and Koch (2005) introduced another manipulation, called "Continuous Flash Suppression" (CFS), in which robust and prolonged interocular suppression is achieved by presenting flickering patterns to one eye.

Many previous studies have aimed at finding the NCCs by combining these experimental procedures with different measures of neural activity such as scalp magneto/electro-encephalography (M/EEG; Dehaene et al., 2001; Sergent et al., 2005), functional magnetic resonance imaging (fMRI; Lumer et al., 1998; Portas et al., 2000), intracranial EEG (Fisch et al., 2009; Gaillard et al., 2009), and single-cell recordings in human (Kreiman et al., 2002; Quian Quiroga et al., 2008) and non-human primates (Logothetis, 1998; Macknik and Livingstone, 1998). In general, the methodology undertaken for this line of research is the contrastive analysis, i.e., comparing the neural activity elicited by"seen"versus"unseen" stimuli (Baars, 1993). However, possible drawbacks associated to this empirical approach have been raised in the latest years (e.g., Overgaard, 2004; Aru et al., 2012a). Complementary to these observations, here we discuss a set of considerations to improve the search for the NCCs using the contrastive analysis.

#### **MANIPULATION OF THE PHYSICAL STIMULI**

The first step toward finding the NCCs is to select an experimental method (e.g., backward masking, CFS, etc.) to induce lack of awareness. A simple approach would be to implement this manipulation only for a set of "unseen" trials and contrast the results with a set of "seen" trials, where the manipulation is not used. For example, in the case of CFS, this would imply comparing the neural activity elicited during interocular suppression with a dioptic control in which flickering patterns are absent (Sterzer et al., 2009; Kang et al., 2011; Axelrod et al., 2014). However, the limitation of this approach is that the neural activity induced by the manipulation (e.g., flickering masks in the example of CFS) is absent in the "seen" condition. Therefore, the contrast between "seen" and "unseen" trials could be partially reflecting the processing of different physical stimuli.

An alternative is to use a milder version of the manipulation for the "seen" trials. This can be achieved, for example, by adding different amounts of noise to the stimulus (Jemel et al., 2003) or, in the case of CFS, by changing the contrast of the flickering masks (Kaunitz et al., 2011). But still, differences in low-level features such as luminance, contrast, or spatial frequency can largely modulate brain activity (Scholte et al., 2009). In fact, it has been argued that the ultra-fast detection of faces in natural scenes is partly explained by such features (Honey et al., 2008). One way to reduce these effects is by changing the perceptual ambiguity but controlling for a certain number of low-level variables (Portilla and Simoncelli, 2000; Willenbockel et al., 2010). This strategy ensures that this particular set of variables (e.g., contrast, luminance, and spatial frequency) do not explain differences in the neural activation between "seen" and "unseen" trials. However, the possibility of a hidden low-level variable explaining the differences observed in the neural activations cannot be ruled out.

In order to get rid of possible confounds introduced by the physical stimuli, several studies have proposed to compare different perceptual outputs using identical visual stimuli (Sergent et al., 2005; Quian Quiroga et al., 2008; Lamy et al., 2009; Aru et al., 2012b; Navajas et al., 2013). The underlying idea is to implement a manipulation that leads to ∼50% recognition performance, and then to contrast the activity elicited by these two sets of trials. The challenge, of course, is to find an experimental manipulation to be at the threshold of perception so that a certain stimulus is equally likely to be recognized or not. For example, Sergent et al. (2005) used the AB paradigm and compared "seen" and "unseen" trials with other set of trials in which the stimulus was absent. Several studies were successful in implementing this approach to uncover the neural basis of visibility (Marois et al., 2004; Sergent et al., 2005).  In general, due to interindividual differences (Kanai and Rees, 2011), previous works have proposed to adjust the stimuli ambiguity on a subject-by-subject basis (Fisch et al., 2009; Aru et al., 2012b; Navajas et al., 2013). For example, Navajas et al. (2013) used a modified backward-masking paradigm in which different degrees of zero-mean Gaussian noise were added to the stimuli. Critically, the variance of the noise was tuned across trials following a double-staircase procedure (Cornsweet, 1962; **Figure 1A**). In this way, "seen" and "unseen" trials were extracted from same noise levels, enabling a comparison across different perceptual states but keeping constant the physical stimulation.

Using the contrastive analysis with identical visual stimulation allows ruling out physical effects that can otherwise contaminate the comparison between conditions. Nonetheless, this approach does not exempt the contrastive analysis from other possible confounds (e.g., Aru et al., 2012a). For example, previous efforts have sought to dissociate the NCCs from the effects of attention (Tse et al., 2005; Bahrami et al., 2007), confidence (Sergent et al., 2005; Li et al., 2014), unconscious processing (Lamy et al., 2009; Salti et al., 2012), and introspection (Pitts et al., 2012; Fraessle et al., 2014). Whether the neural correlates of these processes can be entirely disentangled from the NCCs is still matter of extended debate (Lamme, 2003; Block, 2005; Dehaene et al., 2006; Koch and Tsuchiya, 2007; Kouider et al., 2010).

## **DIFFERENT CONSCIOUS PROCESSES IN THE BRAIN**

**DO DIFFERENT NCCS NECESSARILY CONTRADICT EACH OTHER?** In the last decades, vast empirical and theoretical efforts have been yielded to unravel the NCCs. However, to date, there is little agreement about the areas, timing, and mechanisms involved in eliciting a conscious percept. As an example, our own work has recently provided dissimilar evidence in terms of modulations by awareness occurring at different times and in different areas: (1) An evoked potential measured from the scalp in the OTC peaking at ∼170 ms that predicts conscious face perception (Navajas et al., 2013; **Figure 1B**); (2) Single-cell firing at ∼300–400 ms of highly selective neurons in the medial temporal lobe (MTL) appearing only upon conscious recognition (Quian Quiroga et al., 2008; **Figure 2**); and (3) A deflection in the local-field potential (LFP) preceding the firing of MTL neurons that is present only in recognized trials (Rey et al., 2014; **Figure 2**). To put together these results into a coherent framework, we propose to distinguish two different neural processes associated with conscious perception, namely, perceptual and full awareness.

#### **PERCEPTUAL AWARENESS IN OBJECT-SELECTIVE CORTICAL AREAS**

The search for the NCCs has posed the problem of finding which of the neural activations along the visual system correlate with conscious perception (Crick and Koch, 1990). In this line, evidence from psychophysical (He and MacLeod,2001), physiological (Gawne and Martin, 2000), and neuroimaging (Haynes and Rees, 2005) studies supports the notion that the primary visual cortex (V1) is not directly involved in eliciting conscious percepts (Crick and Koch, 1995; Rees et al., 2002). However, alternative views about the role of V1 in visual awareness were also proposed (Tong, 2003). In turn, object-selective responses in the inferotemporal cortex (ITC) have been consistently shown to be modulated by conscious perception (for a review see Logothetis, 1998). Similarly, an evoked potential in the 5 to 15 Hz frequency band at ∼170 ms was repeatedly shown to be modulated by conscious perception using the contrastive analysis with identical visual stimulation (Fisch et al.,2009; Navajas et al.,2013; Sandberg et al.,2013; **Figure 1B**). Furthermore, conscious reports about face perception can be decoded at the single-trial level based on the peak amplitude of these evoked responses (**Figure 1C**).

In principle, this set of results showing correlations between awareness and brain activity at 100–200 ms after stimulus onset could be in conflict with theoretical proposals arguing that the

NCCs are reflected by "late" (>300 ms) activations (Dehaene and Changeux, 2011). Alternatively, these neural modulations can be regarded as pre-requisites of consciousness (Aru et al., 2012a) occurring after stimulus onset (VanRullen, 2011), thus reflecting a preconscious state (Dehaene et al., 2006). We believe that this evidence supports the existence of a conscious process for visual recognition, namely perceptual awareness, which is linked to a boost in the activity of object-selective neural assemblies in highlevel visual areas. However, we do not claim that this is the only conscious process in the brain. Instead, we propose that the role of perceptual awareness is to rapidly recognize visual stimuli and to

feed this information to other neural circuits involved in different cognitive functions. Among the subset of processes, we will focus on one occurring in the MTL that is directly involved in memory processes.

#### **CONTEXTUAL AWARENESS IN THE MEDIAL TEMPORAL LOBE**

Patients with pharmacologically intractable epilepsy, implanted with intracranial electrodes for clinical reasons, provide the unique opportunity to record, with the proper setup, single-cell activity from the conscious human brain (for a review see Engel et al., 2005). With these recordings, it was found that neurons in the

MTL respond to different images in a remarkably selective and invariant manner. For instance, one neuron fired to seven different pictures of the actress Jennifer Aniston and not to other 80 pictures of other celebrities, animals and places (Quian Quiroga et al., 2005). Moreover, these responses could be triggered not only by pictures but also by the written name of the person and by the name pronounced by a synthetized voice (Quian Quiroga et al., 2009). Altogether, these results support the idea that the function of these neurons is to provide an explicit and abstract representation of the meaning of stimuli, thus being called *concept cells* (Quian Quiroga, 2012).

The latency of the firing of concept neurons is about 300 ms, although parahippocampal cells fire 50–100 ms before (Mormann et al., 2008). This timing is consistent with a set of "late" (>300 ms) activations that were shown to correlate with access to consciousness (Del Cul et al., 2007; Dehaene and Changeux, 2011). In this line, previous research has shown that neurons in the MTL modulate their firing activity with conscious perception (Kreiman et al., 2000, 2002; Reddy et al., 2006; Quian Quiroga et al., 2008). For example, when two incongruent pictures are presented to each eye, the firing of these neurons follows subjective perception (Kreiman et al., 2002). Similarly, in a CB paradigm, Reddy et al. (2006) showed that concept cells do not follow retinal input, as they were only active upon the perception of the changes. Using a backward-masking paradigm, previous research has also shown that concept cells fire only when the subject recognized the stimulus (Quian Quiroga et al., 2008). Remarkably, the responses appear in an all-or-none fashion, even if the visual stimuli were identical – i.e., the same picture at the same duration (Quian Quiroga et al., 2008).

These studies provide critical evidence for asserting that conscious perception is accompanied by these neural responses in the MTL. However, it was argued that this stage of processing might reflect the consequences of conscious recognition (NCCco), rather than recognition *per se* (Aru et al., 2012a; Quian Quiroga, 2012). This claim is supported by the fact that damage to MTL structures does not impair conscious perception (Kensinger

and Corkin, 2000; Postle, 2009). Likewise, here we propose that perceptual awareness precedes the firing of concept cells and is correlated with neuronal firing at 100–200 ms after stimulus onset, probably in the ITC (Logothetis, 1998). Indeed, we believe that a different and more sophisticated conscious process is triggered when this information is propagated to the MTL, activating these highly selective and sparsely firing neurons that represent the meaning of the stimulus for declarative, and particularly episodic, memory functions (Quian Quiroga, 2012). In particular, we support the idea that full awareness of the stimulus is elicited by this sensory-independent conceptual representation.

#### **FROM RECOGNITION TO CONTEXT: A LINKING MECHANISM?**

One of the most intriguing aspects of concept cells is the fact that their mean firing onset is too late (∼300 ms) to be explained by direct projections from high-level visual areas (ITC). In this line, it was argued that this delay might be crucial to enable the integration of information from different cortical areas, giving rise to a unified concept (Quian Quiroga, 2012). A recent study has shown a global LFP deflection in the theta-band (4–8 Hz) that precedes the response onset of concept cells (**Figure 2**) and is present only when the stimulus is consciously recognized (Rey et al., 2014). Moreover, the precise onset of concept cell responses is characterized by an increase in phase locking between the spikes and the LFPs in the theta band.

Even though the neural origin of this LFP deflection remains unclear, we believe that it is not originated from within the MTL. This is partially accounted by the fact that the human hippocampus is not thought to produce substantial contributions to the low-frequency LFP signals due to its structure (Buzsaki et al., 2012). More importantly, since the theta activation was seen globally in the MTL, if it were generated within the MTL, single cell activity responsible for this should have been observed prior to the change in the LFP. However, this situation was not observed (Rey et al., 2014). In turn, we hypothesize that this LFP response may reflect an activation generated by reverberating activity in the ITC crossing a certain threshold and triggering perception. This

LFP would provide a temporal window so that perceptual information can reach the MTL for further processes, such as memory functions.

## **CONCLUDING REMARKS**

This review discussed two methodological considerations in the study of the NCCs. In particular, we first argued for the implementation of paradigms where "seen" and "unseen" trials are obtained through the use of identical stimuli. Using contrastive analysis with identical visual stimulation allows ruling out physical effects that can otherwise contaminate the comparison between conditions. In the second part we discussed a selection of studies in which different NCCs were found at different timings and different brain areas (Quian Quiroga et al., 2008; Navajas et al., 2013; Rey et al., 2014). These seemingly contradictory results can be put together into a coherent framework by discriminating two different neural processes associated with conscious perception (i.e., perceptual and contextual awareness).

Interestingly, other distinctions between different conscious processes in the brain were previously proposed (Block, 1995; Kouider et al., 2010). For example, Block introduced the dichotomy between"phenomenal"and"access" consciousness (for a review, see Block, 2005), which is mainly centered on the question of whether we can have cognitive access to all our perceptual experiences, and thus whether we can see more than we can report (Block, 2011, 2012; Kouider et al., 2012). In this review, we proposed to discriminate between two different types of consciousness for already perceived stimuli (that can be reported) – i.e., processes beyond the distinction of phenomenal and access consciousness. In particular, we distinguish between a type of consciousness that relies on the firing of concept cells (*contextual awareness*) and the one that can be experienced even in the absence of MTL structures (*perceptual awareness*). The most remarkable examples of *perceptual* without *contextual* awareness are provided by patients with bilateral MTL resection or damage, such as patients H.M. (Scoville and Milner, 1957), R.B. (Zola-Morgan et al., 1986), and K.C. (Steinvorth et al., 2005). This condition led to a severe impairment in recollecting autobiographical events with no temporal gradient (Steinvorth et al., 2005), deficits in imagining new experiences (Hassabis et al., 2007), as well as in retaining and retrieving any type of episodic memory (Moscovitch et al., 2005). However, many other cognitive functions remained unaltered in these patients, such as the recognition of faces encoded before the surgery/accident (Kensinger and Corkin, 2000; O'Kane et al., 2004). Altogether, this evidence indicates that the MTL is not involved in the recognition of semantic entities, a process that we propose to be triggered by object-selective cortical areas (*perceptual awareness*). But bilateral damage or resection of MTL structures prevents subjects from having an enriched representation of the context, associations, and episodic memories linked to the specific stimulus, which we argue is elicited by the firing of concept cells (*contextual awareness*).

## **ACKNOWLEDGMENTS**

This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/H051651/1] and the Medical Research Council [grant number G1002100].

#### **REFERENCES**

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 12 June 2014; accepted: 12 August 2014; published online: 29 August 2014. Citation: Navajas J, Rey HG and Quian Quiroga R (2014) Perceptual and contextual awareness: methodological considerations in the search for the neural correlates of consciousness. Front. Psychol. 5:959. doi: 10.3389/fpsyg.2014.00959*

*This article was submitted to Consciousness Research, a section of the journal Frontiers in Psychology.*

*Copyright © 2014 Navajas, Rey and Quian Quiroga. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited andthatthe original publication inthis journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Investigating ongoing brain oscillations and their influence on conscious perception – network states and the window to consciousness

## *Philipp Ruhnau\*, Anne Hauswald and Nathan Weisz*

*Center for Mind/Brain Sciences, University of Trento, Trento, Italy*

#### *Edited by:*

*Jaan Aru, University of Tartu, Estonia*

#### *Reviewed by:*

*Rufin VanRullen, Centre de Recherche Cerveau et Cognition, France Niko Busch, Charité – Universitätsmedizin Berlin, Germany*

#### *\*Correspondence:*

*Philipp Ruhnau, Center for Mind/Brain Sciences, University of Trento, Via delle Regole 101, 38060 Mattarello, Trento, Italy e-mail<EMAIL_ADDRESS>
In cognitive neuroscience, *prerequisites* of consciousness are of high interest. Within recent years it has become more commonly understood that ongoing brain activity, mainly measured with electrophysiology, can predict whether an upcoming stimulus is consciously perceived. One approach to investigate the relationship between ongoing brain activity and conscious perception is to conduct near-threshold (NT) experiments and focus on the pre-stimulus period. The current review will, in the first part, summarize main findings of pre-stimulus research from NT experiments, mainly focusing on the alpha band (8–14 Hz). It is probable that the most prominent finding is that local (mostly sensory) areas show enhanced excitatory states prior to detection of upcoming NT stimuli, as putatively reflected by decreased alpha band power. However, the view of a solely local excitability change seems to be too narrow. In a recent paper, using a somatosensory NT task,Weisz et al. (2014) replicated the common alpha finding and, furthermore, conceptually embedded this finding into a more global framework called "Windows to Consciousness" (Win2Con). In this review, we want to further elaborate on the crucial assumption of "open windows" to conscious perception, determined by pre-established pathways connecting sensory and higher order areas. Methodologically, connectivity and graph theoretical analyses are applied to source-imaging magnetoencephalographic data to uncover brain regions with strong network integration as well as their connection patterns. Sensory regions with stronger network integration will more likely distribute information when confronted with weak NT stimuli, favoring its subsequent conscious perception. First experimental evidence confirms our aforementioned "open window" hypothesis. We therefore emphasize that future research on *prerequisites* of consciousness needs to move on from investigating solely local excitability to a more global view of network connectivity.

**Keywords: alpha oscillations, consciousness, near-threshold perception, network connectivity,Win2Con**

### **INTRODUCTION**

Consciousness is one of the main enigmas of philosophy and has always been of great interest to experimental psychology. In recent years, understanding consciousness has also been a focus of cognitive neuroscience and can be described as one of the most important challenges in unraveling the neural basis of human cognition (Zeki, 2003; Dehaene et al., 2006; Lamme, 2006; Cohen and Dennett, 2011). Since advancing a mechanistic understanding of how phenomenological states emerge from neural activity patterns appears not currently feasible (hard problem, Chalmers, 1995), a popular strategy in cognitive neuroscience has been to first identify the so-called neural correlates of consciousness (NCC), defined as "the minimal set of neural events that give rise to a specific aspect of a conscious percept" (Crick and Koch, 1998).

Neural correlates of consciousness have been investigated with many different neuroimaging methods and electrophysiology. A strong focus initially lay on the differential response evoked by similar stimuli that were either reportable (i.e., consciously accessible) or not. To achieve a similar signal to noise ratio (i.e., equal amount perceived and non-perceived) these stimuli are oftentimes presented at the individual perception threshold. Therefore, a typically applied paradigm to investigate NCCs is the near-threshold (NT) paradigm. The design idea is simple; participants encounter a stimulus at a certain intensity such that it is equally likely to be perceived or not perceived, i.e., at perception threshold. The advantage of this design is that it is not the stimulus that changes from trial to trial but the quality of its perception (perceived; nonperceived). By investigating neural responses to stimuli that have been perceived and comparing them with those that have not been perceived, researchers have the unique opportunity to investigate neural processes based on (absent/present) consciousness.

A recent review has made a distinction between different NCCs (Aru et al., 2012). In order of appearance they are: Neural correlates of prerequisite processes (NCC-pr) – which are necessary for conscious access, yet they do not carry conscious content. Secondly, the NCC directly related to conscious experience – a neural correlate that, if present, would result in a specific conscious experience. Finally, consequences of conscious experience (NCC-co) represent after-effects of conscious experience (Aru et al., 2012). In typical event-related studies focusing on periods following the stimulus, a distinction between the "true" NCC and NCCco is challenging due to strong overlaps; furthermore, even in the period following the stimulus onset there may be an additional impact of NCC-pr (Aru et al., 2012). In the current review we will focus on the prerequisite of consciousness (NCC-pr). We will emphasize the importance of ongoing oscillatory brain activity and suggest a network perspective when investigating prerequisites of consciousness. The importance of distributed network activity in supporting conscious perception has already been proposed by others (e.g., in the global workspace theory, Dehaene et al., 1998; Baars, 2005; and similarly, for a unitary percept within the micro-consciousness framework, Zeki and Bartels, 1999), however, the idea was confined to processing and propagation of already presented stimuli and has not been extended to the period preceding upcoming inflow.

The claim that pre-activated networks (or network connections) are required for conscious perception of NT stimuli has been made in a recent work by Weisz et al. (2014) and first evidence for this hypothesis has been provided. The "Windows to Consciousness" (Win2Con) framework proposes pre-established pathways, which enable the propagation of stimulus representations from sensory areas to higher order cognitive areas (such as prefrontal cortex) and thereby predisposing a conscious representation. We will describe the study by Weisz et al. (2014) in more detail in later parts of this review (see Windows to Conscious Perception) and we will also focus on the implications of the results for neuroscientific research of consciousness (see also other articles in this research topic).

In the first part of this review we will, however, summarize main findings related to the pre-stimulus period, that is, the time window preceding a stimulus. A main focus will be on the alpha band (8–14 Hz), which in terms of significance to, and attention of the neuroscience community has experienced a roller-coaster ride. Being the oldest rhythm described in the history of human electrophysiology, it has long been labeled as the brain's "idling" response, since it is dominant during restful wakefulness and decreases during stimulation (Pfurtscheller and Silva, 1999). Current frameworks of alpha oscillations stress their functional significance as an inhibitory rhythm under top–down control (Klimesch et al., 2007). Alpha oscillations seem to serve as a mechanism to gate sensory information by inhibiting irrelevant or interfering regions and releasing inhibition from necessary areas (Jensen and Mazaheri, 2010). Furthermore, electrophysiological research found ongoing brain activity in the alpha range preceding upcoming stimuli that can predict whether the stimulus can be reported (i.e., consciously accessed) or not (e.g., Hanslmayr et al., 2007; van Dijk et al., 2008). These findings were remarkable and they have provoked quite a few following studies, summarized in the next paragraph.

## **PRE-STIMULUS OSCILLATORY ACTIVITY PREDICTS CONSCIOUS PERCEPTION**

There are a wide variety of studies investigating conscious access to low contrast stimuli. Since the late 1990s, many researchers have identified event-related components of conscious processing of similar stimuli (for a review see Dehaene et al., 2006). This approach, at least implicitly, holds that neural processes relevant to conscious processing are time-locked to the presentation of a stimulus. A few years ago, several studies challenged this notion finding that alpha band power preceding a low-contrast or NT stimulus was significantly lower when the stimulus would be reported as perceived as compared to when it was not perceived (Hanslmayr et al., 2007; Romei et al., 2008a; van Dijk et al., 2008). Importantly, alpha effects were largely constrained to posterior regions involved in visual processing, demonstrating that pre-stimulus effects can be regionally circumscribed and arguing against interpretations that overall arousal/wakefulness levels explain the differences. This general finding intrigued also, because it suggested that conscious perception can be determined by ongoing brain oscillations, which have conventionally been regarded as functionally irrelevant noise. The study and understanding of pre-stimulus oscillatory effects can now be considered as a central aspect in the investigation of NCC prerequisites.

A strong alpha rhythm in posterior regions predicts that an upcoming visual NT stimulus will not be perceived (Ergenoglu et al., 2004; Hanslmayr et al., 2007; Romei et al., 2008a; van Dijk et al., 2008; Busch et al., 2009; Wyart and Tallon-Baudry, 2009). Typically, these studies presented NT targets for a brief time (Busch et al., 2009; Busch and VanRullen, 2010; Lange et al., 2013) and masked them after offset to avoid sensory memory effects (van Dijk et al., 2008; Hanslmayr et al., 2009). Sustained power differences between subsequent perceived and non-perceived trials have been found in the second before the presentation of the NT stimulus. These pre-stimulus alpha decreases in perceived, as compared to non-perceived, trials showed a parietal to occipital topographical distribution, which was localized to visual cortex but also to parietal areas (Romei et al., 2008a,b; van Dijk et al., 2008; Lange et al., 2013). These localizations and the behavioral effects of alpha power increases (i.e., missing NT stimuli), as well as top–down modulated increases of alpha in task-irrelevant areas (Jensen et al., 2002; Thut et al., 2006; Jokisch and Jensen, 2007; Ciesielski et al., 2010), are supportive of the functional inhibition framework (Klimesch et al., 2007; Jensen and Mazaheri, 2010) wherein alpha oscillations are modulating the excitatory state of cortical regions. First invasive evidence for this hypothesis exists. A study on monkeys showed a correlation between alpha oscillations and neural firing, such that low alpha power goes together with an increase in neural firing (carrying the sensory representation, Haegens et al., 2011). Thus, alpha activity does directly affect excitability in local cortical areas. In the case of the visual NT design one might phrase as follows: a low contrast (NT) stimulus that enters the visual cortex during high alpha power does not (or only weakly) activate sensory neurons and – due to this failure – does not influence downstream areas essential for conscious perception.

Near-threshold experiments similar to the ones described above have also been done in the somatosensory modality. Alpha modulations in posterior regions and over somatosensory cortices have been found to modulate detection performance (Linkenkaer-Hansen et al., 2004; Sauseng et al., 2009; Schubert et al., 2009; Weisz et al., 2014). The relationship between alpha power (or in case of the somatosensory system, also called mu) and conscious perception, however, seems not to be as straightforward as in vision. Especially for primary somatosensory areas, alpha power and perception are more related in an inverted U-shape manner (lowest detection rates for low and high alpha power, highest detection rate with medium alpha power, Linkenkaer-Hansen et al., 2004). On the other hand, secondary somatosensory cortices as well as parietal areas show a very similar linear relationship as in the visual domain, increasing detection rates of NT stimuli with decreasing alpha power (Schubert et al., 2009; Weisz et al., 2014). Furthermore, evidence from research on a monkey showed a similar relationship between alpha in the somatosensory system and perception (Haegens et al., 2011). This study, however, applied a (difficult) tactile discrimination (perceptual decisionmaking) task. A recent study (Haegens et al., 2014) investigated thalamic and primary somatosensory cortex (S1) activity in an NT paradigm in two monkeys. This study showed a relation of alpha power in S1 to response bias, such that increased alpha power led to more overall reports of perceived stimuli. To conclude, even though not always completely consistent in terms of the pattern, alpha oscillations also seem to play a crucial role in the perception of tactile stimuli by modulating local excitability of somatosensory cortex.

Surprisingly, in the auditory domain a study well matched to the NT studies described above is still missing (for fMRI see Sadaghiani et al., 2009). This might be because measuring auditory alpha with non-invasive electroencephalography/magnetoencephalography (EEG/MEG) is challenging and therefore evidence on alpha in the auditory system is in general sparse (but see Frey et al., 2014; Strauß et al., 2014; Wilsch et al., 2014). However, several studies on auditory phantom percepts (Leske et al., 2013; Müller et al., 2013) – illusions without actual sensory stimulation, therefore a conceptual antagonist to NT stimulation – showed similar oscillatory modulations as in vision (for a recent comprehensive review on phantom percepts and oscillations see Lange et al., 2014). For instance, Leske et al. (2013) used the Zwicker tone, a brief auditory "afterimage" following the presentation of notch-filtered noise, to investigate the role of auditory alpha in illusory perception. Applying different types of experimental and analytic modulations they found that, generally speaking, lower oscillatory power in the alpha and beta range (10–20 Hz) coincided with a stronger perception of the illusion. Power modulations were most pronounced in Heschl's gyrus, incorporating primary auditory cortex. Similar findings were provided for an illusion of music within presented noise (Müller et al., 2013), and an imbalance of cortical alpha is correlated with a pathological illusion: in tinnitus patients alpha power in auditory cortex regions is lower than in controls (Weisz et al., 2005; Hartmann et al., 2014). Thus, alpha oscillations play a crucial role in the generation of auditory illusions, resembling a similar inhibitory function as in other modalities.

It is worth noting that modulations of alpha power are not merely correlational. Studies using transcranial magnetic stimulation (TMS) showed a direct relationship between the (induced) alpha power and behavior (Romei et al., 2010). They presented lateralized visual NT stimuli and, using TMS, rhythmically stimulated the contra- or ipsilateral occipital cortex at 10 Hz. Participants detected substantially more stimuli when their ipsilateral occipital cortex was stimulated as compared to when the stimulation was contralateral. Furthermore, Romei et al. (2010) could show that the effect was specific to stimulation frequencies in the alpha range (10 Hz) but not to other frequencies (5 and 20 Hz).

As mentioned above, many studies on neural oscillatory activity showed an inhibitory function of the alpha band (Foxe and Snyder, 2011; Haegens et al., 2011; Mathewson et al., 2011; Spaak et al., 2012). This inhibitory function is not restricted to NT paradigms; in fact it has been shown that inhibition caused by alpha oscillation can even increase with stimulus intensity (Chaumon and Busch, 2014). Furthermore, alpha activity seems to be under top–down control (Jensen et al., 2002; Thut et al., 2006; Jokisch and Jensen, 2007; Ciesielski et al., 2010), such that taskirrelevant regions are inhibited by an increase of alpha power. It seems plausible that the neural system is inhibiting undesired routes, thereby actively opening specific gates to guide information through a distributed network architecture (Jensen and Mazaheri, 2010). To perform optimally, our neural system needs a strong inhibitor, which alpha activity seems to provide.

Oscillations are not only characterized by amplitude but also by phase. The idea (and in fact the scientific proof) that the phase of brain activity in relevant areas is directly correlated to how well that area can process information inflow is not new (Lindsey, 1952). In recent years, however, there has been an enhanced focus on this aspect. For instance, there is some evidence that visual perception samples the information inflow at a sampling rate of around 10 Hz (VanRullen and Macdonald, 2012). Thus, it is not surprising that, besides oscillatory power, the phase (or the phase distribution) before an actual stimulus can also be predictive of its perceptibility (Busch et al., 2009; Mathewson et al., 2009; Thorne et al., 2011). Effects of phase are not limited to vision but have also been shown in the somatosensory (Ai and Ro, 2014) and the auditory modality (Henry and Obleser, 2012; Neuling et al., 2012). Recent approaches using entrainment in the alpha range go so far as to ascribe phase the dominant role, causally related to behavior (Spaak et al., 2014). Crucially, however, phase effects are more closely aligned to the upcoming stimulus, and are typically found around stimulus onset (Mathewson et al., 2009; Thorne et al., 2011) or slightly before (Busch et al., 2009). While power modulations are more representative of a sustained excitability change in a task relevant region, phase seems to reflect increased excitability at certain points in time. Thus, it is not surprising that phase modulations are observed for expectation (Stefanics et al., 2010) and attention (Lakatos et al., 2008) when the timing of upcoming stimuli is known. A recent paper by Zoefel and Heil (2013) reminds us, however, that among all the recent phase-craze some caution may be warranted, by showing that contaminations by the evoked response can cause phase effects akin to those reported in the literature.

In summary, evidence from NT paradigms in different modalities showed a prerequisite NCC reflected in a modulation of cortical alpha power in task relevant, mostly local sensory areas. Low alpha power seems to be related to high excitability in sensory areas. Thus, in the case of reduced alpha power, an upcoming low contrast (NT) stimulus leads to an ignition of relevant neural assemblies in sensory areas and thereby a perceptual report is more likely. Local excitability, however, is not the only prerequisite for conscious perception as the Win2Con framework predicts (see Windows to Conscious Perception).

## **ESSENTIAL NODES AND NETWORK PROPAGATION ARE NECESSARY FOR CONSCIOUSNESS**

There are many findings showing specialized areas in the human brain representing the conscious percept and not just mere sensory stimulus input. For instance, the fusiform face area (FFA) and the parahippocampal place area (PPA) alternate in activity depending on whether the conscious percept is a face (FFA active) or a house (PPA active) with the sensory stimulation being identical (Tong et al., 1998). These areas, necessary for a specific content of consciousness, are sometimes called essential nodes (Zeki and Bartels, 1999). Many studies based on the GlobalWorkspace theory (Baars, 1989) provided evidence that essential nodes are not responsible for a conscious percept by themselves, but that a propagation of the stimulus representation, mainly to a fronto-parietal network, is required for a stimulus to become conscious (Dehaene et al., 2001; Dehaene and Changeux, 2005; Nakamura et al., 2005).

For example, NT or masked visual stimuli that are not consciously accessible still elicit activations in respective lower level sensory brain regions (Dehaene et al., 2006). Yet, only stimuli with enough energy such that their representation is propagated up to higher-order areas lead to a re-activation of sensory areas (re-entrant/recurrent process) and"ignition"of the conscious process (Lamme and Roelfsema, 2000; Dehaene and Changeux, 2005; Dehaene et al., 2006). Furthermore, evidence from masking studies shows that the mask interrupts recurrent processes (Lamme et al., 2002). Several electrophysiological studies could demonstrate an enhanced functional connectivity for a consciously perceived stimulus as compared to the identical stimulus that went unnoticed (Palva et al., 2005; Melloni et al., 2007). Furthermore, multivariate decoding patterns applied to visual cortex can predict the presented stimulus, even in the absence of a conscious percept (Haynes et al., 2005). Thus, despite being an important component of the NCC, there is more to conscious perception than the activation of essential nodes. The model that initially proposed essential nodes, which are active for different aspects/features of the stimulus, is the model by Zeki and Bartels (1999) and Zeki (2001, 2003), which proposes micro-consciousness in each of the active nodes. All representations (nodes) exist independently. However, a unitary conscious percept can only exist when they are bound together (Bartels and Zeki, 2006). Thus, even though the basic representation of features is different, and no fronto-parietal areas are engaged *per se*, the crucial element that forms a unitary consciousness is again how individual parts of the percept are connected.

Most NCC research still follows the conventional approach in cognitive neuroscience, in studying local brain activity or functional connectivity states starting with the onset of the stimulus. This implicitly or explicitly degrades preceding and fluctuating ongoing brain activity as irrelevant noise. However, numerous works (see Pre-stimulus Oscillatory Activity Predicts Conscious Perception) assume and illustrate the high importance of ongoing brain activity for upcoming sensory stimulation. This suggests that presentation of sensory stimuli may fall into a period, described by local activation patterns and interregional functional connectivity patterns, which may be favorable or unfavorable for conscious perception (see also Windows to Conscious Perception). First evidence for the importance of network connections in ongoing activity comes from a recent study by Hipp et al. (2011). The study used an ambiguous stimulus, two bars that could either be perceived as bouncing off or passing each other, and investigated networks associated with the different percepts. They found that pre-stimulus activity in a fronto-parietal beta network was predictive of a bouncing or passing percept. This indicates that the content of consciousness is predisposed by ongoing activity in a large neuronal network.

In summary, whether consciousness is represented by activity in a global workspace or by the integration of information of multiple essential nodes with micro-consciousness, the common factor is the necessary interconnection, without which conscious access seems impossible. In the following chapter we will argue that this is similarly true for prerequisites of consciousness.

### **WINDOWS TO CONSCIOUS PERCEPTION**

The framework proposed by Weisz et al. (2014) added a new angle to the discussion of determinants of consciousness, or NCC-pr to stay within the terminology of Aru et al. (2012). The rationale of the framework is motivated by what is seemingly a paradox: if local excitability is not a sufficient explanation for conscious perception of a stimulus (e.g., Dehaene et al., 2006) in a post-stimulus period then why should local excitability be the crucial pre-stimulus ingredient of upcoming conscious perception? Indeed, as summarized convincingly by Dehaene et al. (2006), temporally early sensory activity, i.e., not caused by reentrant processes, does not differentiate between consciously perceived and non-perceived (identical) stimuli in post-stimulus intervals. This however would be expected if the main feature leading to subsequent perception of NT stimuli is the excitability level in sensory areas (e.g., neurons closer to firing threshold inV1 discharging following weak afferent input in the case of perceived stimuli).

Weisz et al. (2014) argue that in addition (or potentially even as a substitute) to local excitability, specific functional connections between sensory and higher order areas are predicting conscious perception (**Figure 1A**). It is therefore a matter of pre-established pathways forming windows to consciousness (thus the frameworks name: Win2Con) that allow a stimulus to be forwarded and consequently become conscious. Due to the abundance of pre-stimulus alpha effects in the literature, Win2Con builds upon the "gatingby-inhibition" hypothesis (Jensen and Mazaheri, 2010), which proposes that alpha activity can functionally inhibit (and release) nodes along the sensory pathway and thereby govern which content reaches consciousness and which is suppressed. It seems likely that alpha band activity plays a role in pre-established networks, although our framework makes no specific predictions about the frequency band involved. Crucially, the Win2Con framework predicts that the functional pathways activated in this gating process are already pre-activated, thereby constituting open windows to

connections to higher order areas (e.g., prefrontal, parietal) are essential. **(B)** Local excitability changes in the somatosensory cortex (SII) as indexed by alpha decreases prior to perceived as compared to not perceived trials. **(C)** Additionally, local changes of connectivity (efficiency) have been observed increased with specific regions: mainly the left anterior temporal/inferior frontal areas, and other sensorimotor regions (e.g., premotor cortex, inferior parietal lobe). The power and connectivity results presented in **(B–D)** are from Figures 2–4 in Weisz et al. (2014).

consciousness. There is some evidence that this is in fact the case.

In their study, Weisz et al. (2014) used an NT paradigm to investigate pre-stimulus activity in the somatosensory domain (see Wühle et al., 2010 for the original study investigating NCC). Two tactile stimuli were presented successively, one of them always supra-threshold the other one at perception threshold. Participants reported whether they perceived one or two stimuli. Contrasting pre-stimulus power for perceived and not perceived trials of the NT stimulus, the authors reported enhanced alpha in secondary somatosensory cortex [as well as middle frontal gyrus (MFG)] for subsequently not perceived NT stimuli (**Figure 1B**), thereby replicating the finding of local excitability changes in predicting the NT stimulus perception. More relevant within the proposed framework, they also analyzed network properties (global and local graph theoretical measures, see **Box 1**) between perceived and not perceived NT stimuli during the pre-stimulus period. On a global level, enhanced small-worldedness occurred around 17 Hz prior to not perceived NT stimuli compared to perceived ones. As small-worldedness is calculated as the ratio between clustering of a network and path length, both parameters can potentially influence the metric (more details on the relationship between different network properties can be found in **Box 1**). The authors investigated the nature of this effect by calculating global clustering and path length (as well as global

efficiency) for the relevant frequency band and found that the enhanced small-worldedness prior to not perceived NT stimuli was due to a globally more clustered network compared to perceived ones. As global efficiency suggested shorter communication pathways prior to perceived stimuli, the authors concluded that networks preceding not perceived NT stimuli show more characteristics of local integration while those preceding perceived NT stimuli are more defined by global integration properties. These global differences underlying perceived and not perceived NT trials were further investigated using more fine-grained analyses on the frequency identified by the global analyses (17 Hz), namely local graph theoretical measures, which provide the location of specific network properties instead of a description applying to the entire network (**Figure 1C**). Confirming the global effects, increased local clustering and efficiency as well as reduced distance were observed for the secondary somatosensory cortex prior to perceived NT stimuli, presumably a key region for conscious somatosensory perception. Somatosensory cortex not only appears to be generally better integrated prior to perceived NT stimuli, but the communication flow is increased with specific regions (**Figure 1D**): mainly the left anterior temporal/inferior frontal areas and other sensorimotor regions (e.g., premotor cortex, inferior parietal lobe, etc.). Prior to not perceived NT stimuli, only the posterior cingulate gyrus showed enhanced connectivity.

#### **BOX 1 | Graph theory for electrophysiology: a very brief primer.**

Graph theory investigates different network properties of nodes (e.g., neurons, brain regions, or websites) and edges (e.g., axons, structural/functional connections, or hyperlinks). As the brain is a complex network of interconnected neurons, graph theoretical measures can also be applied to understand the dynamics of this complex system better (for reviews see Bullmore and Sporns, 2009; He and Evans, 2010; Sporns, 2012). Within the brain networks, the nature of nodes (i.e., voxels) and edges (i.e., connections) depend on different variables as measures and features of connectivity, brain mapping methods, and anatomical parcellation schemes (Rubinov and Sporns, 2010), all of which can influence the interpretation of the data.

With electrophysiology the following steps need to be taken to investigate frequency specific source space data with graph theory (see Bullmore and Sporns, 2009; and for details Weisz et al., 2014): First, after the projection of sensor space activity to source space (Schoffelen and Gross, 2009) a measure of association between individual nodes needs to be estimated (in Weisz et al., 2014, the imaginary part of coherence; for other approaches see Hipp et al., 2012), resulting in a node by node association matrix. Secondly, a binary adjacency matrix is generated by applying a threshold to the association matrix (for how to choose the threshold see van Wijk et al., 2010). The adjacency matrix (the graphical model of the brain network) can then be used to calculate network parameters of interest. Within the context of Win2Con the most interesting ones would be, for example:

The Brain Connectivity Toolbox (Rubinov and Sporns, 2010; https://sites.google.com/site/bctnet) offers a wide variety of functions in Matlab and C that calculate most of these measures.

The reported study by Weisz et al. (2014) provides first evidence for the Win2Con framework. Different sensory areas (here somatosensory regions) are differentially integrated within a network prior to the relevant stimulus, rendering its perception more or less likely. As proposed in the framework, local alpha modifications predicting the perception of an upcoming event (as suggested in the functional inhibition hypothesis) appear to depend on the level of integration between the region showing alpha modulations and the relevant network. In this sense, local excitability is necessary but not sufficient for conscious perception. Pre-established pathways within the relevant network are another prerequisite for NT stimuli to become consciously accessible. This is supported by findings of double dissociation of alpha modulations and graph theoretical measures: for example, while alpha power showed strong effects in the right MFG, effects of the graph theoretical measures were absent. *Vice versa*, significant changes of graph theoretical measures occurred in left anterior temporal/inferior regions without concomitant effects in alpha power. Accordingly, alpha power should not be mistakenly regarded as a proxy of functional connectivity.

Speaking of pre-established connections might trigger associations with other frequently investigated and reported networks such as, for example, the default mode network (Raichle et al., 2001). Findings from the study by Weisz et al. (2014) might nourish speculations that the default mode network constitutes such a network of pre-established pathways that modify the likeliness of subsequent conscious perception. In the current case of somatosensory NT stimulation, the posterior cingulate cortex, which supposedly is a core region of the default mode network, showed reduced connectedness with the somatosensory cortex prior to not perceived stimuli. Analogously, we want to point out that the idea of pre-established pathways relies on the assumption that, although globally increased connectivity might be a hint to an open window, it is not a general increase in connectedness of sensory regions that renders perception of an upcoming stimulus more likely, but connections with relevant areas, and integration of frontal and parietal regions that are crucially involved in the so-called global workspace (Dehaene et al., 1998).

#### **CONCLUSION AND FUTURE RESEARCH QUESTIONS**

In the current review we gave an overview of previous work regarding factors determining conscious perception with a focus on oscillatory alpha modulations prior to NT stimulation. In summary, lower alpha power in task relevant areas appears to be

### **FIGURE 2 | Schematic representation of the predictions of the functional inhibition theory and the windows to consciousness**

**framework.** The functional cortex images on top of each line plot show the hypothesized contrast of perceived vs. not perceived stimuli in terms of functional activity. **(A)** Functional inhibition hypothesis. Cortical excitability is modulated in sensory regions before NT stimulus presentation. Low alpha goes hand in hand with increased excitability and thus an NT stimulus entering sensory areas yields a stronger neural signal. The signal propagates to higher order areas, which send feed-back signals to sensory areas. The second peak in the sensory region reflects activity due to re-entrant activation. **(B)** Windows to consciousness framework. Under similar excitation levels, modulations of connectivity are expected. In case of pre-established pathways (high connectivity, dark green) early sensory activation, expected to be similar for perceived and not perceived trials, is propagated to high-level areas. This leads to re-entrant activation of sensory areas. Crucially, only late activity differences in sensory areas are expected.

predictive of conscious perception of upcoming NT stimulation. However, it is worth mentioning that there is sparse evidence that the local excitability changes are bound to sensory regions as only a few studies have estimated the sources of alpha modulations (van Dijk et al., 2008; Lange et al., 2013; Weisz et al., 2014). Also, these local excitability alterations may not always be predictive of NT stimulus perception, at least in a linear manner (Linkenkaer-Hansen et al., 2004). Importantly, we claim that there exists an explanatory gap between the standard interpretation of pre-stimulus alpha effects that emphasize the role of local excitability, and the very sparse evidence showing an implication of respective regions at early latencies (see **Figure 2** for a schematic presentation of the predictions of the functional inhibition and the Win2Con framework). Consequently, we argued that local alpha power is indicative but not sufficient for conscious perception (at least for stimuli of weak intensity). As was proposed in a recent study (Weisz et al., 2014) introducing the Win2Con framework, a prerequisite for conscious perceptions is the integration of relevant areas in a broad network. Thus, only when local sensory excitability meets pre-established pathways, i.e., integration of the relevant area, will an NT stimulus be perceived.

There are still a few open questions that it is important to address: Firstly, as mentioned above, the actual location of the frequently reported alpha modulations prior to stimulus perception is often not known. Sources within sensory areas would have different theoretical implications than downstream sources: for example, the distinction between visual cortex and parietal regions is not always evident from sensor topographies alone. While the first would underline the importance of the current state of a specific sensory system, the latter may point toward a generalized increased attentive state. Therefore, supportive evidence should come from source localization efforts and/or intracranial recordings in animals or, for example, epileptic patients. Secondly, while building upon the alpha literature makes sense in light of the current evidence, the question remains whether power modulations in sensory areas are indeed necessary or whether the connectivity states alone are sufficient to predict conscious perception. Thirdly, and somewhat related, the exact relation between power and connectivity or local and inter-areal synchronization needs to be better understood. Fourthly, are pre-stimulus networks and post-stimulus networks comparable and, if yes, how do they relate? Fifthly, what kind of impact does pre-stimulus activity have on stimulus processing as indexed by evoked/induced brain activity? This can be investigated, for example, by using correlational approaches. Sixth, networks within the alpha–beta frequency range are not only theoretically good candidates but, as elaborated above, there is evidence for their involvement in conscious perception. Nevertheless, the question about other frequencies ranges and their interplay remains open. Finally, how causal are pre-stimulus connectivity patterns? This could be investigated using real-time experiments in which particular states (as previously identified in offline studies) can be detected and stimuli presented at times of a specifically beneficial or detrimental state.

While the current review and introduction to our framework was geared toward the case of NT stimuli to illustrate our point, in real life many stimuli are clearly supra-threshold. In this case it is likely that sensory areas will be sufficiently activated to drive downstream areas, rendering the stimulus to be consciously perceived. However, it is also likely that trial-by-trial coupling fluctuations between sensory and downstream areas will influence post-stimulus activity patterns and thereby phenomenological experience. Apart from clever paradigms that allow for graded

quantification of subjective experiences related to a stimulation, analysis methods would need to sufficiently develop to allow for the assessment of single-trial connectivity using MEG/EEG.

#### **ACKNOWLEDGMENTS**

Philipp Ruhnau and Nathan Weisz were funded by the European Research Council (ERC StG 283404 – WIN2CON). We would like to thank Julia N. Frey for her helpful input on a previous version of the manuscript and Hayley Prins for proof reading the manuscript. Also, we would like to acknowledge the two reviewers for their very constructive comments.

#### **REFERENCES**

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 03 July 2014; accepted: 09 October 2014; published online: 30 October 2014. Citation: Ruhnau P, Hauswald A and Weisz N (2014) Investigating ongoing brain oscillations andtheir influence on conscious perception – network states andthe window to consciousness. Front. Psychol. 5:1230. doi: 10.3389/fpsyg.2014.01230*

*This article was submitted to Consciousness Research, a section of the journal Frontiers in Psychology.*

*Copyright © 2014 Ruhnau, Hauswald and Weisz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited andthatthe original publication inthis journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Intracranial spectral amplitude dynamics of perceptual suppression in fronto-insular, occipito-temporal, and primary visual cortex

## *Juan R. Vidal 1,2,3,4 \*, Marcela Perrone-Bertolotti 3,4 , Philippe Kahane5 and Jean-Philippe Lachaux1,2*

*1 INSERM U1028, CNRS UMR5292, Brain Dynamics and Cognition Team, Lyon Neuroscience Research Center, Lyon, France*

*2 Université Claude Bernard, Lyon, France*

*3 Laboratoire de Psychologie et NeuroCognition, University Grenoble Alpes, F-38040 Grenoble, France*

*4 CNRS, Laboratoire de Psychologie et NeuroCognition, UMR 5105, F-38040 Grenoble, France*

*5 CHU Grenoble and Department of Neurology, INSERM U704, F-38043 Grenoble, France*

#### *Edited by:*

*Jaan Aru, University of Tartu, Estonia*

#### *Reviewed by:*

*Melanie Wilke, California Institute of Technology, USA Theofanis Panagiotaropoulos, Max Planck Institute for Biological Cybernetics, Germany*

#### *\*Correspondence and present address:*

*Juan R. Vidal, CNRS, Laboratory of Psychology and NeuroCognition, UMR 5105, University Pierre Mendès-France University of Grenoble-Alpes, Bâtiment Sciences de l'Homme et Mathématiques BP 47, 38040 Grenoble Cedex 9, France e-mail<EMAIL_ADDRESS>
If conscious perception requires global information integration across active distant brain networks, how does the loss of conscious perception affect neural processing in these distant networks? Pioneering studies on perceptual suppression (PS) described specific local neural network responses in primary visual cortex, thalamus and lateral prefrontal cortex of the macaque brain. Yet the neural effects of PS have rarely been studied with intracerebral recordings outside these cortices and simultaneously across distant brain areas. Here, we combined (1) a novel experimental paradigm in which we produced a similar perceptual disappearance and also re-appearance by using visual adaptation with transient contrast changes, with (2) electrophysiological observations from human intracranial electrodes sampling wide brain areas. We focused on broadband high-frequency (50–150 Hz, i.e., gamma) and low-frequency (8–24 Hz) neural activity amplitude modulations related to target visibility and invisibility. We report that low-frequency amplitude modulations reflected stimulus visibility in a larger ensemble of recording sites as compared to broadband gamma responses, across distinct brain regions including occipital, temporal and frontal cortices. Moreover, the dynamics of the broadband gamma response distinguished stimulus visibility from stimulus invisibility earlier in anterior insula and inferior frontal gyrus than in temporal regions, suggesting a possible role of fronto-insular cortices in top–down processing for conscious perception. Finally, we report that in primary visual cortex only low-frequency amplitude modulations correlated directly with perceptual status. Interestingly, in this sensory area broadband gamma was not modulated during PS but became positively modulated after 300 ms when stimuli were rendered visible again, suggesting that local networks could be ignited by top–down influences during conscious perception.

**Keywords: perceptual suppression, broadband gamma, conscious perception, anterior insula, primary visual cortex, visibility, intracranial EEG, contrast adaptation**

## **INTRODUCTION**

The flow of our conscious perceptual experiences evidently relate to the ongoing changes in our sensory inputs. In addition to establishing the limits between early sensory neural processing and subsequent conscious-related processing (Dehaene and Changeux, 2011) it has become a critical issue to scrutinize the conditions used in contrastive analyzes which typically compare brain activity for visible and invisible stimuli (Aru et al., 2012; de Graaf et al., 2012). For example, it might be relevant to *a priori* dissociate between perceptual invisibility that is caused by disabling sensory stimuli from reaching consciousness, which occurs when efficiently masking stimuli, from perceptual invisibility that is produced by suppressing a previously visible stimulus. Both invisibilities probably do not involve the same underlying neural mechanisms. In this study we focus on the latter which is called perceptual suppression (PS).

By definition, visual PS consists in making a visible object invisible despite ongoing retinal input. This has been achieved through flash suppression (Wolfe, 1984; Wilke et al., 2003, 2006; Tsuchiya and Koch, 2005). In binocular vision PS can also be induced by background motion, as in motion-induced blindness (MIB; Bonneh et al., 2001, 2014), by transient contrast decreases (May et al., 2003; Simons et al., 2006), or by presenting small visual transients throughout the visual field after short fixation (Kanai and Kamitani, 2003). Sudden contrast reductions have a strong impact so as to make complex visual scenes vanish from conscious perception for a prolonged period (Simons et al., 2006). The latter type of PS has been described and characterized at a psychophysical level, yet remains largely unexplored at the neural level, but could reveal important insights into the neural basis of conscious visual perception.

While previous studies explored the modulation of cortical spectral responses during PS (Wilke et al., 2006; Keliris et al., 2010; Panagiotaropoulos et al., 2012), we ignore if these responses are manipulation-specific, i.e., whether they depend on the type of

experimental manipulation that induces PS, or whether they generalize to other types of manipulations inducing suppressions as well, such as those induced by contrast decreases, thereby reflecting general mechanisms underlying states of stimulus visibility and invisibility. To explore a novel experimental approach to conscious perception and to study neural activity modulation by PS we characterized the neural correlates of PS induced by stimulus contrast decrease (Simons et al., 2006), for the first time within human intracranial electroencephalographic (iEEG) recordings.

In our task participants passively fixated a typical Troxler ringshaped stimulus while it underwent successive contrast increases and decreases, yet always remained physically on-screen. Within our experimental design, the same contrast level can be associated with either a visible or an invisible target. A behavioral account in our study showed that this effect elicited with specific parameters is perceptually robust and highly reproducible from trial-to-trial. We therefore implemented an adapted version of it for the subsequent intracranial recordings. In our analyzes we first focused on the global significant emergence of broadband gamma (50– 150 Hz) and low-frequency alpha/beta (8–24 Hz) responses as compared to a baseline period. We estimated the modulation of these signals in sensory, limbic, and frontal cortices. This first analysis revealed which type of signal amplitude modulations, increases or decreases, are elicited by contrast changes (increases or decreases), respectively, linked to stimulus visibility (VIS) and invisibility (INV). However, this analysis does not directly link local network activity with stimulus visibility status processing, due to a possible confound with the sign of contrast change. Therefore, in a second analysis we pinpointed perceptual processing by contrasting the two previous conditions with a third condition, the stimulus offset (OFF), which also elicits stimulus disappearance but without stimulus sensory processing (see Materials and Methods). Summarizing, our paradigm uses perceptual invisibility during contrast decrease (INV), physical invisibility by removal of physical stimulus (OFF) and physical visibility by contrast increase (VIS).

Here we report that PS induced by contrast reduction elicits a decrease in low-frequency activity in distant cortical networks. Conversely, broadband gamma responses, especially in lateral occipital-temporal cortices, are positively modulated for this

same physical manipulation which is associated with stimulus invisibility in our experimental design. Interestingly, when contrasting this condition with the two other conditions, respectively, stimulus visibility (by contrast increase) and stimulus invisibility (by stimulus offset), we found that in occipito-temporal cortices most effects were perceptual and were found in low-frequency amplitude modulations. In this same region, gamma-band activity modulation was divided between perceptual and sensory effects. Surprisingly, the temporal dynamics of the gammaband amplitude modulation found in primary visual cortex (PVC,=V1), revealed unexpected patterns. There was no gammaband response to stimulus contrast decrease (INV) and a slowly negative progressing gamma-band response to stimulus contrast increase (VIS). Interestingly, after 300 ms VIS responses displayed a positive and transient amplitude rebound, which could be signaling top–down feedback processing associated with the status transition from invisibility to visibility. Finally, we also contribute evidence on a role of frontal cortex in conscious perception by reporting that perceptual effects in the gamma-band appear earlier in fronto-insular networks than in occipito-temporal networks, conveying evidence supporting top–down signals in conscious perception that may arise from these networks. Globally, the results presented in this study shed light on a rich panel of underlying specific processes at work during PS with new insights for the neural basis and neural dynamics of conscious perception.

## **MATERIALS AND METHODS STIMULUS AND TASK** *Behavioral task*

The visual stimulus consisted of a centered dark gray fuzzy circle (Troxler stimulus, inner diameter: 10 cm; width 1 cm) on a gray background (**Figure 1A**). The image's original contrast level was changed for three different stimuli with respective contrast decreases of 10, 50, and 100% (stimulus removal). A small black fixation cross was positioned at the center of the image and stimuli were presented on a screen positioned at ∼1 m from the participants. The behavioral task consisted in the following sequence of events (**Figure 1A**): after a 1–2 s of fixation the stimulus at 100% of contrast was presented for 2 s, followed by the presentation for 1.2 s of one of the three other stimuli with reduced. Thereafter,

a white background screen was presented indicating three alternative response choices indicating full, partial, or null stimulus disappearance. Participants could respond during the 3 s duration of this period. The participants task was to rate on each trial his/her perceptual fading experience of the stimulus after the contrast change. Importantly, participants had the third option to indicate "partial disappearance" only when the stimulus did not fully disappear and remained partially visible. The disappearance probability of each stimulus was estimated by the ratio of the number of full disappearance reports divided by the total number of responses. As shown in **Figure 1B**, at 50% contrast decrease, the disappearance probability of the stimulus was near 95%, reflecting the strength of the PS effect. Similar effects have been shown in previous studies (May et al., 2003). The behavioral task was carried out on a laptop computer (screen: 60 Hz refresh rate).

#### *Intracranial recording task*

The same basic visual stimuli from the behavioral task was used in the experiment for the intracranial recordings. In this task, the stimulus contrast level indicated at 100%, which was equal to the 75% contrast level used in the behavioral task, was relatively reduced and increased to two different contrast levels: 50 and 150%. A small black fixation cross was positioned at the center of the circle and stimuli were presented on a screen positioned at ∼1 m from the participants. When increasing the contrast to its original level or higher the faded ring reappeared immediately. We used this PS effect in an original experimental design with two conditions, described in the following section.

A trial consisted in four successive contrast changes of the same ring-shaped stimulus (**Figures 2B,C**). Each trial started with the presentation of a central fixation cross for a duration of 1.5 s, followed by the onset of the stimulus at 100% contrast for 2.2 s. In the first condition termed INV the contrast changes occurred in the following order and lasted 1.1 s: 150, 100, and 150%. The latency of stimulus onset or stimulus change that includes a temporal jitter of 0–100 ms is preceded by the ∼ symbol. The contrast variations produced stimulus invisibility in the third step when the stimulus contrast was decreased 50% (**Figure 2B**). In the second condition termed VIS the contrast changes occurred in the following order: 50, 100, and 150%. In this series the stimulus became invisible in the second step and was visible again in the third step when the contrast was increased 50%. Importantly, through these different contrast manipulations we could achieve for the same stimulus physical contrast level (100%) two different percepts, either an invisible or a visible stimulus, as illustrated by the pink box relating panels B and C in **Figure 2**. After the fourth step which increased the stimulus contrast from 100 to 150%, the stimulus was set off during the presentation of a white screen (offset, OFF). In 50% of trials the stimulus decreased 50% contrast for a brief 0.2 s before full stimulus offset. We used the other 50% of all trials' offset (both conditions), who did not include this brief transient, in subsequent analyzes detailed in the data analysis section. Participants were instructed to fixate the central cross during all contrast changes and detect this very short stimulus disappearance before stimulus offset (white screen). This detection task was not related to prior perceived contrast changes and its only purpose was to keep

participants focused throughout trials. This experimental design has the advantage to reduce neural processing related to the preparation of subjective report in the period of interest which involves mainly passive fixation of successive stimulus contrast and visibility changes (pink box, **Figures 2B,C**). After a short task training period which concerned the maintenance of gaze fixation throughout contrast changes participants reported a vivid experience that the stimulus disappeared (PS) and subsequently reappeared after contrast increase (PSR; **Figures 2B,C**). There were between 60 and 120 trials per condition, depending on the number of blocks (=30 trials) completed by each participant. The intracranial task was carried out on a desktop computer (screen: 60 Hz refresh rate).

#### *Participants and intracranial recordings*

The behavioral task included 10 healthy participants (five males, five females, mean age 32.8 ± 2.0 SEM), all had normal or corrected-to-normal vision and provided written informed consent. Intracranial recordings were obtained from 9 neurosurgical patients (five females, four males, mean age: 28.7 ± 3.3 SEM)

with intractable epilepsy at the Epilepsy Department of the Grenoble University Hospital and at the Epilepsy Department of the Neurological Hospital in Lyon. All participants had normal or corrected-to-normal vision, gave written informed consent, and the experimental procedures were approved by the Institutional Review Board and by the National French Science Ethical Committee. Electrode implantation was performed according to regular procedures and all target structures for the presurgical evaluation were selected strictly according to clinical considerations with no reference to the current study. Eleven to fifteen semirigid, multi-lead electrodes were stereotactically implanted in each patient, with 10–15 recording sites on each electrode (2 mm wide, 3.5 mm center-to-center; Jerbi et al., 2009b; Lachaux et al., 2012). Intracranial EEG recordings (iEEG) were conducted using a video-iEEG monitoring system (Micromed), which allowed the simultaneous data recording from 128 depth-EEG electrode sites. All electrode contacts were identified on a postimplantation MRI showing the electrodes, and coregistered on a pre-implantation MRI. MNI and Talairach coordinates were computed using the SPM (http://www.fil.ion.ucl.ac.uk/spm/) toolbox.

#### *Data analysis*

Data were sampled at 512 Hz and each recording site was referenced to its adjacent neighbor, (bipolar montage). In this study we extracted spectral responses from neural signals using two spectral analysis techniques: wavelets and Hilbert transform. Based on prior knowledge from previous studies involving visual induced spectral responses in intracranial recordings (Vidal et al., 2010, 2011; Dalal et al., 2011; Hamame et al., 2014; Perrone-Bertolotti et al., 2014) we targeted two frequency bands of interest: the broad gamma band (50–150 Hz) and the low-frequency band (8–24 Hz). As proposed elsewhere (Lachaux et al., 2012), the broad gamma band is currently considered as a functional correlate of local cortical activation while the low frequency bands might reflect long-range interactions involved in top–down processing (Siegel et al., 2012). The amplitude responses within these bands were extracted with a Hilbert transform that is explained further in the text. However, in order to verify that such prior based frequency band selection indeed contained effective signal modulation we first computed a standard time-frequency (TF) wavelet decomposition within the 2–150 Hz band. To estimate the significance of these post-stimulus responses within this frequency range, as compared to baseline ([–200 to – 50]ms) power in a total time window from −200 to 800 ms post-stimulus, we used a paired *t*-test across trials at each recording sites. Illustrative examples of this analysis are shown in **Figure 3**.

To increase signal-to-noise ratio of spectral neural signals in the low and high frequency bands we thus opted to compute the amplitude envelope in the averaged high and low frequency intervals, respectively. We therefore computed the Hilbert transform to evaluate the significance of these neural responses with a

response emergence in PS, PSR and the comparison between the two conditions in **(A)** extra-striate visual area (MNI coordinate: [−39 −75 2]), **(B)** anterior insular cortex (MNI coordinate: [−37 18 9]) and **(C)** supramarginal gyrus (MNI coordinates: [58−21 18]). Broadband gamma power increase and low-frequency power decrease, as compared to pre-stimulus baseline, were

opposite effect pattern was observed for PSR. All TF T-maps are scaled between −8 and 8. **(D)** To study spectral emergence, i.e., the orientation of the different spectral responses as compared to the average baseline level, we averaged the amplitude in the late part of the neural response, bypassing the initial transient response induced by the changing sensory input.

lower degree of complexity across all electrodes. To obtain highfrequency activity amplitudes, for example between 50 and 150 Hz, we applied the following processing steps. First we bandpass filtered iEEG signals in multiple successive 10 Hz-wide frequency bands (e.g., 8–10 bands as [50–60 Hz], [60–70 Hz], etc.) using a zero phase shift non causal finite impulse filter with 0.5 Hz roll-off. For the low-frequency interval between 8 and 24 Hz we used 4 Hz-wide bands. Next, for each bandpass filtered signal we computed the envelope using a standard Hilbert Transform. For each frequency bin (ex: [60–70 Hz]) the time-varying amplitude was divided by its mean across the entire recording period of the experiment and multiplied by 100. This yields instantaneous amplitude envelope values as percentage-of-the-mean. Finally, the envelope signals computed for each consecutive band were averaged to provide one single time series across the entire recording session. The obtained envelopes had a sampling rate of 64 Hz. This procedure has been previously used in various studies in our group (Jerbi et al., 2010; Juphard et al., 2011; Ossandon et al., 2012; Vidal et al., 2012). To estimate the post-stimulus neural activity modulations vs. baseline (neural emergence), excluding the initial activity transient produced by neural sensory changes, we estimated the difference between the average amplitude in the 300–800 ms post-stimulus time interval with the averaged prestimulus (from −200 to −50 ms) baseline amplitude response by the means of a paired *t*-test, across all electrodes and corrected for multiple comparisons across this dimension with a false discovery rate test (FDR; Genovese et al., 2002). To estimate the difference in neural response amplitude between experimental conditions (visible, invisible and offset) averaged across time, we used a repeated measures analysis of variance (ANOVA) combined with a *post hoc* Tukey–Kramer test. All statistical *p*-values were FDR-corrected (False Discovery Rate, Genovese et al., 2002) for multiple comparisons across the number of electrodes. When these analyzes were performed on time resolved data, we equally corrected for multiple comparisons across time samples per ROI. We additionally considered effects significant and of interest when repeated on at least five consecutive time samples. All analyzes were performed using Matlab (The Mathworks, Inc., Natick, MA, USA).

In this study we focused mainly on two frequency ranges. We analyzed a low and high-frequency interval according to preliminary TF analysis (see Results section). The low-frequency interval ranged from 8 to 24 Hz including mainly alpha and beta band frequency response intervals. The high gamma band interval ranged from 50 to 150 Hz. This interval captures the broadband spectral responses that appear commonly in human intracranial recordings (Ossandon et al., 2012; Vidal et al., 2014). Note that although iEEG signals provide access to neuronal population activity across a wide range of frequencies especially in the gamma band, we focused here on broadband HFA because it has been closely related to population-level neuronal spiking activity (Manning et al., 2009; Ray and Maunsell, 2011) and it is increasingly used as a proxy for active cortical processing (Buzsaki and Wang, 2012; Lachaux et al., 2012). In line with human EEG studies on the link between low and high frequency bands and BOLD signal (Goldman et al., 2002; Laufs et al., 2003; Scheeringa et al., 2011) the high gamma band amplitude fluctuation, also appears to correlate with the fMRI BOLD signal (Logothetis et al., 2001; Lachaux et al., 2007a; Worrell et al., 2012) and has been shown to help neural decoding and real-time mapping of cognitive brain function and processes (Lachaux et al., 2007b; Jerbi et al., 2009a; Hamame et al., 2012).

## **RESULTS**

To use PS by stimulus contrast decrease for subsequent intracranial recordings we first investigated the strength of the PS by the level of contrast decrease in a group of healthy participants. The experiment revealed that small contrast decreases of −10% has only a marginal effect on disappearance probability (8% disappearance probability), as compared to the medium contrast decrease of −50%, which induced 89% of disappearance probability (**Figure 1B**). A repeated measures ANOVA revealed a significant difference [*F*(29) = 178.1; *p* < 0.001) between the disappearance probability of the three levels of contrast decreases (−10, −50, and −100%) and the *post hoc* Tuckey–Kramer test revealed that the −10% condition was different from the other two conditions. We concluded from this first study that decreasing half the contrast could achieve full PS of the visual stimulus, statistically indissociable from full stimulus removal (−100% contrast).

We recorded from a total of 976 electrodes widely distributed throughout the cortex (nine patients). The experimental paradigm is detailed in **Figures 2B,C**. We focused our analyzes on the timewindow indicated by the pink box (**Figures 2B,C**) to compare neural activity elicited by stimulus visibility (VIS) and invisibility (INV) at identical physical stimulus contrast (**Figures 2B,C**). Based on evidence from previous studies on spectral responses of visual processing in intracranial recordings (Vidal et al., 2010) we expected strong modulations within the broad high-gamma band (50–150 Hz) and a low-frequency interval grouping both alpha and beta frequency ranges (8–24 Hz). These signals were extracted with a Hilbert transform procedure (see Materials and Methods section) and subsequently statistically analyzed (detailed in the subsequent paragraph). From these results we chose three representative electrodes in three different cortical areas to proceed to a full TF decomposition with a wavelet procedure, and show the described effects can also be visible within the full TF interval. The results of this analysis are described in the next paragraph.

Time-frequency statistical analyzes revealed a significant poststimulus spectral power modulation relative to pre-stimuluschange baseline level (−200 to −50 ms). **Figures 3A–C** show statistically significant spectral power changes for the two experimental conditions, visible (VIS) and invisible (INV), obtained from three illustrative electrodes from three different participants, and the comparison statistics between the two conditions. First, stimulus invisibility induced a sustained power increase in a rather broad portion of the TF spectra (50–150 Hz). This increase occurred beyond the initial transient activity increase yielded by the contrast change. Simultaneously it induced a sustained power decrease in low-frequency bands, including alpha (8–12 Hz) and beta band (16–24 Hz; Middle panels in **Figures 3A,B**). In the right panel of **Figure 3A** the two distinct frequency intervals are marked as HF, high–frequency, and LF, low-frequency. An opposite response pattern was observed for stimulus visibility obtained during PSR, with a broadband gamma power suppression as the contrast increased (Left panels in **Figures 3A,B**). In the low-frequency range (8–24 Hz) power modulations occurred in opposite sign as for the respective high-frequency modulations. Stimulus contrast increases often elicited power increases in the low-frequency interval while stimulus contrast decreases induced power decrease in this band (**Figures 3A–C**). Though power modulations in both frequency intervals occurred in a variety of brain areas (occipital, temporal, frontal, limbic, and parietal), they did not systematically occur simultaneously at the same sites, as illustrated in **Figure 3C**, for an electrode located in the supramarginal gyrus.

Because contrast changes induced either sustained stimulus visibility or invisibility, beyond the contrast transition, we next focused our analysis on the interval after the initial transient neural response induced by the change in contrast, which occurs in the first couple of 100 ms. We therefore excluded the amplitude signals between 0 and 300 ms post-stimulus change (**Figure 3D**) and operated all subsequent statistical comparisons on the average signal amplitude in the 300–800 ms post-stimulus time window. Though some authors consider conscious processing to initiate in higher visual cortices after the sensory-gated feedforward sweep, i.e., around 100 ms (Lamme, 2003, 2006), others consider this to occur later in time and involving feedback processing from frontal areas, around 300 ms after stimulus onset (Dehaene et al., 2006). After this temporal limit in our recordings, stimulus invisibility by contrast decreases elicited gamma-band amplitude increase mostly in occipital, temporal and frontal cortex (**Figure 4A**). In a majority of medial cortices in temporal and frontal regions (i.e., limbic lobe), gamma-band activity decreased for invisible stimuli. Stimulus visibility elicited fewer responses, and showed mostly gamma-band decreases in temporal, occipital and frontal cortex (**Figure 4A**). Stimulus invisibility induced low-frequency amplitudes suppression on most recording sites across frontal, limbic, parietal, occipital, and temporal lobes (**Figure 4B**). Stimulus visibility induced mostly amplitude increases in all responsive cortical networks (**Figure 4B**). The electrode locations eliciting all the above significant effects within the different cortical lobes are depicted in **Figure 4C**. Importantly, though these effects occur, respectively, for periods of stimulus visibility and invisibility, they also correspond to opposite steps in contrast change (respectively, contrast increase and contrast decrease) and their singular contrastive analysis may not yield per se neural correlates of conscious perception (Aru et al., 2012; de Graaf et al., 2012). Moreover, they may also be affected by the previous states and state transitions, which also imply different contrast levels and contrast level transitions. See the discussion section for further implications of this particularity of our experimental design.

In order to further discriminate the functional processing that might be reflected by local networks we further characterized PS by comparing the amplitude responses elicited by contrast reduction (to 50%) to those elicited by full stimulus offset (**Figures 2B,C**), in line with previous studies and to dissociate sensory from perceptual processing components of neural responses (Wilke et al., 2006, 2009). Although the offset condition differ in terms of low-level physical properties such as time of

presentation and background color, we used this contrast mainly because it elicits a phenomenal perceptual experience of stimulus disappearance highly similar to the one produced by the condition of stimulus contrast reduction. We especially considered neuronal populations located in occipito-temporal and frontal cortical regions who process rather highly integrated visual information and whose activity is modulated by conceptual content of images and their conscious perception (Kreiman et al., 2000, 2002; Quiroga et al., 2007, 2008a,b; Vidal et al., 2010, 2014; Libedinsky and Livingstone, 2011; Panagiotaropoulos et al., 2012). To avoid any interference from the detection task at the end of the contrastchange series, we only used those trials (50%) which were not associated with a behavioral response (see Materials and Methods) but who were equally distributed among the VIS and INV conditions.

**(C)** Brain wide distribution of recording sites displaying either type of neural

emergence response.

By contrasting these three conditions, instead of two conditions, we were able to obtain more information on the involvement of the local neural network in three types of functional processing: (a) *perceptual processing (PERCEPTUAL)*, which differentiates conditions regarding stimulus visibility, independently of stimulus presence or absence, (b) *sensory processing (SEN-SORY)*, which differentiates conditions of stimulus processing independently of stimulus visibility or not, and (c) *suppression processing (SUPPRESSION)*, which differentiates the conditions eliciting stimulus invisibility while processing a sensory stimulus, from the two other conditions. Importantly, these effects are considered independently of sign. We compared the time-averaged amplitude of the three conditions (one way repeated measures ANOVA and Tuckey–Kramer *post hoc* test, FDR-corrected for multiple comparisons), respectively, for lowfrequency and high-frequency responses, and grouped the effects between these conditions into three previously defined functional categories: PERCEPTUAL effects ([INV = OFF]=VIS), SEN-SORY effects ([VIS = INV]=OFF) and SUPPRESSION effects ([VIS = OFF]=INV). In the high-frequency band we found all three response types in most cortical areas (**Figure 5A**). PERCEP-TUAL effects ([INV = OFF]=VIS) in the high-frequency range were found in frontal, temporal, and occipital cortices (**Figure 5A**). SENSORY effects ([VIS = INV]=OFF) were effectively represented in occipital, parietal and temporal cortex, and more surprisingly were also found in frontal sites (**Figure 5A**). Interestingly, SUPPRESSION effects ([VIS = OFF]=INV) were mostly absent in occipital cortex and mainly observed in medial and lateral cortices of the temporal lobe (**Figure 5A**). Low-frequency responses displayed proportionally overall more PERCEPTUAL effects ([INV = OFF]=VIS) in occipital, temporal and parietal networks (**Figure 5B**). SUPPRESSION effects ([VIS = OFF]=INV) were mostly observed in frontal and limbic cortices, while SENSORY effects ([VIS = INV]=OFF) were mostly present in the frontal and limbic lobe.

We now focus on the PVC. In this study one participant (P4) had three electrodes implanted in PVC which have been retinotopically mapped to mid- and upper right quadrant of the visual field (see **Figure 6** for a representation). We were specifically interested in the frequency-specific activity modulation across trials, considering previous studies on PS who studied spike rate and gamma-band activity modulation in V1 of the macaque brain. We found various interesting results (**Figure 6**):

(1) Stimulus offset (OFF) positively modulated broadband gamma activity during the whole temporal interval. The effect started very early due of the temporal smearing inherent to the spectral extraction method (Hilbert) used in our analysis. Interestingly, there was a total absence of amplitude modulation in the INV condition, which involved a stimulus contrast decrease entailing stimulus invisibility. In the VIS condition where the stimulus's contrast was increased, we first

**FIGURE 6 | Grand average of time-resolved high and low frequency responses and their comparison statistics for stimulus visibility (VIS), invisibility (INV), and stimulus offset (OFF) in the PVC**. Above **(A)**, left panel, a retinotopic receptive field map of recording sites, and right panel, localization of electrodes projected on MNI reference brain. **(A)** Broadband gamma responses, significant time-samples of neural emergence test and of condition contrasts. **(B)** Low-frequency responses, same conventions as in **(A)**.

observed a slow progressive negative amplitude modulation, which appeared unrelated to the contrast change of the stimulus but did relate to the perceptual state in the previous period (**Figure 6**). 312 ms after contrast increase we observed a positive amplitude re-bound that shortly after 400 ms became positively modulated as compared to baseline, and lasted until 670 ms.

Finally, we explored the dynamics of the PERCEPTUAL effects across different frontal and temporal recordings sites. It has been shown that neuronal discharges in temporal cortex (Sheinberg and Logothetis, 1997) and in lateral prefrontal cortex (Panagiotaropoulos et al., 2012) reflect unambiguously stimulus visibility at similar latencies, yet no study on PS has ever directly compared these latencies simultaneously in intracranial recordings to explore the information flow of perceptual information between these structures. Two participants within the group of patients who took part of our study had electrodes implanted in both regions (P4 and P7). Though we did not have access to multi-unit or single-unit recordings in those patients, we analyzed broadband gamma amplitude modulations which are often considered as a proxy of spiking activity at the population level (Nir et al., 2008; Manning et al., 2009; Ray and Maunsell, 2011; Buzsaki and Wang, 2012).

Twenty-four recording sites from participants elicited PERCEP-TUAL effects in the broadband gamma range in three regions of interest: temporal cortex (fusiform gyrus/inferior temporal cortex, ITC/FG, *N* = 11), anterior insula (AI, *N* = 7) and inferior frontal gyrus (IFG, *N* = 7). To estimate the latency of these average PERCEPTUAL effects ([INV = OFF] =VIS) we applied a repeated measure ANOVA (conditions: VIS, INV, and OFF) across all trials per region of interest across the full time-interval 0–800 ms, and corrected for multiple comparisons in time. We considered the first significant time sample of a minimum of five consecutive time sample. Here we report that PERCEPTUAL effects initiated, respectively, in the AI at 65 ms [*F*(1992) = 5.16; *p* < 0.01; *post hoc* T–K test, *p* < 0.05], in the IFG at 174 ms

[*F*(1735) = 8.44; *p* < 0.0005; *post hoc* T–K test, *p* < 0.05] and in the ITC/FG at 483 ms [*F*(2978) = 65.3; *p* < 0.0005; *post hoc* T–K test, *p* < 0.05] (**Figures 7A–C**). In all three ROI, the INV condition elicited a positive amplitude modulation while the VIS condition elicited a negative modulation (**Figures 7A–C**). We conducted the same analysis for the low-frequency responses at these three ROI. We found that PERCEPTUAL effects initiated, respectively, in the ITC/FG at 268 ms [*F*(3007) = 6; *p* < 0.05; *post hoc* T–K test, *p* < 0.05], in AI at 377 ms [*F*(2022) = 6.2; *p* < 0.05; *post hoc* T–K test, *p* < 0.05] and in the IFG at 424 ms [*F*(1786) = 5.15; *p* < 0.05; *post hoc* T–K test, *p* < 0.05] (**Figures 7D–F**).

## **DISCUSSION**