Standardization of electroencephalography for multi-site, multi-platform and multi-investigator studies: Insights from the canadian biomarker integration network in depression

Subsequent to global initiatives in mapping the human brain and investigations of neurobiological markers for brain disorders, the number of multi-site studies involving the collection and sharing of large volumes of brain data, including electroencephalography (EEG), has been increasing. Among the complexities of conducting multi-site studies and increasing the shelf life of biological data beyond the original study are timely standardization and documentation of relevant study parameters. We presentthe insights gained and guidelines established within the EEG working group of the Canadian Biomarker Integration Network in Depression (CAN-BIND). CAN-BIND is a multi-site, multi-investigator, and multiproject network supported by the Ontario Brain Institute with access to Brain-CODE, an informatics platform that hosts a multitude of biological data across a growing list of brain pathologies. We describe our approaches and insights on documenting and standardizing parameters across the study design, data collection, monitoring, analysis, integration, knowledge-translation, and data archiving phases of CAN-BIND projects. We introduce a custom-built EEG toolbox to track data preprocessing with open-access for the scientific community. We also evaluate the impact of variation in equipment setup on the accuracy of acquired data. Collectively, this work is intended to inspire establishing comprehensive and standardized guidelines for multi-site studies

Characterizing the Role of Neural Dynamics in the Treatment of Depression

Selecting an appropriate treatment for patients with depression is challenging for several reasons. There is no clear understanding on (i) the pathophysiology of depression, (ii) heterogeneity in depression, and (iii) targets for successful treatment outcome. As such, although treatments for depression are effective, their average efficacy seems to be poor. It is widely accepted that seizures induced in the brain are highly effective for severe, treatment-resistant cases of depression. Seizures are also known to impact the dynamics of neural activity. Based on this knowledge, we investigated whether treatments for depression impact neural dynamics for therapeutic efficacy. We also evaluated whether measures of neural dynamics can predict response. Seizure therapy (electroconvulsive therapy and magnetic seizure therapy) and pharmacotherapy (escitalopram) were studied. It is hypothesized that modulations of neural dynamics in several frequencies, timescales, regions and networks, previously shown to be affected in depression, are associated with therapeutic outcome. These modulations are also hypothesized to be distinct from modulations associated with non-response. In this work, measures of neural dynamics were derived from power spectral density analysis, multiscale entropy analysis and microstate analysis of resting-state, eyes-closed EEG data. Results suggest that successful seizure therapy potentially impacts several characteristics of neural dynamics for therapeutic efficacy. In responders of seizure therapy, modulation of neural dynamics was observed in regions (posterior cingulate cortex, precuneus, occipital pole) and networks (salience, fronto-parietal) previously known to be impaired in depression. In responders of escitalopram, modulation of neural dynamics was observed after 2 weeks into the 8-week course of escitalopram treatment. These changes were observed in regions known to be impaired in depression (posterior cingulate cortex, precuneus, posterior cingulate cortex) but not within networks. In non-responders of escitalopram, an early modulation of neural dynamics (i.e., baseline to 2 weeks) was observed. Finally, using measures of neural dynamics, prediction of response to escitalopram achieved an accuracy of 83.2%. Knowledge from this work will guide the development of antidepressant response prediction tools and potentially improve treatment efficacy in depression.Ph.D.2021-08-16 00:00:0

TMSEEG: A MATLAB-Based Graphical User Interface for Processing Electrophysiological Signals during Transcranial Magnetic Stimulation

Concurrent recording of electroencephalography (EEG) during transcranial magnetic stimulation (TMS) is an emerging and powerful tool for studying brain health and function. Despite a growing interest in adaptation of TMS-EEG across neuroscience disciplines, its wide-spread utility is limited by signal processing challenges. These challenges arise due to the nature of TMS and the sensitivity of EEG to artifacts that often mask TMS-evoked potentials (TEPs). With an increase in the complexity of data processing methods and a growing interest in multi-site data integration, analysis of TMS-EEG data requires the development of a standardized method to recover TEPs from various sources of artifacts. This paper introduces TMSEEG, an open-source MATLAB application comprised of multiple algorithms organized to facilitate a step-by-step procedure for TMS-EEG signal processing. Using a modular design and interactive GUIs, this toolbox aims to streamline TMS-EEG signal processing for both novice and experienced users. Specifically, TMSEEG provides (i) targeted removal of TMS-induced and general EEG artifacts, (ii) a step-by-step modular workflow with flexibility to modify existing algorithms and add customized algorithms, (iii) a comprehensive display and quantification of artifacts, (iv) quality control check points with visual feedback of TEPs throughout the data processing workflow, and (v) capability to label and store a database of artifacts. In addition to these features, the software architecture of TMSEEG ensures minimal user effort in initial setup and configuration of parameters for each processing step. This is partly accomplished through a close integration with EEGLAB, a widely used open-source toolbox for EEG signal processing. In this paper we introduce TMSEEG, validate its features, and demonstrate its application in extracting TEPs across several single- and multi-pulse TMS protocols. As the first open-source GUI-based pipeline for TMS-EEG signal processing, this toolbox intends to promote the wide-spread utility and standardization of an emerging technology in brain research

Selective modulation of brain network dynamics by seizure therapy in treatment-resistant depression

Background: Electroconvulsive therapy (ECT) is highly effective for treatment-resistant depression, yet its mechanism of action is still unclear. Understanding the mechanism of action of ECT can advance the optimization of magnetic seizure therapy (MST) towards higher efficacy and less cognitive impairment. Given the neuroimaging evidence for disrupted resting-state network dynamics in depression, we investigated whether seizure therapy (ECT and MST) selectively modifies brain network dynamics for therapeutic efficacy. Methods: EEG microstate analysis was used to evaluate resting-state network dynamics in patients at baseline and following seizure therapy, and in healthy controls. Microstate analysis defined four classes of brain states (labelled A, B, C, D). Source localization identified the brain regions associated with these states. Results: An increase in duration and decrease in frequency of microstates was specific to responders of seizure therapy. Significant changes in the dynamics of States A, C and D were observed and predicted seizure therapy outcome (specifically ECT). Relative change in the duration of States C and D was shown to be a strong predictor of ECT response. Source localization partly associated C and D to the salience and frontoparietal networks, argued to be impaired in depression. An increase in duration and decrease in frequency of microstates was also observed following MST, however it was not specific to responders. Conclusion: This study presents the first evidence for the modulation of global brain network dynamics by seizure therapy. Successful seizure therapy was shown to selectively modulate network dynamics for therapeutic efficacy. Keywords: Microstate analysis, Network dynamics, Treatment-resistant depression, Electroconvulsive therapy, Magnetic seizure therapy, Electroencephalograph

TMSEEG:A MATLAB-based graphical user interface for processing electrophysiological signals during transcranial magnetic stimulation

Concurrent recording of electroencephalography (EEG) during transcranial magnetic stimulation (TMS) is an emerging and powerful tool for studying brain health and function. Despite a growing interest in adaptation of TMS-EEG across neuroscience disciplines, its widespread utility is limited by signal processing challenges. These challenges arise due to the nature of TMS and the sensitivity of EEG to artifacts that often mask TMS-evoked potentials (TEP)s. With an increase in the complexity of data processing methods and a growing interest in multi-site data integration, analysis of TMS-EEG data requires the development of a standardized method to recover TEPs from various sources of artifacts. This article introduces TMSEEG, an open-source MATLAB application comprised of multiple algorithms organized to facilitate a step-by-step procedure for TMS-EEG signal processing. Using a modular design and interactive graphical user interface (GUI), this toolbox aims to streamline TMS-EEG signal processing for both novice and experienced users. Specifically, TMSEEG provides: (i) targeted removal of TMS-induced and general EEG artifacts; (ii) a step-by-step modular workflow with flexibility to modify existing algorithms and add customized algorithms; (iii) a comprehensive display and quantification of artifacts; (iv) quality control check points with visual feedback of TEPs throughout the data processing workflow; and (v) capability to label and store a database of artifacts. In addition to these features, the software architecture of TMSEEG ensures minimal user effort in initial setup and configuration of parameters for each processing step. This is partly accomplished through a close integration with EEGLAB, a widely used open-source toolbox for EEG signal processing. In this article, we introduce TMSEEG, validate its features and demonstrate its application in extracting TEPs across several single- and multi-pulse TMS protocols. As the first open-source GUI-based pipeline for TMS-EEG signal processing, this toolbox intends to promote the widespread utility and standardization of an emerging technology in brain research