Electrophysiological signatures of inequity-dependent reward encoding in the human OFC

Abstract:

Social decision making requires the integration of reward valuation and social cognition systems, both dependent on the orbitofrontal cortex (OFC). How these two OFC functions interact is largely unknown. We recorded intracranial activity from the OFC of ten patients making choices in a social context where reward inequity with a social counterpart varied and could be either advantageous or disadvantageous. We find that OFC high-frequency activity (HFA; 70–150Hz) encodes self-reward, consistent with previous reports. We also observe encoding of the social counterpart’s reward, as well as the type of inequity being experienced. Additionally, we find evidence of inequity-dependent reward encoding: depending on the type of inequity, electrodes rapidly and reversibly switch between different reward-encoding profiles. These results provide direct evidence for encoding of self- and other rewards in the human OFC and highlight the dynamic nature of encoding in the OFC as a function of social context.

Authors:

• Deborah Marciano

• Brooke R. Staveland

• Jack J. Lin

• Ignacio Saez

• Ming Hsu

• Robert T. Knight

Date: 2023

DOI: https://doi.org/10.1016/j.celrep.2023.112865

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A rapid theta network mechanism for flexible information encoding

Abstract:

Flexible behavior requires gating mechanisms that encode only task-relevant information in working memory. Extant literature supports a theoretical division of labor whereby lateral frontoparietal interactions underlie information maintenance and the striatum enacts the gate. Here, we reveal neocortical gating mechanisms in intracranial EEG patients by identifying rapid, within-trial changes in regional and inter-regional activities that predict subsequent behavioral outputs. Results first demonstrate information accumulation mechanisms that extend prior fMRI (i.e., regional high-frequency activity) and EEG evidence (inter-regional theta synchrony) of distributed neocortical networks in working memory. Second, results demonstrate that rapid changes in theta synchrony, reflected in changing patterns of default mode network connectivity, support filtering. Graph theoretical analyses further linked filtering in task-relevant information and filtering out irrelevant information to dorsal and ventral attention networks, respectively. Results establish a rapid neocortical theta network mechanism for flexible information encoding, a role previously attributed to the striatum.

Authors:

• Elizabeth L. Johnson

• Jack J. Lin

• David King-Stephens

• Peter B. Weber

• Kenneth D. Laxer

• Ignacio Saez

• Fady Girgis

• Mark D’Esposito

• Robert T. Knight

• David Badre

Date: 2023

DOI: https://doi.org/10.1038/s41467-023-38574-7

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Gender bias in academia: A lifetime problem that needs solutions

Summary:

Despite increased awareness of the lack of gender equity in academia and a growing number of initiatives to address issues of diversity, change is slow, and inequalities remain. A major source of inequity is gender bias, which has a substantial negative impact on the careers, work-life balance, and mental health of underrepresented groups in science. Here, we argue that gender bias is not a single problem but manifests as a collection of distinct issues that impact researchers’ lives. We disentangle these facets and propose concrete solutions that can be adopted by individuals, academic institutions, and society.

Authors:

• Anaïs Llorens

• Athina Tzovara

• Ludovic Bellier

• Ilina Bhaya-Grossman

• Aurélie Bidet-Caulet

• William K Chang

• Zachariah R Cross

• Rosa Dominguez-Faus

• Adeen Flinker

• Yvonne Fonken

• Mark A Gorenstein

• Chris Holdgraf

• Colin W Hoy

• Maria V Ivanova

• Richard T Jimenez

• Soyeon Jun

• Julia WY Kam

• Celeste Kidd

• Enitan Marcelle

• Deborah Marciano

• Stephanie Martin

• Nicholas E Myers

• Karita Ojala

• Anat Perry

• Pedro Pinheiro-Chagas

• Stephanie K Riès

• Ignacio Saez

• Ivan Skelin

• Katarina Slama

• Brooke Staveland

• Danielle S Bassett

• Elizabeth A Buffalo

• Adrienne L Fairhall

• Nancy J Kopell

• Laura J Kray

• Jack J Lin

• Anna C Nobre

• Dylan Riley

• Anne-Kristin Solbakk

• Joni D Wallis

• Xiao-Jing Wang

• Shlomit Yuval-Greenberg

• Sabine Kastner

• Robert T Knight

• Nina F Dronkers

Date: 2021

DOI: https://doi.org/10.1016/j.neuron.2021.06.002

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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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