Intracerebral mechanisms explaining the impact of incidental feedback on mood state and risky choice

  1. Romane Cecchi
  2. Fabien Vinckier
  3. Jiri Hammer
  4. Petr Marusic
  5. Anca Nica
  6. Sylvain Rheims
  7. Agnès Trebuchon
  8. Emmanuel J Barbeau
  9. Marie Denuelle
  10. Louis Maillard
  11. Lorella Minotti
  12. Philippe Kahane
  13. Mathias Pessiglione
  14. Julien Bastin  Is a corresponding author
  1. Grenoble Alpes University, France
  2. Université de Paris, France
  3. University Hospital in Motol, Czech Republic
  4. Centre Hospitalier Universitaire de Rennes, France
  5. Hospices Civils de Lyon, France
  6. Hôpital de la Timone, France
  7. CNRS, University of Toulouse Paul Sabatier, France
  8. Centre Hospitalier Universitaire de Toulouse, France
  9. University Hospital of Nancy, France
  10. Centre Hospitalier Universitaire de Grenoble, France
  11. Pitié-Salpêtrière Hospital, France

Abstract

Identifying factors whose fluctuations are associated with choice inconsistency is a major issue for rational decision theory. Here, we investigated the neuro-computational mechanisms through which mood fluctuations may bias human choice behavior. Intracerebral EEG data were collected in a large group of subjects (n = 30), while they were performing interleaved quiz and choice tasks that were designed to examine how a series of unrelated feedbacks affects decisions between safe and risky options. Neural baseline activity preceding choice onset was confronted first to mood level, estimated by a computational model integrating the feedbacks received in the quiz task, and then to the weighting of option attributes, in a computational model predicting risk attitude in the choice task. Results showed that 1) elevated broadband gamma activity (BGA) in the ventromedial prefrontal cortex (vmPFC) and dorsal anterior insula (daIns) was respectively signaling periods of high and low mood, 2) increased vmPFC and daIns BGA respectively promoted and tempered risk taking by overweighting gain versus loss prospects. Thus, incidental feedbacks induce brain states that correspond to different moods and bias the evaluation of risky options. More generally, these findings might explain why people experiencing positive (or negative) outcome in some part of their life tend to expect success (or failure) in any other.

Data availability

Due to ethical restrictions on data sharing, we are unable to share raw data for this manuscript to preserve participant anonymity. However, anonymized iEEG data in BIDS format can be made available upon request to the corresponding author (JB) and source data files with anonymized regression estimates are available for download.The custom codes used to (i) extract the different frequency envelopes, and in particular the broadband gamma activity (BGA), from the raw intracranial data, (ii) perform the regression analyses at recording site level, and (iii) compute the second level statistics (across all recording sites of a ROI) are available at: https://gitlab.com/romane-cecchi/publications-code/2022-ieeg-mood-and-risky-choice (Cecchi, 2022; copy archived at https://archive.softwareheritage.org/swh:1:dir:ec43d71a97bf2e3c56b38688eaea2dfde19ad573).

Article and author information

Author details

  1. Romane Cecchi

    Grenoble Alpes University, Grenoble, France
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6149-939X
  2. Fabien Vinckier

    Université de Paris, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
  3. Jiri Hammer

    University Hospital in Motol, Prague, Czech Republic
    Competing interests
    The authors declare that no competing interests exist.
  4. Petr Marusic

    University Hospital in Motol, Prague, Czech Republic
    Competing interests
    The authors declare that no competing interests exist.
  5. Anca Nica

    Centre Hospitalier Universitaire de Rennes, Rennes, France
    Competing interests
    The authors declare that no competing interests exist.
  6. Sylvain Rheims

    Hospices Civils de Lyon, Lyon, France
    Competing interests
    The authors declare that no competing interests exist.
  7. Agnès Trebuchon

    Hôpital de la Timone, Marseille, France
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8632-3454
  8. Emmanuel J Barbeau

    Brain and Cognition Research Centre (CerCo), CNRS, University of Toulouse Paul Sabatier, Toulouse, France
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0836-3538
  9. Marie Denuelle

    Centre Hospitalier Universitaire de Toulouse, Toulouse, France
    Competing interests
    The authors declare that no competing interests exist.
  10. Louis Maillard

    University Hospital of Nancy, Nancy, France
    Competing interests
    The authors declare that no competing interests exist.
  11. Lorella Minotti

    Centre Hospitalier Universitaire de Grenoble, Grenoble, France
    Competing interests
    The authors declare that no competing interests exist.
  12. Philippe Kahane

    Centre Hospitalier Universitaire de Grenoble, Grenoble, France
    Competing interests
    The authors declare that no competing interests exist.
  13. Mathias Pessiglione

    Pitié-Salpêtrière Hospital, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
  14. Julien Bastin

    Grenoble Alpes University, Grenoble, France
    For correspondence
    julien.bastin@univ-grenoble-alpes.fr
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0533-7564

Funding

Université Grenoble Alpes (ANR-17-CE37-0018)

  • Julien Bastin

Université Grenoble Alpes (ANR-18-CE28-0016)

  • Julien Bastin

Université Grenoble Alpes (ANR-13-TECS-0013)

  • Philippe Kahane
  • Julien Bastin

The Czech Science Foundation (20-21339S)

  • Jiri Hammer

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: All patients gave written, informed consent before their inclusion in the present study, which received approval from the local ethics committees (CPP 09-CHUG-12, study 0907; CPP18-001b / 2017-A03248-45; IRB00003888; CER No. 47-0913).

Copyright

© 2022, Cecchi et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,159
    views
  • 254
    downloads
  • 11
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Romane Cecchi
  2. Fabien Vinckier
  3. Jiri Hammer
  4. Petr Marusic
  5. Anca Nica
  6. Sylvain Rheims
  7. Agnès Trebuchon
  8. Emmanuel J Barbeau
  9. Marie Denuelle
  10. Louis Maillard
  11. Lorella Minotti
  12. Philippe Kahane
  13. Mathias Pessiglione
  14. Julien Bastin
(2022)
Intracerebral mechanisms explaining the impact of incidental feedback on mood state and risky choice
eLife 11:e72440.
https://doi.org/10.7554/eLife.72440

Share this article

https://doi.org/10.7554/eLife.72440

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Daniel Hui, Scott Dudek ... Marylyn D Ritchie
    Research Article

    Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGSBMI–covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge genome-wide association studies effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

    1. Computational and Systems Biology
    2. Neuroscience
    Cesare V Parise, Marc O Ernst
    Research Article

    Audiovisual information reaches the brain via both sustained and transient input channels, representing signals’ intensity over time or changes thereof, respectively. To date, it is unclear to what extent transient and sustained input channels contribute to the combined percept obtained through multisensory integration. Based on the results of two novel psychophysical experiments, here we demonstrate the importance of the transient (instead of the sustained) channel for the integration of audiovisual signals. To account for the present results, we developed a biologically inspired, general-purpose model for multisensory integration, the multisensory correlation detectors, which combines correlated input from unimodal transient channels. Besides accounting for the results of our psychophysical experiments, this model could quantitatively replicate several recent findings in multisensory research, as tested against a large collection of published datasets. In particular, the model could simultaneously account for the perceived timing of audiovisual events, multisensory facilitation in detection tasks, causality judgments, and optimal integration. This study demonstrates that several phenomena in multisensory research that were previously considered unrelated, all stem from the integration of correlated input from unimodal transient channels.