Abstract

How do we process stimuli that stem from the external world and stimuli that are self-generated? In the case of voice perception it has been shown that evoked activity elicited by self-generated sounds is suppressed compared with the same sounds played-back externally. We here wanted to reveal whether neural excitability of the auditory cortex—putatively reflected in local alpha band power—is modulated already prior to speech onset, and which brain regions may mediate such a top-down preparatory response. In the left auditory cortex we show that the typical alpha suppression found when participants prepare to listen disappears when participants expect a self-spoken sound. This suggests an inhibitory adjustment of auditory cortical activity already before sound onset. As a second main finding we demonstrate that the medial prefrontal cortex, a region known for self-referential processes, mediates these condition-specific alpha power modulations. This provides crucial insights into how higher-order regions prepare the auditory cortex for the processing of self-generated sounds. Furthermore, the mechanism outlined could provide further explanations to self-referential phenomena, such as “tickling yourself”. Finally, it has implications for the so-far unsolved question of how auditory alpha power is mediated by higher-order regions in a more general sense.

Introduction

Even if our own voice is often intermingled with external voices, the brain can distinguish between speech sounds that are produced by the brain itself and speech sounds that stem from the external world. A vast amount of literature indicates that the auditory cortex is inhibited when we process self-generated compared with played-back speech sounds. Most of these studies looked at evoked potentials or evoked magnetic fields (Curio et al. 2000; Houde et al. 2002; Ford and Mathalon 2004; Heinks-Maldonado et al. 2005; Martikainen et al. 2005; Baess et al. 2011) and showed that evoked activity is reduced in amplitude for self-generated speech sounds compared with externally played-back speech sounds even if they had the same (or similar) physical characteristics. Most of these results are interpreted in the framework of the so-called “efference copies”, meaning that the motor system is sending a copy of the motor command to the respective sensory area, where corollary discharge elicited by this copy is combined with the sensory feedback (Holst and Mittelstaedt 1950; Sperry 1950; Ford and Mathalon 2004). Beyond that, studies on monkeys show that self-produced vocalization lead to reduced neuronal firing rates in a majority of auditory cortical neurons (Ploog 1981; Eliades and Wang 2003). In line with that, recordings in epilepsy patients disclosed a suppression of ongoing activity in middle and superior temporal gyrus neurons (Creutzfeldt et al. 1989) and a suppression of gamma power in the temporal lobe during speech production (Towle et al. 2008; Flinker et al. 2010). Most interestingly, animal data (Eliades and Wang 2003) and also the data derived from the intracranial recordings by Creutzfeld and colleagues (2003) point to a suppression of brain activity starting already a few hundred milliseconds before sound onset. These findings suggest that the suppression of neuronal activity in the auditory cortex results could, in part, result from internal modulatory mechanisms prior to sound onset.

It has been demonstrated that synchronous oscillatory activity in the alpha frequency band (∼10 Hz) are inversely related to the excitability of the respective brain regions (Klimesch et al. 2007; Jensen and Mazaheri 2010), an assumption that has recently received strong support from invasive recordings (Haegens et al. 2011). An increase of alpha power in a sensory region is associated with a functional inhibition of that region when sensory stimuli are processed (Jensen and Mazaheri 2010). This has been shown in the visual modality (Worden et al. 2000; Thut 2006; Romei et al. 2008; Siegel et al. 2008; van Dijk et al. 2008; Bahramisharif et al. 2010; Hanslmayr et al. 2011), in the somatosensory modality (Jones et al. 2010; Haegens et al. 2012; Lange et al. 2012) and recently also in the auditory modality (Gomez-Ramirez et al. 2011; Muller and Weisz 2012; Weisz et al. 2014; Frey et al. 2014). The aim of the present study was to investigate if the aforementioned inhibition of the auditory cortex prior and during speech production can also be explained by a top-down modulation of auditory alpha power, preceding voice onset. Crucially, any differences in neuronal activity due to differences in sound characteristics (own voice vs. played-back own voice) can be ruled out, by measuring brain signals generated in the time intervals preceding sound onset. Our prediction being, that the inhibition of the auditory cortex for self-spoken versus played-back voices becomes evident in a relative increase in auditory alpha power. Such a finding would give evidence on the processes preceding the modulations of evoked activity in the context of voice perception and would, for the first time, provide evidence on a possible internal mechanism modulating auditory cortex excitability when expecting self-generated sensory input.

Materials and Methods

Participants

Twenty right-handed volunteers reporting normal hearing participated in the current study (9 m/11 f, mean age 22.6). Participants were recruited via flyers posted at the University of Konstanz and were paid following the experiment. The Ethics Committee of the University of Konstanz approved the experimental procedure and all participants gave their written informed consent prior to taking part in the study. Two participants had to be excluded due to an excessive amount of artifacts.

Experimental Procedure

Firstly, participants were introduced to the lab facilities and informed about the experimental procedure, which consisted of 2 phases (voice recordings and main magnetoencephalography [MEG] experiment). For the voice recordings participants were asked to repeat the sound “Aah” 50 times, while their voice was recorded by means of a microphone (Zoom H4 USB-microphone). Then on- and off-set of each “Aah”-sound was determined and cut out automatically by a Matlab script so that 50 sound files resulted. After verifying manually that the sounds were cut out correctly they were copied to the stimulation computer for the subsequent MEG experiment. The voice recordings were done in order to keep physical characteristics of the self-spoken and externally played-back sounds as similar as possible. Loudness of the sounds was adjusted later in the MEG so that participants perceived the self-spoken and the externally played-back sounds as equally loud. For this purpose a random “Aah”-sound was selected and presented to the participant in the MEG scanner. Participants had to rate if the played-back sound was louder or weaker compared with the self-spoken sound, whereupon loudness of the played-back sound was adjusted. This procedure was repeated until the participant rated the played-back and self-spoken sounds as equally loud. After that the root mean square amplitude of the other recorded sounds was matched to the selected reference sound.

Subsequently, the individual headshapes were collected and the main experiment, consisting of 4 blocks, started. In half of the 4 blocks participants were instructed to say the sound “Aah” after a go-signal while in the other half of the blocks they were asked to listen to the sound “Aah” (that was randomly taken from the 50 “Aah”-sounds generated before the experiment). Each experimental trial started with a baseline period of 500 ms, upon which a red fixation cross was shown for 1.5 s (preparation period). After 1.5 s, the red fixation cross turned into a green one, which was the go-signal instructing participants to either say the sound “Aah” (speak condition) or listen to it (listen condition). The next trial started 2–3 s after sound-offset. There were a total of 200 trials. The presentation of visual and auditory stimulus material during MEG recordings was controlled using Psyscope X (Cohen et al. 1993), an open-source environment for the design and control of behavioral experiments (http://psy.ck.sissa.it/) and R version 2.11.1 for Mac OS X (http://www.R-project.org). The procedure of the experiment is illustrated in Figure 1.

Figure 1.

Experimental design. Each experimental trial began with a baseline period of 500 ms, upon that a red fixation cross was shown for 1.5 s (preparation period). After 1.5 s, the red fixation cross turned into a green one, upon that participants were instructed to either say the sound “Aah” (self-spoken condition) or listen to it (play-back condition). The next trial started 2–3 s after sound-offset. In total there were 200 trials.

Figure 1.

Experimental design. Each experimental trial began with a baseline period of 500 ms, upon that a red fixation cross was shown for 1.5 s (preparation period). After 1.5 s, the red fixation cross turned into a green one, upon that participants were instructed to either say the sound “Aah” (self-spoken condition) or listen to it (play-back condition). The next trial started 2–3 s after sound-offset. In total there were 200 trials.

Data Acquisition

The MEG recordings were carried out using a 148-channel whole-head magnetometer system (MAGNESTM 2500 WH, 4D Neuroimaging, San Diego, USA) installed in a magnetically shielded chamber (Vakuumschmelze Hanau). Prior to the recordings, individual head shapes were collected using a digitizer. Participants lay in a comfortable supine position and were asked to keep their eyes open and to focus on the fixation cross displayed by a video projector (JVCTM, DLA-G11E) outside of the MEG chamber and projected to the ceiling in the MEG chamber by means of a mirror system. Participants were instructed to hold still and to avoid eye blinks and movements as best as possible. A video camera installed inside the MEG chamber allowed the investigator to monitor participants throughout the experiment. MEG signals were recorded with a sampling rate of 678.17 Hz and a hardwired high-pass filter of 0.1 Hz. The recorded and RMS–matched “Aah”-sounds (see above) were presented through a tube system with a length of 6.1 m and a diameter of 4 mm (Etymotic Research, ER30). Structural images were acquired with a Philips MRI Scanner (Philips Gyroscan ACS-T 1.5 T, field of view 256 × 256 × 200 sagittal slices).

Data Analysis

Preprocessing

We analyzed the data sets using Matlab (The MathWorks, Natick, MA, Version 7.5.0 R 2007b) and the Fieldtrip toolbox (Oostenveld et al. 2011). From the raw continuous data, we extracted epochs of 5 s lasting from 2.5 s before onset of the red fixation cross to 2.5 s after onset of the red fixation cross. This was done for the 2 conditions separately (self-spoken sound, played-back sound) and resulted in 100 trials for each condition. As participants could not avoid blinking sufficiently we decided to perform an independent component analysis (ICA) in order to minimize the influence of the blinks. For ICA correction we first did a coarse visual artifact rejection, removing trials including strong muscle artifacts and dead or very noisy channels. After coarse artifact rejection the data sets (concatenated across conditions) were downsampled to 300 Hz. On a subset of trials an ICA was performed (RUNICA, Delorme and Makeig 2004) and the affected components (eye movements) visually selected. After that ICA was again applied to the data sets of the 2 original conditions and the raw data were reconstructed with the respective components removed. Finally, the resulting data sets were again visually inspected for artifacts and the residual artifactual trials rejected. To ensure a similar signal-to-noise ratio across conditions, the trial numbers were equalized for the compared conditions (self-spoken vs. played-back) by random omission (60–90 trials remained). Finally, data were downsampled to 500 Hz.

Evoked Activity

In order to replicate the results of previous studies for quality control purposes, we assessed the evoked activity elicited by the sound stimuli. First, data were high-pass filtered by 1 Hz and low-pass filtered by 45 Hz. Evoked activity was obtained by averaging the single trials. This was done for both conditions separately (self-spoken vs. played-back, equal trial numbers). Evoked activity was then tested statistically by point-wise 2-tailed paired samples t-tests.

Spectral Power Analyses

Time–frequency distributions of the epochs preceding self-spoken and externally played-back sounds were compared at the sensor and source level. We estimated task-related changes in oscillatory power using a multitaper FFT time–frequency transformation (Percival 1993) with frequency-dependent Hanning tapers (time window: Δt = 4/f sliding in 50 ms steps). We calculated power from 3 to 30 Hz in steps of 1 Hz and for both conditions separately. The obtained time–frequency representations were then baseline normalized (baseline: −400 to −100 ms before onset of the red fixation cross, relative change).

In order to test if power modulations are significantly different between conditions (expecting self-spoken vs. played-back own voice) we performed a nonparametric cluster-based permutation test on the baseline-normalized time–frequency representations (Maris and Oostenveld 2007), test based on 2-tailed paired t-tests). This test was chosen to correct for multiple comparisons.

As a next step, we estimated the generators of the sensor effects in source space using the frequency-domain adaptive spatial filtering algorithm Dynamic Imaging of Coherent Sources (DICS, Gross et al. 2001). For each participant an anatomically realistic headmodel (Nolte 2003) was created and leadfields for a 3-dimensional grid covering the entire brain volume (resolution: 1 cm) calculated. Together, with the sensor-level cross-spectral density matrix (2 time intervals early 0.5–1 s and late 1–1.5 s, 13 ± 3 Hz, multitaper analysis, conditions concatenated), we could estimate common spatial filters, optimally passing information for each grid point while attenuating influences from other regions for the frequency and time window of interest (according to the cluster permutation test at sensor level: 0.5–1.5 s, 13 ± 3 Hz). The common spatial filters were then applied to the Fourier-transformed data for both conditions separately (same parameters). After that the resulting activation volumes were interpolated onto the individual MRI. In cases where we could not get a structural scan (5 out of 18), we created “pseudo”-individual MRIs that were created based on an affine transformation of the headshape of an Montreal Neurological Institute (MNI) template and the individually gained headshape points. The interpolated activation volumes were then normalized to a template MNI brain provided by the SPM8 toolbox (http://www.fil.ion.ucl.ac.uk/spm/software/spm8). Finally, source solutions for the 2 conditions were compared using a voxel-wise dependent samples t-statistic. From that analysis the left auditory cortex (Brodman Areas 21/22 and Brodman Area 41), the right precentral cortex and the medial prefrontal cortex (BA 8) were derived as main regions showing a significant increase of alpha power for self-generated versus externally played-back sounds. This is illustrated in the results. To get a better estimate of how alpha power in the auditory cortex is modulated we averaged the power within the left auditory cortex for each participant and for both conditions separately. These values were then tested against baseline values by 2-tailed paired t-tests and for both conditions separately. Beyond that, we tested the baseline values of the speak condition against the baseline values of the listen condition again by a 2-tailed paired t-test to rule out the possibility that the relative effects were determined due to baseline differences.

Power–Power Correlations

After spectral power analysis we aimed at shedding light onto the question of how the condition-specific relative alpha increases in the auditory cortex are mediated. We therefore correlated left auditory alpha power with low-frequency power (from 2–26 Hz) in all other regions of the brain (for 1–1.5 s). We did this in MNI grid space.

First, a template grid was created (using a template head model based on a segmented template MNI brain provided by the SPM8 toolbox). Using this template grid an individual grid was generated by warping the template grid to the individual MRI for each participant separately. Importantly, the warped individual grids have an equal number of points with equal positions in MNI space, so that the individual grids of different participants can be compared directly (grid points of Subject 1 correspond to grid points of Subject 2).

These individual MNI grids were then used for source analysis. Source analysis was done for the single trials and using the DICS beamformer algorithm (MNI grid, 1–1.5 s after red-trigger onset, 13 Hz ± 3, same settings as for alpha power source analysis despite the use of the individual MNI grids). We calculated source solutions for frequencies from 2 to 26 Hz in increments of 2 Hz. Thereby, power values for each participant, each condition, each trial, each frequency and each grid point were obtained. We then calculated correlations between alpha power at the reference voxel, which was defined as the grid point being closest to the main alpha power effect as derived from source analysis (MNI coordinates: −55 −28 2, left auditory cortex), and all other grid points. We repeated this for all frequencies (2–26 Hz) and fisher z-transformed the correlation values afterwards. We thereby obtained a 2-D matrix for both conditions (grid points × frequencies). Afterwards, the frequency × grid point maps were tested for significant differences between conditions across subjects using a nonparametric cluster-based permutation test (Maris and Oostenveld 2007); neighbors were defined as grid points that had a distance of <3 cm resulting in average 75 neighbors per grid point, which reflects ∼3% of all grid points). This analysis yielded that alpha power in the left auditory cortex is strongly correlated with low-frequency power (6–14 Hz) in the medial prefrontal cortex when participants expect a self-generated sound. To get a better estimate of how connectivity between the medial prefrontal cortex and the auditory cortex is modulated in both conditions separately we averaged the correlation values within the significant region for each participant and for both conditions separately. These values were then tested against correlation values (within the same region) that were obtained during the baseline period by 2-tailed paired t-tests, and for both conditions separately.

Partial Directed Coherence Between Auditory Cortex and Medial Frontal

As a final step we wanted to elucidate the direction of information flow between the auditory cortex (MNI coordinates: −55 −28 2) and the medial prefrontal cortex (peak voxel of correlation effect, MNI coordinates: −4 44 −6), assessed via partial directed coherence (PDC, Baccala and Sameshima 2001). PDC is a measure of effective coupling that is based on multivariate autoregressive (MVAR) modeling. For a pair of voxels the information flow can be assessed in both directions. We first projected the raw time series into source space by multiplying the raw time series for both conditions separately with a common spatial filter. The spatial filter was created using the LCMV beamformer (Van Veen et al. 1997) and the concatenated data of both conditions (2–26 Hz, time window including baseline and activation −0.5 to 1.5 s). We thereby obtained time series for both conditions and both sources (auditory cortex, medial prefrontal cortex) separately. For these time series a MVAR model was fitted (“bsmart”). The model order was set to 15, according to previous analysis approaches (Supp et al. 2007; Weisz et al. 2014). Then a Fourier transform was performed on the resulting coefficients of the MVAR model. These Fourier-transformed coefficients were then used to calculate partial directed coherence between the auditory and medial prefrontal cortex. The PDC values were baseline normalized using the baseline interval (−0.5 to 0) by first subtracting and then dividing the values by the values of the baseline interval. Finally, the PDC values were tested for differences between conditions (speak vs. listen) using paired t-tests.

Results

The current study aimed at disentangling brain activity preceding the processing of participants' own voice that was either self-spoken or played-back externally. We investigated brain activity on a local and on a network level in the time interval before voice onset and with a focus on low-frequency oscillatory power.

Evoked Responses

The event related response was significantly stronger for the externally played-back speech sound compared with the self-generated one between 150 and 200 ms after sound onset (uncorrected). This is comparable to the previous literature (Curio et al. 2000; Houde et al. 2002; Flinker et al. 2010). Results are shown in Figure 2 (upper panel).

Figure 2.

Auditory results. The upper panel shows the event-related activity elicited by self-generated (blue) versus externally played-back (red) voice sounds. The left side displays global field power at left temporal sensors averaged across participants. The topography for the significant time interval is shown at the right side. Black dots show significant sensors (uncorrected). The event-related activity following the self-generated sound is clearly diminished compared with the externally played-back one (statistically significant in shaded area between 150 and 200 ms after sound onset and at left and right sensors, uncorrected). This points to an inhibition of the auditory cortex, when participants process self-generated compared with externally played-back speech sounds. The middle panel displays the time–frequency representation of power differences between self-produced and externally played-back speech sounds, in the interval before voice onset. Shown are t-values. Higher values (red) indicate a relative increase of power when participants expect to process their self-spoken versus played-back voice. Power between 10–16 Hz is significantly increased at rather frontal sensors in the beginning of the preparation period and frontal and left temporal sensors versus the end of the preparation period. The left lower panel shows how the alpha power modulation (10–16 Hz) is distributed in the brain for the early and late preparation period (0.5–1 s and 1–1.5 s). Alpha power is relatively increased in the left auditory cortex. Shown are t-values that are masked with P < 0.01. The right lower panel shows mean left auditory power modulations during preparation (1–1.5 s) versus baseline for the 2 conditions. Left auditory cortex alpha power is significantly reduced when participants prepare to listen to the externally played-back voice (P < 0.01), while there is no modulation of alpha power when participants prepare to listen to a self-generated voice (no statistical difference compared with baseline in “speak”-condition).

Figure 2.

Auditory results. The upper panel shows the event-related activity elicited by self-generated (blue) versus externally played-back (red) voice sounds. The left side displays global field power at left temporal sensors averaged across participants. The topography for the significant time interval is shown at the right side. Black dots show significant sensors (uncorrected). The event-related activity following the self-generated sound is clearly diminished compared with the externally played-back one (statistically significant in shaded area between 150 and 200 ms after sound onset and at left and right sensors, uncorrected). This points to an inhibition of the auditory cortex, when participants process self-generated compared with externally played-back speech sounds. The middle panel displays the time–frequency representation of power differences between self-produced and externally played-back speech sounds, in the interval before voice onset. Shown are t-values. Higher values (red) indicate a relative increase of power when participants expect to process their self-spoken versus played-back voice. Power between 10–16 Hz is significantly increased at rather frontal sensors in the beginning of the preparation period and frontal and left temporal sensors versus the end of the preparation period. The left lower panel shows how the alpha power modulation (10–16 Hz) is distributed in the brain for the early and late preparation period (0.5–1 s and 1–1.5 s). Alpha power is relatively increased in the left auditory cortex. Shown are t-values that are masked with P < 0.01. The right lower panel shows mean left auditory power modulations during preparation (1–1.5 s) versus baseline for the 2 conditions. Left auditory cortex alpha power is significantly reduced when participants prepare to listen to the externally played-back voice (P < 0.01), while there is no modulation of alpha power when participants prepare to listen to a self-generated voice (no statistical difference compared with baseline in “speak”-condition).

Pre-voice Power Differences—Sensor Level

In a first step, we assessed differences in low-frequency power for self-spoken versus externally played-back voices on sensor level. We found a significant power increase (cluster P < 0.05) peaking between 10 and 16 Hz and encompassing frontal and left temporal sensors for self-generated versus externally played-back speech sounds. Interestingly, at a descriptive level, power modulations at frontal sensors were most dominant in the first part of the preparation period, while left temporal power modulations became stronger towards the end of the preparation period, shortly before voice onset. For comparison see Figure 2 (middle panel).

Source Localization of Alpha Power Differences Before Voice Onset

In order to get a better estimate of where in the brain the low-frequency power modulations take place, we performed a source analysis of the power modulations derived from sensor level (10–16 Hz, 0.5–1 s and 1–1.5 s after onset of the red fixation cross). Source results indicate that, besides extra-auditory areas (right precentral, medial dorsolateral prefrontal cortex), the left auditory cortex shows a strong relative increase of alpha power, becoming most evident in the last part of the preparation period (1–1.5 s, P < 0.01, including Brodman areas 21/22/41). There were no significant differences in auditory activity during baseline (P = 0.19). Interestingly, when extracting the power modulations for the 2 conditions separately, it turned out that the relative increase of alpha power is due to a decrease of alpha power compared with baseline when participants prepare to process their externally played-back voice (P < 0.01), while this alpha power decrease seems to be abolished (no statistical difference compared with baseline) when participants expect to process their self-spoken voice. For comparison see Figure 2 (lower panel).

Beside the auditory power modulations, alpha power was increased in the medial dorsolateral prefrontal cortex (P < 0.01, BA 9 and 32) and the right precentral cortex (P < 0.01, BA 4). Within these extra-auditory regions alpha power was significantly increased compared with baseline when participants prepared to speak (P < 0.01), while we found no significant differences in alpha power compared with baseline when participants prepared to listen (Fig. 3).

Figure 3.

Extra-auditory results. The left panel shows that alpha power is increased in the right precentral and the dorsolateral prefrontal cortex when participants expect to process self-spoken versus played-back voice. Shown are t-values that are masked with P < 0.01. The right panel shows mean power modulations for these regions during preparation (1–1.5 s) versus baseline for the 2 conditions separately. In both regions (precentral and dorsolateral prefrontal) alpha power is significantly increased compared with baseline when participants prepare to listen to their self-generated voice (P < 0.01), while there is no modulation of alpha power when participants prepare to listen to externally played-back voice (no statistical difference compared with baseline in “listen-condition).

Figure 3.

Extra-auditory results. The left panel shows that alpha power is increased in the right precentral and the dorsolateral prefrontal cortex when participants expect to process self-spoken versus played-back voice. Shown are t-values that are masked with P < 0.01. The right panel shows mean power modulations for these regions during preparation (1–1.5 s) versus baseline for the 2 conditions separately. In both regions (precentral and dorsolateral prefrontal) alpha power is significantly increased compared with baseline when participants prepare to listen to their self-generated voice (P < 0.01), while there is no modulation of alpha power when participants prepare to listen to externally played-back voice (no statistical difference compared with baseline in “listen-condition).

Note, that alpha power in the right precentral region was already modulated during baseline (probably due to the blocked design). During baseline alpha power was significantly decreased in the “speak blocks” compared with the “listen blocks”. Baseline differences of the entire brain are shown in Supplementary Fig. 1.

Modulations of Connectivity with the Left Auditory Cortex Before Voice Onset

The second main analysis tackled the question of how the condition-specific alpha power modulations in the auditory cortex are mediated. Therefore we looked at power–power correlations between alpha power in the left auditory cortex and low-frequency power in the other regions of the brain. This was conducted on a single-trial level and for the time interval preceding voice onset when auditory alpha power modulations were strongest (1–1.5 s after onset of the red fixation cross). We took strong power–power correlations as an indicator of a possible communication between the accordant brain regions (Park et al. 2011). A cluster-based permutation test revealed the medial prefrontal cortex as the main region differentially communicating with the left auditory cortex when participants expected to listen to their self-produced versus externally played-back voice (cluster P < 0.05, BA 11). The effect was strongest for power correlations between 6 and 14 Hz. When looking at the effect more precisely, it turned out that communication between the left auditory cortex and the medial prefrontal cortex was significantly enhanced compared with baseline when participants expected their self-spoken voice (P < 0.05) and significantly reduced compared with baseline when they expected their played-back voice (P < 0.05). See Figure 4 (upper panel) for comparison.

Figure 4.

Connectivity results. The upper panel shows the spatial dimension of the significant cluster derived from power–power correlations with the left auditory cortex. Alpha power in the auditory cortex was significantly stronger correlated with low-frequency power in the medial prefrontal cortex (depicted by black circle) when participants expected to listen to their self-spoken versus externally played-back voice (cluster P < 0.05, 1–1.5 s, 6–14 Hz, medial prefrontal cortex). The effect was due to a significant increase of power correlations compared with baseline when participants were expecting their self-produced voice (P < 0.05) and a significant decrease of power correlations when they expected their externally played-back voice (P < 0.05). The lower panel depicts effective connectivity between the left auditory cortex and the medial prefrontal cortex, quantified by Partial Directed Coherence. Information flow from the auditory cortex to the medial prefrontal cortex did not differ between conditions (left). In contrast, information flow from the medial prefrontal cortex to the left auditory cortex was significantly stronger for the self-spoken versus externally played-back speech sounds (right), with the effect being strongest for the time interval slightly preceding the main auditory alpha power modulations (0.7–1 s).

Figure 4.

Connectivity results. The upper panel shows the spatial dimension of the significant cluster derived from power–power correlations with the left auditory cortex. Alpha power in the auditory cortex was significantly stronger correlated with low-frequency power in the medial prefrontal cortex (depicted by black circle) when participants expected to listen to their self-spoken versus externally played-back voice (cluster P < 0.05, 1–1.5 s, 6–14 Hz, medial prefrontal cortex). The effect was due to a significant increase of power correlations compared with baseline when participants were expecting their self-produced voice (P < 0.05) and a significant decrease of power correlations when they expected their externally played-back voice (P < 0.05). The lower panel depicts effective connectivity between the left auditory cortex and the medial prefrontal cortex, quantified by Partial Directed Coherence. Information flow from the auditory cortex to the medial prefrontal cortex did not differ between conditions (left). In contrast, information flow from the medial prefrontal cortex to the left auditory cortex was significantly stronger for the self-spoken versus externally played-back speech sounds (right), with the effect being strongest for the time interval slightly preceding the main auditory alpha power modulations (0.7–1 s).

Finally we wanted to assess the direction of the information flow between the left auditory and the medial prefrontal cortex. In order to do that, we calculated Partial Directed Coherence between the 2 regions. Information flow from the auditory cortex to the medial prefrontal cortex showed no significant differences between conditions (all P > 0.05). In contrast, information flow from the medial prefrontal cortex to the left auditory cortex was significantly enhanced when participants prepared for speaking (P < 0.05). The effect was strongest for the time interval slightly preceding the main auditory alpha power modulations (0.7–1 s after onset of the red fixation cross). For comparison see Figure 4 (lower panel).

Discussion

In the present study we investigated if and how pre-speech brain activity is modulated when participants expect a self-spoken sound. We concentrated on modulations in low-frequency power with a focus on the auditory cortex and its communication with non-auditory regions. Results show that the alpha power suppression, typically present when participants expect sounds, is absent in the left auditory cortex when participants expect their own voice. They further show that the medial prefrontal cortex mediates this effect. The absence of the auditory alpha power suppression can be interpreted as inhibition of the auditory cortex when participants expect self-spoken sounds. This is in line with the previous literature postulating an inhibition of the auditory cortex when processing self-spoken sounds and extends the previous literature by showing that brain activity in the auditory cortex is inhibited already before sound onset and on a macroscopic scale. So far a suppression of brain activity in the auditory cortex before speech onset has only been shown for ongoing activity in single neurons (Creutzfeldt et al. 1989; Eliades and Wang 2003) and not for local field potentials. In addition, the mediation of the auditory cortex's increase in alpha power via medial prefrontal cortex suggests a mechanism of how auditory cortex excitability is adjusted. This gives new insights into the processes going on within the tested paradigm and, crucially, provides first evidence of how auditory alpha power could be causally modulated by higher-order regions.

Auditory Alpha Power Modulations

As described above we found a decrease of auditory alpha power compared with baseline in the “listen” condition and an abolishment of that effect in the “speak” condition. There were no significant differences of auditory alpha activity during the baseline. This points to a relative inhibition of that brain region (Klimesch et al. 2007; Jensen and Mazaheri 2010), in this case a relative inhibition of the auditory cortex (Gomez-Ramirez et al. 2011; Muller and Weisz 2012; Weisz et al. 2014) and is therefore consistent with previous literature postulating an inhibition of the auditory cortex when processing self-generated sounds compared with externally played-back ones (Creutzfeldt et al. 1989; Curio et al. 2000; Houde et al. 2002; Eliades and Wang 2003; Ford and Mathalon 2004). A growing number of studies in the visual and somatosensory system (Worden et al. 2000; Thut 2006; Romei et al. 2008; Siegel et al. 2008; van Dijk et al. 2008; Jones et al. 2010; Händel et al. 2011; Haegens et al. 2012; Lange et al. 2012) and also in the auditory system (Gomez-Ramirez et al. 2011; Muller and Weisz 2012; Müller et al. 2013; Weisz et al. 2014; Frey et al. 2014) have convincingly shown that alpha power modulations have the potential to dynamically adjust the excitability of brain regions with respect to the according task demands (e.g., attention, near-threshold detection, memory), and thereby make neuronal processing adaptive and most effective (Jensen and Mazaheri 2010). Based on that the current results can be interpreted as follows: the auditory system is by default in an inhibited state to filter out the vast amount of auditory information it is exposed to. This is in accord with the observation that alpha in sensory cortices is high when subjects are awake and not engaged in any task (Basar et al. 1997; Klimesch et al. 2007; Jensen and Mazaheri 2010). If participants expect to process an external sound they reduce auditory alpha power in order to enhance processing capacities for the incoming auditory stimulus, as it is the case in “normal” auditory perception (Gomez-Ramirez et al. 2011; Hartmann et al. 2012; Muller and Weisz 2012; Weisz et al. 2014). In contrast, if participants expect a self-generated sound, auditory alpha power is kept high meaning that, in that case, processing capacities are not enhanced compared with baseline. We thus propose that even if alpha power is not increased beyond baseline levels, the “relative increase” (i.e., the one arising from the condition contrast) goes in line with the hypothesis of an inhibition of processing when participants prepare to listen to their self-spoken voice. Such a mechanism could explain why we process and perceive self-spoken sounds differentially from played-back ones. Interestingly, it has been shown that an increase in alpha power has an impact on neuronal firing (Haegens et al. 2011) and also on event-related responses (Basar and Stampfer 1985; Ergenoglu et al. 2004; Klimesch et al. 2007). The present findings could thus be the prerequisite of the reported inhibition of the auditory cortex during the processing of self-generated sounds as reported in literature (Creutzfeldt et al. 1989; Curio et al. 2000; Houde et al. 2002; Eliades and Wang 2003; Ford and Mathalon 2004; Heinks-Maldonado et al. 2005), however such a direct relation would have to be tested within further studies. Also findings postulating that the suppression in the auditory cortex is very specific for the self-generated sounds and does not block auditory processing in general thereby will have to be taken into account (McGuire et al. 1996; Heinks-Maldonado et al. 2005; Fu et al. 2006). We here suggest that the abolishment of the usual alpha power reduction when expecting self-generated sounds is an active and top-down modulated process helping to differentiate between self-spoken and externally played-back sounds. According to the connectivity results, which are explained in more detail below, this seems to be indeed the case.

Left Auditory Alpha Power Modulations

We found that the condition-specific alpha power modulations are lateralized to the left auditory cortex. This is in line with the literature on the processing of self-generated speech sounds showing that the suppression effects are dominant in the left auditory cortex (Curio et al. 2000; Houde et al. 2002; Heinks-Maldonado et al. 2005; Kauramaki et al. 2010).

Non-auditory Alpha Power Modulations

Despite the absence of alpha power suppression in the auditory cortex we derived an increase of alpha power in a prefrontal region (BA 8), encompassing the medial dorsolateral prefrontal cortex and a power increase in the right precentral cortex. Brodman area 8 is involved in planning, cognitive control, and maintaining attention (MacDonald 2000; Seamans et al. 2008) and also in guiding decisions (Seamans et al. 2008). A modulation of that region during speech preparation could point to an active disengagement from the expected and to-be-inhibited auditory input. However, the role of alpha power in higher-order regions is not understood so far so that possible modulations within these prefrontal regions during speech preparation will have to be tested by further studies.

Concerning the precentral cortex, it is important to clarify that the effect is due to differences in the baseline (see Supplementary Fig. 1 for comparison). During baseline alpha power is reduced in the left and right precentral cortex for the “speak” compared with the “listen” condition. The left precentral alpha power decrease is still present in the preparation interval what is in line with the modulations we would expect in the precentral cortex during motor preparation (Jasper and Penfield 1949; Pfurtscheller et al. 1996; Sauseng et al. 2009). Interestingly, however, the right precentral power decrease for the “speak” versus “listen” conditions disappears, leading to the impression of an increase of alpha power in the precentral cortex when subjects prepare to speak. These hemispheric differences could be due to the dominance of the left hemisphere for speech production (Llorens et al. 2011; Price et al. 2011).

Auditory Alpha Power Modulation Mediated by the Medial Prefrontal Cortex

Another crucial question to answer was how the condition-specific auditory alpha power modulations are mediated. We could elucidate the medial prefrontal cortex (BA 11) as the main region showing increased communication in this process. Crucially, this increase in communication was driven by an increase of unilateral communication from the medial prefrontal cortex to the left auditory cortex. This provides clear evidence for a condition-specific modulation of auditory alpha power communicated by the medial prefrontal cortex. The medial prefrontal cortex is involved in self-referential thinking (meta-analyses, Johnson et al. 2002; Heatherton et al. 2006; Northoff et al. 2006; van der Meer et al. 2010), in comparing the self with others (Moore et al. 2013) and self-reflective judgments (Macrae et al. 2004). It is thus also from a theoretical point of view very likely that the medial prefrontal cortex has a crucial role in mediating the excitability of the auditory cortex when we process our own voice. Interestingly, the increase of information flow from the medial prefrontal cortex to the auditory cortex was strongest shortly before the relative increase of auditory alpha power. All in all, we suggest that the medial prefrontal cortex triggers alpha power in the auditory cortex so that self-generated sounds are processed less intensely and we can easily distinguish between self-generated and external sounds.

Conclusion

With the present study our aims were to disentangling brain activity associated with the expectation of a self-spoken sound. We concentrated on alpha power modulations in the auditory cortex and on how these modulations are mediated by non-auditory brain regions. We can show that the typical alpha power suppression when participants expect external sounds is absent in the left auditory cortex when participants expect self-spoken sounds. This points to a relative inhibition of the auditory cortex that is already present before speech onset and is in line with the previous literature showing a suppression of brain activity mainly during sound production. Importantly, the current findings complement the existing evidence on modulations of evoked activity and rather local modulations in auditory activity (as derived by ECoG and animal studies) by elucidating that also the state/excitability of the auditory cortex is modulated when processing self-generated sounds, which became evident in the auditory alpha power modulations. As second main finding we demonstrate that the medial prefrontal cortex, a region known for self-referential processes, mediates these condition-specific alpha power modulations. This provides crucial insights into how higher-order regions prepare the auditory cortex for the processing of self-generated sounds and seems interesting as a mechanism itself having the potential to explain similar phenomena related to self-referential processing like for instance “tickling yourself”. Beyond that, the findings also have implications for the so–far unsolved question of how auditory alpha power is mediated by higher-order regions in a more general sense.

Supplementary Material

Supplementary material can be found at: http://www.cercor.oxfordjournals.org/.

Funding

This work was supported by the European Research Council (grant number: 283404) and the Deutsche Forschungsgemeinschaft (grant number: 4156/2-1).

Notes

We thank Nick Peatfield for proofreading the manuscript. Conflict of Interest: None declared.

References

Baccala
LA
Sameshima
K
.
2001
.
Partial directed coherence: a new concept in neural structure determination
.
Biol Cybern
 .
84
:
463
474
.
Baess
P
Horváth
J
Jacobsen
T
Schröger
E
.
2011
.
Selective suppression of self-initiated sounds in an auditory stream: An ERP study
.
Psychophysiology
 .
48
:
1276
1283
.
Bahramisharif
A
van Gerven
M
Heskes
T
Jensen
O
.
2010
.
Covert attention allows for continuous control of brain–computer interfaces
.
Eur J Neurosci
 .
31
:
1501
1508
.
Basar
E
Schurmann
M
Basar-Eroglu
C
Karakas
S
.
1997
.
Alpha oscillations in brain functioning: an integrative theory
.
Int J Psychophysiol
 .
26
:
5
29
.
Basar
E
Stampfer
HG
.
1985
.
Important associations among EEG-dynamics, event-related potentials, short-term memory and learning
.
Int J Neurosci
 .
26
:
161
180
.
Cohen
J
MacWhinney
B
Flatt
M
Provost
J
.
1993
.
PsyScope: an interactive graphic system for designing and controlling experiments in the psychology laboratory using Macintosh computers
.
Behav Res Methods Instrum Comput
 .
25
:
257
271
.
Creutzfeldt
O
Ojemann
G
Lettich
E
.
1989
.
Neuronal activity in the human lateral temporal lobe
.
Exp Brain Res
 .
77
:
451
475
.
Curio
G
Neuloh
G
Numminen
J
Jousmaki
V
Hari
R
.
2000
.
Speaking modifies voice evoked activity in the human auditory cortex
.
Hum Brain Mapp
 .
9
:
183
191
.
Delorme
A
Makeig
S
.
2004
.
EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis
.
J Neurosci Methods
 .
134
:
9
21
.
Eliades
SJ
Wang
X
.
2003
.
Sensory-motor interaction in the primate auditory cortex during self-initiated vocalizations
.
J Neurophysiol
 .
89
:
2194
2207
.
Ergenoglu
T
Demiralp
T
Bayraktaroglu
Z
Ergen
M
Beydagi
H
Uresin
Y
.
2004
.
Alpha rhythm of the EEG modulates visual detection performance in humans
.
Cogn Brain Res
 .
20
:
376
383
.
Flinker
A
Chang
EF
Kirsch
HE
Barbaro
NM
Crone
NE
Knight
RT
.
2010
.
Single-trial speech suppression of auditory cortex activity in humans
.
J Neurosci
 .
30
:
16643
16650
.
Ford
JM
Mathalon
DH
.
2004
.
Electrophysiological evidence of corollary discharge dysfunction in schizophrenia during talking and thinking
.
J Psychiatr Res
 .
38
:
37
46
.
Frey
J
Mainy
N
Lachaux
JP
Müller
N
Bertrand
O
Weisz
N
.
2014
.
Selective modulation of auditory cortical alpha activity in an audiovisual spatial attention task
.
J Neurosci
 .
34
(19)
:
6634
6639
.
Fu
CH
Vythelingum
GN
Brammer
MJ
Williams
SCR
Amaro
E
Andrew
CM
Yágüez
L
van Haren
NEM
Matsumoto
K
McGuire
PK
.
2006
.
An fMRI study of verbal self-monitoring: neural correlates of auditory verbal feedback
.
Cereb Cortex
 .
16
:
969
977
.
Gomez-Ramirez
M
Kelly
SP
Molholm
S
Sehatpour
P
Schwartz
TH
Foxe
JJ
.
2011
.
Oscillatory sensory selection mechanisms during intersensory attention to rhythmic auditory and visual inputs: a human electrocorticographic investigation
.
J Neurosci
 .
31
:
18556
18567
.
Gross
J
Kujala
J
Hämäläinen
M
Timmermann
L
Schnitzler
A
Salmelin
R
.
2001
.
Dynamic imaging of coherent sources: studying neural interactions in the human brain
.
Proc Natl Acad Sci
 .
98
:
694
699
.
Haegens
S
Luther
L
Jensen
O
.
2012
.
Somatosensory anticipatory alpha activity increases to suppress distracting input
.
J Cogn Neurosci
 
24
:
677
685
.
Haegens
S
Nácher
V
Luna
R
Romo
R
Jensen
O
.
2011
.
α-Oscillations in the monkey sensorimotor network influence discrimination performance by rhythmical inhibition of neuronal spiking
.
Proc NatlAcad Sci
 .
108
:
19377
19382
.
Händel
BF
Haarmeier
T
Jensen
O
.
2011
.
Alpha oscillations correlate with the successful inhibition of unattended stimuli
.
J Cogn Neurosci
 .
23
:
2494
2502
.
Hanslmayr
S
Gross
J
Klimesch
W
Shapiro
KL
.
2011
.
The role of alpha oscillations in temporal attention
.
Brain Res Rev
 .
67
:
331
343
.
Hartmann
T
Schlee
W
Weisz
N
.
2012
.
It's only in your head: expectancy of aversive auditory stimulation modulates stimulus-induced auditory cortical alpha desynchronization
.
NeuroImage
 .
60
:
170
178
.
Heatherton
TF
Wyland
CL
Macrae
CN
Demos
KE
Denny
BT
Kelley
WM
.
2006
.
Medial prefrontal activity differentiates self from close others
.
Soc Cogn Affect Neurosci
 .
1
:
18
25
.
Heinks-Maldonado
TH
Mathalon
DH
Gray
M
Ford
JM
.
2005
.
Fine-tuning of auditory cortex during speech production
.
Psychophysiology
 .
42
:
180
190
.
Holst
E
Mittelstaedt
H
.
1950
.
Das Reafferenzprinzip
.
Naturwissenschaften
 .
37
:
464
476
.
Houde
JF
Nagarajan
SS
Sekihara
K
Merzenich
MM
.
2002
.
Modulation of the auditory cortex during speech: an MEG study
.
J Cogn Neurosci
 .
14
:
1125
1138
.
Jasper
H
Penfield
W
.
1949
.
Electrocorticograms in man: Effect of voluntary movement upon the electrical activity of the precentral gyrus
.
Archiv für Psychiatrie und Zeitschrift Nervenkrankheiten
 .
183
:
163
174
.
Jensen
O
Mazaheri
A
.
2010
.
Shaping functional architecture by oscillatory alpha activity: gating by inhibition
.
Front Hum Neurosci
 .
4
:
186
.
Johnson
SC
Baxter
LC
Wilder
LS
Pipe
JG
Heiserman
JE
Prigatano
GP
.
2002
.
Neural correlates of self reflection
.
Brain
 .
125
:
1808
1814
.
Jones
SR
Kerr
CE
Wan
Q
Pritchett
DL
Hämäläinen
M
Moore
CI
.
2010
.
Cued spatial attention drives functionally relevant modulation of the Mu rhythm in primary somatosensory cortex
.
J Neurosci
 .
30
:
13760
13765
.
Kauramaki
J
Jaaskelainen
IP
Hari
R
Mottonen
R
Rauschecker
JP
Sams
M
.
2010
.
Lipreading and covert speech production similarly modulate human auditory-cortex responses to pure tones
.
J Neurosci
 .
30
:
1314
1321
.
Klimesch
W
Sauseng
P
Hanslmayr
S
.
2007
.
EEG alpha oscillations: the inhibition–timing hypothesis
.
Brain Res Rev
 .
53
:
63
88
.
Lange
J
Halacz
J
van Dijk
H
Kahlbrock
N
Schnitzler
A
.
2012
.
Fluctuations of prestimulus oscillatory power predict subjective perception of tactile simultaneity
.
Cereb Cortex
 .
22
:
2564
2574
.
Llorens
A
Trébuchon
A
Liégeois-Chauvel
C
Alario
F-X
.
2011
.
Intra-cranial recordings of brain activity during language production
.
Front Psychol
 .
2
:
375
.
MacDonald
AW
.
2000
.
Dissociating the role of the dorsolateral prefrontal and medial dorsolateral prefrontal cortex in cognitive control
.
Science
 .
288
:
1835
1838
.
Macrae
CN
Moran
JM
Heatherton
TF
Banfield
JF
Kelley
WM
.
2004
.
Medial prefrontal activity predicts memory for self
.
Cereb Cortex
 .
14
:
647
654
.
Maris
E
Oostenveld
R
.
2007
.
Nonparametric statistical testing of EEG- and MEG-data
.
J Neurosci Methods
 .
164
:
177
190
.
Martikainen
MH
Kaneko
K
Hari
R
.
2005
.
Suppressed responses to self-triggered sounds in the human auditory cortex
.
Cereb Cortex
 .
15
:
299
302
.
McGuire
PK
Silbersweig
DA
Frith
CD
.
1996
.
Functional neuroanatomy of verbal self-monitoring
.
Brain
 .
119
(Pt 3)
:
907
917
.
Moore
WE
Merchant
JS
Kahn
LE
Pfeifer
JH
.
2013
.
“Like me?”: ventromedial prefrontal cortex is sensitive to both personal relevance and self-similarity during social comparisons
.
Soc Cogn Affect Neurosci
 .
9
4
:
421
426
.
Müller
N
Keil
J
Obleser
J
Schulz
H
Grunwald
T
Bernays
R-L
Huppertz
H-J
Weisz
N
.
2013
.
You can't stop the music: reduced auditory alpha power and coupling between auditory and memory regions facilitate the illusory perception of music during noise
.
NeuroImage
 .
79
:
383
393
.
Muller
N
Weisz
N
.
2012
.
Lateralized auditory cortical alpha band activity and interregional connectivity pattern reflect anticipation of target sounds
.
Cereb Cortex
 .
22
:
1604
1613
.
Nolte
G
.
2003
.
The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors
.
Phys Med Biol
 .
48
:
3637
.
Northoff
G
Heinzel
A
de Greck
M
Bermpohl
F
Dobrowolny
H
Panksepp
J
.
2006
.
Self-referential processing in our brain—a meta-analysis of imaging studies on the self
.
NeuroImage
 .
31
:
440
457
.
Oostenveld
R
Fries
P
Maris
E
Schoffelen
J-M
.
2011
.
FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data
.
Comput Intell Neurosci
 
2011
:
1
9
.
Park
H
Kang
E
Kang
H
Kim
JS
Jensen
O
Chung
CK
Lee
DS
.
2011
.
Cross-frequency power correlations reveal the right superior temporal gyrus as a hub region during working memory maintenance
.
Brain Connectivity
 .
1
:
460
472
.
Percival
DB
.
1993
.
Spectral analyses for physical applications
 .
Cambridge
:
Cambridge
University Press
.
Pfurtscheller
G
Stancak
AJ
Neuper
C
.
1996
.
Event-related synchronization (ERS) in the alpha band —an electrophysiological correlate of cortical idling: a review
.
Int J Psychophysiol
 .
24
:
39
46
.
Ploog
D
.
1981
.
Inhibition of auditory cortical neurons during phonation
.
Brain Res
 .
215
:
61
76
.
Price
CJ
Crinion
JT
Macsweeney
M
.
2011
.
A generative model of speech production in Broca's and Wernicke's Areas
.
Front Psychol
 .
2
:
237
.
Romei
V
Rihs
T
Brodbeck
V
Thut
G
.
2008
.
Resting electroencephalogram alpha-power over posterior sites indexes baseline visual cortex excitability
.
Neuroreport
 .
19
:
203
208
.
Sauseng
P
Klimesch
W
Gerloff
C
Hummel
FC
.
2009
.
Spontaneous locally restricted EEG alpha activity determines cortical excitability in the motor cortex
.
Neuropsychologia
 .
47
:
284
288
.
Seamans
JK
Lapish
CC
Durstewitz
D
.
2008
.
Comparing the prefrontal cortex of rats and primates: insights from electrophysiology
.
Neurotox Res
 .
14
:
249
262
.
Siegel
M
Donner
TH
Oostenveld
R
Fries
P
Engel
AK
.
2008
.
Neuronal synchronization along the dorsal visual pathway reflects the focus of spatial attention
.
Neuron
 .
60
:
709
719
.
Sperry
RW
.
1950
.
Neural basis of the spontaneous optokinetic response produced by visual inversion
.
J Comp Physiol Psychol
 .
43
:
482
489
.
Supp
GG
Schlögl
A
Trujillo-Barreto
N
Müller
MM
.
2007
.
Directed Cortical Information Flow during Human Object Recognition: Analyzing Induced EEG Gamma-Band Responses in Brain's Source Space
.
PLoS ONE
 .
2
(8)
:
e684
.
Thut
G
.
2006
.
-Band Electroencephalographic Activity over Occipital Cortex Indexes Visuospatial Attention Bias and Predicts Visual Target Detection
.
J Neurosci
 .
26
:
9494
9502
.
Towle
VL
Yoon
HA
Castelle
M
Edgar
JC
Biassou
NM
Frim
DM
Spire
JP
Kohrman
MH
.
2008
.
ECoG gamma activity during a language task: differentiating expressive and receptive speech areas
.
Brain
 .
131
:
2013
2027
.
Van der Meer
L
Costafreda
S
Aleman
A
David
AS
.
2010
.
Self-reflection and the brain: a theoretical review and meta-analysis of neuroimaging studies with implications for schizophrenia
.
Neurosci Biobehav Rev
 
34
:
935
946
.
Van Dijk
H
Schoffelen
JM
Oostenveld
R
Jensen
O
.
2008
.
Prestimulus Oscillatory Activity in the Alpha Band Predicts Visual Discrimination Ability
.
J Neurosci
 .
28
:
1816
1823
.
Van Veen
BD
Van Drongelen
W
Yuchtman
M
Suzuki
A
.
1997
.
Localization of brain electrical activity via linearly constrained minimum variance spatial filtering
.
Biomedical Engineering
 .
44
:
867
880
.
Weisz
N
Muller
N
Jatzev
S
Bertrand
O
.
2014
.
Oscillatory alpha modulations in right auditory regions reflect the validity of acoustic cues in an auditory spatial attention task
.
Cereb Cortex
 .
24
:
2579
2590
.
Worden
MS
Foxe
JJ
Wang
N
Simpson
GV
.
2000
.
Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha-band electroencephalography increases over occipital cortex
.
J Neurosci
 .
20
:
RC63
.