Representations of the temporal envelope of sounds in human auditory cortex: Can the results from invasive intracortical “depth” electrode recordings be replicated using non-invasive MEG “virtual electrodes”?

NeuroImage 64 (2013) 185–196

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Representations of the temporal envelope of sounds in human auditory cortex: Can the results from invasive intracortical “depth” electrode recordings be replicated using non-invasive MEG “virtual electrodes”? Rebecca E. Millman ⁎, Garreth Prendergast, Mark Hymers, Gary G.R. Green York Neuroimaging Centre, The Biocentre, York Science Park, Heslington, YO10 5DG, UK

a r t i c l e

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Article history: Accepted 10 September 2012 Available online 15 September 2012 Keywords: Phase-locking Time-frequency analyses Magnetoencephalography Beamforming Virtual electrodes

a b s t r a c t Magnetoencephalography (MEG) beamformer analyses use spatial filters to estimate neuronal activity underlying the magnetic fields measured by the MEG sensors. MEG “virtual electrodes” are the outputs of beamformer spatial filters. The present study aimed to test the hypothesis that MEG virtual electrodes can replicate the findings from intracortical “depth” electrode studies relevant to the processing of the temporal envelopes of sounds [e.g. Nourski et al. (2009) “Temporal envelope of time-compressed speech represented in the human auditory cortex,” J. Neurosci. 29:15564–15574]. Specifically we aimed to determine whether it is possible to use non-invasive MEG virtual electrodes to characterise the representation of temporal envelopes of 6-Hz sinusoidal amplitude modulation (SAM) and speech using both auditory evoked fields (AEFs) and patterns of power changes in high-frequency (>70 Hz) bands. MEG signals were analysed using a location of interest (LOI) approach by seeding virtual electrodes in the left and right posteromedial Heschl's gyri. AEFs showed phase-locking to the temporal envelope of SAM and speech stimuli. Time-frequency analyses revealed no clear differences in high gamma power between the pre-stimulus baseline and the poststimulus presentation periods. Nevertheless the patterns of changes in high gamma power were significantly correlated with the temporal envelopes of 6-Hz SAM and speech in the majority of participants. The present study reveals difficulties in replicating clear augmentations in high gamma power changes using MEG virtual electrodes cf. intracortical “depth” electrode studies (Nourski et al., 2009). © 2012 Elsevier Inc. All rights reserved.

Introduction The temporal envelopes of sounds contain important cues for auditory perception e.g. the temporal envelope of speech is crucial for speech perception under quiet listening conditions (e.g. Drullman et al., 1994; Shannon et al., 1995). Many neuroimaging studies of temporal envelope processing using EEG (e.g. Abrams et al., 2008; Aiken and Picton, 2008; Picton et al., 1987, 2003; Rees et al., 1986), MEG (e.g. Ahissar et al., 2001; Herdman et al., 2003; Luo and Poeppel, 2007; Ross et al., 2000; Simon and Wang, 2005) and intracortical electrodes (e.g. Gourevitch et al., 2008; Liegeois-Chauvel et al., 2004) have focussed on measuring activity that is temporally synchronised, i.e. phase-locked, to the temporal envelope of the auditory stimulus. However, studies by Nourski and colleagues (Brugge et al., 2009; Nourski et al., 2009) suggested that phase-locking to the temporal envelope of sounds is not the only mechanism involved in the representation of the temporal envelopes of auditory stimuli. Using intracortical “depth” electrodes placed along the length of Heschl's gyrus (HG), Nourski et al. (2009) showed that the ⁎ Corresponding author. Fax: +44 1904 435356. E-mail address: [email protected] (R.E. Millman). 1053-8119/$ – see front matter © 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.neuroimage.2012.09.017

representation of the speech temporal envelope in posteromedial HG is characterised not only by phase-locked activity but also by increases in event-related band power (ERBP) within the high gamma (>70 Hz) frequency band that were modulated by the temporal envelope of speech. These patterns of high gamma power changes driven by the stimulus temporal envelope occurred even when there was no visible phase-locking to the stimulus envelope. Changes in high-gamma ERBP in more lateral recording sites were relatively modest and did not exhibit modulation by the stimulus envelope seen in posteromedial HG. Brugge et al. (2009) also reported stimulus-related increases in high gamma ERBP within posteromedial HG for relatively simple auditory stimuli (click trains). The aim of the present study was to test the hypothesis that it is possible to use magnetoencephalography (MEG) to replicate the intracortical “depth” electrode work of Nourski et al. (2009) i.e. to characterise the representation of the temporal envelopes of auditory stimuli based on both phase-locked activity, i.e. auditory evoked fields (AEFs), and increases in high-frequency (>70 Hz) total power (i.e. the sum of phase-locked and non-phase-locked power) and non-phaselocked power. MEG is a non-invasive neuroimaging technique that provides direct measurements of neural activity with a millisecond temporal resolution. The magnetic fields measured in MEG pass

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through the head with minimal distortion and the use of MEG beamforming analyses permits spatiotemporal characterisation of brain activity with a relatively high spatial resolution (Barnes and Hillebrand, 2003; Barnes et al., 2004). Beamforming analyses create spatial filters throughout the brain that can be used to reconstruct time series of neural activity at given locations throughout the brain. The outputs of beamformer spatial filters have been termed “virtual channels” (e.g. Robinson and Rose, 1992) or “virtual electrodes” (e.g. Barnes and Hillebrand, 2003). An important application of MEG beamformer and virtual electrode analyses is the potential ability to link non-invasive MEG measurements and invasive electrophysiological recordings in animals (e.g. Zumer et al., 2010) and humans (e.g. Dalal et al., 2008, 2009; Hall et al., 2005; Litvak et al., 2010; Sedley et al., 2012). A small number of studies have compared human MEG data with electrocorticography recordings from surgical candidates and patients undergoing neurosurgery (Dalal et al., 2008, 2009; Litvak et al., 2010; Sedley et al., 2012). Such studies generally report a good correspondence in spatial, temporal and frequency domains between these invasive and non-invasive measures, although it should be noted that reconstructions of high gamma power using MEG virtual electrodes are not always successful (Dalal et al., 2009). An influential cognitive model of speech perception, the asymmetric sampling in time (AST) model (e.g. Poeppel, 2003), predicts that speech is represented on functionally asymmetrical time-scales in the left and right hemispheres. According to the AST model (e.g. Poeppel, 2003), the right hemisphere preferentially encodes low frequencies in the theta frequency range (~4–8 Hz). In contradistinction, the AST model (e.g. Poeppel, 2003) predicts that the left hemisphere operates on a relatively faster time scale, consistent with the gamma frequency band (~20–50 Hz). Consistent with the predictions of the AST model, an EEG study (Abrams et al., 2008) in which speech stimuli were presented monaurally to child participants, suggested that evoked activity from the right hemisphere phase-locks more strongly to the temporal envelope of speech than the left hemisphere. Using MEG dipole fits, Ding and Simon (2012) also found that the spectrotemporal features of speech were more faithfully encoded in the associative auditory cortex i.e. superior temporal gyrus (STG) in the right hemisphere. Nourski et al. (2009) could not assess hemispheric asymmetries in phase-locking to speech temporal envelopes in their study as the “depth” electrode recording sites were restricted to either the left or right auditory cortices of individual patients, depending on the clinical requirements. One advantage of the MEG beamforming and virtual electrode analysis approach over invasive intracortical electrode recordings is that the MEG virtual electrodes may be seeded in both left and right posteromedial HG. Therefore MEG beamformer-based analyses may help to identify hemispheric lateralisations in the representation of sounds in human auditory cortex. In the present study MEG virtual electrode analyses were used to reconstruct the time series in locations of interest (LOIs) within the human auditory cortex. The LOIs were the posteromedial left and right HG, the putative sites of human primary auditory cortices. These LOIs in posteromedial HG are sites where Nourski et al. (2009) successfully measured both AEFs phase-locked to the speech temporal envelopes and increases in high gamma ERBP. The time series of virtual electrodes seeded in the LOIs were used to measure phase-locking to the temporal envelopes of a simple auditory stimulus (sinusoidal amplitude modulation, SAM) and a speech stimulus in both left and right posteromedial HG. These phase-locking measures were used to test the hypothesis of right-hemisphere dominance in coding the speech temporal envelope (e.g. Abrams et al., 2008; Luo and Poeppel, 2007; Poeppel, 2003). Time-frequency plots generated from virtual electrode time series were used to assess whether non-phase-locked power changes within high-frequency bands (70–150 Hz) were modulated by the temporal dynamics of the

stimulus temporal envelopes (e.g. Brugge et al., 2009; Nourski et al., 2009). Examining high-frequency (>70 Hz) changes in power with non-invasive MEG methods is of particular interest to MEG researchers as there are potential methodological difficulties associated with extracting gamma band activity from non-invasive recordings (e.g. Dalal et al., 2009; Hoogenboom et al., 2006; Trujillo et al., 2005), such as low signal-to-noise within the gamma/high gamma band or inadequate spatial resolution. Materials and methods Participants Seven participants (age range 20 to 54 years) with no history of neurological disorders took part in this LOI study. All participants gave their written consent prior to the experiment and the local ethics committee approved the study. Stimuli Stimuli were created using custom programmes in MATLAB (The MathWorks, Natick, MA) with a sampling frequency of 44.1 kHz. Stimuli were delivered monaurally to the left ear of participants via Etymotic insert earphones (Etymotic Research ER3-A) at a sound level of 80 dB SPL. Two auditory stimuli were used in two separate MEG recording sessions: a simple temporal envelope (SAM) and a complex temporal envelope (speech). Sinusoidal amplitude modulation (SAM) SAM applied to a sinusoidal carrier was used as an auditory stimulus with a simple temporal envelope. The equation describing the SAM stimulus was: xðt Þ ¼ ð1 þ m sinð2πf m t ÞÞ sinð2πf c t Þ

ð1Þ

where x is the signal, t is time, m is the modulation depth (m = 1), fm is the modulation frequency (fm = 6 Hz) and fc is the carrier frequency (fc = 1 kHz). During the MEG experiment 6-Hz SAM was presented for a duration of 2.5 s, including 20-ms raised cosine rise/fall times. In practice, the 6-Hz SAM was followed immediately by another modulated stimulus (5-Hz SAM) with a duration of 1.5 s. The use of 6-Hz and 5-Hz SAMs within the same epoch was for an unrelated experiment. Therefore for the purposes of the present study, only the MEG signal during the 2.5-s presentation of the 6-Hz SAM was analysed. The total epoch duration was 8.5 s as the 6-Hz SAM was always preceded by 4.5 s of silence (pre-trigger). The 6-Hz SAM was presented 90 times. The upper panel (A) of Fig. 1 shows the waveform of the 6-Hz SAM stimulus. Speech A single sentence from the IEEE corpus (“A large size in shoes is hard to sell”) spoken by a male speaker was used as the speech stimulus. The temporal envelope of the speech sentence was obtained by passing the speech stimulus through a 64-channel model of the auditory filters (Slaney, 1994), which is analogous to using a tone vocoder (e.g. Dorman et al., 1998; Dudley, 1939; Stone et al., 2008). Estimates of the speech temporal envelopes were obtained by calculating the magnitude of the Hilbert transform of the output of each channel and combining the resultant Hilbert envelopes. The unfiltered amplitude envelope was low-pass filtered with a cut-off frequency of 60 Hz to isolate the frequencies consistent with the perceptually important components of the speech temporal envelope (e.g. Rosen, 1992). The speech temporal envelope was down sampled by a factor of 65 to ensure that the speech temporal envelope and MEG data were sampled at the same rate. Note that although Abrams et al. (2008) took a

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To carry out artefact rejection, the raw data from each epoch were inspected visually. Epochs contaminated with either physiological or non-physiological artefacts were manually removed. Co-registration For the source-space analyses, the landmark locations were matched with the individual participants' anatomical magnetic resonance (MR) scans using a surface-matching technique adapted from Kozinska et al. (2001). T1-weighted MR images were acquired with a GE 3.0-T Signa Excite HDx system (General Electric, Milwaukee, USA) using an 8-channel head coil and a 3-D fast spoiled gradient recall sequence. The parameters were TR/TE/flip angle = 8.03 ms/3.07 ms/20°, spatial resolution of 1.13 mm× 1.13 mm× 1.0 mm, and in-plane resolution of 256 × 256 × 176 contiguous slices. Beamformer and virtual electrode analyses

Fig. 1. Temporal waveforms of the stimuli used in the experiments. The top panel (A) shows the waveform of the 6-Hz SAM stimulus with a simple temporal envelope. The lower panel (B) shows the waveform of a speech sentence with a more complex temporal envelope.

vocoder-based approach (e.g. Drullman et al., 1994) to obtain broadband speech temporal envelopes, Nourski et al. (2009) used an alternative method to extract the speech temporal envelope: Nourski et al. (2009) calculated the Hilbert envelope of the broadband speech signal, i.e. without filtering the speech signal into a number of narrower bands, and then low-pass filtered the Hilbert envelope with a cut-off frequency of 50 Hz. The duration of the speech sentence was 2.8 s. The speech sentence was presented 90 times. The inter-stimulus interval was 3.5 s. The lower panel (B) of Fig. 1 shows the waveform of the speech sentence.

MEG recording Data were collected using a Magnes 3600 whole-head 248channel magnetometer (4-D Neuroimaging Inc., San Diego). The data were recorded at a sample rate of 678.17-Hz and were bandpass filtered online between 1 and 200 Hz using a finite impulse response (FIR) filter. Prior to recording individual facial and scalp landmarks (left and right preauricular points, Cz, nasion and inion) were spatially co-registered using a Polhemus Fastrak System. The landmark locations in relation to the sensor positions are derived on the basis of a precise localization signal provided by 5 spatially-distributed head coils with a fixed spatial relation to the landmarks. These head coils provided a measurement of a participant's head movement at the beginning and end of each stimulus block.

An MEG beamformer estimates the contribution of a given voxel (grid point) in the brain to the signal measured at the MEG sensors. Independent beamformers (spatial filters) are constructed for each grid point. Each beamformer is an optimal spatial filter dedicated to a given grid point. The MEG sensors are linearly weighted to focus the sensor array on a given target source location. In the present study a vectorised linearly-constrained minimum-variance beamformer (VLCMV) (Huang et al., 2004; Van Veen et al., 1997) was used to obtain these spatial filters. The three orthogonal spatial filters were implemented as a single 3-D system (see Johnson et al., 2011). Principal component analysis (PCA) was used to reduce the output of the VLCMV beamformer to a single dimension. Virtual electrodes were used to reconstruct the source activity at given voxels as described in Millman et al. (2010). Virtual electrodes were reconstructed using a bandwidth of 1–200 Hz. The virtual electrode time window for the 6-Hz SAM condition was − 0.5 to 2.5 s post-stimulus presentation. The virtual electrode time window for the speech condition was − 0.5 to 2.8 s post-stimulus presentation. Choice of LOIs The aim of the present study was to analyse LOIs within the right and left posteromedial HG. Posteromedial HG is the putative site of human primary auditory cortex. The LOIs were chosen to be similar in location to intracortical electrode recording sites where Brugge et al. (2009) and Nourski et al. (2009) identified both phase-locked AEPs and augmentations in high-frequency (>70 Hz) power. Nourski and colleagues (Brugge et al., 2009; Nourski et al., 2009) recorded from multiple intracortical “depth” electrodes along the length of HG and were able to reveal functional differences between posteromedial and anterolateral HG: the best responses, in terms of both AEPs and increases in high gamma ERBP, were recorded from the most medial two-thirds of HG. In contrast AEPs and ERBP analyses from anterolateral HG showed little or no evidence of envelope following. Based on the results from Brugge et al. (2009) and Nourski et al. (2009), we inferred that if phase-locking to the temporal envelope of either SAM or speech could be measured, then the LOI must be in an appropriate location to measure both AEFs and changes in high gamma power. If the MEG virtual electrode placed in the LOI was sampling from mainly anterolateral HG, there should be no evidence of phase-locking to the temporal envelope of the auditory stimuli (Brugge et al., 2009; Nourski et al., 2009). LOIs were manually seeded in the left and right HG based on an individual participant's anatomy because the anatomy of HG varies considerably amongst individuals (e.g. Rademacher et al., 2001). Fig. 2 shows the location of the LOIs in the left and right posteromedial HG in an individual participant (S1).

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Spatial specificity of LOIs The spatial resolution of MEG beamformer spatial filters is inhomogeneous across the brain, and is related to the signal-to-noise ratio (SNR) of the source of interest (e.g. Barnes and Hillebrand, 2003; Barnes et al., 2004; Gross et al., 2001; Van Veen et al., 1997). Barnes et al. (2004) examined the effects of inhomogeneous spatial resolution of beamformer source space on virtual electrode locationof-interest (LOI) analyses. Barnes et al. (2004) measured the full-width at half-maxima (FWHM) of beamformer spatial filters in a visual steady-state paradigm and found that the distribution of FWHM is complex. Following the methods described in Barnes and Hillebrand (2003), we obtained estimates of the FWHM of the spatial filter used to create the virtual electrode for each individual at the chosen LOIs in the right and left posteromedial HG for both the 6-Hz SAM and speech conditions. FWHM are reported as the mean FWHM of the three directions of the VLCMV beamformer used to generate the spatial filters. Phase-locked AEFs In the time domain, phase-locked activity was characterised by the virtual electrode time series or auditory evoked fields (AEFs). In the case of the AEF analyses, the primary PCA component did not necessarily best represent phase-locking to the temporal envelope of the auditory stimuli, as the PCA implementation was dominated by the total power in the MEG signal. Therefore, the PCA component with the strongest phase-locking was selected to reduce the

dimensionality of the data. The representation of the stimulus temporal envelopes in the AEFs was quantified in the time domain using crosscorrelation analysis (e.g. Abrams et al., 2008; Nourski et al., 2009). Cross-correlations between the Hilbert envelope of the 6-Hz SAM/ speech and the AEF were performed using the “xcov” function in Matlab during the “envelope-following period” (Abrams et al., 2008). The “envelope following period” was defined as a 500–2500 ms post-stimulus presentation for the SAM stimulus and 500–2800 ms post-stimulus presentation for the speech stimulus. The absolute value of the peak in the cross-correlation function was found for lags of the response between 0 and 150 ms (Nourski et al., 2009). “High gamma” power analyses In the case of EEG/MEG/intracortical electrode time–frequency analyses changes in power may be phase-locked (evoked) or nonphase-locked with respect to the onset of the stimulus. Evoked power (e.g., Pantev, 1995) is both time-locked and phase-locked to the stimulus. Induced power (e.g., Crone et al., 2001) is time-locked but not phase-locked to the stimulus. The total power includes evoked and induced responses. An estimate of non-phase-locked power can be obtained by subtracting the evoked activity from individual trials before transformation into time-frequency plots (e.g., Crone et al., 2001). Time-frequency analyses used here were as similar as possible to the methods used in Nourski et al. (2009) but initially PCA was applied to the output of the VLCMV beamformer to reduce the dimensionality of the vectorised beamformer. However, Sedley et al. (2012) reported that high gamma power changes within the auditory cortex occurred

Fig. 2. Structural MR T1-weighted images of the locations of interest (LOIs) in the left and right posteromedial HG in an individual participant (S1). These images reveal the common structural asymmetries in the location and size of the left and right HG.

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predominantly in the 3rd PCA component and therefore all 3 PCA components of the MEG virtual electrodes were analysed. In the present study, time-frequency analyses were carried out on changes in total power, also termed the ERBP (e.g. Brugge et al., 2009; Nourski et al., 2009), and changes in non-phase-locked power. As in Brugge et al. (2009) and Nourski et al. (2009), changes in total or non-phase-locked power following stimulus onset were calculated relative to a 300-ms pre-stimulus reference period (−400 to −100 ms). Briefly, timefrequency analyses were carried out using complex Morlet wavelets, with centre frequencies ranging from 1 to 200 Hz in 1 Hz steps. The mean power at each centre frequency in the 300-ms pre-stimulus reference period was compared with power measurements at each centre frequency and each time point in 300-ms windows following stimulus presentation. The representation of the stimulus temporal envelope in total or non-phase-locked power changes was quantified by cross-correlating the Hilbert envelope of the stimulus with the Hilbert envelope of total/non-phase-locked power within the high gamma frequency band. As for the AEF analyses, the absolute value of the peak in the cross-correlation function was found for lags in the response between 0 and 150 ms (Nourski et al., 2009). In the present study we report cross-correlations between the Hilbert envelope of the stimuli and the Hilbert envelope of the high gamma frequency band for the whole epoch, including the stimulus onset (e.g. Nourski et al., 2009). Cross-correlation analyses were carried out on the whole epoch for the high gamma band because the representation of the speech temporal envelope in the high gamma band is evident immediately following the stimulus presentation (Nourski et al., 2009): Nourski et al. (2009) showed in their highly-compressed speech conditions (i.e. compression ratios of 0.3 and 0.2) that whereas the ability of the AEP to phase-lock to the first few hundred milliseconds of the speech temporal envelope was hampered by the neural response to the onset of sound, representation of the speech temporal envelope in the high gamma band occurs immediately after the stimulus presentation.

Statistical analyses Representation of Hilbert envelopes of stimuli in AEFs and Hilbert envelopes of high gamma non-phase-locked power Statistical analyses of representations of the temporal envelopes of the stimuli in either the AEFs or patterns in high gamma power were carried out only on the data of individual participants in this LOI study. Non-parametric permutation tests (e.g. Nichols and Holmes, 2001), based on 5000 permutations, were used to determine whether the representations of the Hilbert envelopes of the auditory stimuli (based on both the AEFs and bandpass-filtered Hilbert envelopes of non-phase-locked high gamma power) were statistically significant based on peak cross-correlation values. The null hypothesis stated that there was no representation of the Hilbert envelopes of the stimuli in either the AEFs or bandpass-filtered Hilbert envelopes of non-phase-locked high gamma power. For the AEF analyses, the permutation scheme was based on the implementation used by Prendergast et al. (2011) in which the evoked component of the response was attenuated in each shuffle of the data by phase-inverting half of the epochs at random. If no genuine phase-locked response exists then this random inversion should result in permuted cross-correlation values similar to the observed crosscorrelation value. If a phase-locked response were present, the observed cross-correlation value should fall in the tail of the data-driven null distribution (greater than or equal to the 95th percentile). The analysis of high-gamma power focuses on non-phase-locked power, rather than purely evoked activity, and therefore a phase-inversion of random epochs would be ineffective. For this analysis, a random number of epochs were selected and the phase of the high gamma Hilbert envelope

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of these epochs was inverted. This manipulation allows a null distribution to be built up in the same way as for the AEF analyses. Lateralisation of phase-locking to stimuli Hilbert envelopes Following Abrams et al. (2008), the peak cross-correlation values were first Fisher transformed before they were subjected to paired t-tests (two tailed), comparing the results from the left and right posteromedial HG. Paired t-test values of p b 0.05 were considered statistically significant. Results Spatial specificity of LOIs Table 1 shows the FWHM of the beamformer spatial filters used to generate the LOIs in the left and right posteromedial HG for both the 6-Hz SAM and speech conditions. The FWHM of the beamformer spatial filters in the right posteromedial HG were in the range of ~ 20 mm. Generally the FWHM of the spatial filters were larger in the left posteromedial HG, ranging from 20 to 40 mm. The length of human HG is about 15–20 mm (e.g. Bidet-Caulet et al., 2007) and therefore only one LOI was used in each HG. AEFs 6-Hz SAM Fig. 3A shows the Hilbert envelope of the 6-Hz SAM stimulus (green dotted line) and the AEFs for the 6-Hz SAM condition in the LOIs in the right posteromedial HG (red line, upper panel) and left posteromedial HG (blue line, lower panel) in an individual participant (S3). For the period from about 0 to about 400 ms post-stimulus presentation, phase-locked activity is dominated by the onset to the modulated stimulus. Phase-locking to the temporal envelope of the 6-Hz SAM is established from about 400 to 500 ms following stimulus presentation i.e. the “envelope-following period” (Abrams et al., 2008). Here we define the “envelope-following period” as the time from 500-ms post-stimulus presentation to the end of the modulated stimulus (2500 ms post-stimulus presentation). The cross-correlation coefficients (c) indicate how well the Hilbert envelope of the 6-Hz SAM stimulus is represented by the AEF during the “envelope-following period” (highlighted by the grey bars). Fig. 3B shows the peak cross-correlation values between the AEFs and the Hilbert envelope of the 6-Hz SAM stimulus measured in the left and right posteromedial HG for all individual participants. All cross-correlations between the AEFs and the Hilbert envelope of the 6-Hz SAM were statistically significant except for one participant (S6) whose AEF results did not show significant phase-locking to the 6-Hz SAM in the left posteromedial HG (this is indicated by the red square in the left panel of Fig. 3B). Cross-correlation coefficients were generally greater in the right HG in comparison with the left

Table 1 FWHM of beamformer spatial filters (i.e. virtual electrodes) seeded in the left and right posteromedial HG for the two auditory stimuli used in the present study (6-Hz SAM and speech). Participant

S1 S2 S3 S4 S5 S6 S7

FWHM of spatial filters (mm) Left posteromedial HG

Right posteromedial HG

6-Hz SAM

Speech

6-Hz SAM

Speech

34.02 22.89 32.08 25.42 26.35 23.81 22.08

40.42 22.68 30.43 23.81 26.35 24.85 27.40

22.27 16.92 21.00 22.08 19.92 19.92 25.72

23.57 18.18 19.64 19.64 18.29 20.21 23.81

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envelope develops from about 400 to 500 ms post-stimulus onset. Again we define the “envelope-following period” as the time from 500 ms prior to the stimulus presentation to the end of the stimulus (2800 ms post-stimulus presentation). The cross-correlation coefficients (c) between the Hilbert envelope of the speech stimulus and the AEF were calculated during the “envelope-following period” (highlighted by the grey bars). Fig. 4B shows the peak cross-correlations between the Hilbert envelope of the speech stimulus and the AEFs in the left (left-hand panel) and right (right-hand panel) posteromedial HG for all individual participants. All peak cross-correlation measurements were statistically significant except for one participant (S3) whose speech AEF results did not show significant phase-locking to the speech

Fig. 3. A: The AEF in response to the 6-Hz SAM in the right posteromedial HG (red line) and in the left posteromedial HG (blue line) in an individual participant (S3). The Hilbert envelope of the 6-Hz SAM stimulus is shown by the green dotted line in both panels. The cross-correlation coefficients (c) indicate how well the Hilbert envelope of the stimulus is represented in the AEFs during the “envelope-following period” (highlighted by the grey bars). B: Peak values of cross-correlograms between the Hilbert envelope of the 6-Hz SAM and AEFs recorded in all individual participants' left (left-hand panel) and right (right-hand panel) posteromedial HG.

HG: peak cross-correlation values between the AEFs and the Hilbert envelope of the 6-Hz SAM ranged from 0.14 to 0.42 in the left HG and from 0.3 to 0.77 in the right HG. These differences in peak cross-correlograms in the left and right posteromedial HG were significant (p = 0.022), indicating that phase-locking to the temporal envelope of the 6-Hz SAM is stronger in the right HG compared with the left HG. Speech Fig. 4A shows the Hilbert envelope of the speech stimulus (green dotted line) and the AEFs for the speech condition from the LOI in the right posteromedial HG (red line, upper panel) and the LOI in the left posteromedial HG (blue line, lower panel) in an individual participant (S1). The stimulus onset dominates the first 0–400 ms following the stimulus presentation and phase-locking to the speech temporal

Fig. 4. A: The AEF in response to speech in the right posteromedial HG (red line) and in the left posteromedial HG (blue line) in an individual participant (S1). The Hilbert envelope of the speech stimulus is shown by the green dotted line in both panels. The cross-correlation coefficients (c) indicate how well the Hilbert envelope of the stimulus is represented in the AEFs during the “envelope-following period” (highlighted by the grey bars). B: Peak values of cross-correlograms between the Hilbert envelope of speech and AEFs recorded in all individual participants' left (left-hand panel) and right (right-hand panel) posteromedial HG.

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Hilbert envelope in the right HG (this is indicated by the red star symbol in the right-hand panel of Fig. 4B). Peak cross-correlation values were similar in both left and right posteromedial HG, with peak values ranging from 0.18 to 0.59 in the right HG and from 0.3 to 0.47 in the left HG. There was no significant difference (p = 0.437) between the peak cross-correlograms in the left and right posteromedial HG, suggesting a bilateral representation of the temporal envelope of speech by AEFs in posteromedial HG. High gamma power analyses The results of the analyses for the total and non-phase-locked power changes were very similar. Therefore only the results for the non-phase-locked power changes are displayed and discussed further. Note that the pre-stimulus baseline period used to normalise the changes in high gamma power does not include the entire pre-stimulus duration used to generate the virtual electrodes, as is plotted in the time-frequency plots in Figs 5A and 6A. The pre-stimulus baseline period used to normalise the power changes is only from − 400 to − 100 ms and fluctuations in power within the high gamma band are generally between 0 and 1 dB. 6-Hz SAM Fig. 5A shows time–frequency plots depicting the changes in non-phase-locked power in response to the 6-Hz SAM relative to a pre-stimulus baseline (− 400 to − 100 ms prior to the stimulus presentation) in the right (upper panel) and left (lower panel) posteromedial HG for an individual participant (S3) in 3 PCA components. Visual inspection of the time-frequency plots showed that the power changes in the high gamma band are noisy and there are no clear differences in high gamma power between the pre-stimulus baseline and the post-stimulus presentation period. The maximal power changes following the stimulus presentation often occurred outside the high gamma frequency range i.e. below 50 Hz. As the time-frequency plots did not show clear increases in high gamma power, the remaining analyses focussed on modulations in the shape of the high gamma Hilbert envelope following the stimulus presentation (Nourski et al. 2009). Fig. 5B shows the pattern of changes in the Hilbert envelope of non-phase-locked high gamma power elicited by the 6-Hz SAM in the right (upper panel) and left (lower panel) posteromedial HG in an individual participant (S3) in 3 PCA components. The Hilbert envelope of the non-phase-locked high gamma power changes in response to the 6-Hz SAM is shown by the red line in the right posteromedial HG (upper panel) and by the blue line in the left posteromedial HG (lower panel). The Hilbert envelope of the 6-Hz SAM stimulus is shown by the green dotted line in all sub-panels of Fig. 5B. The cross-correlation coefficients (c) show the ability of the patterns of changes in non-phase-locked high gamma power, to follow the 6-Hz SAM temporal envelope during the entire stimulus epoch (highlighted by the grey bars). Fig. 5C shows the peak cross-correlations between the Hilbert envelope of the 6-Hz SAM and the Hilbert envelopes of the non-phase-locked high gamma power changes in the left and right posteromedial HG in 3 PCA components. Although the peak cross-correlation values were low in both left and right HG, for some participants these cross-correlation coefficients were deemed statistically significant by non-parametric permutation tests (see Methods' Representation of Hilbert envelopes of stimuli in AEFs and Hilbert envelopes of high gamma non‐phase‐locked power section). Statistically significant cross-correlation coefficients are shown by black symbols in Fig. 5C whereas red symbols indicate the cross-correlation coefficients that were not statistically significant. Cross-correlation coefficients were significant in 3 participants in PCA 1, 4 participants in PCA 2 and only one participant in PCA 3.

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Speech The time-frequency plots in Fig. 6A show the changes in non-phase-locked power elicited by speech relative to a 300-ms pre-stimulus baseline (− 400 to − 100 ms prior to stimulus presentation) in the left and right posteromedial HG for an individual participant (S3) for all 3 PCA components. Visual inspection of Fig. 6A revealed that there were no clear increases in the high gamma band following the stimulus presentation relative to the pre-stimulus baseline. Often the largest increase in high gamma power occurred in frequency bands lower than the high gamma band. Therefore further analyses on the high gamma band were restricted to time-domain cross-correlation analyses to determine whether the Hilbert envelopes of the stimuli were represented in the shape of the Hilbert envelope of the high gamma band (Nourski et al., 2009). Fig. 6B shows the pattern of changes in non-phase-locked high gamma power in response to speech in the right posteromedial HG (upper panel) and left posteromedial HG (lower panel) in an individual participant (S3) for all 3 PCA components. The Hilbert envelopes of the high gamma power in response to speech in the right posteromedial HG is shown by the blue line and by the red line for the left posteromedial HG. The Hilbert envelope of the speech stimulus is shown by the green dotted line in all sub panels in Fig. 6B. The cross-correlation coefficients (c) indicate how well the speech temporal envelope is represented by the pattern of changes in high-frequency non-phase-locked power during the duration of the stimulus presentation (highlighted by the grey bars). Fig. 6C shows the peak cross-correlations between the Hilbert envelope of the speech stimulus and the Hilbert envelopes of the non-phase-locked high gamma power in the left and right posteromedial HG for all individual participants in all 3 PCA components. Significant cross-correlation coefficients are shown by black symbols and non-significant results are shown by red symbols in Fig. 6C. Peak cross-correlation coefficients between the Hilbert envelope of the high gamma power and the Hilbert envelope of the speech stimulus were statistically significant in 1 participant in PCA 1, 3 participants in PCA 2 and 6 participants in PCA 3.

Discussion This study aimed to characterise the representation of a 6-Hz SAM and a speech sentence in human posteromedial HG using both AEFs and patterns within high-gamma power using non-invasive MEG virtual electrodes so that we could compare the outcome with the results from intracortical “depth” electrode work (Brugge et al., 2009; Nourski et al., 2009). Here we used a vocoder-based approach cf. Nourski et al. (2009), to obtain the temporal envelope of the speech sentence used in the present study. In the present study there was clear phase-locking to the temporal envelopes of both the 6-Hz SAM and the speech sentence and therefore vocoder-based extraction of speech temporal envelopes is suitable for investigating how the temporal envelope of speech is represented in the human brain. The results show that an LOI approach using MEG virtual electrodes can successfully measure 1) AEFs phase-locked to the Hilbert envelope of the 6-Hz SAM and speech and 2) patterns of changes in the high gamma Hilbert envelopes that were significantly modulated by the temporal dynamics of the stimuli. However, the results of the present study did not show the clear augmentations in high gamma power as seen in Brugge et al. (2009) and Nourski et al. (2009) following stimulus presentation. On one hand the MEG virtual electrode analyses failed to replicate the clear increases in high gamma power in response to the auditory stimuli as shown by Nourski and colleagues (e.g. Nourski et al., 2009) in intracortical “depth” electrode studies. On the other hand, the significant correlation between the Hilbert envelopes of the high gamma band and the Hilbert envelopes of the stimuli in the majority of participants suggests that the

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presence of both the SAM and speech stimuli did influence the high gamma band in a stimulus-specific manner.

Lateralisation of phase-locked activity In the present study we compared phase-locking to the Hilbert envelope of the 6-Hz SAM and speech in the left and right posteromedial HG. Lateralisation of activity phase-locked to auditory stimuli, and in particular speech, in either the right or left auditory cortex is of interest because the AST model (e.g. Poeppel, 2003) predicts right-hemisphere dominance in coding the slow modulations present in the speech temporal envelope. The results from the present study showed that phase-locking to SAM was dominant in the right posteromedial HG relative to the left posteromedial HG. This result is perhaps not surprising, given the dominance of contralateral connections in the ascending auditory system, and is consistent with some previous studies suggesting a rightward lateralisation in the representation of slow, nonspeech stimuli (e.g. Boemio et al., 2005; Millman et al., 2010). However, we are not aware of previous studies that have compared phase-locking to the temporal envelope of both a simple sensory stimulus (in this case the 6-Hz SAM) and speech within the same individuals. Here we were able to show that although phase-locking to a slow, nonspeech stimulus (6-Hz SAM) is lateralised to the right posteromedial HG, there was no significant difference in phase -locking to the speech temporal envelope in the left and right posteromedial HG within the same individuals. The difference in lateralisation for the non-speech stimulus (6-Hz SAM) and speech within the posteromedial HG adds support to cautions against using nonspeech stimuli to make predictions about asymmetries in the representation of speech (e.g. Rosen et al., 2011): in the present study we found a difference in functional asymmetries for a simple SAM (right-lateralised) and speech (bilateral) in the auditory cortex, suggesting that hemispheric asymmetries revealed by the processing of simple auditory stimuli do not necessarily apply to the processing of speech sentences. We believe that this is an important result because some studies cited as evidence of the AST model relied on hemispheric asymmetries in the representation of relatively simple auditory stimuli (e.g. Boemio et al., 2005; Lehongre et al., 2011; Zatorre et al., 2002) and assumed that the same asymmetries are present for speech stimuli. Consistent with the AST model (Poeppel, 2003), Luo and Poeppel (2007) identified phase-locked theta activity elicited by spoken speech that was lateralised to the right hemisphere. Luo and Poeppel (2007) further hypothesised that the rightward lateralisation of the phaselocked theta activity arises between the “core and belt (and perhaps parabelt) auditory areas”. Abrams et al. (2008) also showed that phase-locking to the temporal envelope of speech lateralised towards the right hemisphere in children, when speech was presented to either the right or left ear. The bilateral representation of activity phase-locked to the temporal envelope of speech measured in the present may seem at odds with the right hemisphere dominance reported in Abrams et al. (2008) and Luo and Poeppel (2007). However, in Abrams et al. (2008) and Luo and Poeppel (2007) analyses of phase-locking to the speech temporal envelope were conducted in EEG/MEG sensor space and no attempts were made to localise these responses to speech. The use of MEG virtual electrode analyses in the present study allowed us to measure phase-locking from specific anatomical locations, i.e. both left and

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right posteromedial HG, the putative sites of human primary auditory cortex. Therefore right-hemisphere dominance for phase-locking to the speech temporal envelope (Abrams et al., 2008; Luo and Poeppel, 2007) or phase-locked activity elicited by the spectrotemporal features of speech (Ding and Simon, 2012) may develop in auditory/temporal cortical areas beyond the posteromedial HG. The results from the present study may be used to add anatomical and functional constraints to models of speech perception: speech is represented bilaterally by phase-locked activity in the posteromedial (cf. Luo and Poeppel, 2007) right and left HG, even when speech is presented monaurally to the left ear. However, phase-locked activity is only one way that the representation of speech can be measured using EEG/MEG/intracortical “depth” electrodes. A combined fMRI/EEG study (Morillon et al., 2010) on hemispheric asymmetries in power changes in speech-relevant frequency bands reported asymmetries only in measures of total power. It is important to keep in mind that other measures of the representation of speech, such as changes in non-phase-locked high gamma power (e.g. Nourski et al., 2009), may or may not lateralise towards the same hemisphere as other measures e.g. phase-locked activity or changes in total power.

MEG beamformer and virtual electrode measures of high gamma power In the present study we used an LOI approach based on MEG virtual electrodes to optimise the potential of our analyses to measure changes in high gamma power (e.g. Dalal et al., 2009) and to examine whether it is possible to replicate the results from invasive “depth” electrode studies (Brugge et al., 2009; Nourski et al., 2009) using non-invasive MEG beamformer-based methods. We did not attempt to separately localise changes in high gamma power because previous intracortical “depth” electrode studies (Brugge et al., 2009; Nourski et al., 2009) showed that both phase-locked AEPs and envelope-following within ERBP analyses co-localise to the same sites within the posteromedial HG. Previous MEG studies have successfully used MEG beamformer and virtual electrode analyses to measure increases in gamma (e.g. Gross et al., 2001; Hall et al., 2005) and high gamma power relative to a pre-stimulus baseline (e.g. Dalal et al., 2008, 2009; Hoogenboom et al., 2006; Litvak et al., 2010) or following the presentation of a specific auditory stimulus (Sedley et al., 2012). In the Sedley et al. (2012) study using MEG virtual electrodes they showed clear increases in high gamma power and these increases were consistent across the range of frequencies within the high gamma band. In the present study the power changes in the high gamma band were noisy and there was no clear change in high gamma power in response to the presentation of the stimuli. As a result of the lack of clear increases in high gamma power, the mean of the high gamma Hilbert envelope did not show a large increase relative to the pre-stimulus baseline (see Figs. 5B and 6B). There are several reasons apparent to us why Sedley et al. (2012) were able to show clear augmentations in high gamma power relative to the pre-stimulus period whereas the present results did not. One difference between the present study and the Sedley et al. (2012) study that may be relevant is the type of beamformer used to generate the virtual electrodes. Recent work suggests differences in the ability of scalar and vectorised implementations of the same class of beamformer to localise and reconstruct high-gamma activity. For example, Dalal et al. (2008) reported that a scalar minimum variance beamformer did not reconstruct high gamma total power successfully. Sedley et al. (2012) reported that a vectorised DICS beamformer

Fig. 5. A: Time-frequency plots of changes in non-phase-locked power in response to the 6-Hz SAM in the right posteromedial HG (upper panel) and left posteromedial HG (lower panel) in an individual participant (S3) for all 3 PCA components. The high gamma frequency band is highlighted by the double vertical lines at the right-hand side of each sub-panel. B: The Hilbert envelope of the 70–150 Hz non-phase-locked power in response to the SAM in the right posteromedial HG (red lines in the upper panel) and in the left posteromedial HG (blue lines in the lower panel) in an individual participant (S3). The Hilbert envelope of the 6-Hz SAM stimulus is shown by the green dotted line in all sub-panels. The cross-correlation coefficients (c) indicate how well the Hilbert envelope of the stimulus is represented by the pattern of changes in non-phase-locked high gamma power following stimulus presentation (highlighted by the grey bars). C: Peak values of cross-correlograms between the Hilbert envelope of the 6-Hz SAM and the Hilbert envelope of the non-phase-locked high gamma power recorded in all individual participants' left and right posteromedial HG.

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was more successful than a scalar DICS beamformer in localising group induced high gamma power in human auditory cortex in response to a pitch stimulus (regular-interval noise, RIN). Both the Sedley et al. (2012) study and the present study used vectorised beamformers to generate the MEG virtual electrodes. However, the DICS beamformer used by Sedley et al. (2012) constructs spatial filters based on the cross-spectral density matrix whereas the output of the VLCMV beamformer used in the present study is based on a weighted sum of source strength estimates at a given grid point. Another important difference between the Sedley et al. (2012) study and current study is that Sedley et al. (2012) included the additional step of applying the coefficients for each PCA component to each point in the time–frequency space i.e. in the methods of Sedley et al. (2012) they write “Activity for each eigenmode was calculated for each point in time–frequency space by matrix multiplying activity across trials and orientations by the weights for the respective principal component”. We did not include this additional step in our analyses because we wanted to keep our time–frequency analyses as similar as possible to the methods used in Nourski et al. (2009). In our opinion it is important to make a like-for-like comparison of the time–frequency plots generated from “depth” electrode recordings and MEG virtual electrodes when testing the hypothesis that MEG virtual electrodes can replicate the results from intracortical “depth” electrodes. Sedley et al. (2012) also presented a group average time–frequency plot based on a group of effectively 26 participants (left and right hemispheres of 13 individuals). In the present study we only present time-frequency plots from individual participants in each hemisphere separately as, in our opinion, this is the most appropriate way to compare the results of non-invasive MEG virtual electrodes with the results from intracortical “depth” electrodes. A further interesting aspect of the cross-correlation analyses presented here is the variation in statistically significant crosscorrelation values across the PCA components. It is sometimes assumed in MEG/EEG analyses that the signal of interest will be found in the primary PCA component (PCA 1). The present results show that if we had relied on PCA 1 we would not have successfully measured significant cross-correlations in the majority of participants. Note that applying PCA to MEG virtual electrodes will not necessarily give the same rotation (orientation of MEG virtual electrodes) across individuals and across conditions. Previous evidence that the signal of interest is not always found in PCA 1 was provided by Sedley et al. (2012), who reported that PCA 3 showed the clearest representation of their pitch stimulus. In the present study the greatest number of significant cross-correlations was found in PCA 2 for the 6-Hz SAM condition and in PCA 3 for the speech condition. PCA 1, which best explains the variance (and hence power) in the signal, did not generally identify significant cross-correlation values between the Hilbert envelopes of the stimuli and the Hilbert envelope of the high gamma band. One of the main aims of the present study was to quantitatively measure patterns of changes in high gamma power that represented the Hilbert envelope of the auditory stimuli (6-Hz SAM and speech) by applying statistical tests to the outcomes of the cross-correlation analyses. In this respect, the current study presents perhaps the most stringent test of the capability of non-invasive MEG beamformer-based methods to reconstruct high-gamma activity to date. The use of a metric such as the Hilbert envelope of the auditory

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stimuli affords a straight-forward method to quantify how well the temporal envelopes of the stimuli were represented in both the AEF and pattern of changes within the high-gamma frequency band. The quantitative nature of our analyses differs from previous MEG studies on reconstructing high gamma responses where qualitative similarities have been reported between invasive “depth” electrode recordings or deep-brain stimulation and non-invasive MEG virtual electrodes (e.g. Dalal et al., 2008; Litvak et al., 2010; Sedley et al., 2012). Whereas these previous studies generally reported convergence between invasive and non-invasive measures of increases in high gamma power (Litvak et al., 2010; Sedley et al., 2012), the quantitative aspect of the measures of high gamma power used in the present study showed that non-invasive reconstructions of the patterns of high gamma activity were successful in the majority of participants. Summary In the present study a VLCMV beamformer using a virtual electrode reconstructed with a broad frequency bandwidth (1–200 Hz) was sufficient to reconstruct patterns of changes in high-gamma power consistent with the Hilbert envelopes of the 6-Hz SAM and speech with reasonable fidelity. This was despite the fact that the low-frequencies within the broadband filter used for the virtual electrode analyses should dominate the calculation of the beamformer weights (e.g. Dalal et al., 2008). However, we could not replicate the clear and consistent increases across the entire high gamma bandwidth shown in intracortical “depth” electrode studies (e.g. Nourski et al., 2009). It remains to be seen whether high gamma power changes will provide an opportunity to bridge the gap between invasive intracortical electrode recordings and non-invasive EEG/MEG beamformer and virtual electrode analyses. Acknowledgments We are grateful to Dr. Kirill Nourski for providing the code for the analyses of the total and non-phase-locked power changes as described in Nourski et al. (2009). We thank two anonymous reviewers for helpful comments on previous versions of this manuscript. References Abrams, D.A., Nicol, T., Zecker, S., Kraus, N., 2008. Right-hemisphere auditory cortex is dominant for coding syllable patterns in speech. J. Neurosci. 28, 3958–3965. Ahissar, E., Nagarajan, S., Ahissar, M., Protopapas, A., Mahncke, H., Merzenich, M.M., 2001. Speech comprehension is correlated with temporal response patterns recorded from auditory cortex. Proc. Natl. Acad. Sci. U. S. A. 98, 13367–13372. Aiken, S.J., Picton, T.W., 2008. Human cortical responses to the speech envelope. Ear Hear. 29, 139–157. Barnes, G.R., Hillebrand, A., 2003. Statistical flattening of MEG beamformer images. Hum. Brain Mapp. 18, 1–12. Barnes, G.R., Hillebrand, A., Fawcett, I.P., Singh, K.D., 2004. Realistic spatial sampling for MEG beamformer images. Hum. Brain Mapp. 23, 120–127. Bidet-Caulet, A., Fischer, C., Besle, J., Aguera, P.-E., Giard, M.-H., Bertrand, O., 2007. Effects of selective attention on the electrophysiological representations of concurrent sounds in the human auditory cortex. J. Neurosci. 27, 9252–9261. Boemio, A., Fromm, S., Braun, A., Poeppel, D., 2005. Hierarchical and asymmetric temporal sensitivity in human auditory cortices. Nat. Neurosci. 8, 389–395. Brugge, J.F., Nourski, K.V., Oya, H., Reale, R.A., Kawasaki, H., Steinschneider, M., Howard, M.A.I.I.I., 2009. Coding of repetitive transients by auditory cortex on Heschl's gyrus. J. Neurophysiol. 102, 2358–2374. Crone, N.E., Boatman, D., Gordon, B., Hao, L., 2001. Induced electrocorticographic gamma activity during auditory perception. Clin. Neurophysiol. 112, 565–582.

Fig. 6. A: Time–frequency plots of changes in non-phase-locked power in response to speech in the right posteromedial HG (upper panel) and left posteromedial HG (lower panel) in an individual participant (S3) in all 3 PCA components. The high gamma frequency band is highlighted by the double vertical lines at the right-hand side of each sub-panel. B: The Hilbert envelope of the 70–150 Hz non-phase-locked power in response to speech in the right posteromedial HG (red lines in upper panel) and in the left posteromedial HG (blue lines in lower panel) in an individual participant (S2). The Hilbert envelope of the speech stimulus is shown by the green dotted line in all sub-panels. The cross-correlation coefficients (c) indicate how well the Hilbert envelope of the stimulus is represented by the pattern of changes in non-phase-locked high gamma power following stimulus presentation (highlighted by the grey bars). C: Peak values of cross-correlograms between the Hilbert envelope of speech and the Hilbert envelope of the non-phase-locked high gamma power recorded in all individual participants' left and right posteromedial HG.

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