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 present the 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 multi-project 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.

Unbiased cluster estimation of electrophysiological brain response.

BACKGROUND: Recent increase in the size and complexity of electrophysiological data from multidimensional electroencephalography (EEG) and magnetoencephalography (MEG) studies has prompted the development of sophisticated statistical frameworks for data analysis. One of the main challenges for such frameworks is the multiple comparisons problem, where the large number of statistical tests performed within a high-dimensional dataset lead to an increased risk of Type I errors (false positives). A solution to this problem, cluster analysis, applies the biologically-motivated knowledge of correlation between adjacent voxels in one or more dimensions of the dataset to correct for the multiple comparisons problem and detect true neurophysiological effects. Cluster-based methods provide increased sensitivity towards detecting neurophysiological events compared to conservative methods such as Bonferroni correction, but are limited by their dependency on an initial cluster-forming statistical threshold (e.g. t-score, alpha) obstructing precise comparisons of results across studies. NEW METHOD: Rather than selecting a single threshold value, unbiased cluster estimation (UCE) computes a significance distribution across all possible threshold values to provide an unbiased overall significance value. COMPARISON TO EXISTING METHODS: UCE functions as a novel extension to existing cluster analysis methods. RESULTS: Using data from EEG combined with brain stimulation study, we showed the impact of statistical threshold on outcome measures and introduction of bias. We showed the application of UCE for different study designs (e.g., within-group, between-group comparisons). CONCLUSION: We propose that researchers consider employing UCE for multidimensional EEG/MEG datasets toward an unbiased comparison of results between subjects, groups, and studies.

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.