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Phase-amplitude coupling supports phase coding in human ECoG

  1. Andrew J Watrous Is a corresponding author
  2. Lorena Deuker
  3. Juergen Fell
  4. Nikolai Axmacher Is a corresponding author
  1. University of Bonn, Germany
  2. Radboud University, Netherlands
  3. German Center for Neurodegenerative Diseases, Germany
  4. Ruhr University Bochum, Germany
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Cite as: eLife 2015;4:e07886 doi: 10.7554/eLife.07886

Abstract

Prior studies have shown that high-frequency activity (HFA) is modulated by the phase of low-frequency activity. This phenomenon of phase-amplitude coupling (PAC) is often interpreted as reflecting phase coding of neural representations, although evidence for this link is still lacking in humans. Here, we show that PAC indeed supports phase-dependent stimulus representations for categories. Six patients with medication-resistant epilepsy viewed images of faces, tools, houses, and scenes during simultaneous acquisition of intracranial recordings. Analyzing 167 electrodes, we observed PAC at 43% of electrodes. Further inspection of PAC revealed that category specific HFA modulations occurred at different phases and frequencies of the underlying low-frequency rhythm, permitting decoding of categorical information using the phase at which HFA events occurred. These results provide evidence for categorical phase-coded neural representations and are the first to show that PAC coincides with phase-dependent coding in the human brain.

https://doi.org/10.7554/eLife.07886.001

eLife digest

Electrocorticography, or ECoG, is a technique that is used to record the electrical activity of the brain via electrodes placed inside the skull. This electrical activity repeatedly rises and falls, and can therefore be represented as a series of waves. All waves have three basic properties: amplitude, frequency and phase. Amplitude describes the height of a wave's peaks (and the depth of its troughs), and frequency defines how many waves are produced per second. The phase of a wave changes from 0° to 360° between two consecutive peaks of that wave and then repeats, similar to the phases of the moon.

Previous studies have shown that brain activity at different frequencies can interact. For instance, neural firing (when nerve impulses are sent from one neuron to the next) is related to ‘high frequency activity’; and the amplitude of high frequency activity can be altered by the phase of other, lower frequency brain activity. It has been suggested that this phenomenon, called ‘phase-amplitude coupling’, might be one way that the brain uses to represent information. This ‘phase coding’ hypothesis has been demonstrated in rodents but is largely untested in humans.

Now, Watrous et al. have explored this hypothesis in epilepsy patients who had ECoG electrodes implanted in their brains for a diagnostic procedure before surgery. These electrodes were used to record brain activity while the patients viewed images from four different categories (houses, scenes, tools and faces).

Watrous et al. found that phase-amplitude coupling occurred in over 40% of the recordings of brain activity. The analysis also revealed that the phase of the lower frequency activity at which the high frequency activity occurred was different for each of the four image categories. This provides support for the phase-coding hypothesis in humans. Furthermore, it suggests that not only how much neural firing occurs but also when (or specifically at what phase) it occurs is important for how the brain represents information. Future studies could now build on this analysis to see if phase-amplitude coupling also supports phase coding and neural representations in other thought processes, such as memory and navigation.

https://doi.org/10.7554/eLife.07886.002

Introduction

Perceptual representations of the environment are critical to an animal's survival and are believed to occur through coactivated neuronal groups known as cell assemblies. Human neuronal firing (Ekstrom et al., 2007; Kraskov et al., 2007; Chan et al., 2011; Rey et al., 2014) and increases in high-frequency activity (HFA) in the gamma range (above 30 Hz; Jacobs and Kahana, 2009; Jacobs et al., 2012; van Gerven et al., 2013) carry information about perceptual and mnemonic representations. Several recent studies have shown that these two signals are positively correlated (Ray et al., 2008; Manning et al., 2009; Whittingstall and Logothetis, 2009; Miller et al., 2014; Rey et al., 2014; Burke et al., 2015) and are each modulated by the phase of low frequency oscillations (LFO) (O'Keefe and Recce, 1993; Bragin et al., 1995; Skaggs et al., 1996; Canolty et al., 2006; Jacobs et al., 2007; Tort et al., 2009; Axmacher et al., 2010; Rutishauser et al., 2010; McGinn and Valiante, 2014). This modulation is detectable as phase-amplitude coupling (PAC) of gamma amplitude to LFO phase (Buzsaki, 2010; Miller et al., 2014; Aru et al., 2015).

Together, these findings have motivated models positing that LFO phase may organize cell assemblies (Kayser et al., 2012; Lisman and Jensen, 2013; Jensen et al., 2014; Watrous et al., 2015), a form of phase coding (O'Keefe and Recce, 1993). Supporting this view, LFO phase can be used to decode behaviorally relevant information (Belitski et al., 2008, 2010; Fell et al., 2008; Schyns et al., 2011; Lopour et al., 2013; Ng et al., 2013) and phase coded neural activity has been demonstrated in rodents (O'Keefe and Recce, 1993; Skaggs et al., 1996) and monkeys (Kayser et al., 2009; Siegel et al., 2009). Although the PAC observed in humans (Canolty et al., 2006; Axmacher et al., 2010) has been thought to reflect phase-coding, this assumption has yet to be validated because prior studies have not investigated the relation between PAC and decoding from LFO phases.

We have recently proposed that the frequency-specific phase of LFO coordinates neural firing to support neural representations (Watrous and Ekstrom, 2014; Watrous et al., 2015). Here, we tested this prediction, a form of the phase-coding hypothesis in humans, by examining the relation between PAC and neural representations for categories. We analyzed intracranial recordings from 167 electrodes in six patients with pharmaco-resistant epilepsy as they viewed pictures of houses, tools, scenes, and faces. First, we identified PAC on individual electrodes by using a recently developed metric which allows for the characterization of PAC across individual HFA events. On electrodes exhibiting PAC, we then assessed the distinctiveness of each category's phase-coded representation during periods with and without pronounced HFA. Our results suggest that during periods with pronounced HFA, categorical representations can be recovered based on the phase of low-frequency oscillations, supporting the idea of phase-coded neural representations in humans.

Results

We analyzed data from a total of 167 intracranially-recorded EEG electrodes from six patients with pharmaco-resistant epilepsy as they viewed pictures of houses, faces, tools, and outdoor scenes (Figure 1A), testing whether these categories may be represented based on HFA activity at different phases of the LFO (Figure 1B). We first sought to identify electrodes exhibiting PAC and used a data-driven method which allows for the identification of predominant modulating and modulated frequencies (Dvorak and Fenton, 2014; see Figure 1—figure supplement 1 for analysis schematic). Figure 2A–D shows the PAC modulation profile of an example electrode from the basal temporal lobe of patient 3. Figure 2A shows the magnitude of the modulatory signal relative to HFA events (time 0) at different frequencies in the HFA band. PAC is evident as red and blue vertical striping, with maximal modulation of activity at 84 Hz (Figure 2B; ‘HFA event’ marked by arrow in Figure 2C) occurring near the trough of the 2.5 Hz oscillation (see also Figure 2D). Notably, PAC was visible in the raw trace (Figure 2C), the modulatory signal showed rhythmicity (Figure 2D), and there was a clear peak in the power spectrum of both the raw signal and the modulatory signal (Figure 2E, see Figure 2—figure supplement 1 for more examples).

Figure 1 with 1 supplement see all
(A) Task structure and timing.

Exemplar images are shown from each category. Each image was presented in pseudo-random order for one second with a jittered inter-stimulus interval. (B) Theoretical model of phase amplitude coupling (PAC) and phase coding, showing how each phenomenon could occur in isolation (left, right) or together (middle). Numbers above distributions indicate difference scores (DSs), the total number of categories one category differs from. High-frequency activity (HFA) may occur at specific phases but not differ between categories, leading to PAC without phase coding (left). Alternatively, HFA may be phase clustered across categories but still occur at different phases for some categories, leading to both PAC and phase coding (middle). In a third scenario (right), category-specific phase clustering could occur without any phase-clustering of HFA across categories, leading to phase coding without PAC.

https://doi.org/10.7554/eLife.07886.003
Figure 2 with 3 supplements see all
Phase amplitude coupling analysis.

(AE) Example of PAC using the OTC method described by Dvorak and Fenton (2014). Data are from one electrode located in the left basal temporal lobe of patient #3. (A) Oscillatory-triggered comodulogram shows phase coupling above 50 Hz, evident as red and blue vertical striped regions. Time zero corresponds to the HFA event. (B) Z-scored modulation strength as a function of frequency relative to 100 surrogate shuffles at pseudo-HFA events (i.e., random time points). (C) Modulation of gamma amplitude (green) by the phase of a 2.5 Hz oscillation (blue) on an example trial. Time zero indicates image onset. Red shaded area and arrowhead indicate an HFA window and HFA event, respectively. Extracting the peak modulatory signal from B (84 Hz) reveals the phase (D, HFA events occur at the trough at time 0), strength (D, peak-to-trough height) and frequency (E; green) of the modulation. The red trace in (E) shows the average normalized power of the entire recording. (F) Group level analysis of HFA event timing. HFA events occurred throughout the stimulus presentation period but increased ∼150 ms after stimulus onset. Magenta trace shows percentage of gamma events as a function of time, averaged across electrodes and categories. The timing of HFA events did not systematically differ by category (Figure 2—figure supplement 2). (G) Group level FFT data, defined at the peak of the modulation strength curve for each PAC+ electrode. Most PAC occurred around 1 Hz. Black bars are relative counts of electrodes with a peak at each frequency. (H) Distribution of modulated frequencies across electrodes. Electrodes were primarily modulated in the low and high gamma bands. (I) Preferred phases for modulation, clustered around the trough of the signal (180°).

https://doi.org/10.7554/eLife.07886.005

Next, we investigated the prevalence of PAC and HFA on each electrode. We found robust evidence for PAC, with at least 20% of electrodes in each patient showing significant PAC (n = 72/167 ‘PAC+’ electrodes, see ‘Materials and methods’ for statistical assessment and inclusion criteria). On PAC+ electrodes, HFA was broadly distributed across trials and time points. Calculating the proportion of trials showing a period of significantly increased HFA (95th percentile, see ‘Materials and methods’ for ‘HFA windows’) on each PAC+ electrode and category, we found that HFA occurred throughout the period of stimulus presentation but increased ∼150 ms after stimulus onset (Figure 2F). 66% of trials had at least one HFA window and this prevalence did not vary by category (Figure 2—figure supplement 2; one-way ANOVA, F(3,284) = 0.6, p > 0.61). These findings converge with prior studies demonstrating increased neural firing and HFA during stimulus presentation and demonstrate pronounced PAC in our paradigm (Canolty et al., 2006; Mormann et al., 2008; Axmacher et al., 2010; Cichy et al., 2014; Rey et al., 2014).

We then determined the frequencies and phases at which PAC is maximal on each PAC+ electrode. Slow-modulating (‘Fphase’) frequencies were significantly clustered in the delta band (0.5–4 Hz; Figure 2G) and HFA modulated frequencies (‘Famp’) were significantly clustered around slow (∼32 Hz) and fast (∼110 Hz) gamma frequencies (Figure 2H, chi-square goodness of fit test across gamma frequencies, p < 0.004, χ2(22) = 43.6, Cohen's d = 0.77). Furthermore, we found that HFA was typically maximal near the trough of the oscillation (i.e., at 180°; Figure 2I; p < 0.05, Rayleigh test; see Figure 2—figure supplement 1 for additional examples and modulation at other phases).

We next tested if PAC occurs for all four categories, which would be necessary if PAC was related to the representation of categorical information. To this end, we tested each category separately for phase clustering of HFA events at the electrode-specific peak modulatory frequency (‘FMAX’). This analysis revealed significant clustering for all four categories on 87% (63/72) of PAC+ electrodes (Rayleigh test p < 0.00004, Bonferroni-corrected p < 0.01 for PAC+ electrodes and categories). Phase clustering was observed in each patient and did not vary across categories at FMAX (one-way ANOVA on resultant vector lengths, F(3,284) = 0.14, p > 0.93). In sum, we found evidence for widespread PAC in each patient at several frequencies and phases of the LFO, similar to single neuron and field potential studies in monkeys (Kayser et al., 2009; Siegel et al., 2009) and humans (Canolty et al., 2006; Jacobs et al., 2007; Axmacher et al., 2010; Maris et al., 2011; Jacobs et al., 2012; van der Meij et al., 2012; Voytek et al., 2015).

HFA occurs at different phases for different categories

Testing the phase-coding hypothesis, we asked if high frequency activity occurred during category-specific phases of the modulatory LFO. Figure 3A shows two traces from an example electrode which are color-coded by the instantaneous phase at Fmax. HFA windows (boxes color coded by 1 Hz phase) occurred during different modulatory phases depending on stimulus category. On this electrode, phases extracted during HFA windows were clustered for each category to different phases, resulting in category-specific phase-clustering (Figure 3B). Similar findings were observed in other patients (Figure 3C), and appeared distinct from representations using power or phase (Figure 3—figure supplements 1, 2).

Figure 3 with 3 supplements see all
HFA occurs at category-specific low-frequency phases.

(A) Two example trials from patient #6 demonstrating that HFA windows occur at different phases for different categories. The signal is color-coded by the phase of 1 Hz oscillation only during the stimulus period. Times prior to stimulus period are shown in order to visualize the 1 Hz modulatory signal. HFA windows are indicated by the boxes, color-coded by the 1 Hz phase at which they occur. (B) Summary circular histograms and resultant vectors for this electrode. Categorical phase-clustering to different phases was prominent at Fmax, allowing for the decoding of categorical information based on the phase at which HFA events occur. DSs are plotted for each category in the lower panel. (C) Another example, from a different patient (#4), showing phase-clustered HFA windows for different categories (upper) along with DSs (lower). (D) Proportion of electrodes in each patient showing category specific phase-clustered HFA. (E) Average absolute phase difference across categories and electrodes for increasingly distinct phase representations (PRs). (F) Circular distribution of phases for each level of DS, pooled over electrodes and categories. Phase coded representations were equally likely to occur at each phase.

https://doi.org/10.7554/eLife.07886.009

These findings imply that representations might occur by the category-specific phase at which HFA events occur. In order to further quantify this effect, we developed a simple metric, the difference score (‘DS’), which allowed us to identify the distinctiveness of each category's phase distribution during HFA windows. We applied this metric to the subset of 63 PAC+ electrodes showing significant phase-clustered HFA for each category. This was necessary in order to exclude spurious phase differences between categories occurring in the absence of phase clustering. Across all patients, 78% (49/63) of PAC+ electrodes showed a unique phase-clustering profile for one category compared with each other category (e.g., Figure 1B; DS = 3 for at least one category, p < 10−9, Watson Williams test, Bonferroni corrected across comparisons). This pattern was consistent both within and across patients, with at least 15% of electrodes in each patient showing these effects (Figure 3D).

We next calculated the average phase difference between categories, expecting this measure to increase with increasing DS. Indeed, categories with larger DSs exhibited larger phase differences with other categories such that maximally distinct representations were 35° phase offset from all other categories (Figure 3E).

As described above, PAC was most likely to occur at the oscillatory trough (Figure 2I and Figure 2—figure supplement 1). Nonetheless, on individual electrodes or for individual categories, HFA could occur at different phases. In fact, across electrodes, phase-coding was equally likely to occur at all phases and for all categories; phase-coded categories were not clustered at particular phases at any level of DS (Rayleigh test, all p > 0.19; Figure 3F) and phase-coding was equally likely for each category (χ2(3) = 1.6, p = 0.64). Thus, a large proportion of PAC+ electrodes also show category-specific phase clustering of HFA events to different phases (Video 1), suggesting that PAC is related to phase-coding (Figure 1B, middle).

Video 1
Significant electrodes rendered onto a glass brain.

Each point represents an electrode, and each color represents different effects. Black electrodes (n = 95) did not show significant phase-amplitude coupling (PAC). Green electrodes (n = 9) only showed significant PAC. Yellow electrodes (n = 14) showed significant PAC and phase-clustering of HFA for all 4 categories. Red electrodes (n = 49) showed significant PAC, phase-clustering for all 4 categories, and phase-coding of high-frequency activity (i.e., difference score of 3 for at least one category).

https://doi.org/10.7554/eLife.07886.013

Decoding category identity from HFA event phases

To link these findings more directly to neural coding, we used pattern classification to determine if the phase at which HFA events occur is sufficient to recover categorical information (see ‘Materials and methods’). As expected from the analysis using DS, 42 (25% of all) electrodes showed significant decoding accuracy (using LFO phases during HFA windows as features) compared to category label shuffled surrogates and this proportion was significantly higher than would be expected by chance (p < 10−10, binomial test, chance level: 8.3 electrodes, Cohen's d = 0.6). Next, we assessed whether phase-coding of categorical information indeed depended on HFA, as would be expected if PAC supports phase-coding. We compared decoding accuracy during HFA events to decoding accuracy during randomly selected surrogate events. 19 (11%) electrodes showed significantly higher decoding accuracy during HFA events as compared to random event surrogates, and this proportion was significantly higher than would be expected by chance (p < 0.0003, binomial test, chance = 8.3 electrodes, Cohen's d = 0.22). Moreover, 17 (10%) electrodes showed significant enhancements of decoding accuracy during HFA events relative to both label and event shuffled surrogates, with at least two electrodes in each patient showing this pattern. This proportion of electrodes far exceeded that expected by chance (p < 10−10, binomial test, chance = 0.41 electrodes, Cohen's d = 0.46). These findings complement the above results using DS and indicate that the phase at which HFA events occur carries sufficient information to decode image category, suggesting such information may be a relevant component of the neural code.

We performed several control analyses to rule out alternative explanations. First, if slow oscillatory phase relates to category-specific representations, we expect phase-locking across trials to different categories. We observed significant phase locking on many electrodes to specific categories (Figure 3—figure supplement 3, Rayleigh test, p < 0.000001), similar to previous studies which have identified phase-locked activity (e.g., Fell et al., 2008). Second, we excluded the possibility that our PAC+ or phase-clustering inclusion criteria biased our findings by computing a composite measure of phase representation (PR) on each electrode (see ‘Supplement results’). This analysis again revealed that phase coding is largest on PAC+ electrodes and is enhanced during HFA windows. Third, for comparison with prior PAC methods, we recomputed PAC using the modulation index (MI, Tort et al., 2009) in different low-frequency bands, again finding PAC that was most prevalent in the delta band (Figure 2—figure supplement 3).

Lastly, several models predict that neural processes forming representations will show frequency-specificity (Siegel et al., 2012; Watrous and Ekstrom, 2014; Womelsdorf et al., 2014). We therefore recalculated phase-clustering and DS at the minimum modulatory frequency (FMIN; see ‘Materials and methods’ and Figure 4—figure supplement 1 for individual subject values) using the same criteria detailed above. As one would expect, on PAC+ electrodes, phase clustering was larger at FMAX compared to at FMIN, both on individual electrodes (Figure 4A, B) and at the group level (Figure 4C; paired t-test on resultant vector lengths, t(287) = 8, p < 10−10, Cohen's d = 0.32). Moreover, only 20% (15/72) of PAC+ electrodes showed significant phase-clustering at FMIN for all 4 categories and only 1 electrode showed category-selective phase-clustering of HFA events. Given that the phase of slower frequencies varies less over time and that we primarily identified Fmax at slow frequencies, this result might be biased towards finding enhanced phase clustering at Fmax. We therefore recalculated phase clustering across the full range of frequencies (1–12 Hz, 0.1 Hz steps). Again, we found enhanced phase-clustering around 0.5 and 1 Hz (Figure 4—figure supplement 2), but not at adjacent frequencies as would be expected from this alternative account. Taken together, these results support the conclusion that HFA at distinct phases and frequencies reflect representations for different categories.

Figure 4 with 2 supplements see all
HFA clusters to specific phases and frequencies for different categories.

(A) Example electrode showing phase clustering at the maximum modulatory signal (Fmax; frequency with maximum power in the FFT, see Figure 2E) but not at the minimum modulatory signal (panel B; Fmin; frequency with minimum power in the FFT). HFA events are marked in color as the phase of the oscillation at the respective frequencies. (C) At the group level, phase clustering was more prominent at the maximum frequency (Fmax; maroon) compared to the minimum frequency (Fmin; black) across categories and PAC+ electrodes.

https://doi.org/10.7554/eLife.07886.014

Discussion

We tested the hypothesis that PAC reflects a phase-coding mechanism, measuring both PAC and categorical PR in intracranial recordings from six patients who viewed pictures from different categories. Our analyses show that on a large subset of electrodes showing PAC, the frequency-specific phase at which HFA occurs varies with categorical information. Therefore, to the extent that HFA reflects increases in local neuronal activity (Crone et al., 1998; Miller et al., 2014), our results suggest that neural representations for categories might occur by the phase at which neurons fire. These findings thus provide a novel link between PAC and phase-coded neural representations in humans.

Critically, although PAC and phase-coded representations share some attributes, such as phase-clustering of activity, they are not necessarily identical processes. High frequency activity could occur at particular phases of LFOs, as reflected by PAC, but these phases may not vary with stimulus category (Figure 1B, left). In other words, there could be PAC without phase coding. This is in fact the null hypothesis we have tested and would manifest as PAC with DSs of zero. On the other hand, categorical information may be represented by specific low-frequency phases independent of HFA, leading to DS without phase-clustering across HFA events (PAC; Figure 1B, right). We did not find a complete overlap between PAC+ and phase-coding electrodes, indicating that each can occur in isolation, but instead found a compromise between these extremes (Figure 1B, middle). These results suggest that PAC in many cases reflects phase coding because of the significant overlap between the two phenomena (Video 1).

Phase-coding, in the form of phase-modulated neuronal firing, has been identified in rodents, monkeys, and humans (O'Keefe and Recce, 1993; Skaggs et al., 1996; Jacobs et al., 2007; Kayser et al., 2009; Rutishauser et al., 2010). Although the mechanisms which guide such a neuronal phase preference remain poorly understood, previous studies have found enhanced PAC during learning and memory tasks (Tort et al., 2008; Axmacher et al., 2010; Kendrick et al., 2011; Friese et al., 2013; Lega et al., 2014). Our findings provide a potentially unifying account of these observations, suggesting that PAC may be promoting the formation of phase-coded neural assemblies (Canolty and Knight, 2010; Watrous and Ekstrom, 2014; Watrous et al., 2015). Follow-up studies will need to test this account of PAC as it relates to other putative roles for PAC (Canolty and Knight, 2010; Voytek et al., 2015).

While epilepsy is marked by increased synchronized neuronal activity which could potentially manifest as HFA or PAC, we believe several factors weigh against this interpretation. First, we only analyzed electrodes overlying putatively healthy tissue, typically from the hemisphere contralateral to the epileptic focus, as assessed by our clinical team. Electrodes showing epileptic spiking were systematically removed from our analysis and all analyzed trials were visually inspected for artifacts related to epilepsy. Next, the PAC metric allows for assessment of the modulatory signal. Visual inspection of these signals did not reveal a similarity to epileptic spikes (Figure 2—figure supplement 1). Finally, it seems unlikely that epileptic activity at different phases would systematically differ by category. Similar reasoning excludes saccade-related artifacts (Yuval-Greenberg et al., 2008; Kovatch et al., 2011) as a parsimonious account of our results. We therefore conclude that similar findings would translate into healthy human populations.

Another caveat is that our results provide evidence for categorical phase-coding based on a restricted image set. This was necessary in the present study to maximize the chances of identifying category-selective responses while still ensuring that these responses were generalizable across a few exemplars. Follow-up studies should test the generalizability of these findings using more exemplars within a category and using other categories.

PAC has typically been investigated using pre-defined low and high-frequency filters which may optimize statistical power for detecting PAC but do not adequately deal with the time-resolved nature of cognition (Aru et al., 2015). Here, we leveraged a recent method which can identify PAC and subsequently test mechanistically interesting questions related to the modulation of HFA, such as its temporal profile and its dependence on phase, frequency, and behavioral requirements. Notably, this method may conservatively estimate PAC because it is based on transient increases in HFA, which do not necessarily occur in all cases of PAC. Our findings demonstrate that PAC and large HFA events can be identified and subsequently linked to categorically distinct representations. These results thus extend previous research which has decoded neural representations using either low or high frequency activity (Jacobs and Kahana, 2009; Schyns et al., 2011; van Gerven et al., 2013) and may provide new avenues for decoding the human representational system.

Intriguingly, phase-coding of categorical information extended beyond brain areas associated with higher-order vision. Thus, our findings of category-specificity do not appear to exclusively relate to perception but may also involve other more complex, and idiosyncratic, associations to these stimuli. Our findings are nonetheless in line with prior work (Majima et al., 2014; Yaffe et al., 2014; Zhang et al., 2015) which has found spatially-distributed content-specific representations.

We identified frequency-specific PRs in humans, consistent with a growing body of evidence implicating the relevance of frequency-specific oscillatory activity to human cognition (Daitch et al., 2013; Watrous et al., 2013; Fontolan et al., 2014; Freudenburg et al., 2014). These findings are therefore consistent with models implicating frequency-specific oscillations as central to higher-order cognition (Siegel et al., 2012; Watrous and Ekstrom, 2014; Watrous et al., 2015). It has recently been shown that the frequency of LFOs contributes to several neuronal properties such that relatively slower LFOs lead to decreased firing threshold and increased spike timing variability (Cohen, 2014). It is not immediately clear how this relates to our finding that PAC predominantly occurs with modulating frequencies in the delta band, particularly around 1 Hz. It is possible that our findings reflect the activation of assemblies during ‘up’ states which show a similar frequency profile (Destexhe et al., 2007) or that the applied method of identifying peaks in the spectrum biased our findings to find PAC at lower frequencies.

A third possibility, more likely in our view based on our results indicating multiple modulating frequencies per electrode (Figure 2G), is that the timing of our task (1 image per second with a jittered inter-stimulus interval) partially entrained slow oscillations forming an oscillatory hierarchy (Lakatos et al., 2005). Similarly, our results showing PAC at a variety of phases and frequencies (Maris et al., 2011; van der Meij et al., 2012), particularly near 32 Hz, might reflect a form of ‘nested coupling’ (Kopell et al., 2010) distinct from ‘broadband’ high gamma, which has been suggested to reflect population spiking (Manning et al., 2009; Miller et al., 2014). Future research may clarify this issue by comparing single neuron activity and HFA modulation during different perceptual tasks and by investigating their relation to hierarchical cross-frequency coupling.

To summarize, by identifying electrodes exhibiting both PAC and phase-coded neural representations for categories, our results employing direct brain recordings explicitly link phase-coupled neural activity to phase coding in humans.

Materials and methods

Epilepsy patients

Six right handed patients with pharmacoresistant epilepsy (mean age 31.8 years; 3 female) participated in the study. All patients were stereotactically implanted for diagnostic purposes. Medial temporal depth electrodes (AD-Tech, Racine, WI, USA) with 10 cylindrical platinum-iridium contacts (diameter: 1.3 mm) were implanted in 1 patient, and 5 patients were implanted with subdural grid and strip electrodes with stainless-steel contacts (diameter: 4 mm) at temporal, frontal, and parietal sites (Video 2). Recordings were performed using a Stellate recording system (Stellate GmbH, Munich, Germany) at the Department of Epileptology, University of Bonn, Germany. The study was conducted according to the latest version of the Declaration of Helsinki and approved by the ethical committee of the medical faculty at the University of Bonn (approval identifier 280/08). All patients provided written informed consent to participate in the study and for the results to be published in a pseudonymized manner.

Video 2
Electrode locations for each patient, rendered onto a glass brain of the average MNI template.

Each point represents an electrode, and each color represents a different patient. Electrodes were primarily located in the temporal lobe.

https://doi.org/10.7554/eLife.07886.017

Experimental design

Patients performed an object–location association task, though here we focus on neural representations for categories independent of memory encoding per se. Patients viewed greyscale images taken from four different categories (houses, tools, scenes, and faces) and each category had four unique stimuli, resulting in a stimulus set of 16 unique images. Example images from each category are shown in Figure 1A. Each image was presented 30 times in pseudo random order (total of 480 trials) and was followed by a white square in a fixed location. Patients were instructed to form object–location associations and to rate if they liked or disliked each image, thus ensuring that they were attending to each image presentation. Images were presented on a laptop placed in front of the patient. Each image was presented for 1 s, followed by the white square presented for 1 s, and finally a jittered inter-stimulus interval ranging from 1800–2200 ms. A fixation cross was presented between images.

Recording and analyses

Intracranial EEG recordings (sampled at 1000 Hz) were referenced to linked mastoids and band-pass filtered (0.01 Hz [6 dB/octave] to 300 Hz [12 dB/octave]). Recordings from the hemisphere contralateral to the epileptogenic focus were analyzed. To boost our electrode sampling, an additional 32 electrodes from an ipsilateral left lateral temporal grid were included from patient 5 based on the physicians' report, which indicated a left hippocampal focus and no evidence of neocortical lesion based on an magnetic resonance imaging (MRI). Signals from this grid were carefully visually inspected for artifacts and did not show increased artifacts associated with epilepsy. Qualitatively similar results were observed when excluding these electrodes from the analysis, with the proportions of electrodes showing any reported effect changing by no more than 3%.

Electrode locations were determined by post-implantation MRI such that electrodes were mapped by co-registering pre- and post-implantation MRIs, normalizing the pre-implantation MRI and applying the normalization matrix to the post-implantation MRI. The anatomical locations of contacts were then identified by comparison with standardized anatomical atlases and using custom software (published at http://pylocator.thorstenkranz.de/). In total, 167 implanted electrode contacts were analyzed across all patients (Video 2).

Raw EEG signals were extracted from 750 ms before to 1500 ms after image onset. EEG trials were visually inspected for artifacts (e.g., epileptiform spikes), and trials with artifacts were excluded from further analysis (15% of all trials on average). Trial epochs were then concatenated for subsequent analysis described below. We analyzed an average of 103 trials per category and subject and there were no differences in total number of trials analyzed across categories (F(3,20) = 0.38, p = 0.76).

Oscillatory triggered coupling (OTC) analysis

PAC was detected using the methods described by Dvorak and Fenton (2014). This method is conceptually similar to an event-locked analysis around periods of enhanced HFA. All analyses were conducted using standard routines in EEGLab (Delorme and Makeig, 2004) and Matlab based on previously published algorithms (Rizzuto et al., 2006; Berens, 2009; Tort et al., 2009; Dvorak and Fenton, 2014). In brief, the power and phase of the signal on each electrode was computed in the low frequency (Fphase, 0.5–12 Hz, 0.5 Hz steps) and gamma (Famp, center frequencies at 32–120 Hz, 4 Hz steps) bands using Morlet wavelet convolution with 7 cycles. At each center HFA frequency, the time course of power values was z-scored and time periods exceeding the 95th percentile of these values were identified (we refer to these as ‘HFA windows’, red shaded area in Figure 2C). The time point of the largest power value within each window was identified and taken as the time-locking ‘HFA event’ for OTC analyses (arrow, Figure 2C). Two-second segments (1 s before to 1 s after each HFA event) of the raw signal were extracted around these timestamps and raw signal segments were summed at each time point across segments, resulting in the modulatory signal at each center HFA frequency.

The strength of modulation was determined based on the peak to trough height of the modulatory signal. Surrogate modulatory signals (n = 100) were constructed at each modulated frequency based on choosing an equal number of pseudo-HFA events at random timestamps and repeating the above procedure. Surrogate modulation strengths were extracted and used to z-score the observed modulation strength. PAC+ electrodes were identified as electrodes (1) with a modulation strength z-score >4.35 for at least one gamma frequency and (2) with a clear peak in the power spectrum of the raw signal at Fphase (Aru et al., 2015). This z-score threshold was calculated by identifying the z value equivalent to a Bonferroni corrected (across 23 gamma frequencies and 167 electrodes) alpha threshold of p < 0.05 and corresponded to p < 0.00005. Peaks were identified by normalizing the power spectrum of both the raw and modulatory signal to their respective maxima and ensuring that both normalized signals were maximal (i.e., 1) at the same frequency.

We identified the peak HFA modulation frequency as the frequency with the largest z-score and extracted the modulatory signal (see Figure 2B). The phase and frequency content of the modulatory signal was determined using a Hilbert transform and fast Fourier transform, respectively. The modulatory signal was mean-centered prior to FFT in order to remove DC components. The maximum (‘FMAX’) and minimum (‘FMIN’) of this FFT output indicate the strongest and weakest slow-modulating frequencies in the 0.5–12 Hz band, respectively.

DS and phase clustering calculation

Phase comparisons were conducted using a Watson Williams test following Rizzuto et al (2006) and using code taken from these eegtoolbox available at (http://memory.psych.upenn.edu/Software). Statistical testing was performed between the phases extracted during HFA windows for all pairs of conditions (4 categories; 6 total category pairs). DSs were computed for each category as the total number of significant differences (p < 0.001) between the phase distribution for one category and the remaining categories and thus ranged from 0 (no difference to any other category) to 3 (significant difference to all other categories). Phase clustering scores were defined as the resultant vector length for each category's phase distribution.

Pattern classification analysis

We used a pattern classification approach for comparison with our DS metric, classifying image category based on the phase at which HFA events occur. To this end, we trained support vector machines using a linear kernel and fivefold cross-validation. Phase values at Fmax were extracted at moments in time when HFA events occurred during image presentation and were used as input features for the classifier. Similar to previous approaches (Lopour et al., 2013; Majima et al., 2014), the sine and cosine of the phase values were used as input features for phase. Classifiers were run separately on each electrode and the classifier output was a prediction of the category label for each HFA event. Classification accuracy was defined as the average proportion of correctly classified HFA events across folds.

Chance classification performance varies across electrodes because we classified the category label associated with each HFA event and the number of HFA events per category varied across electrodes. We thus opted to report significance based on permutation testing which accounts for the varying chance level across electrodes and assessed the significance of classification using two separate analyses which both utilized permutation testing. First, we randomized the category labels associated with HFA events and assessed classification accuracy. Second, we used random time points (also corresponding to random phases) as surrogate HFA events and assessed classification accuracy. Each type of permutation test was performed 50 times, resulting in a distribution of pseudo classification accuracy values for each test. Observed classification accuracies at or above the 95th percentile of each of these distributions were deemed significant. Thus, we fixed the type 1 error rate for each test at 5% and, assuming independence between tests, we would therefore expect 0.0025 × 167 = 0.42 electrodes to show significance by chance for both permutation tests.

Supplement information

Methods: control analyses and PR scoring

Conducting an alternative analysis, we aimed to determine if phase coding was more prominent during HFA windows. To this end, we created a composite measure of PR on each electrode. Direct comparison of the observed DS value to a surrogate distribution is problematic because DS assumes values between 0–3. Therefore, in the case of an observed DS of 3, it is impossible to identify any surrogates larger than our observation. We thus created a composite and continuous measure of PR by multiplying DS and phase clustering values on each category–electrode pair.

Permutation tests were then performed by shuffling the temporal position of HFA windows. This method is similar to a method used previously (Axmacher et al., 2008). Specifically, we randomly reordered the positions of HFA and non-HFA windows and then recalculated DS, phase-clustering, and PR values for each category. Notably, this method maintains the distribution of HFA and non-HFA window durations while shuffling these windows relative to the phase series. Surrogate PR values were calculated 200 times per electrode and the observed PR value was compared against the 95th percentile of this surrogate distribution. PR values were also extracted during non-HFA windows as a second control condition.

Supplement results

We computed a composite measure of PR by weighting each category's DS by its phase-clustering value. This measure combines two intuitive features of phase-coding, namely that information is represented at different phases and that activity is concentrated at these phases (captured by DS and phase-clustering values, respectively). Testing the specificity of HFA windows for phase coding, we found that 56% (94/167) of electrodes showed PR that was larger during HFA windows compared to both time-shifted surrogates and non-HFA windows for at least one category. Moreover, PR values were significantly larger on PAC+ electrodes compared to electrodes without significant PAC (two-sample t-test, p < 0.000005). Thus, we find that phase coding is largest on PAC+ electrodes and is enhanced during HFA windows.

We calculated the MI (Tort et al., 2009) by binning HFA (51–200 Hz) amplitude according to phase in either the delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz) or the entire low frequency (0.5–12 Hz) bands. Following surrogate control analyses, in which we randomly shuffled gamma values 500 times prior to calculating MI, we observed significant PAC in each band relative to shuffled gamma MI values. Consistent with our primary results, PAC was most prominent in the delta band using these methods. Furthermore, the magnitude of gamma band activity was maximal at a similar phase of delta oscillations (180°, oscillatory trough) as when assessed using the OTC method. Based on the MI based metric, we found that 102/167 electrodes exhibited significant PAC with delta band phase. These results are shown in Figure 2—figure supplement 3.

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Decision letter

  1. Howard Eichenbaum
    Reviewing Editor; Boston University, United States

eLife posts the editorial decision letter and author response on a selection of the published articles (subject to the approval of the authors). An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent (see review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.

Thank you for submitting your work entitled “Phase-amplitude coupling supports phase coding in human ECoG” for peer review at eLife. Your submission has been favorably evaluated by Timothy Behrens (Senior Editor), a Reviewing Editor, and three reviewers.

The reviewers have discussed their reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

All of the reviewers felt the paper was strong, but there were several concerns that need to be addressed:

Reviewer #1:

Watrous et al. tackle the important question of whether phase coding is a mechanism for information encoding in the human brain. They test whether cross-frequency coupling (CFC) subserves phase dependent coding of distinct visual categories. The study is highly relevant for the field of systems neuroscience.

1) The authors may be unintentionally inflating their findings by defining PAC by power increases in HFA through the oscillation triggered coupling (OTC) procedure. PAC does not require a change in power, but a change in the distribution of power relative to the phase of the low-frequency oscillation. Evoked activity might distort CFC results. Did the authors attempt to study evoked and non-stimulus locked activity separately?

2) OTC needs more explanation in this paper. Given the major claims of the paper, the authors should compare the OTC to more established PAC methods, so that the reader can better assess the results. The findings may be inflated by the chosen metric.

3) There are reports (Mathewson, 2009; Schyns, 2011) that indicate that phase coding only becomes evident in stages of high power. It is necessary to test the phase vs. power coding and then assess phase coding separately for high and low power trials. This is important, since the decoding results imply that HFA alone might explain some of the findings. FMAX and FMIN values per subject should be reported. In order to establish PAC as a putative mechanism, one needs to rule out that neither HFA nor low frequency phase/power alone can explain the findings.

4) Could the distribution of phase angles at stimulus onset exhibit a systematic bias with respect to the visual stream? To rule out entrainment to the rhythmic stream the authors could employ ITC or phase-locking to the sensory stream (e.g. Thut et al., 2011).

5) A major concern is the use of wavelets for extracting oscillatory phase. Wavelets constitute an acausal approach to determine the phase of a signal, since an underlying oscillation is assumed. This leads to frequency, but also phase smoothing, e.g. that a transient ERP contaminates the pre-stimulus phase estimates by backward ‘smearing’. The paper would benefit from a causal approach. Please consult Zoefel and Heil (2013, FIPSY).

6) The difference score is introduced in the second paragraph of the subsection “HFA occurs at different phases for different categories” and requires earlier explanation. A schematic for OTC and DS would be preferable.

7) Were the phase and power extracted from the same electrode? Was PAC calculated on epoched data? Both things could possibly lead to spurious findings.

8) Why did the authors only analyze the frequency spectrum up to 120 Hz? Often findings in HG in ECoG only start at 100 Hz and then extend up to 250 Hz.

9) Were eye movements recorded in any of the subjects? The authors argue that this is an unlikely source of artifactual data but eye-tracking data would be more assuring.

Reviewer #2:

A growing literature in systems neuroscience has observed a consistent relationship between the timing of spikes in local ensembles, relative to the phase position of local oscillatory field potentials. This putative phase-coding mechanism provides an interesting means of coordinating population coding, and makes clear predictions about temporal constraints on computation. To date, phase-coding has been studied in rodent models and non-human primates. In their submission, Watrous et al. seek to quantify a correlate of phase-coding in human cortex using direct intracranial recordings. As a means of quantifying phase-coding, the authors focus on phase-amplitude coupling (PAC) in the cortical surface potential, specifically low frequency phase modulation of high-frequency amplitude (HFA). This approach is supported by the correlation between HFA and local population spiking, and previous evidence of phase modulation of HFA. In this regard, HFA PAC reflects a macro-scale measure of population phase-coding.

The role of PAC in cognition is an active area of investigation and the author's attempts to link this phenomenon to phase-coding using human intracranial recordings is of great interest. At a general level, the relationship between phase-coding and local field potential PAC is unclear given the population level readout of spiking in the HFA. In their study, Watrous et al. attempt to identify unique PAC based phase-coding for different visual categories. The authors provide a clear motivation, and employ sophisticated analysis methods towards this endeavor.

1) Anatomy: While the authors provide evidence for PAC, and phase based coding of visual categories, their conclusions make no reference to functional neuroanatomy. Recording sites across subjects come from a wide variety of regions; this strikes me as a challenge when interpreting the specific coding of visual categories. The supplemental movie suggests a range of distant regions, not typically associated with higher-level vision, display some degree of phase-coding/representation of visual categories. This seems inconsistent with previous electrophysiology and functional imaging work (which is not cited or discussed in any detail). On a related note, the diversity of recording sites also makes the use of count statistics as somewhat arbitrary. Percentage of electrodes displaying an effect of interest can be equally meaningful for large or small percentages (e.g. low % for anatomically specific effect or high % for a trivial global effect). The authors should clarify why macro-scale phase-coding exists for visual categories across many cortical regions, rather than focused to the more classical regions of categorical selectivity.

2) Task/decoding: In their task, the authors present only four exemplars for each category, repeating each 33 times (if I understand correctly). While this may serve to aid mnemonic encoding, it does limit the claim of categorical decoding. Specifically, in developing a category decoder, the large number of stimulus repetitions limits insight, given the similarity between any training and test set. On a related note, I found the reporting of the basic decoding results unclear (is the decoder working above expected chance levels? >25%). Given the authors’ aims, it seems that a better use of the data would be quantifying the consistency of phase-coding metrics across repetitions of stimuli, as well as within/between class comparison. This approach would focus more on extracting single trial features and testing similarity across repeated trials (I note issues of repetition suppression come into play here). This approach of displaying consistency of stimulus phase-coding would provide more robust evidence for the authors’ claims.

Reviewer #3:

The manuscript by Watrous and colleagues is an interesting look at phase coding in the human cortex and medial temporal lobe. While the authors have a great deal of experience with ECoG analyses, including phase coding and PAC, and their manuscript is generally of interest, I have a number of questions and concerns.

Technical comments:

1) In Figure 2H, Figure 2–figure supplement 1, the authors show “low gamma” PAC and “high gamma” PAC. This 32 Hz coupling mode seems striking, because it's likely that coupling extends even further below this range into the beta range. This low gamma has been argued to be distinct from more “broadband” high gamma (Kai Miller and Dora Hermes' work), which is correlated with population spiking (Mukamel, Science; Manning, J. Neurosci.), in contrast to low gamma, which is more oscillatory. Thus, the low gamma effect may be more a form of “nested” coupling as has been argued by Nancy Kopell.

2) There appears to be a disproportionate PAC effect at 0.5Hz and 1.0Hz, but with surprising specificity, and not between those two frequencies as seen in Figure 4–figure supplement 1. Why do the authors believe this occurs, and why do they believe their PAC effects are so restricted to this delta range, in contrast to what others have observed in ECoG?

3) How sensitive is detection of HFA event times to the filtering method?

4) With regards to electrode choice, the rationale for only using electrodes in the contralateral hemisphere is unclear. Why systematically reject an entire hemisphere (except for 1 subject, oddly) when you visually inspect channels for epileptic activity anyway? Additionally, what is the medical justification for implanting patients with electrodes in what is putatively a healthy hemisphere?

Statistics comments:

1) Watson-Williams test assumes a von Mises distribution. Is this true for the distributions studied here? If not, use the Wheeler-Watson test.

2) For the resampling statistics: the images were shown in groups of four, but the resampling seems to use random permutation. Resampling should be performed such that the labels for the “chunks” should be shuffled, but within these 4-trial chunks, the labels should be kept the same. This would control for any effect of this chunking.

3) Are there still significant differences between categories? How many electrodes have a category with DS=3?

4) It would be nice to also be given an estimate of effect size wherever a p-value is given.

5) For the SVM bootstrapping estimates, are the two bootstrapping experiments actually independent in order to support the expected false alarm rate of 0.42 electrodes?

General comments:

1) Are there spatial clusters among the electrodes that have phase coding for each of the different categories (c.f. Vidal et al, 2010)?

2) Please make all rose phase plots opaque as in Figure 3C so that we can see the phase distributions for each category.

3) For these phase plots, it would be nice to see the true number of high frequency activity events within each phase bin.

4) It is unclear how Figure 3–figure supplement 1 should be interpreted. For example, the primary effect in the paper is in the delta range, but this figure seems to show poor delta phase clustering. Why?

https://doi.org/10.7554/eLife.07886.018

Author response

Reviewer #1:

1) The authors may be unintentionally inflating their findings by defining PAC by power increases in HFA through the oscillation triggered coupling (OTC) procedure. PAC does not require a change in power, but a change in the distribution of power relative to the phase of the low-frequency oscillation. Evoked activity might distort CFC results. Did the authors attempt to study evoked and non-stimulus locked activity separately?

As described in response to concern #2, we choose this relatively uncommon and novel metric of PAC because it is not biased by inter-trial phase locking of low-frequency activity. As the reviewer correctly points out, the OTC method is based on transient increases in HFA, which do not necessarily occur in all cases of PAC. We assessed PAC as both an increase in HFA and a phase-locking of HFA to slow oscillation phases. Given this additional constraint in our analysis, we believe we are conservatively estimating PAC because, as the reviewer notes, PAC is not necessarily associated with a change in power. This becomes apparent in our alternative metric (see response to concern #2). Regarding the issue of evoked activity, we observed HFA events distributed throughout the stimulus presentation period (Figure 2F; Figure 3B–figure supplement 1; Figure 3C–figure supplement 1), which is inconsistent with the typical notion of stimulus evoked activity. We have added the following text to the manuscript: “Notably, this method may conservatively estimate PAC because it is based on transient increases in HFA, which do not necessarily occur in all cases of PAC”.

2) OTC needs more explanation in this paper. Given the major claims of the paper, the authors should compare the OTC to more established PAC methods, so that the reader can better assess the results. The findings may be inflated by the chosen metric.

We thank the reviewer for this suggestion and have conducted several analyses to this end. We calculated the Modulation index (MI: Tort et al, 2009, PNAS) by binning HFA (51-200 Hz; see response to concern #8 for justification of this more extended high-frequency range) amplitude according to low-frequency phase in either the delta (.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz) or the entire low frequency (.5-12 Hz) bands. Following surrogate control analyses, we observed significant PAC in each band. Consistent with our primary results, PAC was most prominent in the delta band using these methods. Furthermore, the magnitude of gamma band activity was maximal at a similar phase of delta oscillations (180°, oscillatory trough) as when assessed using the OTC method. Based on the MI based metric, we found that 102/167 electrodes exhibited significant PAC with delta band phase. However, we found more electrodes showing significant PAC using the more traditional MI based metric compared to the OTC metric (72/167 PAC+ electrodes). We believe this is because, as the reviewer points out, MI does not require a change in power, whereas OTC is more conservative in requiring a change in power.

We opted for OTC rather than other PAC measures because we sought to interrogate the relation between PAC and phase-coding, which is thought to rely on precise timing and is calculated at individual time points. Any conventional time-resolved PAC metric such as MI (calculated across trials) is potentially biased by phase locking (and becomes impossible to calculate if inter-trial phase locking is perfect, because then no phase variance exists). On the other hand, we could not calculate PAC across time because of our relatively short trial periods (related to the reviewer’s concern regarding calculating PAC on epoched data). This reasoning motivated us to use the OTC method.

In addition, we have added a description of the analysis into the supplemental materials and have added the following text to the Results section to address this point: “Third, for comparison with prior PAC methods, we recomputed PAC using the Modulation Index (Tort et al., 2009) in different low-frequency bands, again finding PAC that was most prevalent in the delta band (Figure 2–figure supplement 3).”

3) There are reports (Mathewson, 2009; Schyns, 2011) that indicate that phase coding only becomes evident in stages of high power. It is necessary to test the phase vs. power coding and then assess phase coding separately for high and low power trials. This is important, since the decoding results imply that HFA alone might explain some of the findings. FMAX and FMIN values per subject should be reported. In order to establish PAC as a putative mechanism, one needs to rule out that neither HFA nor low frequency phase/power alone can explain the findings.

We fully agree with the reviewer, and in fact, an earlier version of this manuscript included such an analysis assessing categorical decoding using low frequency power/phase or HFA along with our difference score approach. We now include results from this analysis for the example electrodes in Figure 3. The left panel in Figure 3–figure supplement 1 shows the time-resolved and trial-averaged delta power/phase or HFA power values, along with difference scores, for each category. For comparison, the right panel shows HFA windows for each category (similar to Figure 4). Consistent with prior work (reviewed in Watrous et al., 2015), these examples indicate that some categorical information is contained in each measure. In the electrode example in Figure 3–figure supplement 1, faces can be well distinguished from the other categories based on power information between around 300-700ms, based on low-frequency phase between around 100-150ms, and based on HFA between around 250-300ms. Critically, however, maximal phase and HFA differences scores (e.g. for faces) do not overlap and these enhanced DS periods do not neatly map onto HFA windows (compare to right panel). In this example, faces can be decoded via phase-clustered HFA (i.e. PAC) at many time points throughout the entire stimulus presentation period, while decoding based on low-frequency phase or power alone or based on the magnitude of HFA alone is only possible in restricted time periods. Moreover, unlike what we observe in our primary analysis based on PAC (Figure 3), categorical decoding for all categories is not possible with HFA alone (which in this example only allows distinguishing faces from non-faces, but not between any of the other categories).

Another example is shown in Figure 3–figure supplement 2 (same patient and electrode as in Figure 3C). In this case, PAC-based decoding allows separating of both houses and scenes from all three respective other categories (PAC-based DS of 3 for houses and scenes in Figure 3C). By contrast, unequivocal decoding by phase is only possible at specific time points for faces and tools (left panel in Figure 3–figure supplement 2), and decoding by HFA alone only allows separating faces from the three other categories at a short time window late in the trial.

These examples highlight a complex relationship between each measure and categorical representation, which certainly warrants further investigation. We believe a full characterization of these effects is beyond the scope of this manuscript. However, our examples clearly indicate that some categorical information can be recovered from low-frequency phase, power and HFA alone, but also demonstrate that additional information can be recovered based on PAC.

Finally, we now plot individual subject FMAX and FMIN values in Figure 4–figure supplement 1.

4) Could the distribution of phase angles at stimulus onset exhibit a systematic bias with respect to the visual stream? To rule out entrainment to the rhythmic stream the authors could employ ITC or phase-locking to the sensory stream (e.g. Thut et al., 2011).

We provided a preliminary evaluation of the issue regarding phase-locking in the former Figure 3–figure supplement 1 (now Figure 3–figure supplement 3). This figure shows the proportion of electrodes with significant phase-locking exclusively for each of the four categories as a function of time and frequency. We find evidence that there is some category-selective phase locking in our recordings. Importantly, our PAC results were strongest in the delta band using both OTC and MI methods (Tort et al., 2009, PNAS) and thus do support some level of visual entrainment to stimulus presentation as described in the Discussion. However, overall only relatively small numbers of electrodes showed category specific phase-clustering (5 percent of all electrodes for all categories apart from tools, where 16 percent showed phase-clustering), while PAC-based decoding of each of these categories was possible in more than twice as many electrodes (12 to 17 percent of all electrodes had a difference score of 3 for each of the four categories). Again, this shows that PAC-based decoding cannot be explained only by phase-locking.

5) A major concern is the use of wavelets for extracting oscillatory phase. Wavelets constitute an acausal approach to determine the phase of a signal, since an underlying oscillation is assumed. This leads to frequency, but also phase smoothing, e.g. that a transient ERP contaminates the pre-stimulus phase estimates by backward ‘smearing’. The paper would benefit from a causal approach. Please consult Zoefel and Heil (2013, FIPSY).

We agree that extracting the phase of the signal using wavelets could potentially introduce phase-smoothing based on the assumption of an underlying oscillation but believe several considerations argue against such an effect confounding our results. First, visual comparison of raw signals and extracted phases (using wavelets) did not reveal any systematic shift in phase estimates (e.g. Figure 3A). More importantly, we tested the assumption of an underlying oscillation directly by insuring that all PAC+ electrodes showed a peak in the power spectrum of the modulatory signal at the same frequency as the raw signal. Third, given that we are not exclusively looking at evoked activity, the potential of an ERP “backward smearing” our phase estimates is minimized, in theory. Finally, such phase smoothing might result in uniformly distributed (i.e. Von Mises) phases, which is the opposite of what we found when calculating phase-clustering (using wavelet-extracted phase estimates) on PAC+ electrodes. These considerations led us to use wavelets because they were used in the original implementation of OTC by Dvorak and Fenton.

6) The difference score is introduced in the second paragraph of the subsection “HFA occurs at different phases for different categories” and requires earlier explanation. A schematic for OTC and DS would be preferable.

Following up on this helpful comment, we have added a schematic flow chart for both the OTC method and DS as Figure 1–figure supplement 1.

7) Were the phase and power extracted from the same electrode? Was PAC calculated on epoched data? Both things could possibly lead to spurious findings.

Phase and power were extracted on the same electrode because we were interested in how categorical representation via phase might occur locally. Mathematically, phase and power are independent. Practically, however, they depend on the synchronization of neural assemblies nearby to the recording contact and thus are interdependent (e.g. high synchronization will result in a robust phase estimate and probably high inter-trial phase locking, but also in high power). In this study, it was no real option for us to use different electrodes because a main aim was to first assess PAC, which has primarily been characterized as a local phenomenon. Although some reports indicate that information might be represented between electrode sites (Canolty et al., 2010 PNAS; Majima et al., 2014 Neuroimage), we believe this is the most straightforward way to understand phase coding. Furthermore, our new results on difference scores based on low-frequency power and low-frequency phase (see Figure 3–figure supplement 1 and Figure 3–figure supplement 2) show independent decoding from phase and power, speaking against the idea that these two metrics are strongly inter-related. PAC was calculated on concatenated epochs of artifact-rejected data using the OTC methods as described by Dvorak and Fenton, 2014. Our analysis did not include HFA events detected near the concatenation border and thus we observed similar results when calculating OTC on unepoched or epoched but concatenated data (example taken from Figure 2–figure supplement 1 Patient #4, Results).

8) Why did the authors only analyze the frequency spectrum up to 120 Hz? Often findings in HG in ECoG only start at 100 Hz and then extend up to 250 Hz.

We opted to analyze the spectrum up to 120 Hz in order to avoid potential confounds associated with epilepsy, such as pathological “fast ripples” (above 150 Hz; Staba et al, 2007, Epilepsia; Menendez de la Prida et al, 2015, Journal of Clinical Neurophysiology). 120 Hz was also the upper frequency bound used by Dvorak and Fenton in the original paper describing OTC, and several other papers investigating PAC in humans have similar cutoff frequencies at or below 150 Hz (Canolty et al., 2006, Science; Lega et al., 2014, Cerebral Cortex; Voytek et al., 2015, Nature Neuroscience). Given that the exact relation between normal/pathological HFA and cognition remains unclear, we thought it was most important to take a conservative approach on this matter though we do not dispute other studies which have found high gamma/HFA effects at higher frequencies.

We also note that we found similar results using the Modulation Index with the “full” gamma frequency range (50-200 Hz). Based on these reasons, we would suggest keeping the original frequency range. Nonetheless, we are willing to perform this additional analysis if the reviewer feels it is necessary.

9) Were eye movements recorded in any of the subjects? The authors argue that this is an unlikely source of artifactual data but eye-tracking data would be more assuring.

We instructed patients to keep their eyes on the fixation cross between stimuli in order to minimize any effects associated with eye movements. Unfortunately, electrooculogram/eye trackers are not well-tolerated by patients who already experience head discomfort associated with electrode implantation. Therefore, eye movement data was not systematically collected in this study, and thus eye movements are a potential confound which we cannot fully rule out (Kovach et al., 2011, Neuroimage). However, even when entertaining a scenario in which some of our HFA events were related to eye movements, it does not necessarily follow that: 1) these HFA events should show phase-locked activity, and 2) are also diagnostic of image category. Furthermore, eye movements occur predominantly at specific time points, in particular at around 200ms, where they may account for apparent induced gamma-band responses, while PAC in our study occurred widely distributed throughout the entire stimulus presentation phase (Figure 2–figure supplement 2).

Reviewer #2:

1) Anatomy: While the authors provide evidence for PAC, and phase based coding of visual categories, their conclusions make no reference to functional neuroanatomy. Recording sites across subjects come from a wide variety of regions; this strikes me as a challenge when interpreting the specific coding of visual categories. The supplemental movie suggests a range of distant regions, not typically associated with higher-level vision, display some degree of phase-coding/representation of visual categories. This seems inconsistent with previous electrophysiology and functional imaging work (which is not cited or discussed in any detail). On a related note, the diversity of recording sites also makes the use of count statistics as somewhat arbitrary. Percentage of electrodes displaying an effect of interest can be equally meaningful for large or small percentages (e.g. low % for anatomically specific effect or high % for a trivial global effect). The authors should clarify why macro-scale phase-coding exists for visual categories across many cortical regions, rather than focused to the more classical regions of categorical selectivity.

We thank the reviewer for this point and have added the following paragraph to the Discussion: “Intriguingly, phase-coding of categorical information extended beyond brain areas associated with higher-order vision. […] Our findings are nonetheless in line with prior work (Zhang et al., 2014; Majima et al., 2014; Yaffe et al., 2014) which has found spatially- distributed content-specific representations.”

2) Task/decoding: In their task, the authors present only four exemplars for each category, repeating each 33 times (if I understand correctly). While this may serve to aid mnemonic encoding, it does limit the claim of categorical decoding. Specifically, in developing a category decoder, the large number of stimulus repetitions limits insight, given the similarity between any training and test set.

We agree with the reviewer that the repetition of each exemplar (4 exemplars from 4 categories; 16 total images repeated 30 times each) limits the generalizability of our findings to other stimuli within these categories. Such compromises between generalizability and statistical sampling/power are a common problem in the field without any agreed upon solution. We opted for this design in order to maximize the chances of identifying category-selective responses while still ensuring that these responses were generalizable across a few exemplars. We have added the following text related to this point to the Discussion: “Another caveat is that our results provide evidence for categorical phase-coding based on a restricted image set. […] Follow-up studies should test the generalizability of these findings using more exemplars within a category and using other categories.”

On a related note, I found the reporting of the basic decoding results unclear (is the decoder working above expected chance levels? >25%).

We apologize for the lack of clarity in the reporting of the basic decoding results. Yes, all electrodes reported to be significant were at or above the 95th percentile of chance classification accuracies obtained using permutation testing. We classified the category label associated with each HFA event and the number of HFA events per category varied across electrodes. Therefore, chance classification performance varied across electrodes and was not fixed at 25%. We thus opted to report significance based on permutation testing which accounts for the varying chance level across electrodes. We have added to this effect in the Methods section: “Chance classification performance varies across electrodes because we classified the category label associated with each HFA event and the number of HFA events per category varied across electrodes. We thus opted to report significance based on permutation testing which accounts for the varying chance level across electrodes.”

Given the authors’ aims, it seems that a better use of the data would be quantifying the consistency of phase-coding metrics across repetitions of stimuli, as well as within/between class comparison. This approach would focus more on extracting single trial features and testing similarity across repeated trials (I note issues of repetition suppression come into play here). This approach of displaying consistency of stimulus phase-coding would provide more robust evidence for the authors’ claims.

We are not entirely clear what the reviewer is proposing here but feel that our analysis likely already incorporates these ideas. Our analyses were in fact based on the extraction of single-trial features. We insured that there was within class consistency for these features and then tested for how these might differ between categories. More specifically, we insured that the phases associated with HFA events (our single trial features) for each category were consistent by assessing phase-clustering locked to the onset of HFA events. We then compared these phase distributions between categories to test for phase-coding.

Reviewer #3:

1) In Figure 2H, Figure 2–figure supplement 1, the authors show “low gamma” PAC and “high gamma” PAC. This 32 Hz coupling mode seems striking, because it's likely that coupling extends even further below this range into the beta range. This low gamma has been argued to be distinct from more “broadband” high gamma (Kai Miller and Dora Hermes' work), which is correlated with population spiking (Mukamel, Science; Manning, J Neurosci), in contrast to low gamma, which is more oscillatory. Thus, the low gamma effect may be more a form of “nested” coupling as has been argued by Nancy Kopell.

We thank the reviewer for this insightful comment. We agree that our results might imply several different “coupling modes”, perhaps with different forms of narrowband (low frequency) or broadband (higher frequency) gamma. We view this as a fruitful avenue for future study, and have added text to this effect in the Discussion: “Similarly, our results showing PAC at a variety of phases and frequencies (Maris et al., 2011; van der Meij et al., 2012), particularly near 32 Hz, might reflect a form of ‘nested coupling’ (Kopell et al., 2010) distinct from ‘broadband’ high gamma, which has been suggested to reflect population spiking (Manning et al., 2009; Miller et al., 2014)”.

2) There appears to be a disproportionate PAC effect at 0.5Hz and 1.0Hz, but with surprising specificity, and not between those two frequencies as seen in Figure 4–figure supplement 1. Why do the authors believe this occurs, and why do they believe their PAC effects are so restricted to this delta range, in contrast to what others have observed in ECoG?

We agree that this result is somewhat surprising and made several attempts to rule out trivial, signal processing based interpretations. We first wanted to make sure that the results weren’t associated with the 1/f nature of the signal and excluded this account based on the fact that there are distinct peaks at .5 and 1 Hz, rather than a smooth falloff with increasing frequency. We also find the strongest PAC effects in the delta band using the Modulation Index (Tort et al., 2009) and can thus exclude the possibility that our comparatively new analytic method (OTC) influenced this finding. Nonetheless, we also observed significant coupling to theta and/or alpha phase.

Given this, we trust the delta effects we observe and believe that several factors may account for these results. First, our stimulus presentations occurred in the delta band (with some jitter) and may have therefore partially entrained the visual system. Second, the literature is dominated by PAC findings using phases in a pre-defined frequency band. This is typically theta band activity, in large part motivated by findings in rodents. Recent evidence suggests that the closest analog to rodent theta is in fact human delta oscillations (Watrous et al., 2013; Hippocampus; Jacobs et al., 2014, Philosophical Transactions of the Royal Society B). Thus, it is conceivable that previous studies would have also observed strong delta band PAC effects had they considered this band. We note that one strength of the OTC method is that it does not require assumptions about the coupling frequency.

3) How sensitive is detection of HFA event times to the filtering method?

As described above (Reviewer 1, comment 2), we calculated PAC using the Modulation Index and found comparable results. Thus, identifying PAC via detection of HFA events and the exact filtering method do not appear to have an appreciable impact on our primary findings. This comports well with the previous literature (van Vugt et al., 2007, Journal of Neuroscience Methods). We ultimately utilized wavelets based on the OTC method, in which the authors provide an in-depth discussion of the importance of various filtering parameters (Dvorak and Fenton, 2014, Journal of Neuroscience Methods).

4) With regards to electrode choice, the rationale for only using electrodes in the contralateral hemisphere is unclear. Why systematically reject an entire hemisphere (except for 1 subject, oddly) when you visually inspect channels for epileptic activity anyway? Additionally, what is the medical justification for implanting patients with electrodes in what is putatively a healthy hemisphere?

In order to avoid affecting our results by epileptic artifacts, we took an extremely conservative approach by only analyzing electrodes derived from the contralateral hemisphere and subsequently checking these electrodes manually for any remaining epileptic activity. Regarding the medical justification, surgeons typically implant electrodes bilaterally when there is uncertainty regarding the seizure onset zone. In fact, this is a primary reason for performing these implantations and the diagnosis of the affected hemisphere is made well after implantation, often after we have conducted our experiments. The classification of the hemispheres (pathological vs healthy) was based on the results of presurgical diagnostics as well as clinical evaluation of the intracranial recordings. These decisions are made purely based on clinical considerations and none of the authors on this study were involved in this process.

Statistics comments:

1) Watson-Williams test assumes a von Mises distribution. Is this true for the distributions studied here? If not, use the Wheeler-Watson test.

We are in entire agreement with the reviewer. Our implementation of the Watson test used the identical code following Rizzuto et al (2006, Neuroimage) which is publicly available from Mike Kahana’s website. This method explicitly tests the assumption of Von Mises distributions by calculating and comparing the circular dispersion of each distribution. In situations in which the distributions do not share similar dispersions, the test is modified following Fisher (1993).

2) For the resampling statistics: the images were shown in groups of four, but the resampling seems to use random permutation. Resampling should be performed such that the labels for the “chunks” should be shuffled, but within these 4-trial chunks, the labels should be kept the same. This would control for any effect of this chunking.

We apologize for the confusion, which likely arose from our caption for Figure 1. Images were presented in pseudo-random order, not in “chunks”. Our permutation testing is therefore appropriate. We have added text indicating the pseudo-random order of image presentation into the figure caption.

3) Are there still significant differences between categories? How many electrodes have a category with DS=3?

We are unsure what the reviewer means by “still” in this context but 49 of the 63 electrodes showing PAC and phase-clustered HFA events for each of the 4 categories had at least one category with DS=3.

4) It would be nice to also be given an estimate of effect size wherever a p-value is given.

We now include effect sizes for statistically significant (any p<.05) effects which were conducted with binomial, paired t-tests, or chi-square tests. Effect sizes for circular variables appears trickier; after a literature and internet search, we are unaware of effect size calculations for circular data (Rayleigh test, Watson-Williams test). We would be happy to include effect sizes for these tests if the reviewer has further suggestions

5) For the SVM bootstrapping estimates, are the two bootstrapping experiments actually independent in order to support the expected false alarm rate of 0.42 electrodes?

Yes, each of the bootstrapping estimates was performed separately. We either shuffled category labels or used phases at random HFA events. To the extent that shuffling was performed either across trials or across HFA events (and never at the same time), we assume that these tests are independent. Nonetheless, our results do show that 17 of the 19 electrodes showing significant decoding compared to random HFA events also shows significant decoding with trial label shuffling. This indicates that an electrode showing significant decoding is likely to show this compared to either of these two types of surrogate testing.

General comments:

1) Are there spatial clusters among the electrodes that have phase coding for each of the different categories (c.f. Vidal et al, 2010)?

We thank the reviewer for this suggestion. We tested this by calculating the average inter-electrode distance between each pairwise combination of electrodes that were selective for a specific category, and compared this to a matched number of randomly drawn electrodes (with replacement). We repeated this randomization procedure 1000 times in order to estimate a distribution of inter-electrode spacings that would be expected by chance. As seen in Author response image 2, we neither observed significant spatial clustering nor significantly spatially diffuse electrode configurations (dark blue bars between the 5th and 95th percentile of simulated inter-electrode distances). We believe this lack of clustering is because we were sampling category-selective responses, which extended beyond higher order vision, and have added to text to this effect in the Discussion (see Reviewer 2, comment 1 above for exact text).

2) Please make all rose phase plots opaque as in Figure 3C so that we can see the phase distributions for each category.

We have updated the figure accordingly.

3) For these phase plots, it would be nice to see the true number of high frequency activity events within each phase bin.

We now include plots showing HFA windows (color coded by the FMAX phase) for each category for these electrodes in the rightmost panels of Figure 3B–figure supplement 1 and Figure 3C–figure supplement 1. We elected to show these, rather than histograms, because they allow for a better, time-resolved comparison of HFA windows and decoding using power, phase, or HFA (see Reviewer 1, comment 3).

4) It is unclear how Figure 3–figure supplement 1 should be interpreted. For example, the primary effect in the paper is in the delta range, but this figure seems to show poor delta phase clustering. Why?

Figure 3–figure supplement 1 shows the proportion of electrodes showing category specific phase-clustering for each category. The largest proportion of electrodes with the most sustained effects is in the delta range at ∼ .5 and 1 Hz (with the exception of Tools). It is also worth noting that since this analysis investigates category specific effects, any significant electrode showing phase clustering will only show up in any one of the 4 panels. Finally, we report phase-clustered HFA events for all 4 categories at FMAX in 63 of 72 PAC+ electrodes. Thus, we observe robust phase-clustering in the delta band which is often category-specific and feel that Figure 3–figure supplement 1 accurately reflects this finding.

https://doi.org/10.7554/eLife.07886.019

Article and author information

Author details

  1. Andrew J Watrous

    Department of Epileptology, University of Bonn, Bonn, Germany
    Contribution
    AJW, Conception and design, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents
    For correspondence
    andrew.j.watrous@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
  2. Lorena Deuker

    1. Department of Epileptology, University of Bonn, Bonn, Germany
    2. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
    Contribution
    LD, Conception and design, Acquisition of data, Analysis and interpretation of data, Contributed unpublished essential data or reagents
    Competing interests
    The authors declare that no competing interests exist.
  3. Juergen Fell

    Department of Epileptology, University of Bonn, Bonn, Germany
    Contribution
    JF, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  4. Nikolai Axmacher

    1. German Center for Neurodegenerative Diseases, Bonn, Germany
    2. Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
    Contribution
    NA, Conception and design, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents
    For correspondence
    nikolai.axmacher@ruhr-uni-bochum.de
    Competing interests
    The authors declare that no competing interests exist.

Funding

Deutsche Forschungsgemeinschaft (DFG AX82/2)

  • Nikolai Axmacher

Deutsche Forschungsgemeinschaft (SFB 1089)

  • Juergen Fell
  • Nikolai Axmacher

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

NA was supported by funding from an Emmy Noether grant by the DFG (AX82/2). NA and JF received funding via SFB 1089. We thank Hui Zhang and Marcin Leszczynski for comments on an initial manuscript version.

Ethics

Human subjects: The study was conducted according to the latest version of the Declaration of Helsinki and approved by the local ethics committee, and all patients provided written informed consent.

Reviewing Editor

  1. Howard Eichenbaum, Reviewing Editor, Boston University, United States

Publication history

  1. Received: April 2, 2015
  2. Accepted: August 25, 2015
  3. Accepted Manuscript published: August 26, 2015 (version 1)
  4. Version of Record published: September 23, 2015 (version 2)

Copyright

© 2015, Watrous et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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