Introducing RELAX: An automated pre-processing pipeline for cleaning EEG data - Part 1: Algorithm and application to oscillations.

OBJECTIVE: Electroencephalographic (EEG) data are often contaminated with non-neural artifacts which can confound experimental results. Current artifact cleaning approaches often require costly manual input. Our aim was to provide a fully automated EEG cleaning pipeline that addresses all artifact types and improves measurement of EEG outcomes METHODS: We developed RELAX (the Reduction of Electroencephalographic Artifacts). RELAX cleans continuous data using Multi-channel Wiener filtering [MWF] and/or wavelet enhanced independent component analysis [wICA] applied to artifacts identified by ICLabel [wICA_ICLabel]). Several versions of RELAX were compared using three datasets (N = 213, 60 and 23 respectively) against six commonly used pipelines across a range of artifact cleaning metrics, including measures of remaining blink and muscle activity, and the variance explained by experimental manipulations after cleaning. RESULTS: RELAX with MWF and wICA_ICLabel showed amongst the best performance at cleaning blink and muscle artifacts while preserving neural signal. RELAX with wICA_ICLabel only may perform better at differentiating alpha oscillations between working memory conditions. CONCLUSIONS: RELAX provides automated, objective and high-performing EEG cleaning, is easy to use, and freely available on GitHub. SIGNIFICANCE: We recommend RELAX for data cleaning across EEG studies to reduce artifact confounds, improve outcome measurement and improve inter-study consistency.

RELAX part 2: A fully automated EEG data cleaning algorithm that is applicable to Event-Related-Potentials.

OBJECTIVE: Electroencephalography (EEG) is often used to examine neural activity time-locked to stimuli presentation, referred to as Event-Related Potentials (ERP). However, EEG is influenced by non-neural artifacts, which can confound ERP comparisons. Artifact cleaning reduces artifacts, but often requires time-consuming manual decisions. Most automated methods filter frequencies <1 Hz out of the data, so are not recommended for ERPs (which contain frequencies <1 Hz). Our aim was to test the RELAX (Reduction of Electroencephalographic Artifacts) pre-processing pipeline for use on ERP data. METHODS: The cleaning performance of multiple versions of RELAX were compared to four commonly used EEG cleaning pipelines across both artifact cleaning metrics and the amount of variance in ERPs explained by different conditions in a Go-Nogo task. Results RELAX with Multi-channel Wiener Filtering (MWF) and wavelet-enhanced independent component analysis applied to artifacts identified with ICLabel (wICA_ICLabel) cleaned data most effectively and produced amongst the most dependable ERP estimates. RELAX with wICA_ICLabel only or MWF_only may detect effects better for some ERPs. CONCLUSIONS: RELAX shows high artifact cleaning performance even when data is high-pass filtered at 0.25 Hz (applicable to ERP analyses). SIGNIFICANCE: RELAX is easy to implement via EEGLAB in MATLAB and freely available on GitHub. Given its performance and objectivity we recommend RELAX to improve artifact cleaning and consistency across ERP research.

Utilising TMS-EEG to Assess the Response to Cerebellar-Brain Inhibition.

Cerebellar-brain inhibition (CBI) is a transcranial magnetic stimulation (TMS) paradigm indexing excitability of cerebellar projections to motor cortex (M1). Stimulation involved with CBI is often considered to be uncomfortable, and alternative ways to index connectivity between cerebellum and the cortex would be valuable. We therefore sought to assess the utility of electroencephalography in conjunction with TMS (combined TMS-EEG) to record the response to CBI. A total of 33 volunteers (25.7 ± 4.9 years, 20 females) participated across three experiments. These investigated EEG responses to CBI induced with a figure-of-eight (F8; experiment 1) or double cone (DC; experiment 2) conditioning coil over cerebellum, in addition to multisensory sham stimulation (experiment 3). Both F8 and DC coils suppressed early TMS-evoked EEG potentials (TEPs) produced by TMS to M1 (P < 0.05). Furthermore, the TEP produced by CBI stimulation was related to the motor inhibitory response to CBI recorded in a hand muscle (P < 0.05), but only when using the DC coil. Multisensory sham stimulation failed to modify the M1 TEP. Cerebellar conditioning produced changes in the M1 TEP that were not apparent following sham stimulation, and that were related to the motor inhibitory effects of CBI. Our findings therefore suggest that it is possible to index the response to CBI using TMS-EEG. In addition, while both F8 and DC coils appear to recruit cerebellar projections, the nature of these may be different.

Neurobiological effects of transcranial direct current stimulation in younger adults, older adults and mild cognitive impairment.

Transcranial direct current stimulation (tDCS) has been investigated as a way to improve motor and cognitive functioning, with largely variable results. Currently, relatively little is known about the neurobiological effects, and possible drivers of variability, in either healthy or clinical populations. Therefore, this study aimed to characterise the neurobiological effects to tDCS in younger adults, older adults and adults with mild cognitive impairment (MCI), and their relationship to cognitive performance. 20 healthy younger adults, 20 healthy older adults and 9 individuals with MCI participated in the study. All completed neuropsychological tasks and TMS-EEG, prior to and following delivery of 20 min of anodal tDCS to the left dorsolateral prefrontal cortex (DLPFC). EEG was also recorded during the 2-Back working memory task. Following tDCS, younger adults demonstrated alterations in early TMS-Evoked Potentials (TEPs), namely P30 and P60. Both younger and older adults exhibited a larger task-related N250 amplitude after stimulation, with contrasting relationships to cognitive performance. The MCI group showed no change in TEPs or ERPs over time. Comparisons between the groups revealed differences in the change in amplitude of early TEP (P60) and ERP (N100) peaks between younger and older adults. Our findings indicate that tDCS was able to modulate cortical activity in younger and older healthy adults, but in varying ways. These findings suggest that varied response to tDCS may be related to factors such as age and the presence/absence of cognitive impairment, and these factors should be considered when assessing the effectiveness of tDCS in healthy and pathological aging.

Differentiating responders and non-responders to rTMS treatment for depression after one week using resting EEG connectivity measures.

BACKGROUND: Non-response to repetitive transcranial magnetic stimulation (rTMS) treatment for depression is costly for both patients and clinics. Simple and cheap methods to predict response would reduce this burden. Resting EEG measures differentiate responders from non-responders, so may have utility for response prediction. METHODS: Fifty patients with treatment resistant depression and 21 controls had resting electroencephalography (EEG) recorded at baseline (BL). Patients underwent 5-8 weeks of rTMS treatment, with EEG recordings repeated at week 1 (W1). Forty-two participants had valid BL and W1 EEG data, and 12 were responders. Responders and non-responders were compared at BL and W1 in measures of theta (4-8 Hz) and alpha (8-13 Hz) power and connectivity, frontal theta cordance and alpha peak frequency. Control group comparisons were made for measures that differed between responders and non-responders. A machine learning algorithm assessed the potential to differentiate responders from non-responders using EEG measures in combination with change in depression scores from BL to W1. RESULTS: Responders showed elevated theta connectivity across BL and W1. No other EEG measures differed between groups. Responders could be distinguished from non-responders with a mean sensitivity of 0.84 (p = 0.001) and specificity of 0.89 (p = 0.002) using cross-validated machine learning classification on the combination of all EEG and mood measures. LIMITATIONS: The low response rate limited our sample size to only 12 responders. CONCLUSION: Resting theta connectivity at BL and W1 differ between responders and non-responders, and show potential for predicting response to rTMS treatment for depression.

Responders to rTMS for depression show increased fronto-midline theta and theta connectivity compared to non-responders.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression, but only some individuals respond. Predicting response could reduce patient and clinical burden. Neural activity related to working memory (WM) has been related to mood improvements, so may represent a biomarker for response prediction. PRIMARY HYPOTHESES: We expected higher theta and alpha activity in responders compared to non-responders to rTMS. METHODS: Fifty patients with treatment resistant depression and twenty controls performed a WM task while electroencephalography (EEG) was recorded. Patients underwent 5-8 weeks of rTMS treatment, repeating the EEG at week 1 (W1). Of the 39 participants with valid WM-related EEG data from baseline and W1, 10 were responders. Comparisons between responders and non-responders were made at baseline and W1 for measures of theta (4-8 Hz), upper alpha (10-12.5 Hz), and gamma (30-45 Hz) power, connectivity, and theta-gamma coupling. The control group's measures were compared to the depression group's baseline measures separately. RESULTS: Responders showed higher levels of WM-related fronto-midline theta power and theta connectivity compared to non-responders at baseline and W1. Responder's fronto-midline theta power and connectivity was similar to controls. Responders also showed an increase in gamma connectivity from baseline to W1, with a concurrent improvement in mood and WM reaction times. An unbiased combination of all measures provided mean sensitivity of 0.90 at predicting responders and specificity of 0.92 in a predictive machine learning algorithm. CONCLUSION: Baseline and W1 fronto-midline theta power and theta connectivity show good potential for predicting response to rTMS treatment for depression.