Human communication can be described as two dynamic systems (i.e., speaker and listener) that are coupled via sensory information (Silbert et al., 2014) and operate according to principles of active inference by minimizing prediction errors (Friston and Frith, 2015). Within this framework, the speaker’s predictive processing model steers speech production, allowing adjustments in volume, speed, or articulation based on proprioceptive and auditory feedback. Similarly, the listener’s predictive processing model generates anticipations about upcoming speech which are continually updated and compared with incoming sensory data. The implementation of this model in the human brain was shown to be associated with brain rhythms (Abbasi et al., 2023; Arnal and Giraud, 2012; Park et al., 2015).

Brain rhythms have been extensively investigated during continuous speech perception. A consistent finding in these studies is the synchronization of frequency-specific brain activity, with the rhythmic amplitude modulation in continuous speech (Giraud and Poeppel, 2012; Gross et al., 2013b). The exploration of the brain networks that underpin speech perception has also revealed a stronger causal influence from higher-order brain regions (such as the left inferior frontal gyrus and left motor areas) to the auditory cortex for intelligible speech compared to unintelligible speech (Park et al., 2015). In the domain of speech production, both invasive (Llorens et al., 2011; Ozker et al., 2022; Riès et al., 2017) and non-invasive (Ganushchak et al., 2011; Janssen et al., 2020) electrophysiological studies have made significant contributions due to their superior temporal resolution compared to fMRI. However, it is important to note that invasive recordings offer only limited spatial coverage from specific recording sites in patients. Magnetoencephalography (MEG) has been used by several research teams to explore the intricate relationship between brain frequency-specific dynamics and speech production. Ruspantini et al. observed notable coherence between MEG activity originating from the motor cortex and Electromyography (EMG) activity in lip muscles, with a peak frequency of approximately 2–3 Hz (Ruspantini et al., 2012). In a separate investigation conducted by Alexandrou et al., they identified high-frequency band modulation (60–90 Hz) within bilateral temporal, frontal, and parietal brain regions (Alexandrou et al., 2017). In our recent study, we also explored speech production and perception using MEG to map the cortical networks engaged during these two experimental conditions (Abbasi et al., 2023). We reported significant connectivity from the motor area to superior temporal gyrus (STG) in lower-frequency bands (up to beta) during speaking, and the reverse pattern in high gamma frequencies (Abbasi et al., 2023).

Subcortical areas also contribute in updating the brain’s internal model during speech production and perception. Thalamic nuclei serve as central nodes for circuits associated with language processing, interacting with the frontal cortex, basal ganglia, cerebellum, and dopaminergic groups. Notably, the motor-related thalamic nuclei link the basal ganglia and cerebellum with the frontal cortex, contributing to both motor and cognitive aspects of language (Barbas et al., 2013; Silveri, 2021). Relatedly, several previous studies have reported the cerebellum’s role in several cognitive functions such as speech production and perception (Giraud et al., 2007; Skipper and Lametti, 2021) movement coordination, timing, motor programming, speech motor control, and sensory prediction (Parrell et al., 2017). Lesion studies, neuroimaging investigations, and brain stimulation studies have consistently linked the cerebellum to critical aspects of timing and predictive processing in speech perception (Ackermann, 2008; Manto et al., 2012; Parrell et al., 2017). Temporo-cerebellar coupling has been suggested to support the continuous updating of internal models of the auditory environment (Kotz et al., 2014). In a more recent study, Stockert et al. reported bidirectional temporo-cerebellar anatomical connectivity (Stockert et al., 2021). The authors suggested that these anatomical temporo-cerebellar connections may support the encoding and modeling of rapidly modulated auditory spectro-temporal patterns.

However, to the best of our knowledge no study has so far investigated spectrally resolved, directed functional coupling between cortical and subcortical brain areas during listening and speaking. In this MEG study, we intend to investigate the specific frequency-related connections between both cortical and subcortical brain regions while participants engaged in speaking and listening tasks. Participants answered seven questions, each lasting 60 s (speaking condition), and also listened to recordings of their own speech from the previous speaking session (listening condition; see Methods for details). Initially, we identified the cortical and subcortical brain regions involved in speech perception and production through meta-analysis studies. Subsequently, we examined the brain network and directed connections within it to shed light on the cognitive processes underlying listening and speaking. This involved a direct examination of frequency-specific communication channels for both feedforward and feedback signaling during speech production and perception using multivariate Granger causality. Our connectivity findings confirmed the participation of subcortical regions such as the thalamus and cerebellum in both speech production and perception. Our analysis using directed Granger causality revealed stronger connectivity from the cerebellum to the auditory area at slower frequencies (below 40 Hz). This connectivity pattern might reflect the influence of cerebellar predictions on auditory processing. Conversely, connectivity in the opposite direction, from the auditory area to the cerebellum, was stronger at faster frequencies (above 40 Hz). This pattern could potentially be indicative of prediction error signals being relayed back to the cerebellum for updating its predictions.

In this study, we investigated the connectivity between brain regions that are involved in speech production and perception. OurROI-based (Region of Interest) analysis utilized the automatic meta-analysis provided by neurosynth.org using the term ‘speech’ that resulted in 642 fMRI studies highlighting the brain areas involved in speech production and perception. We extracted the MNI coordinates of active areas from the resulting neurosynth statistical map, resulting in 14 parcels. For each voxel in a given parcel, the beamformer-derived time-series for all the three source orientations were subjected to singular value decomposition (SVD). In every parcel, we used the three strongest components for further analysis. For estimating pairwise functional connectivity between all parcels (n = 14), we used multivariate nonparametric Granger causality (mGC; Schaum et al., 2021) and related them to the hallmarks of predictive processing models during continuous speaking and listening (see Figure 1). The method uses three-dimensional representations of parcel activity and thus estimates connectivity more accurately than traditional one-dimensional representations (Chalas et al., 2022).

Figure 1

Download asset Open asset

The calculation of mGC between two parcels yielded two spectra reflecting both directions (A->B and B->A). Next, we computed the directed asymmetry index (DAI) for each pair of spectra (Bastos et al., 2015). This measure captures the predominant direction of mGC between two parcels based on the relative difference between both directions (see ‘Methods’ for more information). A positive DAI indicates the dominant directionality from A toward B, while a negative DAI indicates the opposite directionality. First, we used group statistics to identify brain areas where DAI values in specific frequency bands in the speaking as well as listening conditions differed significantly from zero. We performed group statistics from 0 to 100 Hz. Next, we defined the following canonical frequency bands: Delta/Theta (0–7 Hz), alpha (7–13 Hz), beta (15–30 Hz), gamma (30–60 Hz), and high gamma (60–90 Hz).

Using connectogram plots, we visualized the connectivity between the selected 14 ROIs and examined neural mechanisms associated with speech production and perception, as well as predictive processing models. The number of outgoing and incoming edges for each node was also calculated using the brain connectivity toolbox and represented as the node strength (Rubinov and Sporns, 2010). The upper row of Figure 2 illustrates the strength for three different nodes in the speaking condition. In L-SCEF (pre-supplementary motor area) and R-CB6 (right cerebellum lobule VI), the number of outgoing edges in low frequencies is higher compared to incoming edges. However, we observed a reverse pattern in high frequencies. Interestingly, in the left STG (L-A5), there are more incoming connections compared to outgoings in low frequencies, and this changes in the higher frequencies, with more outgoing connections seen in this area. This result indicates that in low frequencies, the sensorimotor area and cerebellum predominantly transmit information, while in higher frequencies, they are more involved in receiving it. On the other hand, the left STG receives information in low frequencies and transmits it in high frequencies. The connectograms in the middle parts of Figure 2 illustrate the connections of the left sensorimotor area (left plots; L-3b and L-SCEF) and the right cerebellum (right plots) to other cortical and subcortical parcels in low (top: 7–20 Hz) and high (bottom: 60–90 Hz) frequencies during the speaking condition. In low frequencies, both the sensorimotor area and the right cerebellum primarily send information to other parcels, including the left and right temporal regions, while in the high frequencies, these nodes primarily receive information. Interestingly, our connectivity results illustrate that the top–down effects from higher-order cortical areas to lower-order areas during speaking occur in distinct frequency bands. Figure 2 (lower panels) depicts that the strongest top–down connectivity from the left pre-supplementary motor area (pre-SMA; L-SCEF) to lower-order cortical areas such as L-A5 occur in the theta band (peak at 5–7 Hz). However, the top–down connectivity from L-3b to L-A5 was strongest in two distinct theta (peak at 7 Hz) and beta (peak at 21 Hz) frequency bands. Moreover, these results revealed significant bottom–up connectivity from lower-order cortical areas, namely L-A5 to left sensorimotor area (L-3b and L-SCEF) in high-frequency bands. As we previously showed (Abbasi et al., 2023), this striking reversal indicates a dissociation of bottom–up and top–down information flow during speaking in distinct frequency bands. Signals communicated top–down are predominantly transmitted in low-frequency ranges, while those communicated bottom–up are transmitted in high-frequency ranges. Notably, Figure 2 (bottom-right panel) also depicts directional connectivity from the right cerebellum lobule VI (R-CB6) to the left STG (L-A5) in frequencies below 50 Hz. In contrast, in frequencies above 50 Hz, we observed reverse signaling from the left STG to the right cerebellum during speaking.

Figure 2

Download asset Open asset

The connectograms in Figure 3 provide a comprehensive overview of significant connectivities across various frequency bands among the selected fourteen brain areas during speaking and listening conditions. Specifically highlighting the first column’s connectograms during speaking, we observe comparable connectivity patterns to those of the left sensorimotor area. Connections from the right higher-order cortical regions, such as R-SCEF and R-3b, toward lower-order areas like R-A5, are noticeable in low-frequency bands (<30 Hz). Conversely, for higher frequencies (>30 Hz), we noticed the reverse directional connections, from lower- to higher-order brain regions. Furthermore, Figure 4 (upper panel) reveals strong top–down connectivity from bilateral 3b and left FOP (frontal opercular area) to bilateral A5 and left STS (superior temporal sulcus), particularly prominent in two distinct frequency bands: theta (peak at 7 Hz) and beta (peak at 21 Hz). Additionally, similar to the left hemisphere, our results in Figure 4 (upper panel) show significant bottom–up connectivity from lower-order cortical areas (bilateral A5 and left STS) to higher-order cortical regions (bilateral 3b and bilateral SCEF) across both low- and high-frequency bands.

Figure 3

Download asset Open asset

Figure 4

Download asset Open asset

We observed a similar pattern during the listening condition compared to the speaking condition, including reversed directionality between low and high frequencies (Figure 3, second column). However, both top–down effects in the low-frequency band and bottom–up effects in the high-frequency band were less pronounced during listening compared to speaking. The third column in Figure 3 as well as the third panel of Figure 4 confirm these results by showing the direct comparisons of DAI connectivity between speaking and listening. This figure illustrates significant differences in connectivity between speaking and listening conditions in different frequency bands. Specifically, stronger top–down signals originating from higher-order cortical areas (such as bilateral SCEF and 3b) toward lower-order brain areas, such as bilateral A5 and left STS were observed during speaking compared to listening. Conversely, the individual spectrally resolved DAI plots for the listening conditions, along with the connectograms, indicate the presence of significant connectivity solely from low-order areas (such as bilateral A5) toward higher-order cortical areas (bilateral SCEF and 3b) during the speaking condition, as opposed to the listening condition. More detailed spectrally resolved DAI between all ROIs and both conditions are illustrated in Figure 4.

Notably, we found significant connections between subcortical and cortical areas, indicating their involvement in speech production and perception networks. Figure 4 depicts the connectivity from the right cerebellum to other cortical and subcortical parcels. In contrast with what we reported for the speaking condition, during listening, there is only a significant connectivity in low frequency to the left temporal area but not a reverse connection in the high frequencies. However, a direct comparison between speaking and listening conditions did not reveal a significant difference. Our analysis also revealed robust connectivity between the right cerebellum and the left parietal cortex, evident in both speaking and listening conditions, with stronger connectivity observed during speaking. Notably, Figure 4 depicts information flows from the cerebellum to the parietal areas in low-frequency ranges. There is also an intriguing connection between the left thalamus and right cerebellum, which reveals a distinct pattern of connectivity. Specifically, we found significant connectivity from the cerebellum to the thalamus in the low-frequency range during speaking, while the opposite pattern was observed in the high-frequency range (Figure 4).

Finally, we examined the relationship between the connectivity patterns from the selected cortical parcels to the right cerebellum and the coupling of the speech envelope with the oscillations in the STG. The speech–brain coupling was computed using a multivariate mutual information (MI) approach presented in our recently published study (Abbasi et al., 2023). We investigated the correlations between the directional connectivity indices and MI values across participants for each parcel and frequency band in speaking condition. Our analysis showed significant negative correlations between the RCB6 to L_A5 connectivity and the speech–STG coupling in the theta band (at 130 ms lag) for speaking condition (Figure 5; p < 0.05).

Figure 5

Download asset Open asset

The present study utilized multivariate Granger causality analysis to investigate the frequency-specific neural representations of the internal forward model during continuous speaking and listening. Our results revealed significant connectivity from bilateral higher-order cortical areas to bilateral auditory areas (STG and STS) in lower-frequency bands (up to beta) during speaking, and in the opposite direction in gamma frequencies. Notably, subcortical areas also contributed to the internal forward model, with directional communication observed from the right cerebellum lobule VI (R-CB6) to the left auditory area below 30 Hz. Conversely, in frequencies above 30 Hz, we observed reverse signaling from the left auditory areas to the right cerebellum.

Custom-made scripts used for data analysis are publicly available at link: https://osf.io/9fq47/. Additionally, an example dataset is included at the same repository to facilitate understanding of the data structure. The raw data contains participants' audio speech files and is protected by data privacy laws, preventing public sharing. However, we are committed to responsible data sharing and can make the raw data available upon reasonable request, subject to data privacy regulations. To access the raw data, interested researchers can contact the corresponding author of this manuscript by informal email. They only need to briefly explain their research goals and the specific data subsets they require. No formal proposal is required.

1. 1. Abbasi O

   (2022) Open Science Framework

   ID 9fq47. Preprocessed and anonymised data set example as well as source codes of speech perception and production study!.

   https://osf.io/9fq47/

1. 1. Abbasi O
   2. Hirschmann J
   3. Schmitz G
   4. Schnitzler A
   5. Butz M

   (2016) Rejecting deep brain stimulation artefacts from MEG data using ICA and mutual information

   Journal of Neuroscience Methods 268:131–141.

   https://doi.org/10.1016/j.jneumeth.2016.04.010

   • PubMed
   • Google Scholar
2. 1. Abbasi O
   2. Gross J

   (2020) Beta-band oscillations play an essential role in motor-auditory interactions

   Human Brain Mapping 41:656–665.

   https://doi.org/10.1002/hbm.24830

   • PubMed
   • Google Scholar
3. 1. Abbasi O
   2. Steingräber N
   3. Gross J

   (2021) Correcting MEG Artifacts Caused by Overt Speech

   Frontiers in Neuroscience 15:682419.

   https://doi.org/10.3389/fnins.2021.682419

   • PubMed
   • Google Scholar
4. 1. Abbasi O
   2. Steingräber N
   3. Chalas N
   4. Kluger DS
   5. Gross J

   (2023) Spatiotemporal dynamics characterise spectral connectivity profiles of continuous speaking and listening

   PLOS Biology 21:e3002178.

   https://doi.org/10.1371/journal.pbio.3002178

   • PubMed
   • Google Scholar
5. 1. Ackermann H
   2. Wildgruber D
   3. Daum I
   4. Grodd W

   (1998) Does the cerebellum contribute to cognitive aspects of speech production? A functional magnetic resonance imaging (fMRI) study in humans

   Neuroscience Letters 247:187–190.

   https://doi.org/10.1016/s0304-3940(98)00328-0

   • PubMed
   • Google Scholar
6. 1. Ackermann H

   (2008) Cerebellar contributions to speech production and speech perception: psycholinguistic and neurobiological perspectives

   Trends in Neurosciences 31:265–272.

   https://doi.org/10.1016/j.tins.2008.02.011

   • PubMed
   • Google Scholar
7. 1. Alexandrou AM
   2. Saarinen T
   3. Mäkelä S
   4. Kujala J
   5. Salmelin R

   (2017) The right hemisphere is highlighted in connected natural speech production and perception

   NeuroImage 152:628–638.

   https://doi.org/10.1016/j.neuroimage.2017.03.006

   • PubMed
   • Google Scholar
8. 1. Arnal LH
   2. Giraud AL

   (2012) Cortical oscillations and sensory predictions

   Trends in Cognitive Sciences 16:390–398.

   https://doi.org/10.1016/j.tics.2012.05.003

   • PubMed
   • Google Scholar
9. 1. Attal Y
   2. Schwartz D

   (2013) Assessment of subcortical source localization using deep brain activity imaging model with minimum norm operators: a MEG study

   PLOS ONE 8:e59856.

   https://doi.org/10.1371/journal.pone.0059856

   • PubMed
   • Google Scholar
10. 1. Barbas H
    2. García-Cabezas MÁ
    3. Zikopoulos B

    (2013) Frontal-thalamic circuits associated with language

    Brain and Language 126:49–61.

    https://doi.org/10.1016/j.bandl.2012.10.001

    • PubMed
    • Google Scholar
11. 1. Bastos AM
    2. Vezoli J
    3. Bosman CA
    4. Schoffelen JM
    5. Oostenveld R
    6. Dowdall JR
    7. De Weerd P
    8. Kennedy H
    9. Fries P

    (2015) Visual areas exert feedforward and feedback influences through distinct frequency channels

    Neuron 85:390–401.

    https://doi.org/10.1016/j.neuron.2014.12.018

    • PubMed
    • Google Scholar
12. 1. Cao L
    2. Thut G
    3. Gross J

    (2017) The role of brain oscillations in predicting self-generated sounds

    NeuroImage 147:895–903.

    https://doi.org/10.1016/j.neuroimage.2016.11.001

    • PubMed
    • Google Scholar
13. 1. Chalas N
    2. Daube C
    3. Kluger DS
    4. Abbasi O
    5. Nitsch R
    6. Gross J

    (2022) Multivariate analysis of speech envelope tracking reveals coupling beyond auditory cortex

    NeuroImage 258:119395.

    https://doi.org/10.1016/j.neuroimage.2022.119395

    • PubMed
    • Google Scholar
14. 1. Crosson B

    (2013) Thalamic mechanisms in language: a reconsideration based on recent findings and concepts

    Brain and Language 126:73–88.

    https://doi.org/10.1016/j.bandl.2012.06.011

    • PubMed
    • Google Scholar
15. 1. Dhamala M
    2. Rangarajan G
    3. Ding M

    (2008) Analyzing information flow in brain networks with nonparametric Granger causality

    NeuroImage 41:354–362.

    https://doi.org/10.1016/j.neuroimage.2008.02.020

    • PubMed
    • Google Scholar
16. 1. Fairs A
    2. Michelas A
    3. Dufour S
    4. Strijkers K

    (2021) The same ultra-rapid parallel brain dynamics underpin the production and perception of speech

    Cerebral Cortex Communications 2:tgab040.

    https://doi.org/10.1093/texcom/tgab040

    • PubMed
    • Google Scholar
17. 1. Ferro D
    2. van Kempen J
    3. Boyd M
    4. Panzeri S
    5. Thiele A

    (2021) Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention

    PNAS 118:e2022097118.

    https://doi.org/10.1073/pnas.2022097118

    • PubMed
    • Google Scholar
18. 1. Floegel M
    2. Fuchs S
    3. Kell CA

    (2020) Differential contributions of the two cerebral hemispheres to temporal and spectral speech feedback control

    Nature Communications 11:2839.

    https://doi.org/10.1038/s41467-020-16743-2

    • PubMed
    • Google Scholar
19. 1. Fontolan L
    2. Morillon B
    3. Liegeois-Chauvel C
    4. Giraud AL

    (2014) The contribution of frequency-specific activity to hierarchical information processing in the human auditory cortex

    Nature Communications 5:4694.

    https://doi.org/10.1038/ncomms5694

    • PubMed
    • Google Scholar
20. 1. Friston KJ
    2. Frith CD

    (2015) Active inference, communication and hermeneutics

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 68:129–143.

    https://doi.org/10.1016/j.cortex.2015.03.025

    • PubMed
    • Google Scholar
21. 1. Ganushchak LY
    2. Christoffels IK
    3. Schiller NO

    (2011) The use of electroencephalography in language production research: a review

    Frontiers in Psychology 2:208.

    https://doi.org/10.3389/fpsyg.2011.00208

    • PubMed
    • Google Scholar
22. 1. Giglio L
    2. Ostarek M
    3. Sharoh D
    4. Hagoort P

    (2024) Diverging neural dynamics for syntactic structure building in naturalistic speaking and listening

    PNAS 121:e2310766121.

    https://doi.org/10.1073/pnas.2310766121

    • PubMed
    • Google Scholar
23. 1. Giraud AL
    2. Kleinschmidt A
    3. Poeppel D
    4. Lund TE
    5. Frackowiak RSJ
    6. Laufs H

    (2007) Endogenous cortical rhythms determine cerebral specialization for speech perception and production

    Neuron 56:1127–1134.

    https://doi.org/10.1016/j.neuron.2007.09.038

    • PubMed
    • Google Scholar
24. 1. Giraud AL
    2. Poeppel D

    (2012) Cortical oscillations and speech processing: emerging computational principles and operations

    Nature Neuroscience 15:511–517.

    https://doi.org/10.1038/nn.3063

    • PubMed
    • Google Scholar
25. 1. Gross J
    2. Baillet S
    3. Barnes GR
    4. Henson RN
    5. Hillebrand A
    6. Jensen O
    7. Jerbi K
    8. Litvak V
    9. Maess B
    10. Oostenveld R
    11. Parkkonen L
    12. Taylor JR
    13. van Wassenhove V
    14. Wibral M
    15. Schoffelen J-M

    (2013a) Good practice for conducting and reporting MEG research

    NeuroImage 65:349–363.

    https://doi.org/10.1016/j.neuroimage.2012.10.001

    • PubMed
    • Google Scholar
26. 1. Gross J
    2. Hoogenboom N
    3. Thut G
    4. Schyns P
    5. Panzeri S
    6. Belin P
    7. Garrod S

    (2013b) Speech rhythms and multiplexed oscillatory sensory coding in the human brain

    PLOS Biology 11:e1001752.

    https://doi.org/10.1371/journal.pbio.1001752

    • PubMed
    • Google Scholar
27. 1. Janssen N
    2. van der Meij M
    3. López-Pérez PJ
    4. Barber HA

    (2020) Exploring the temporal dynamics of speech production with EEG and group ICA

    Scientific Reports 10:3667.

    https://doi.org/10.1038/s41598-020-60301-1

    • PubMed
    • Google Scholar
28. 1. Kotz SA
    2. Stockert A
    3. Schwartze M

    (2014) Cerebellum, temporal predictability and the updating of a mental model

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 369:20130403.

    https://doi.org/10.1098/rstb.2013.0403

    • PubMed
    • Google Scholar
29. 1. Lakatos P
    2. Gross J
    3. Thut G

    (2019) A new unifying account of the roles of neuronal entrainment

    Current Biology 29:R890–R905.

    https://doi.org/10.1016/j.cub.2019.07.075

    • PubMed
    • Google Scholar
30. 1. Llorens A
    2. Trébuchon A
    3. Liégeois-Chauvel C
    4. Alario FX

    (2011) Intra-cranial recordings of brain activity during language production

    Frontiers in Psychology 2:375.

    https://doi.org/10.3389/fpsyg.2011.00375

    • PubMed
    • Google Scholar
31. 1. Manto M
    2. Bower JM
    3. Conforto AB
    4. Delgado-García JM
    5. da Guarda SNF
    6. Gerwig M
    7. Habas C
    8. Hagura N
    9. Ivry RB
    10. Mariën P
    11. Molinari M
    12. Naito E
    13. Nowak DA
    14. Oulad Ben Taib N
    15. Pelisson D
    16. Tesche CD
    17. Tilikete C
    18. Timmann D

    (2012) Consensus paper: roles of the cerebellum in motor control--the diversity of ideas on cerebellar involvement in movement

    Cerebellum 11:457–487.

    https://doi.org/10.1007/s12311-011-0331-9

    • PubMed
    • Google Scholar
32. 1. Maris E
    2. Oostenveld R

    (2007) Nonparametric statistical testing of EEG- and MEG-data

    Journal of Neuroscience Methods 164:177–190.

    https://doi.org/10.1016/j.jneumeth.2007.03.024

    • PubMed
    • Google Scholar
33. 1. Martikainen MH
    2. Kaneko K
    3. Hari R

    (2005) Suppressed responses to self-triggered sounds in the human auditory cortex

    Cerebral Cortex 15:299–302.

    https://doi.org/10.1093/cercor/bhh131

    • PubMed
    • Google Scholar
34. 1. Nolte G

    (2003) The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors

    Physics in Medicine and Biology 48:3637–3652.

    https://doi.org/10.1088/0031-9155/48/22/002

    • PubMed
    • Google Scholar
35. 1. Oostenveld R
    2. Fries P
    3. Maris E
    4. Schoffelen JM

    (2011) FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

    Computational Intelligence and Neuroscience 2011:156869.

    https://doi.org/10.1155/2011/156869

    • PubMed
    • Google Scholar
36. 1. Ozker M
    2. Doyle W
    3. Devinsky O
    4. Flinker A

    (2022) A cortical network processes auditory error signals during human speech production to maintain fluency

    PLOS Biology 20:e3001493.

    https://doi.org/10.1371/journal.pbio.3001493

    • PubMed
    • Google Scholar
37. 1. Park H
    2. Ince RAA
    3. Schyns PG
    4. Thut G
    5. Gross J

    (2015) Frontal top-down signals increase coupling of auditory low-frequency oscillations to continuous speech in human listeners

    Current Biology 25:1649–1653.

    https://doi.org/10.1016/j.cub.2015.04.049

    • PubMed
    • Google Scholar
38. 1. Parrell B
    2. Agnew Z
    3. Nagarajan S
    4. Houde J
    5. Ivry RB

    (2017) Impaired feedforward control and enhanced feedback control of speech in patients with cerebellar degeneration

    The Journal of Neuroscience 37:9249–9258.

    https://doi.org/10.1523/JNEUROSCI.3363-16.2017

    • PubMed
    • Google Scholar
39. 1. Riès SK
    2. Dhillon RK
    3. Clarke A
    4. King-Stephens D
    5. Laxer KD
    6. Weber PB
    7. Kuperman RA
    8. Auguste KI
    9. Brunner P
    10. Schalk G
    11. Lin JJ
    12. Parvizi J
    13. Crone NE
    14. Dronkers NF
    15. Knight RT

    (2017) Spatiotemporal dynamics of word retrieval in speech production revealed by cortical high-frequency band activity

    PNAS 114:E4530–E4538.

    https://doi.org/10.1073/pnas.1620669114

    • PubMed
    • Google Scholar
40. 1. Rubinov M
    2. Sporns O

    (2010) Complex network measures of brain connectivity: uses and interpretations

    NeuroImage 52:1059–1069.

    https://doi.org/10.1016/j.neuroimage.2009.10.003

    • PubMed
    • Google Scholar
41. 1. Ruspantini I
    2. Saarinen T
    3. Belardinelli P
    4. Jalava A
    5. Parviainen T
    6. Kujala J
    7. Salmelin R

    (2012) Corticomuscular coherence is tuned to the spontaneous rhythmicity of speech at 2-3 Hz

    The Journal of Neuroscience 32:3786–3790.

    https://doi.org/10.1523/JNEUROSCI.3191-11.2012

    • PubMed
    • Google Scholar
42. 1. Schaum M
    2. Pinzuti E
    3. Sebastian A
    4. Lieb K
    5. Fries P
    6. Mobascher A
    7. Jung P
    8. Wibral M
    9. Tüscher O

    (2021) Right inferior frontal gyrus implements motor inhibitory control via beta-band oscillations in humans

    eLife 10:e61679.

    https://doi.org/10.7554/eLife.61679

    • PubMed
    • Google Scholar
43. 1. Schneider M
    2. Broggini AC
    3. Dann B
    4. Tzanou A
    5. Uran C
    6. Sheshadri S
    7. Scherberger H
    8. Vinck M

    (2021) A mechanism for inter-areal coherence through communication based on connectivity and oscillatory power

    Neuron 109:4050–4067.

    https://doi.org/10.1016/j.neuron.2021.09.037

    • PubMed
    • Google Scholar
44. 1. Sedley W
    2. Gander PE
    3. Kumar S
    4. Kovach CK
    5. Oya H
    6. Kawasaki H
    7. Howard MA
    8. Griffiths TD

    (2016) Neural signatures of perceptual inference

    eLife 5:e11476.

    https://doi.org/10.7554/eLife.11476

    • PubMed
    • Google Scholar
45. 1. Silbert LJ
    2. Honey CJ
    3. Simony E
    4. Poeppel D
    5. Hasson U

    (2014) Coupled neural systems underlie the production and comprehension of naturalistic narrative speech

    PNAS 111:E4687–E4696.

    https://doi.org/10.1073/pnas.1323812111

    • PubMed
    • Google Scholar
46. 1. Silveri MC

    (2021) Contribution of the cerebellum and the basal ganglia to language production: speech, word fluency, and sentence construction-evidence from pathology

    Cerebellum 20:282–294.

    https://doi.org/10.1007/s12311-020-01207-6

    • PubMed
    • Google Scholar
47. 1. Skipper JI
    2. Lametti DR

    (2021) Speech perception under the tent: a domain-general predictive role for the cerebellum

    Journal of Cognitive Neuroscience 33:1517–1534.

    https://doi.org/10.1162/jocn_a_01729

    • PubMed
    • Google Scholar
48. 1. Stockert A
    2. Schwartze M
    3. Poeppel D
    4. Anwander A
    5. Kotz SA

    (2021) Temporo-cerebellar connectivity underlies timing constraints in audition

    eLife 10:e67303.

    https://doi.org/10.7554/eLife.67303

    • PubMed
    • Google Scholar
49. 1. Stolk A
    2. Todorovic A
    3. Schoffelen JM
    4. Oostenveld R

    (2013) Online and offline tools for head movement compensation in MEG

    NeuroImage 68:39–48.

    https://doi.org/10.1016/j.neuroimage.2012.11.047

    • PubMed
    • Google Scholar
50. Preprint

    1. Tait L
    2. Özkan A
    3. Szul MJ
    4. Zhang J

    (2020) Cortical Source Imaging of Resting-State MEG with a High Resolution Atlas: An Evaluation of Methods

    bioRxiv.

    https://doi.org/10.1101/2020.01.12.903302

    • Google Scholar
51. 1. Todorović S
    2. Anton JL
    3. Sein J
    4. Nazarian B
    5. Chanoine V
    6. Rauchbauer B
    7. Kotz SA
    8. Runnqvist E

    (2024) Cortico-cerebellar monitoring of speech sequence production

    Neurobiology of Language 5:701–721.

    https://doi.org/10.1162/nol_a_00113

    • PubMed
    • Google Scholar
52. 1. Vinck M
    2. Uran C
    3. Spyropoulos G
    4. Onorato I
    5. Broggini AC
    6. Schneider M
    7. Canales-Johnson A

    (2023) Principles of large-scale neural interactions

    Neuron 111:987–1002.

    https://doi.org/10.1016/j.neuron.2023.03.015

    • PubMed
    • Google Scholar
53. 1. Yarkoni T
    2. Poldrack RA
    3. Nichols TE
    4. Van Essen DC
    5. Wager TD

    (2011) Large-scale automated synthesis of human functional neuroimaging data

    Nature Methods 8:665–670.

    https://doi.org/10.1038/nmeth.1635

    • PubMed
    • Google Scholar

Author details

1. Omid Abbasi

   Institute for Biomagnetism and Biosignal Analysis, University of Münster, Münster, Germany

   Contribution

   Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   abbasi@wwu.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2169-8498

2. Nadine Steingräber

   Institute for Biomagnetism and Biosignal Analysis, University of Münster, Münster, Germany

   Contribution

   Data curation, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

3. Nikos Chalas

   1. Institute for Biomagnetism and Biosignal Analysis, University of Münster, Münster, Germany
   2. Otto-Creutzfeldt-Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany

   Contribution

   Data curation, Writing – original draft

   Competing interests

   No competing interests declared

4. Daniel S Kluger

   Otto-Creutzfeldt-Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany

   Contribution

   Visualization, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0691-794X

5. Joachim Gross

   Otto-Creutzfeldt-Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3994-1006

• 181

  views

• 8

  downloads

• 0

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.