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. 2024 Sep 10;13:RP94198. doi: 10.7554/eLife.94198

Speech-induced suppression and vocal feedback sensitivity in human cortex

Muge Ozker 1,2,✉, Leyao Yu 1,3, Patricia Dugan 1, Werner Doyle 4, Daniel Friedman 1, Orrin Devinsky 1, Adeen Flinker 1,3
Editors: Supratim Ray5, Barbara G Shinn-Cunningham6
PMCID: PMC11386952  PMID: 39255194

Abstract

Across the animal kingdom, neural responses in the auditory cortex are suppressed during vocalization, and humans are no exception. A common hypothesis is that suppression increases sensitivity to auditory feedback, enabling the detection of vocalization errors. This hypothesis has been previously confirmed in non-human primates, however a direct link between auditory suppression and sensitivity in human speech monitoring remains elusive. To address this issue, we obtained intracranial electroencephalography (iEEG) recordings from 35 neurosurgical participants during speech production. We first characterized the detailed topography of auditory suppression, which varied across superior temporal gyrus (STG). Next, we performed a delayed auditory feedback (DAF) task to determine whether the suppressed sites were also sensitive to auditory feedback alterations. Indeed, overlapping sites showed enhanced responses to feedback, indicating sensitivity. Importantly, there was a strong correlation between the degree of auditory suppression and feedback sensitivity, suggesting suppression might be a key mechanism that underlies speech monitoring. Further, we found that when participants produced speech with simultaneous auditory feedback, posterior STG was selectively activated if participants were engaged in a DAF paradigm, suggesting that increased attentional load can modulate auditory feedback sensitivity.

Research organism: Human

eLife digest

The brain lowers its response to inputs we generate ourselves, such as moving or speaking. Essentially, our brain ‘knows’ what will happen next when we carry out these actions, and therefore does not need to react as strongly as it would to unexpected events. This is why we cannot tickle ourselves, and why the brain does not react as much to our own voice as it does to someone else’s. Quieting down the brain’s response also allows us to focus on things that are new or important without getting distracted by our own movements or sounds.

Studies in non-human primates showed that neurons in the auditory cortex (the region of the brain responsible for processing sound) displayed suppressed levels of activity when the animals made sounds. Interestingly, when the primates heard an altered version of their own voice, many of these same neurons became more active. But it was unclear whether this also happens in humans.

To investigate, Ozker et al. used a technique called electrocorticography to record neural activity in different regions of the human brain while participants spoke. The results showed that most areas of the brain involved in auditory processing showed suppressed activity when individuals were speaking. However, when people heard an altered version of their own voice which had an unexpected delay, those same areas displayed increased activity. In addition, Ozker et al. found that the higher the level of suppression in the auditory cortex, the more sensitive these areas were to changes in a person’s speech.

These findings suggest that suppressing the brain’s response to self-generated speech may help in detecting errors during speech production. Speech deficits are common in various neurological disorders, such as stuttering, Parkinson’s disease, and aphasia. Ozker et al. hypothesize that these deficits may arise because individuals fail to suppress activity in auditory regions of the brain, causing a struggle when detecting and correcting errors in their own speech. However, further experiments are needed to test this theory.

Introduction

A major question in neuroscience is how do animals distinguish between stimuli originating from the environment and those produced by their own actions. Sensorimotor circuits share a common mechanism across the animal kingdom in which sensory responses to self-generated motor actions are suppressed. It is commonly hypothesized that suppressing responses to predicted self-generated stimuli increases sensitivity of the sensory system to external stimuli (Poulet and Hedwig, 2002; Poulet and Hedwig, 2006; Crapse and Sommer, 2008; Schneider and Mooney, 2018). Furthermore, it enables detection and correction of motor errors by providing a template of the predicted sensory outcome to compare with the actual sensory outcome. In the domain of speech, this mechanism is described in models which suggest that neural responses in the auditory cortex are suppressed during speech production. When there is a mismatch between the predicted auditory outcome and the actual auditory feedback, responses in the auditory regions are enhanced to encode the mismatch and inform vocal-motor regions to correct vocalization (Hickok et al., 2011; Houde and Nagarajan, 2011; Tourville and Guenther, 2011).

A common experimental strategy to generate mismatch between the predicted auditory outcome and the actual auditory feedback is to perturb auditory feedback during speech production. Auditory feedback perturbations are usually applied either by delaying auditory feedback (DAF), which disrupts speech fluency (Lee, 1950; Fairbanks, 1955; Stuart et al., 2002), or by shifting voice pitch and formants, which result in compensatory vocal changes in the opposite direction of the shift (Houde and Jordan, 1998; Jones and Munhall, 2000; Niziolek and Guenther, 2013). Numerous electrophysiological and neuroimaging studies investigated neural responses during speech production both in the absence and presence of auditory feedback perturbations. In support of speech production models, these studies have repeatedly reported suppressed responses in auditory cortex during speaking compared with passive listening to speech (Numminen et al., 1999; Wise et al., 1999; Curio et al., 2000, Houde et al., 2002; Christoffels et al., 2007, Ford et al., 2010, Niziolek et al., 2013), as well as enhanced responses when auditory feedback was perturbed indicating sensitivity to auditory feedback (Tourville et al., 2008; Behroozmand et al., 2009; Chang et al., 2013; Greenlee et al., 2013, Kort et al., 2014; Behroozmand et al., 2015; Ozker et al., 2022). However, it is not clear whether the same or distinct neural populations in the auditory cortex show speech-induced suppression and sensitivity to auditory feedback.

While auditory responses are largely suppressed during speech production, detailed investigations using neurosurgical recordings revealed that the degree of suppression was variable across cortical sites, and auditory cortex also exhibited non-suppressed and enhanced responses (albeit less common) (Creutzfeldt and Ojemann, 1989; Flinker et al., 2010; Greenlee et al., 2011), mirroring results from non-human primate studies using single-unit recordings (Eliades and Wang, 2003; Eliades and Wang, 2008). In the same non-human primate study, it was reported that neurons that were suppressed during vocalization showed increased activity when auditory feedback was perturbed (Eliades and Wang, 2008). Based on this finding, we predicted that if speech-induced suppression enables detection and correction of speech errors, suppressed auditory sites should be sensitive to auditory feedback, thus exhibit enhanced neural responses to feedback perturbations. Alternatively, if suppression and speech monitoring are unrelated processes, then suppressed sites should be distinct from the ones that are sensitive to auditory feedback.

The level of attention during speech monitoring can vary depending on the speech task. During normal speech production, speech monitoring does not require a conscious effort, however it is a controlled, attentional process during an auditory feedback perturbation task (Hashimoto and Sakai, 2003). It is well known that selective attention enhances auditory responses and improves speech perception under noisy listening conditions or when multiple speech streams are present (Mesgarani and Chang, 2012, Zion Golumbic et al., 2013). We predicted that increased attention to auditory feedback under adverse speaking conditions, such as during an auditory feedback perturbation task, should increase feedback sensitivity and elicit larger responses in the auditory cortex compared to normal speech production.

To summarize, in this study we aimed to test the hypothesis that speech-induced suppression increases sensitivity to auditory feedback in human neurophysiological recordings. We predicted that auditory sites showing speech-induced suppression would elicit enhanced responses to auditory feedback perturbations. Further, we aimed to investigate the role of attention in auditory feedback sensitivity by comparing auditory responses during an auditory feedback perturbation task compared with normal speech production.

To address these aims, we used intracranial electroencephalography (iEEG) recordings in neurosurgical participants, which offers a level of spatial detail and temporal precision that would not be possible to achieve using non-invasive techniques. We first identified the sites that show auditory suppression during speech production, and then employed a DAF paradigm to test whether the same sites show sensitivity to perturbed feedback. Our results revealed that overlapping sites in the superior temporal gyrus (STG) exhibited both speech-induced auditory suppression and sensitivity to auditory feedback with a strong correlation between the two measures, supporting the hypothesis that auditory suppression predicts sensitivity to speech errors in humans. Further, we showed that auditory responses in the posterior STG are enhanced in a DAF task compared to normal speech production, even for trials in which participants receive simultaneous auditory feedback (no-delay condition). This result suggests that increased attention during an auditory feedback perturbation task can modulate auditory feedback sensitivity and posterior STG is a critical region for this attentional modulation.

Results

In order to assess cortical responses during perception and production of speech, and quantify speech-induced auditory suppression, participants (n=35) performed an auditory word repetition (AWR) task. We examined the response patterns in seven different cortical regions including STG, middle temporal gyrus (MTG), supramarginal gyrus (SMG), inferior frontal gyrus (IFG), middle frontal gyrus (MFG), precentral gyrus (preCG), and postcentral gyrus (postCG) (Figure 1A). As an index of the neural response, we used the high gamma broadband signal (70–150 Hz, see Materials and methods), which correlates with the spiking activity of the underlying neuronal population (Mukamel et al., 2005; Crone et al., 2006; Cardin et al., 2009; Ray and Maunsell, 2011; Lachaux et al., 2012).

Figure 1. Cortical responses during speech tasks.

Figure 1.

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(A) Electrodes from all participants (n=35) are shown on a template brain with different colors corresponding to different regions (number of electrodes in each region denoted in the parentheses). (B) High gamma broadband responses (70–150 Hz) for individual trials in an auditory word repetition task are shown for each region. (C) High gamma responses for individual trials in a visual word reading task are shown for each region. Trials are sorted with respect to speech onset (white line). (D) Mean high gamma broadband responses averaged across trials are shown for each region with the width representing the standard error of the mean across electrodes. Time = 0 indicates speech production onset.

We analyzed the responses in two different time windows: during passive listening of the auditory stimulus (0–500 ms after stimulus onset) and during speaking when participants repeated the perceived auditory stimulus (0–500 ms after articulation onset). Average responses were larger during passive listening in STG (average % signal change ± SEM; Listen: 62.1±0.6, Speak: 29.8±0.4), MTG (32.7±0.9, 22.3±0.9), and SMG (27.4±0.8, 25.8±0.7) compared with speaking. Conversely, responses were larger during speaking in IFG (29.2±1.3, 31.2±1.3), MFG (28.3±1.6, 31.4±1.3), preCG (27.4±0.4, 37±0.5), and postCG (26±0.4, 42±0.5). These results suggested that auditory regions responded more strongly during passive listening compared to speaking, verifying previous reports of neural response suppression to self-generated speech in auditory cortex (Figure 1B–D).

In the AWR task, participants heard the same auditory stimulus twice in each trial, once from a recorded female voice and once from their own voice. It is well known that repeated presentation of a stimulus results in the suppression of neural activity in regions that process that stimulus, a neural adaptation phenomenon referred to as repetition suppression (Grill-Spector et al., 2006; Todorovic and de Lange, 2012). To ensure that our observed suppression of neural activity in auditory regions was not due to repetition suppression, but rather was induced by speech production, we performed a visual word reading (VWR) task, in which participants hear the auditory stimulus only once (from their own voice). Response magnitudes during speaking in the AWR and VWR tasks were similar (paired t-test: t(466)=0.62, p=0.53), characterized by a strong correlation across electrodes (Pearson’s correlation: r=0.9006, p=0). These results suggested that repetition of the auditory stimulus in the AWR task did not affect response magnitudes and the observed reduction in response magnitudes was induced by speech production.

To quantify the amount of speech-induced suppression, we calculated a suppression index (SuppI) for each electrode by comparing neural responses during listening versus speaking in the AWR task (SuppI = Listen-Speak/Listen+Speak; see Materials and methods). A positive SuppI indicated a response suppression during speaking compared to listening and was observed most strongly in middle to posterior parts of STG, followed by MTG and SMG. A negative SuppI indicated a response enhancement during speaking compared to listening and was observed in motor regions, most strongly in the postCG (Figure 2A and B).

Figure 2. Spatial topography of speech-induced auditory suppression.

Figure 2.

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(A) Suppression indices for all electrodes are shown on a template brain. Red color tones indicate smaller neural activity during speaking, while blue electrodes indicate larger neural activity during speaking compared to listening in the auditory word repetition task. (B) Suppression indices averaged across electrodes are shown for each region sorted from largest to smallest mean suppression index. Boxplots indicate mean ± SD.

After mapping the topographical distribution of SuppI across the cortex, we focused on understanding the functional role of auditory suppression in speech monitoring. We hypothesized that the degree of speech-induced auditory suppression should be tightly linked to sensitivity to speech errors, as predicted by current models (Houde and Nagarajan, 2011; Tourville and Guenther, 2011) and neural data in non-human primates (Eliades and Wang, 2008). To test this hypothesis, we used an additional task, in which we delayed the auditory feedback (DAF) during speech production to disrupt speech fluency. In this task, 14 participants repeated the VWR task while they were presented with their voice feedback through earphones either simultaneously (no-delay) or with a delay (50, 100, and 200 ms; see Materials and methods). In a previous study (Ozker et al., 2022), using the same dataset, we demonstrated that participants slowed down their speech in response to DAF (articulation duration; DAF0: 0.698, DAF50: 0.726, DAF100: 0.737, and DAF200: 0.749 ms). Moreover, auditory regions exhibited an enhanced response that varied as a function of feedback delay, likely representing an auditory error signal encoding the mismatch between the expected and the actual feedback. However, those results were not directly linked to auditory suppression.

Here, we compared neural responses in the AWR and the DAF tasks to test whether auditory regions that exhibit strong speech-induced suppression also exhibit large auditory error responses to DAF, which would indicate strong sensitivity to speech errors. In a single participant, we demonstrated that a representative electrode on the STG with strong auditory suppression (average % signal change in 0–500 ms; Listen: 124±7, Speak: 20±3, SuppI: 0.27) exhibited significant response enhancement (DAF0: 135±12, DAF50: 134±8, DAF100: 175±10, DAF200: 208±17, ANOVA: F(3, 116)=8.5, p=3.7e-05) (Figure 3A and B), while a nearby electrode with weaker auditory suppression (Listen: 116±6, Speak: 80±4, SuppI: 0.06) did not exhibit significant response enhancement with feedback delays (DAF0: 360±29, DAF50: 328±24, DAF100: 379±31, DAF200: 419±30, ANOVA: F(3, 116)=1.73, p=0.16) (Figure 3C and D).

Figure 3. Speech-induced auditory suppression and sensitivity to delayed auditory feedback (DAF) in representative electrodes in a single participant.

Figure 3.

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(A) High gamma broadband response (70–150 Hz) in electrode G63 showing a large amount of auditory suppression during speaking words compared to listening to the same words. Error bars indicate SEM over trials. (B) High gamma responses in electrode G63 to articulation of words with DAF. 0 s indicate the onset of the perceived auditory feedback. Inset figure shows the cortical surface model of the left hemisphere brain of a single participant. Black circles indicate the implanted electrodes. White highlighted electrodes are located on the middle (G63) and caudal (G54) superior temporal gyrus (STG). (C) High gamma response in electrode G54 showing a small degree of auditory suppression during speaking words compared to listening. (D) High gamma response in electrode G54 locked to articulation of words during DAF. 0 s indicate the onset of the perceived auditory feedback.

To quantify the auditory error response and measure the sensitivity of a cortical region to DAF, we calculated a sensitivity index (SensI) for each electrode by correlating the delay condition and the average neural response across trials (see Materials and methods). A large SensI indicated a strong response enhancement (large auditory error response) with increasing delays. The degree of both speech-induced suppression and sensitivity to DAF were highly variable across the cortex, SuppI ranging from –0.46 to 0.53 and SensI ranging from –0.62 to 0.70. The largest SuppI and SensI as well as a strong overlap between the two measures were observed in the STG, suggesting that auditory electrodes that show speech-induced suppression are also sensitive to auditory feedback perturbations (Figure 4A–C). We validated this relationship by revealing a significant correlation between SuppI and SensI of auditory electrodes (n=57, Pearson’s correlation: r=0.4006, p=0.002) supporting our hypothesis and providing evidence for a common neural mechanism (Figure 4D).

Figure 4. Correlation between speech-induced auditory suppression and sensitivity to delayed auditory feedback (DAF).

Figure 4.

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(A) Sensitivity indices (SensI) for all electrodes are shown on a template brain (both right and left hemisphere electrodes were shown on the left hemisphere). Red tones indicate larger neural activity to increasing amount of delays in the DAF task, while blue tones indicate the opposite. (B) Suppression indices (SuppI) for all electrodes are shown on a template brain. Red tones indicate larger neural activity during listening compared to speaking in the auditory word repetition task, while blue tones indicate the opposite. (C) Electrodes that show either sensitivity to DAF (positive SensI value) or speech-induced auditory suppression (positive SuppI value), or both are shown on a template brain. (D) Scatter plot and fitted regression showing a significant correlation between sensitivity to DAF and speech-induced auditory suppression across auditory electrodes. Each circle represents an electrode’s SensI and SuppI.

Our neural analysis revealed that response magnitudes in auditory cortex were much larger when participants heard their simultaneous voice feedback in a DAF paradigm compared with producing speech without any feedback (DAF0: no-delay trials) (average % signal change in 0–500 ms; DAF0: 113±14, VWR: 41±7, compare gray lines in Figure 3A and C with black lines in Figure 3B and D, respectively). We were interested in dissociating if these larger responses were merely an effect of perceiving voice feedback through earphones instead of air or rather were specific to our DAF design, likely due to increased attentional demands. Therefore, four participants performed an additional VWR task in which they were presented with their simultaneous voice feedback through earphones (VWR with auditory feedback [VWR-AF]). As previous studies have reported that DAF can increase voice intensity (Yates, 1963, Howell and Archer, 1984), we first verified whether participants spoke louder during the DAF task. A comparison of their voice intensity between DAF0 (no-delay trials in the DAF task) and the VWR-AF (standard word reading with simultaneous feedback through earphones) conditions did not show a significant difference (voice intensity; DAF0: 50±11 dB, VWR: 49±12 dB; paired t-test: t(118)=1.8, p=0.08). After verifying that the sound volume entering the auditory system is not statistically different in the two conditions, we compared the responses in the auditory cortex and found that overall response magnitudes were now on par across the two conditions (DAF0: 89±17, VWR-AF: 82±17, Figure 5A). However, a detailed inspection of individual electrode responses revealed that some electrodes showed larger response to DAF0, while others showed either larger responses to VWR-AF or similar responses to both conditions (Figure 5B). In a single participant, we demonstrated that adjacent electrodes in the STG that are only 5 mm apart exhibited completely different response patterns. Electrodes in the more posterior parts of STG showed larger responses to DAF0, while electrodes in more anterior parts showed similar responses to DAF0 and VWR-AF (Figure 5C). To determine an anatomical landmark at which the reversal of response patterns occurred in the STG, we used the lateral termination of the transverse temporal sulcus (TTS) (Greenlee et al., 2011; Nourski et al., 2016) based on the individual FreeSurfer segmentation of the participant’s preoperative MRI. Across participants, this landmark corresponded to y coordinate = –22±2.

Figure 5. Effect of the delayed auditory feedback (DAF) paradigm on neural responses during speech.

Figure 5.

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(A) High gamma broadband responses (70–150 Hz) averaged across auditory electrodes are similar during no-delay condition in the DAF task (DAF0) and during visual word reading with auditory feedback (VWR-AF). Error bars indicate SEM across electrodes. (B) Scatter plot shows averaged high gamma responses (0–500 ms) for VWR-AF versus DAF0 conditions for auditory electrodes. (C) High gamma responses for DAF0 and VWR-AF are shown in representative auditory electrodes in a single participant. Electrodes that are posteriorly located on the superior temporal gyrus (STG) show larger responses to DAF0 condition, while electrodes that are anteriorly located on the STG show similar responses to the two conditions. The lateral termination of the transverse temporal sulcus (TTS) is identified as a landmark (white zigzagged line) that separates the two different response patterns. (D) High gamma responses for DAF0 and VWR conditions were compared and resulting t-values are shown for all electrodes on a template brain. Pink color tones indicate larger responses to DAF0, while green color tones indicate larger responses to VWR condition. (E) t-values calculated by comparing responses to DAF0 and VWR conditions are shown for all auditory electrodes with respect to their anterior-to-posterior positions to the TTS.

Next, we compared the response patterns in the two conditions for all electrodes across participants by calculating a t-value for each electrode (unpaired t-test: average responses from –200 to 500 ms). We demonstrated that auditory regions in posterior STG showed larger responses to DAF0 condition, while frontal motor regions showed larger responses to VWR-AF (Figure 5D). Lastly, we examined STG electrodes alone, sorted by their anterior-to-posterior positions with respect to the TTS. In line with the results from the single participant, electrodes that were located posteriorly within a 1 cm distance from this anatomical landmark showed significantly larger responses to the DAF0 condition (Figure 5E). These results suggest that posterior STG is more activated when participants are engaged in a speech production task that requires increased effort and attention.

Discussion

Our study provides a detailed topographical investigation of speech-induced auditory suppression in a large cohort of neurosurgical participants. We found that while the strongest auditory suppression was observed in the STG, the degree of suppression was highly variable across different recording sites. To explain this variability, we considered the functional role of auditory suppression in speech monitoring. We showed that delaying auditory feedback during speech production enhanced auditory responses in the STG. The degree of sensitivity to feedback delays was also variable across different recording sites. We found a significant correlation between speech-induced suppression and feedback sensitivity, providing evidence for a shared mechanism between auditory suppression and speech monitoring. While there was no anatomical organization for auditory suppression and feedback sensitivity in the STG, we found an anterior-posterior organization for the effect of attention on feedback sensitivity. Auditory sites that lie posterior to the lateral termination of the TTS in the STG showed stronger activation during the DAF task compared to a standard word reading task, even for trials in which participants received simultaneous feedback, demonstrating attentional modulation of feedback sensitivity.

We observed the strongest speech-induced suppression in the middle and posterior parts of the STG. In line with previous iEEG studies, we found that degree of suppression was variable across different recording sites in the STG without any anatomical organization (Flinker et al., 2010; Greenlee et al., 2011; Nourski et al., 2016). So far, a clear gradient for speech-induced suppression has never been reported in the STG but only in the Heschl’s gyrus and superior temporal sulcus by studies that used comprehensive depth electrode coverage within the temporal lobe (Nourski et al., 2016; Nourski et al., 2021).

We found only a few sites with speech-induced enhancement and several sites with no response change. Based on single-unit recordings in non-human primates, it is known that majority of neurons in the non-core auditory cortex exhibits suppression, while a smaller group exhibits excitation during vocalization. It is difficult to isolate speech-induced enhancement in human studies, because measurements reflect the average response of the underlying neural population, which is dominated by suppressed responses. A previous non-human primate study suggested that there might be a division of labor between the suppressed and excited neurons. They showed that when an external auditory stimulus is presented concurrently during vocalization, neurons that showed vocalization-induced suppression did not respond to the external stimulus. In contrary, neurons that showed vocalization-induced excitation responded even more when external stimulus is concurrently presented during vocalization, suggesting a role in maintaining sensitivity to the external acoustic environment (Eliades and Wang, 2003). In humans there might be a similar division of labor between auditory sites that were suppressed and non-suppressed, such that while suppressed sites are engaged in monitoring self-generated sounds, non-suppressed sites maintain sensitivity to external sounds. But unfortunately, our study did not include the necessary experimental conditions to directly test this hypothesis.

Our broad topographical search using subdural electrodes revealed additional sites outside the canonical auditory regions in the STG that showed speech-induced suppression, mainly in the MTG, and a few others in the SMG and preCG. Sensorimotor regions in the preCG including inferior frontal and premotor cortices are known to activate during passive listening tasks (Wilson et al., 2004; Pulvermüller et al., 2006; Cogan et al., 2014), and show tuning to different acoustic properties of speech similar to the auditory regions in the STG (Mesgarani et al., 2014; Cheung et al., 2016). Our results showed that isolated sites in these frontal motor regions were sensitive to DAF, confirming their auditory properties and suggesting their involvement in speech monitoring.

Current models of speech motor control predicted a shared mechanism between auditory suppression and sensitivity to speech errors, suggesting a role for auditory suppression in speech monitoring (Houde and Nagarajan, 2011; Tourville and Guenther, 2011). Behavioral evidence in human studies showed that when auditory feedback is delayed in real time, speakers attempt to reset or slow down their speech (Lee, 1950; Fairbanks, 1955; Stuart et al., 2002). Similarly, when fundamental frequency (pitch) or formant frequencies of the voice are shifted, speakers change their vocal output in the opposite direction of the shift to compensate for the spectral perturbation (Houde and Jordan, 1998; Jones and Munhall, 2000; Niziolek and Guenther, 2013). Neurosurgical recordings and neuroimaging studies that investigate the brain mechanism of auditory feedback processing demonstrated that these feedback-induced vocal adjustments are accompanied by enhanced neural responses in various auditory regions (Tourville et al., 2008; Behroozmand et al., 2009; Behroozmand et al., 2015; Ozker et al., 2022). However, it has not been clear whether it is the same or different neural populations that exhibit speech-induced suppression and enhanced responses to auditory feedback perturbations. Only in a non-human primate study, which recorded single-unit activity in auditory neurons of marmoset monkeys, it was shown that neurons that were suppressed during vocalization exhibited increased activity during frequency-shifted feedback (Eliades and Wang, 2008). In contrast, to replicate this finding in humans, a previous iEEG study by Chang et al., 2013, used frequency-shifted feedback during vowel production and found that most suppressed auditory sites did not overlap with those sensitive to feedback alterations. Using DAF instead of frequency-shifted feedback, we demonstrated a significant overlap of two neural populations in the STG, along with a strong correlation between the degree of speech-induced suppression and sensitivity to auditory feedback. This discrepancy may be due to different methods of calculating sensitivity to altered feedback. In our study, sensitivity was determined by comparing responses to delayed and non-delayed feedback during production, whereas Chang et al. compared perturbed feedback responses during production and listening. One possibility is that our approach identifies a larger auditory neural population in the STG sensitive to altered feedback. Alternatively, it could indicate a larger population highly sensitive to temporal rather than spectral perturbations in auditory feedback. Thus, we observe a wide overlap of the two neural populations in the STG showing both speech-induced suppression and sensitivity to auditory feedback. Replaying a recording of the participants’ own delayed voice back to them, which we were unable to complete in this study, would have made the results of the two studies more comparable while also completely eliminating the possibility of a sensory explanation for the observed response enhancement.

Forward models of speech production suggest that a mismatch between the predicted and the actual auditory feedback is encoded by a response enhancement in the auditory cortex signifying an error signal (Houde and Nagarajan, 2011; Tourville and Guenther, 2011; Hickok, 2012). Our results suggested that attention to one’s own speech stream during adverse speaking conditions, such as during an auditory feedback perturbations task, might also contribute to the response enhancement in the auditory cortex. Auditory feedback control of speech was thought to be involuntary and not subject to attentional control, because several previous studies showed that participants produced compensatory responses to pitch shifts even when they were told to ignore feedback perturbations (Munhall et al., 2009; Zarate et al., 2010; Keough et al., 2013). However, prolonging pitch shift duration resulted in an early vocal response that opposes the pitch shift direction and a later vocal response that follows the pitch shift direction suggesting an interplay between reflexive and top-down processes in controlling voice pitch (Hain et al., 2000; Burnett and Larson, 2002). More recent EEG studies demonstrated that dividing attention between auditory feedback and additional visual stimuli or increasing the attentional load of the task affected vocal responses as well as the magnitude of ERP components, suggesting that attention modulates auditory feedback control on both a behavioral and a cortical level (Tumber et al., 2014; Hu et al., 2015; Liu et al., 2015; Liu et al., 2018). In our study, we found that neural responses in the posterior STG were larger for DAF0 (randomly presented simultaneous feedback condition in the DAF task) as compared with the VWR-AF condition (consistent simultaneous feedback throughout standard word reading task), even though participants displayed similar vocal behavior in these two conditions. In light of the previous literature, we interpret these response differences as arising from an attentional load difference between the two tasks. In the DAF experiment, the auditory feedback was not consistent since no-delay trials were randomized with delay trials. This randomized structure of the paradigm with interleaved long delay trials (causing slowed speech) required conscious effort for speech monitoring and thus sustained attention. While remaining cautious about this interpretation and our study’s limitation in attentional controls, we believe that this response enhancement represents an increased neural gain driven by attention to auditory feedback (Hillyard et al., 1998), and highlights the critical role of the posterior STG in auditory-motor integration during speech monitoring (Hickok and Poeppel, 2000), with its close proximity to the human ventral attention network comprising temporoparietal junction (Vossel et al., 2014). We leave it to future studies to include additional conditions to manipulate the direction and load of attention to further validate the influence of attention on speech monitoring.

Materials and methods

Participant information

The Institutional Review Board of NYU Grossman School of Medicine approved all experimental procedures. After consulting with the clinical-care provider, a research team member obtained written and oral consent from each participant. 35 neurosurgical epilepsy patients (19 females, mean age: 31, 23 left, 9 right, and 3 bilateral hemisphere coverage) implanted with subdural and depth electrodes provided informed consent to participate in the research protocol. Electrode implantation and location were guided solely by clinical requirements. Three patients were consented separately for higher density clinical grid implantation, which provided denser sampling of underlying cortex.

iEEG recording

iEEG was recorded from implanted subdural platinum-iridium electrodes embedded in flexible silicon sheets (2.3 mm diameter exposed surface, 8×8 grid arrays, and 4–12 contact linear strips, 10 mm center-to-center spacing, Ad-Tech Medical Instrument, Racine, WI, USA) and penetrating depth electrodes (1.1 mm diameter, 5–10 mm center-to-center spacing 1×8 or 1×12 contacts, Ad-Tech Medical Instrument, Racine, WI, USA). Three participants consented to a research hybrid grid implanted which included 64 additional electrodes between the standard clinical contacts (16×8 grid with sixty-four 2 mm macro contacts at 8×8 orientation and sixty-four 1 mm micro contacts in between, providing 10 mm center-to-center spacing between macro contacts and 5 mm center-to-center spacing between micro/macro contacts, PMT Corporation, Chanhassen, MN, USA). Recordings were made using one of two amplifier types: NicoletOne amplifier (Natus Neurologics, Middleton, WI, USA), bandpass filtered from 0.16 to 250 Hz and digitized at 512 Hz. Neuroworks Quantum Amplifier (Natus Biomedical, Appleton, WI, USA) recorded at 2048 Hz, bandpass filtered at 0.01–682.67 Hz and then downsampled to 512 Hz. A two-contact subdural strip facing toward the skull near the craniotomy site was used as a reference for recording and a similar two-contact strip screwed to the skull was used for the instrument ground. iEEG and experimental signals (trigger pulses that mark the appearance of visual stimuli on the screen, microphone signal from speech recordings and feedback voice signal) were acquired simultaneously by the EEG amplifier in order to provide a fully synchronized dataset.

Experimental design

Experiment 1: AWR

35 participants performed the experiment. Stimuli consisted of 50 items (nouns) taken from the revised Snodgrass and Vanderwart object pictorial set (e.g. ‘drum’, ‘hat’, ‘pencil’) (Rossion and Pourtois, 2004; Shum et al., 2020). Auditory words presented randomly (two repetitions) through speakers. Participants were instructed to listen to the presented words and repeat them out loud at each trial.

Experiment 2: VWR

The same 35 participants performed the experiment. Stimuli consisted of the same 50 words used in Experiment 1, however visually presented as text stimuli on the screen in a random order (two repetitions). Participants were instructed to read the presented word out loud at each trial.

Experiment 3: DAF

A subgroup of 14 participants performed this experiment. Stimuli consisted of 10 different three-syllable words visually presented as text stimuli on the screen (e.g. ‘envelope’, ‘umbrella’, ‘violin’). Participants were instructed to read the presented word out loud at each trial. As participants spoke, their voices were recorded using the laptop’s internal microphone, delayed at four different amounts (no-delay, 50, 100, 200 ms) using custom script MATLAB, Psychtoolbox-3, available in GitHub (copy archived at Ozker, 2024) and played back to them through earphones. Trials, which consisted of different stimulus-delay combinations, were presented randomly (three to eight repetitions). Behavioral and neural data from the DAF experiment were used in a previous publication from our group (Ozker et al., 2022).

Experiment 4: VWR-AF

A subgroup of four participants performed an additional VWR experiment, in which they were presented with the word stimuli as in Experiment 3 and heard their simultaneous (no-delay) voice feedback through earphones.

Statistical analysis

Electrodes were examined for speech-related activity defined as significant high gamma broadband responses. Unpaired t-tests were performed to compare responses to a baseline for each electrode and multiple comparisons were corrected using the false discovery rate method (q=0.05). Electrodes that showed significant response increase (p<10–4) either before (−0.5 to 0 s) or after speech onset (0–0.5 s) with respect to a baseline period (−1 to –0.6 s) and at the same time had a large signal-to-noise ratio (μ/σ>0.7) during either of these time windows were selected. Electrode selection was first performed for each task separately, then electrodes that were commonly selected were further analyzed. For the analysis of the DAF experiment, one-way ANOVA was calculated using the average neural response as a dependent variable and feedback delay as a factor to assess the statistical significance of response enhancement in a single electrode.

Experimental setup

Participants were tested while resting in their hospital bed in the epilepsy-monitoring unit. Visual stimuli were presented on a laptop screen positioned at a comfortable distance from the participant. Auditory stimuli were presented through speakers in the AWR and VWR experiments and through earphones (Bed Phones On-Ear Sleep Headphones Generation 3) in the DAF and in the VWR-AF experiment. Participants were instructed to speak at a normal voice level and sidetone volume was adjusted to a comfortable level at the beginning of the DAF experiment. DAF and VWR-AF experiments were performed consecutively and sidetone volume was kept the same in the two experiments. Participants’ voice was recorded using an external microphone (Zoom H1 Handy Recorder). A TTL pulse marking the onset of a stimulus, the microphone signal (what the participant spoke), and the feedback voice signal (what the participant heard) were fed into the EEG amplifier as an auxiliary input in order to acquire them in sync with EEG samples. Sound files recorded by the external microphone were used for voice intensity analysis. Average voice intensity for each trial was calculated in dB using the ‘Intensity’ object in Praat software (Boersma, 2001).

Electrode localization

Electrode localization in individual space as well as MNI space was based on co-registering a preoperative (no electrodes) and postoperative (with electrodes) structural MRI (in some cases a postoperative CT was employed depending on clinical requirements) using a rigid-body transformation. Electrodes were then projected to the surface of cortex (preoperative segmented surface) to correct for edema-induced shifts following previous procedures (Yang et al., 2012) registration to MNI space was based on a nonlinear DARTEL algorithm (Ashburner, 2007). Within participant anatomical locations of electrodes were based on the automated FreeSurfer segmentation of the participant’s preoperative MRI. We recorded from a total of 3591 subdural and 1361 depth electrode contacts in 35 participants. Subdural electrode coverage extended over lateral temporal, frontal, parietal, and lateral occipital cortices. Depth electrodes covered additional regions to a limited extent including the transverse temporal gyrus, insula, and fusiform gyrus. Contacts that were localized to the cortical white matter were excluded from the analysis. To categorize electrodes in the STG into anterior and posterior groups, lateral termination of the TTS was used as an anatomical landmark (Greenlee et al., 2011; Nourski et al., 2016).

Neural data analysis

Electrodes with epileptiform activity or artifacts caused by line noise, poor contact with cortex, and high-amplitude shifts were removed from further analysis. A common average reference was calculated by subtracting the average signal across all electrodes from each individual electrode’s signal (after rejection of electrodes with artifacts). The analysis of the electrophysiological signals focused on changes in broadband high gamma activity (70–150 Hz). To quantify changes in the high gamma range, the data were bandpass filtered between 70 and 150 Hz, and then a Hilbert transform was applied to obtain the analytic amplitude.

Recordings from the DAF and VWR-AF experiments were analyzed using the multitaper technique, which yields a more sensitive estimate of the power spectrum with lower variance, thus is more beneficial when comparing neural responses to incremental changes in stimuli. Continuous data streams from each channel were epoched into trials (from –1.5 to 3.5 s with respect to speech onset). Line noise at 60, 120, and 180 Hz were filtered out. Three Slepian tapers were applied in timesteps of 10 ms and frequency steps of 5 Hz, using temporal smoothing (tw) of 200 ms and frequency smoothing (fw) of ±10 Hz. Tapered signals were then transformed to time-frequency space using discrete Fourier transform and power estimates from different tapers were combined (MATLAB, FieldTrip toolbox). The number of tapers (K) were determined by the Shannon number according to the formula: K=2*tw*fw-1 (Percival and Walden, 1993). The high gamma broadband response (70–150 Hz) at each time point following stimulus onset was measured as the percent signal change from baseline, with the baseline calculated over all trials in a time window from –500 to –100 ms before stimulus onset (data files containing high gamma activity recordings are available in GitHub).

SuppI calculation

Suppression of neural activity is measured by comparing responses in two time periods in the AWR task. First time period was during listening the stimulus (0–0.5 s) and the second time period was during speaking (0–0.5 s). For each trial, average responses over Listen and Speak periods were found and suppression was measured by calculating Listen-Speak/Listen+Speak. Then suppression values were averaged across trials to calculate a single SuppI for each electrode. For the neural activity, raw high gamma broadband signal power was used instead of the percent signal change to ensure that the SuppI values varied between –1 and 1, indicating a range from complete enhancement to complete suppression respectively.

SensI calculation

Sensitivity to DAF is measured by comparing neural responses to increasing amounts of feedback delay. Neural responses in each trial were averaged in a time period following the voice feedback (0–0.5 s). For each electrode, a SensI was calculated by measuring the trial-by-trial Spearman correlation between the delay condition and the averaged neural response. A large sensitivity value indicated a strong response enhancement with increasing delays.

Acknowledgements

This study was supported by grants from the NIH (F32 DC018200 to MO and R01NS109367, R01DC018805, R01NS115929 to AF) and the NSF (CRCNS 1912286 to AF) and by the Leon Levy Foundation Fellowship (to MO).

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. Open access funding provided by Max Planck Society.

Contributor Information

Muge Ozker, Email: mozker@gmail.com.

Supratim Ray, Indian Institute of Science Bangalore, India.

Barbara G Shinn-Cunningham, Carnegie Mellon University, United States.

Funding Information

This paper was supported by the following grants:

  • National Institute on Deafness and Other Communication Disorders F32 DC018200 to Muge Ozker.

  • National Institute of Neurological Disorders and Stroke R01NS109367 to Adeen Flinker.

  • National Institute on Deafness and Other Communication Disorders R01DC018805 to Adeen Flinker.

  • National Institute of Neurological Disorders and Stroke R01NS115929 to Adeen Flinker.

  • National Science Foundation CRCNS 1912286 to Adeen Flinker.

  • Leon Levy Foundation Fellowship in Neuroscience to Muge Ozker.

Additional information

Competing interests

No competing interests declared.

Author contributions

Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing.

Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft.

Resources, Data curation, Investigation, Project administration.

Resources.

Resources, Data curation, Investigation, Methodology.

Resources.

Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing.

Ethics

The study was approved by the NYU Grossman School of Medicine Institutional Review Board (approved protocol s14-02101) which operates under NYU Langone Health Human Research Protections. Research studies are performed in accordance with the Department of Health and Human Services policies and regulations at 45 CFR 46. Before obtaining consent, all participants were confirmed to have the cognitive capacity to provide informed consent by a clinical staff member. Participants provided oral and written informed consent before beginning study procedures. They were informed that participation was strictly voluntary, and would not impact their clinical care. Participants were informed that they were free to withdraw participation in the study at any time. All study procedures were conducted in accordance with the Declaration of Helsinki.

Additional files

MDAR checklist

Data availability

Data and code are available in GitHub (copy archived at Ozker, 2024).

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eLife assessment

Supratim Ray 1

The manuscript describes human intracranial neural recordings in the auditory cortex during speech production, showing that the effects of delayed auditory feedback correlate with the degree of underlying speech-induced suppression. This is an important finding, as previous work has suggested that speech suppression and feedback sensitivity often do not co-localize and may be distinct processes, in contrast with findings in non-human primates where there is a strong correlation. The strength of the evidence is convincing, with appropriate experimental methods, data, and analysis.

Reviewer #1 (Public Review):

Anonymous

Summary:

The manuscript describes a series of experiments using human intracranial neural recordings designed to evaluate processing of self-generated speech in the setting of feedback delays. Specifically, the authors aim to address the question about the relationship between speech-induced suppression and feedback sensitivity in the auditory cortex, which, relationship has been conflicting in the literature. They found a correlation between speech suppression and feedback delay sensitivity, suggesting a common process. Additional controls were done for possible forward suppression/adaptation, as well as controlling for other confounds due to amplification, etc.

Strengths:

The primary strength of the manuscript is the use of human intracranial recording, which is a valuable resource and gives better spatial and temporal resolution than many other approaches. The use of delayed auditory feedback is also novel and has seen less attention than other forms of shifted feedback during vocalization. Analyses are robust and include demonstrating a scaling of neural activity with the degree of feedback delay, more robust evidence for error encoding than simply using a single feedback perturbation.

Weaknesses:

Some of the analyses performed differ from those used in past work, which limits the ability to directly compare the results. Notably, past work has compared feedback effects between production and listening, which was not done here. There were also some unusual effects in the data, such as increased activity with no feedback delay when wearing headphones, that the authors attempted to control for with additional experiments, but remain unclear. Confounds by behavioral results of delayed feedback are also unclear.

Overall the work is well done and clearly explained. The manuscript addresses an area of some controversy and does so in a rigorous fashion, namely the correlation between speech-induced suppression and feedback sensitivity (or lack thereof). While the data presented overlap that collected and used for a previous paper, this is expected given the rare commodity these neural recordings represent. Contrasting these results to previous ones using pitch-shifted feedback should spawn additional discussion and research, including verification of the previous finding, looking at how the brain encodes feedback during speech over multiple acoustic dimensions, and how this information can be used in speech motor control.

Reviewer #2 (Public Review):

Anonymous

Summary:

In "Speech-induced suppression and vocal feedback sensitivity in human cortex", Ozker and colleagues use intracranial EEG to understand audiomotor feedback during speech production using a speech production and delayed auditory feedback task. The purpose of the paper is to understand where and how speaker induced suppression occurs, and whether this suppression might be related to feedback monitoring. First, they identified sites that showed auditory suppression during speech production using a single word auditory repetition task and a visual reading task, then observed whether and how these electrodes show sensitivity to auditory feedback using a DAF paradigm. The stimuli were single words played auditorily or shown visually and repeated or read aloud by the participant. Neural data were recorded from regular- and high-density grids from the left and right hemisphere. The main findings were:

• Speaker induced suppression is strongest in the STG and MTG, and enhancement is generally seen in frontal/motor areas except for small regions of interest in dorsal sensorimotor cortex and IFG, which can also show suppression.

• Delayed auditory feedback, even when simultaneous, induces larger response amplitudes compared to the typical auditory word repetition and visual reading tasks. The authors presume this may be due to effort and attention required to perform the DAF task.

• The degree of speaker induced suppression is correlated with sensitivity to delayed auditory feedback, and is strongest for ~200 ms of delayed auditory feedback.

• pSTG (behind TTS) is more strongly modulated by DAF than mid-anterior STG

Strengths:

Overall, I found the manuscript to be clear, the methodology and statistics to be solid, and the findings mostly quite robust. The large number of participants with high density coverage over both the left and right lateral hemispheres allows for a greater dissection of the topography of speaker induced suppression and changes due to audiomotor feedback. The tasks were well-designed and controlled for repetition suppression and other potential caveats.

Weaknesses:

I am happy with the changes the authors made in response to my first round of comments.

The authors addressed my comments relating to plotting relative to the onset of articulation in Figure 1 and also addressed whether the amount of suppression varies according to more interfering delayed auditory feedback (though the correlations between sensitivity and suppression are a little noisy, they are positive). Finally, I am also satisfied with the inclusion of more group data in Figure 4.

eLife. 2024 Sep 10;13:RP94198. doi: 10.7554/eLife.94198.3.sa3

Author response

Muge Ozker 1, Leyao Yu 2, Patricia Dugan 3, Werner Doyle 4, Daniel Friedman 5, Orrin Devinsky 6, Adeen Flinker 7

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript describes a series of experiments using human intracranial neural recordings designed to evaluate the processing of self-generated speech in the setting of feedback delays. Specifically, the authors aim to address the question about the relationship between speech-induced suppression and feedback sensitivity in the auditory cortex, whose relationship has been conflicting in the literature. They found a correlation between speech suppression and feedback delay sensitivity, suggesting a common process. Additional controls were done for possible forward suppression/adaptation, as well as controlling for other confounds due to amplification, etc.

Strengths:

The primary strength of the manuscript is the use of human intracranial recording, which is a valuable resource and gives better spatial and temporal resolution than many other approaches. The use of delayed auditory feedback is also novel and has seen less attention than other forms of shifted feedback during vocalization. Analyses are robust, and include demonstrating a scaling of neural activity with the degree of feedback delay, and more robust evidence for error encoding than simply using a single feedback perturbation.

Weaknesses:

Some of the analyses performed differ from those used in past work, which limits the ability to directly compare the results. Notably, past work has compared feedback effects between production and listening, which was not done here. There were also some unusual effects in the data, such as increased activity with no feedback delay when wearing headphones, that the authors attempted to control for with additional experiments, but remain unclear. Confounds by behavioral results of delayed feedback are also unclear.

Overall the work is well done and clearly explained. The manuscript addresses an area of some controversy and does so in a rigorous fashion, namely the correlation between speech-induced suppression and feedback sensitivity (or lack thereof). While the data presented overlaps that collected and used for a previous paper, this is expected given the rare commodity these neural recordings represent. Contrasting these results to previous ones using pitch-shifted feedback should spawn additional discussion and research, including verification of the previous finding, looking at how the brain encodes feedback during speech over multiple acoustic dimensions, and how this information can be used in speech motor control.

We thank the reviewer for their comments and have addressed the concerns point by point in the section “Recommendation for Authors”.

Reviewer #2 (Public Review):

Summary:

"Speech-induced suppression and vocal feedback sensitivity in human cortex", Ozker and colleagues use intracranial EEG to understand audiomotor feedback during speech production using a speech production and delayed auditory feedback task. The purpose of the paper is to understand where and how speaker-induced suppression occurs, and whether this suppression might be related to feedback monitoring. First, they identified sites that showed auditory suppression during speech production using a single-word auditory repetition task and a visual reading task, then observed whether and how these electrodes show sensitivity to auditory feedback using a DAF paradigm. The stimuli were single words played auditorily or shown visually and repeated or read aloud by the participant. Neural data were recorded from regular- and high-density grids from the left and right hemispheres. The main findings were:

• Speaker-induced suppression is strongest in the STG and MTG, and enhancement is generally seen in frontal/motor areas except for small regions of interest in the dorsal sensorimotor cortex and IFG, which can also show suppression.

• Delayed auditory feedback, even when simultaneous, induces larger response amplitudes compared to the typical auditory word repetition and visual reading tasks. The authors presume this may be due to the effort and attention required to perform the DAF task.

• The degree of speaker-induced suppression is correlated with sensitivity to delayed auditory feedback. • pSTG (behind TTS) is more strongly modulated by DAF than mid-anterior STG

Strengths:

Overall, I found the manuscript to be clear, the methodology and statistics to be solid, and the findings mostly quite robust. The large number of participants with high-density coverage over both the left and right lateral hemispheres allows for a greater dissection of the topography of speaker-induced suppression and changes due to audiomotor feedback. The tasks were well-designed and controlled for repetition suppression and other potential caveats.

Weaknesses:

(1) In Figure 1D, it would make more sense to align the results to the onset of articulation rather than the onset of the auditory or visual cue, since the point is to show that the responses during articulation are relatively similar. In this form, the more obvious difference is that there is an auditory response to the auditory stimulus, and none to the visual, which is expected, but not what I think the authors want to convey.

We agree with the reviewer. We have updated Figure 1 accordingly.

(2) The DAF paradigm includes playing auditory feedback at 0, 50, 100, and 200 ms lag, and it is expected that some of these lags are more likely to induce dysfluencies than others. It would be helpful to include some analysis of whether the degree of suppression or enhancement varies by performance on the task, since some participants may find some lags more interfering than others.

We thank the reviewer for this suggestion. In the original analysis, we calculated a Sensitivity Index for each electrode by correlating the high gamma response with the delay condition across trials. To address the reviewer’s question, we now compared delay conditions in pairs (DAF0 vs DAF50, DAF0 vs DAF100, DAF0 vs DAF200, DAF50 vs DAF100, DAF50 vs DAF200 and DAF100 vs DAF200).

Similar to our Suppression Index calculation, where we compared neural response to listening and speaking conditions (Listen-Speak/Listen+Speak), we now calculated the Sensitivity Index by comparing neural response to two delay conditions as follows:

e.g. Sensitivity Index = (DAF50 – DAF0) / (DAF50 + DAF0). We used the raw high gamma broadband signal power instead of percent signal change to ensure that the Sensitivity Index values varied between -1 to 1.

As shown in the figure below, even when we break down the analysis by feedback delay, we still find a significant association between suppression and sensitivity (except for when we calculate sensitivity indices by comparing DAF50 and DAF100). Strongest correlation (Pearson’s correlation) was found when sensitivity indices were calculated by comparing DAF0 and DAF200.

As the reviewer suggested, participants found DAF200 more interfering than the others and slowed down their speech the most (Articulation duration; DAF0: 0.698, DAF50: 0.726, DAF100: 0.737, and DAF200: 0.749 milliseconds; Ozker, Doyle et al. 2022).

Author response image 1.

Author response image 1.

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(3) Figure 3 shows data from only two electrodes from one patient. An analysis of how amplitude changes as a function of the lag across all of the participants who performed this task would be helpful to see how replicable these patterns of activity are across patients. Is sensitivity to DAF always seen as a change in amplitude, or are there ever changes in latency as well? The analysis in Figure 4 gets at which electrodes are sensitive to DAF but does not give a sense of whether the temporal profile is similar to those shown in Figure 3.

In Figure 4A, electrodes from all participants are color-coded to reflect the correlation between neural response amplitude and auditory feedback delay. A majority of auditory electrodes in the STG exhibit a positive correlation, indicating that response amplitude increases with increasing feedback delays. To demonstrate the replicability of the response patterns in Figure 3, here we show auditory responses averaged across 23 STG electrodes from 6 participants.

Author response image 2.

Author response image 2.

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Response latency in auditory regions also increases with increasing auditory feedback delays. But this delayed auditory response to delayed auditory feedback is expected. In Figure 3, signals were aligned to the perceived auditory feedback onset, therefore we don’t see the latency differences. Below we replotted the same responses by aligning the signal to the onset of articulation. It is now clearer that responses are delayed as the auditory feedback delay increases. This is because participants start speaking at time=0, but they hear their voice with a lag so the response onset in these auditory regions are delayed.

According to models of speech production, when there is a mismatch between expected and perceived auditory feedback, the auditory cortex encodes this mismatch with an enhanced response, reflecting an error signal. Therefore, we referred to changes in response amplitude as a measure of sensitivity to DAF.

(4) While the sensitivity index helps to show whether increasing amounts of feedback delay are correlated with increased response enhancement, it is not sensitive to nonlinear changes as a function of feedback delay, and it is not clear from Figure 3 or 4 whether such relationships exist. A deeper investigation into the response types observed during DAF would help to clarify whether this is truly a linear relationship, dependent on behavioral errors, or something else.

We compared responses to delay conditions in pairs in the analysis presented above (response #2). We hope these new results also clarifies this issue and address the reviewer’s concerns.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major points:

(1) While the correlation between SuppI and SensI is clear here (as opposed to Chang et al), it is unclear if this difference is a byproduct of how SensI was calculated (and not just different tasks). In that paper, the feedback sensitivity was calculated as a metric comparing feedback responses during production and listening, whereas here the SensI is a correlation coefficient during production only. If the data exists, it would be very helpful to also show an analysis similar to that used previously (i.e. comparing DAF effects in both production and playback, either in correlations or just the 200ms delay response). One could imagine that some differences are due to sensory properties, though it is certainly less clear what delay effects would be on listening compared to say pitch shift.

We thank the reviewer for pointing this out. Indeed, the calculation of SensI is different in the two studies. In Chang et al. study, SensI was calculated by comparing perturbed feedback responses during production and passive listening. This is a very meticulous approach as it controls for the acoustic properties of the auditory stimuli under both conditions.

In our study, we didn’t have a passive listening condition. This would require recording the participants’ voice as they were speaking with DAF and playing it back to them in a subsequent passive listening condition. Therefore, we can’t completely eliminate the possibility that some differences are due to sensory properties. However, to address the reviewer’s concern, we examined the voice recordings of 8 participants for acoustic differences. Specifically, we compared voice intensities for different auditory feedback delays (0,50,100 and 200ms) and found no significant differences (F=0, p=0.091).

We think that the difference with the Chang et al. study is an important point to emphasize, therefore we now added in the Discussion:

“In contrast, to replicate this finding in humans, a previous iEEG study by Chang et al. (Chang, Niziolek et al. 2013) used frequency-shifted feedback during vowel production and found that most suppressed auditory sites did not overlap with those sensitive to feedback alterations. Using DAF instead of frequency-shifted feedback, we demonstrated a significant overlap of two neural populations in the STG, along with a strong correlation between the degree of speech-induced suppression and sensitivity to auditory feedback. This discrepancy may be due to different methods of calculating sensitivity to altered feedback. In our study, sensitivity was determined by comparing responses to delayed and non-delayed feedback during production, whereas Chang et al. compared perturbed feedback responses during production and listening. One possibility is that our approach identifies a larger auditory neural population in the STG sensitive to altered feedback. Alternatively, it could indicate a larger population highly sensitive to temporal rather than spectral perturbations in auditory feedback. Thus, we observe a wide overlap of the two neural populations in the STG showing both speech-induced suppression and sensitivity to auditory feedback. Replaying a recording of the participants' own delayed voice back to them, which we were unable to complete in this study, would have made the results of the two studies more comparable while also completely eliminating the possibility of a sensory explanation for the observed response enhancement.”

(2) I am still a bit unclear on how Experiment 4 is different than the no-delay condition in Experiment 3. Please clarify. Also, to be clear, in Experiments 1+2 the subjects were not wearing any headphones and had no additional sidetone?

It is correct that participants were not wearing earphones in Experiments 1&2 (with no additional sidetone), and that they were wearing earphones in Experiments 3&4.

For the “no delay” condition in the DAF experiment (Experiment 3), participants were wearing earphones and reading words with simultaneous auditory feedback. So, this condition was equivalent to visual word reading (Experiment 2), except participants were wearing earphones. Yet, neural responses were much larger for the “no delay” condition in the DAF experiment compared to visual word reading.

We suspected that larger neural responses in the DAF experiment were caused by hearing auditory feedback through earphones. To test and control for this possibility, in a subset of participants, we ran an additional visual word reading experiment (Experiment 4) with earphones and used the same volume settings as in the DAF experiment. We found that response magnitudes were now similar in the two experiments (Experiment 3 and 4) and earphones (with the associated increased sound amplitude) were indeed the reason for larger neural responses. Thus, Experiment 4 differs from the no-delay condition in Experiment 3 only in the stimuli read aloud.

(3) In Figure 3, why is the DAF200 condition activity so much bigger than the other conditions, even prior to the DAF onset? I worry this might bias the rest of the response differences.

In Figure 3B and 3D, time=0 indicates the onset of the perceived auditory feedback. Below we replotted the responses in the same two electrodes but now time=0 indicates the onset of articulation. We see that the peaking time of the responses are delayed as the auditory feedback delay increases. This is because participants start speaking at time=0, but they hear their voice with a lag so the response onset in these auditory regions are delayed. However, like the reviewer pointed out, the response for the DAF200 condition in Electrode G54 is slightly larger even at the very beginning. We think that this small, early response might reflect a response to the bone-conducted auditory feedback, which might be more prominent for the DAF200 condition. Nevertheless, we still see that response amplitude increase with increasing feedback delays in Electrode 63.

(4) Figure 4C, are the labeled recording sites limited to those with significant DAF and/or suppression?

In Figure 4C, we show electrodes that had significant high-gamma broadband responses during all tasks. We write in the Methods: “Electrodes that showed significant response increase (p < 10−4) either before (−0.5 to 0 s) or after speech onset (0 to 0.5 s) with respect to a baseline period (−1 to −0.6 s) and at the same time had a large signal-to-noise ratio (μ/σ > 0.7) during either of these time windows were selected. Electrode selection was first performed for each task separately, then electrodes that were commonly selected were further analyzed.”

(5) Were there any analyses done to control for the effects of vocal changes on the DAF neural responses? The authors' previous paper did note a behavioral effect. This is probably not trivial, as we may not know the 'onset time' of the response, in contrast to pitch shift where it is more regular. If the timing is unknown, one thing that could be tried is to only look early in DAF responses (first 50ms say) to make sure the DAF effects hold.

DAF involves two different perturbations: the absence of feedback at speech onset and the introduction of delayed feedback during playback. The timing of the behavioral effect in response to these two perturbations remains unclear. Aligning the neural responses to the production onset and examining the first 50ms would only capture the response to the acoustic feedback for the no-delay condition within that time window. Conversely, aligning the responses to the playback onset might miss the onset of the behavioral effect, which likely starts earlier as a response to the lack of feedback. We acknowledge the reviewer's point that this is a limitation of the DAF paradigm, and the behavioral effect is not as straightforward as that of pitch perturbation. However, we believe there is no clear solution to this issue.

Minor points:

(1) Figure 3, it might be nice to show the SuppI and SensI on the plots to give the reader a better sense of what those values look like.

We included SuppI and SensI values in the new version of Figure 3.

Reviewer #2 (Recommendations For The Authors):

Minor Comments:

(1) In Figure 1, it is unclear whether the responses shown in B-D correspond to the ROIs shown in Figure A - I am guessing so, but the alignment of the labels makes this slightly unclear, so I suggest these be relabeled somehow for clarity.

This is fixed in the updated version of Figure 1.

(2) In Figure 1D the difference in colors between AWR and VWR is difficult to appreciate - I suggest using two contrasting colors.

This is fixed in the updated version of Figure 1.

(3) Please add y-axis labels for Fig 3B-D. (I believe these are % signal change, but it would be clearer if the label were included).

This is fixed in the updated version of Figure 3.

(4) Can the authors comment on whether the use of speakers for AWR and VWR versus earphones for DAF and VWF- AF may have had an influence on the increased response in this condition? If the AWR were rerun using the headphone setup, or if DAF with 0 ms feedback were run with no other trials including lags, would the large differences in response amplitude be observed?

Participants were not wearing earphones in Experiments 1&2, and that they were wearing earphones in Experiments 3&4.

For the “no delay” condition in the DAF experiment (Experiment 3), participants were wearing earphones and reading words with simultaneous auditory feedback. So, this condition was equivalent to VWR (Experiment 2), except participants were wearing earphones. Yet, neural responses were much larger for the “no delay” condition in the DAF experiment compared to VWR.

Supporting the reviewer’s concerns, we suspected that larger neural responses in the DAF experiment were caused by hearing auditory feedback through earphones. To test and control for this possibility, in a subset of participants, we ran the VWR-AF experiment (Experiment 4) with earphones and used the same volume settings as in the DAF experiment. We found that response magnitudes were now similar in the two experiments (Experiment 3 and 4) and earphones were indeed the reason for larger neural responses.

(5) No data or code were available, I did not see any statement about this nor any github link or OSF link to share their data and/or code.

Data is available in the Github repository: flinkerlab/Sensitivity-Suppression

Associated Data

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    Supplementary Materials

    MDAR checklist
    elife-94198-mdarchecklist1.pdf (533.9KB, pdf)

    Data Availability Statement

    Data and code are available in GitHub (copy archived at Ozker, 2024).

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