Frequency-specific cortico-subcortical interaction in continuous speaking and listening
- Institute for Biomagnetism and Biosignal Analysis, University of Münster, Germany
- Otto-Creutzfeldt-Center for Cognitive and Behavioral Neuroscience, University of Münster, Germany
Figures
Figure 1
Methodological pipeline.
Thirty participants answered seven given questions (60 s each; speaking condition) as well as listened to audio-recordings of their own voice from previous sessions (listening condition) while magnetoencephalography (MEG) data were recorded. Artifacts were removed from the recorded MEG data (Abbasi et al., 2021). Individual MRIs were used to estimate source models per participant which were interpolated to a template volumetric grid. Relevant areas in speech production and perception networks were identified from Neurosynth.org platform. Fourteen corresponding anatomical parcels in HCP and AAL atlases were identified: L-FOP (1), R-PEF+6v (2), L-SCEF (3), R-SCEF (4), L-3b (5), R-3b (6), L-STS (7), L-TPOJ1 (8), L-A5 (9), R-A5 (10), L-TH (11), R-CB-Crus2 (12), L-CB-6 (13), and R-CB-6 (14). For each identified parcel, estimated source time-series were extracted. Next, using a blockwise approach, we considered the first three singular value decomposition (SVD) components of each parcel as a block and estimated the connectivity between each pair of parcels using a multivariate nonparametric Granger causality approach (mGC; Schaum et al., 2021). In this study, the connectivity results are presented using connectogram plots. In the connectograms, nodes represent the brain areas and edges represent the strength and direction of the connections between them. The thickness of the edges indicates the magnitude of the t-values, while color indicates the directionality of the connectivity. In other words, when node A connects to node B, the edge will have the same color as node A, and vice versa when node B connects to node A. Note that only significant connections are shown in the connectograms (p < 0.05, cluster correction). For instance in the illustrated connectogram, the purple edge between L-CB6 and R-PEF shows significant connectivity from L-CB6 to R-PEF. R = right, L = left.
Figure 2
Connectivity analysis between the sensorimotor, cerebellum, and superior temporal gyrus (STG).
Upper panels illustrate the strength for L-SCEF, L-A5, and R-CB6 nodes in the speaking condition. In the middle part, connectograms illustrate connections between the left sensorimotor area (left plots) and the right cerebellum (right plots) during speaking to other cortical and subcortical parcels at low frequencies (top: 7–20 Hz) and high frequencies (bottom: 60–90 Hz). Lower panels illustrate spectrally resolved directed asymmetry index (DAI) from L-3b (left spectra), L-SCEF (middle spectra), and R-CB6 (right spectra) to L-A5. The results represented in the whole figure are significant connectivity patterns that passed a cluster-based permutation test (p <.05, cluster correction).
Figure 3
Connectivity analysis.
Significant connectivity between 14 ROIs involved in speech production and perception. A cluster-based permutation test was used to detect significant connectivity patterns. The results of statistical analysis revealed significant connectivities between different brain areas during speaking (first column of connectograms), listening (second column of connectograms), and the comparison between speaking and listening conditions (third column of connectograms) across various frequency bands. In the connectograms, nodes represent the brain areas and edges represent the strength and direction of the connections between them. The thickness of the edges indicates the magnitude of the t-values, while color indicates the directionality of the connectivity. In other words, when node A connects to node B, the edge will have the same color as node A, and vice versa when node B connects to node A. Note that only significant connections are shown (p < 0.05, cluster correction).
Figure 4
Connectivity results.
Spectrally resolved directed asymmetry index (DAI) between all pairs of ROIs. A cluster-based permutation test was used to detect significant connectivity patterns. The results of statistical analysis revealed significant connectivities between different brain areas during speaking (top), listening (middle), and the comparison between speaking and listening conditions (bottom). The directionality is from y- to x-axis. Note that significant values are highlighted with increased line width (p < 0.05, cluster correction). x- and y-axes represent frequency (Hz) and t-values, respectively.
Figure 5
Correlation between speech–brain coupling and Granger causality.
This plot depicts the relationship between speech–brain coupling in the left superior temporal gyrus (STG) at theta frequency (4–8 Hz) with a positive lag of 130 ms and (right cerebellum to left STG) Granger causality (GC) during the speaking condition. The negative correlation indicates that weaker speech–brain coupling in the theta band is associated with stronger directional information flow from the right cerebellar lobule VI to the left temporal areas in the theta frequency band.
Tables
Table 1
Selected ROI labels from HCP and AAL atlases.
| 1 | L-FOP | Left frontal opercular area |
|---|---|---|
| 2 | R-PEF+6v | Right premotor eyefield + ventral area 6 |
| 3 | L-SCEF | Left supplementary and cingulate eyefield |
| 4 | R-SCEF | Right supplementary and cingulate eyefield |
| 5 | L-3b | Left primary somatosensory cortex |
| 6 | R-3b | Right primary somatosensory cortex |
| 7 | L-STS | Left superior temporal sulcus |
| 8 | L-TPOJ1 | Left TemporoParietoOccipital Junction 1 |
| 9 | L-A5 | Left auditory complex 5 |
| 10 | R-A5 | Right auditory complex 5 |
| 11 | L-TH | Left thalamus |
| 12 | R-CB-Cruss2 | Right cerebellum crus 2 |
| 13 | L-CB-6 | Left cerebellum 6 |
| 14 | R-CB-6 | Right cerebellum 6 |
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Frequency-specific cortico-subcortical interaction in continuous speaking and listening
eLife 13:RP97083.
https://doi.org/10.7554/eLife.97083.2
