Differential modulation of neural oscillations in perception-action links in Tourette syndrome

Abstract

Gilles de la Tourette Syndrome (GTS) is a multi-faceted neuro-psychiatric disorder. While novel conceptions overcoming the criticized categorization of GTS as a movement disorder are on the rise, little is known about their neural implementation and whether there are links to known pathophysiological processes in GTS. This is the case for conceptions suggesting that aberrant perception-action processes reflect a key feature of GTS. Building on the concept that overly strong perception-action associations are pivotal to understanding GTS pathophysiology, we examined how these associations influence response inhibition and used EEG methods to examine the importance of theta, alpha and beta band activity due to their known relevance for GTS pathophysiology. In this case-control study, behavioural analyses revealed that adult patients with GTS experienced greater difficulty during motor response inhibition when perceptual features of Nogo stimuli overlapped with perceptual features of Go stimuli, indicating impaired reconfiguration of perception-action associations. Neurophysiological findings showed robust differential patterns of modulation in theta and alpha band activity between neurotypical (NT) individuals and GTS patients. Specifically, GTS patients exhibited stronger and more extended theta band modulation but weaker and more restricted alpha band modulation during overlapping Nogo trials than NT individuals. Unlike NT individuals, GTS patients did not exhibit beta band modulations necessary for dynamically handling perception-action codes. The findings highlight increased theta band modulation in GTS patients' significant stronger perception-action bindings and a lack of compensatory alpha band modulation. The robust differential modulation observed provides novel insights, emphasizing theta and alpha oscillations as key elements in GTS pathophysiology and offering potential implications for targeted cognitive-behavioural interventions.

Keywords: Tourette syndrome; alpha band activity; perception–action integration; response inhibition; theta band activity.

Graphical Abstract

Figure 1

Depiction of task stimuli. (A) In the non-overlapping condition, the Go and Nogo stimuli differed in both features (i.e. letters and font colour), while in the overlapping condition, Go and Nogo stimuli shared similar features. (B) A trial either ends either with a key press or after 1700 ms. For illustration, a correct non-overlapping Go trial and a correct non-overlapping Nogo trial are presented.

Figure 2

Behavioural data. Boxplots of false alarm rates in Nogo trials in non-overlapping and overlapping trials in neurotypical individuals (NT; N = 30) and patients with GTS (N = 30), along with the overlap effect in both groups. Boxes represent the interquartile range (IQR) from the first quartile (Q1) to the third quartile (Q3), with the median indicated by a horizontal line. Whiskers extend to 1.5 × IQR from Q1 and Q3. Individual data points are shown as grey dots, and outliers are marked with asterisks. The mixed-effects ANOVA of the false alarm rates in Nogo trials revealed an interaction of Overlap ∗ Group (F(1,58) = 12.93, P < 0.001), as the overlap effect was larger in patients with GTS than in NT (t(47.46) = 3.60, P < 0.001).

Figure 3

Neurophysiological results at sensor level. Time–frequency plots show the difference between overlapping and non-overlapping Nogo conditions at electrodes identified as significant in the cluster-based permutation test; time point zero reflects the presentation of the stimulus. Topographic plots highlight significant electrodes, averaged across time. Left side shows NT group (N = 30) results, right side shows GTS group (N = 30) results. (A) Results in the theta frequency band, showing a positive cluster in each NT (T_sum = 23.46, P = 0.026) and in GTS (T_sum = 23.46, P = 0.040). (B) Results in the alpha frequency band, showing a negative cluster in each NT (T_sum = −156.76, P = 0.002) and in GTS (T_sum = −156.76, P = 0.002). (C) Results in the beta frequency band, showing a negative cluster in each NT (T_sum = −61.46, P = 0.006) and in GTS (T_sum = −61.46, P = 0.002). Regarding between-group comparisons, no clusters were found through cluster-based permutation testing in all frequency bands.

Figure 4

Neurophysiological results at source level. The left plots for each parcel show differences between overlapping and non-overlapping Nogo conditions in voxels identified as significant in cluster-based permutation testing. Colour scaling indicates the magnitude of power differences. The right plots highlight the top 1% of voxels with the largest differences. Left side shows NT group (N = 30) results, right side shows GTS group (N = 30) results. (A) Results in the theta frequency band, showing a positive cluster in each NT (T_sum = 27 896.72, P < 0.001) and in GTS (T_sum = 32 810.62, P < 0.001). (B) Results in the alpha frequency band, showing a positive cluster in each NT (T_sum = 4010.10, P = 0.019) and in GTS (T_sum = 2357.45, P = 0.040). (C) Results in the beta frequency band, showing a positive cluster in NT (T_sum = 3733.36, P = 0.034), but no significant cluster in GTS (P ≥ 0.176).

Figure 5

Distribution of the cluster-based permutation test results. Histograms show the distribution of outcome parameters from the leave-one-out approach of cluster-based permutation testing at source level. Parameters include the T_sum of the cluster-based permutation test, the number of significant voxels and the relative T_sum (T_sum normalized by the number of significant voxels; left to right). Results are displayed for NT (light grey; N = 30) and GTS (dark grey; N = 30). (A) Results for the theta frequency band, indicating a larger number of significant voxels (Z = −6.23, P < 0.001), a larger T_sum (Z = −6.24, P < 0.001) and a larger relative T_sum (Z = −6.33, P < 0.001) in the GTS group compared to the neurotypical individuals. (B) Results for the alpha frequency band, indicating a larger number of significant voxels (Z = −3.86, P < 0.001), a larger T_sum (Z = −4.03, P < 0.001) and a larger relative T_sum (Z = −3.68, P < 0.001) in the GTS group compared to the neurotypical individuals when treating the absence of significant clusters as missing value (NT: N = 30; GTS: N = 11). Also when assigning zeros in the case of absence of significant clusters (NT: N = 30; GTS: N = 30), there was a larger number of significant voxels (Z = −6.25, P < 0.001), a larger T_sum (Z = −6.34, P < 0.001) and a larger relative T_sum (Z = −6.16, P < 0.001) in the GTS group compared to the neurotypical individuals.