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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Behavioral and EEG Measures Show no Amplifying Effects of Shared Attention on Attention or Memory

Abstract:

Shared attention experiments examine the potential differences in function or behavior when stimuli are experienced alone or in the presence of others, and when simultaneous attention of the participants to the same stimulus or set is involved. Previous work has found enhanced reactions to emotional stimuli in social situations, yet these changes might represent enhanced communicative or motivational purposes. This study examines whether viewing emotional stimuli in the presence of another person influences attention to or memory for the stimulus. Participants passively viewed emotionally-valenced stimuli while completing another task (counting flowers). Each participant performed this task both alone and in a shared attention condition (simultaneously with another person in the same room) while EEG signals were measured. Recognition of the emotional pictures was later measured. A significant shared attention behavioral effect was found in the attention task but not in the recognition task. Compared to event-related potential responses for neutral pictures, we found higher P3b response for task relevant stimuli (flowers), and higher Late Positive Potential (LPP) responses for emotional stimuli. However, no main effect was found for shared attention between presence conditions. To conclude, shared attention may therefore have a more limited effect on cognitive processes than previously suggested.

Authors:

• Noam Mairon

• Mor Nahum

• Arjen Stolk

• Robert T Knight

• Anat Perry

Date: 2020

DOI: 10.1038/s41598-020-65311-7

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Electrocorticographic dissociation of alpha and beta rhythmic activity in the human sensorimotor system

Abstract:

This study uses electrocorticography in humans to assess how alpha- and beta-band rhythms modulate excitability of the sensorimotor cortex during psychophysically-controlled movement imagery. Both rhythms displayed effector-specific modulations, tracked spectral markers of action potentials in the local neuronal population, and showed spatially systematic phase relationships (traveling waves). Yet, alpha- and beta-band rhythms differed in their anatomical and functional properties, were weakly correlated, and traveled along opposite directions across the sensorimotor cortex. Increased alpha-band power in the somatosensory cortex ipsilateral to the selected arm was associated with spatially-unspecific inhibition. Decreased beta-band power over contralateral motor cortex was associated with a focal shift from relative inhibition to excitation. These observations indicate the relevance of both inhibition and disinhibition mechanisms for precise spatiotemporal coordination of movement-related neuronal populations, and illustrate how those mechanisms are implemented through the substantially different neurophysiological properties of sensorimotor alpha- and beta-band rhythms.

Authors:

• Arjen Stolk

• Loek Brinkman

• Mariska J Vansteensel

• Erik Aarnoutse

• Frans SS Leijten

• Chris H Dijkerman

• Robert T Knight

• Floris P de Lange

• Ivan Toni

Date: 2019

DOI: https://doi.org/10.7554/eLife.48065

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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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Encoding of multiple reward-related computations in transient and sustained high-frequency activity in human OFC

Abstract:

Human orbitofrontal cortex (OFC) has long been implicated in value-based decision making. In recent years, convergent evidence from human and model organisms has further elucidated its role in representing reward-related computations underlying decision making. However, a detailed description of these processes remains elusive due in part to (1) limitations in our ability to observe human OFC neural dynamics at the timescale of decision processes and (2) methodological and interspecies differences that make it challenging to connect human and animal findings or to resolve discrepancies when they arise. Here, we sought to address these challenges by conducting multi-electrode electrocorticography(ECoG) recordings in neurosurgical patients during economic decision making to elucidate the electrophysiological signature, sub-second temporal profile, and anatomical distribution of reward-related computations within human OFC. We found that high-frequency activity (HFA) (70–200 Hz) reflected multiple valuation components grouped in two classes of valuation signals that were dissociable in temporal profile and information content: (1) fast, transient responses reflecting signals associated with choice and outcome processing, including anticipated risk and outcome regret, and (2) sustained responses explicitly encoding what happened in the immediately preceding trial. Anatomically, these responses were widely distributed in partially overlapping networks, including regions in the central OFC (Brodmann areas 11 and 13), which have been consistently implicated in reward processing in animal single-unit studies. Together, these results integrate insights drawn from human and animal studies and provide evidence for a role of human OFC in representing multiple reward computations.

Authors:

• Ignacio Saez

• Jack Lin

• Arjen Stolk

• Edward Chang

• Josef Parvizi

• Gerwin Schalk

• Robert T. Knight

• Ming Hsu

Date: 2018

DOI: 10.1016/j.cub.2018.07.045

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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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