Advances in human intracranial electroencephalography research, guidelines and good practices

Abstract:

Since the second half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

Authors:

• Manuel R. Mercier

• Anne-Sophie Dubarry

• François Tadel

• Pietro Avanzini

• Nikolai Axmacher

• Dillan Cellier

• Maria Del Vecchio

• Liberty S. Hamilton

• Dora Hermes

• Michael J. Kahana

• Robert T. Knight

• Anais Llorens

• Pierre Megevand

• Lucia Melloni

• Kai J. Miller

• Vitória Piai

• Aina Puce

• Nick F. Ramsey

• Caspar M. Schwiedrzik

• Sydney E. Smith

• Arjen Stolk

• Nicole C. Swann

• Mariska J Vansteensel

• Bradley Voytek

• Liang Wang

• Jean-Philippe Lachaux

• Robert Oostenveld

Date: 2022

DOI: https://doi.org/10.1016/j.neuroimage.2022.119438

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International Federation of Clinical Neurophysiology (IFCN) – EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms.

Abstract:

In 1999, the International Federation of Clinical Neurophysiology (IFCN) published “IFCN Guidelines for topographic and frequency analysis of EEGs and EPs” (Nuwer et al., 1999). Here a Workgroup of IFCN experts presents unanimous recommendations on the following procedures relevant for the topographic and frequency analysis of resting state EEGs (rsEEGs) in clinical research defined as neurophysiological experimental studies carried out in neurological and psychiatric patients: (1) recording of rsEEGs (environmental conditions and instructions to participants; montage of the EEG electrodes; recording settings); (2) digital storage of rsEEG and control data; (3) computerized visualization of rsEEGs and control data (identification of artifacts and neuropathological rsEEG waveforms); (4) extraction of “synchronization” features based on frequency analysis (band-pass filtering and computation of rsEEG amplitude/power density spectrum); (5) extraction of “connectivity” features based on frequency analysis (linear and nonlinear measures); (6) extraction of “topographic” features (topographic mapping; cortical source mapping; estimation of scalp current density and dura surface potential; cortical connectivity mapping), and (7) statistical analysis and neurophysiological interpretation of those rsEEG features. As core outcomes, the IFCN Workgroup endorsed the use of the most promising “synchronization” and “connectivity” features for clinical research, carefully considering the limitations discussed in this paper. The Workgroup also encourages more experimental (i.e. simulation studies) and clinical research within international initiatives (i.e., shared software platforms and databases) facing the open controversies about electrode montages and linear vs. nonlinear and electrode vs. source levels of those analyses.

Authors:

• Claudio Babiloni

• Robert J Barry

• Erol Başar

• Katarzyna J Blinowska

• Andrzej Cichocki

• Wilhelmus HIM Drinkenburg

• Wolfgang Klimesch

• Robert T Knight

• Fernando Lopes da Silva

• Paul Nunez

• Robert Oostenveld

• Jaeseung Jeong

• Roberto Pascual-Marqui

• Pedro Valdes-Sosa

• Mark Hallett

Date: 2020

DOI: https://doi.org/10.1016/j.clinph.2019.06.234

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iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology

Description:

The Brain Imaging Data Structure (BIDS) is a community-driven specification for organizing neuroscience data and metadata with the aim to make datasets more transparent, reusable, and reproducible. Intracranial electroencephalography (iEEG) data offer a unique combination of high spatial and temporal resolution measurements of the living human brain. To improve internal (re) use and external sharing of these unique data, we present a specification for storing and sharing iEEG data: iEEG-BIDS.

Authors:

• Christopher Holdgraf

• Stefan Appelhoff

• Stephan Bickel

• Kristofer Bouchard

• Sasha D’Ambrosio

• Olivier David

• Orrin Devinsky

• Benjamin Dichter

• Adeen Flinker

• Brett L. Foster

• Krzysztof J. Gorgolewski

• Iris Groen

• David Groppe

• Aysegul Gunduz

• Liberty Hamilton

• Christopher J. Honey

• Mainak Jas

• Robert T. Knight

• Jean-Philippe Lachaux

• Jonathan C. Lau

• Christopher Lee-Messer

• Brian N. Lundstrom

• Kai J. Miller

• Jeffrey G. Ojemann

• Robert Oostenveld

• Natalia Petridou

• Gio Piantoni

• Andrea Pigorini

• Nader Pouratian

• Nick F. Ramsey

• Arjen Stolk

• Nicole C. Swann

• François Tadel

• Bradley Voytek

• Brian A . Wandell

• Jonathan Winawer

• Kirstie Whitaker

• Lyuba Zehl

• Dora Hermes

Date: 2019

DOI: https://doi.org/10.1038/s41597-019-0105-7

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Integrated analysis of anatomical and electrophysiological human intracranial data

ABSTRACT

Human intracranial electroencephalography (iEEG) recordings provide data with much greater spatiotemporal precision than is possible from data obtained using scalp EEG, magnetoencephalography (MEG), or functional MRI. Until recently, the fusion of anatomical data (MRI and computed tomography (CT) images) with electrophysiological data and their subsequent analysis have required the use of technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that enables complex raw human iEEG data to be converted into more readily comprehensible illustrative representations. The protocol uses an open-source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and used by a large research community. In this protocol, we describe how to analyze complex iEEG datasets by providing an intuitive and rapid approach that can handle both neuroanatomical information and large electrophysiological datasets. We provide a worked example using an example dataset. We also explain how to automate the protocol and adjust the settings to enable analysis of iEEG datasets with other characteristics. The protocol can be implemented by a graduate student or postdoctoral fellow with minimal MATLAB experience and takes approximately an hour to execute, excluding the automated cortical surface extraction.

AUTHORS

• Arjen Stolk

• Sandon Griffin

• Roemer van der Meij

• Callum Dewar

• Ignacio Saez

• Jack J. Lin

• Giovanni Piantoni

• Jan-Mathijs Schoffelen

• Robert T. Knight 

• Robert Oostenveld 

Date: 2018

DOI: 10.1038/s41596-018-0009-6

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