Modulation of Frontal Oscillatory Power during Blink Suppression in Children: Effects of Premonitory Urge and Reward

Increased Alpha-Band Connectivity During Tic Suppression in Children With Tourette Syndrome Revealed by Source Electroencephalography Analyses

BACKGROUND

Tourette syndrome (TS) is a neurodevelopmental disorder involving chronic motor and phonic tics. Most individuals with TS can suppress their tics for at least a short period of time. Yet, the brain correlates of tic suppression are still poorly understood.

METHODS

In the current study, high-density electroencephalography was recorded during a resting-state and a tic suppression session in 72 children with TS. Functional connectivity between cortical regions was assessed in the alpha band (8-13 Hz) using an electroencephalography source connectivity method. Graph theory and network-based statistics were used to assess the global network topology and to identify brain regions showing increased connectivity during tic suppression.

RESULTS

Graph theoretical analyses revealed distinctive global network topology during tic suppression, relative to rest. Using network-based statistics, we found a subnetwork of increased connectivity during tic suppression (p < .001). That subnetwork encompassed many cortical areas, including the right superior frontal gyrus and the left precuneus, which are involved in the default mode network. We also found a condition-by-age interaction, suggesting age-mediated increases in connectivity during tic suppression.

CONCLUSIONS

These results suggest that children with TS suppress their tics through a brain circuit involving distributed cortical regions, many of which are part of the default mode network. Brain connectivity during tic suppression also increases as youths with TS mature. These results highlight a mechanism by which children with TS may control their tics, which could be relevant for future treatment studies.

Revisiting Polarity Indeterminacy of ICA-Decomposed ERPs and Scalp Topographies

We propose an alternative method to align the polarities of independent components (ICs) for group-level IC cluster analysis. Current methods are presently limited in how indeterminacy of IC polarities is handled, as when multiplying a weight matrix to a time-series IC activation, the result from 1 × 1 and - 1 × - 1 are indistinguishable. We first clarify the EEGLAB's default solution and define it as the iterative correlation maximization as it maximizes the within-cluster correlations of the IC scalp topographies to the cluster mean. We then propose the covariance maximization method, which determines the polarity of ICs based on the sign of the largest eigenvalue of covariance matrix. We compared the two methods on datasets from a published visual event-related potential (ERP) study. The results were similar when both methods were applied to the IC scalp topographies. However, when the proposed method was applied to IC ERPs, the number of clusters that showed significant ERP amplitudes increased from 5 to 9 out of 9 due to minimization of within-cluster ERP amplitude cancellation. Our study confirm covariance maximization provides an alternative solution to post-ICA group-level analysis that can maximize sensitivity of IC ERPs.

Automated preprocessing and phase-amplitude coupling analysis of scalp EEG discriminates infantile spasms from controls during wakefulness

OBJECTIVE

Delta-gamma phase-amplitude coupling in EEG is useful for localizing epileptic sources and to evaluate severity in children with infantile spasms. We (1) develop an automated EEG preprocessing pipeline to clean data using artifact subspace reconstruction (ASR) and independent component (IC) analysis (ICA) and (2) evaluate delta-gamma modulation index (MI) as a method to distinguish children with epileptic spasms (cases) from normal controls during sleep and awake.

METHODS

Using 400 scalp EEG datasets (200 sleep, 200 awake) from 100 subjects, we calculated MI after applying high-pass and line-noise filters (Clean 0), and after ASR followed by either conservative (Clean 1) or stringent (Clean 2) artifactual IC rejection. Classification of cases and controls using MI was evaluated with Receiver Operating Characteristics (ROC) to obtain area under curve (AUC).

RESULTS

The artifact rejection algorithm reduced raw signal variance by 29-45% and 38-60% for Clean 1 and Clean 2, respectively. MI derived from sleep data, with or without preprocessing, robustly classified the groups (all AUC > 0.98). In contrast, group classification using MI derived from awake data was successful only after Clean 2 (AUC = 0.85).

CONCLUSIONS

We have developed an automated EEG preprocessing pipeline to perform artifact rejection and quantify delta-gamma modulation index.