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Four core properties of the human brain valuation system demonstrated in intracranial signals

Nature Neuroscience volume 23, pages 664–675 (2020)Cite this article

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Abstract

Estimating the value of alternative options is a key process in decision-making. Human functional magnetic resonance imaging and monkey electrophysiology studies have identified brain regions, such as the ventromedial prefrontal cortex (vmPFC) and lateral orbitofrontal cortex (lOFC), composing a value system. In the present study, in an effort to bridge across species and techniques, we investigated the neural representation of value ratings in 36 people with epilepsy, using intracranial electroencephalography. We found that subjective value was positively reflected in both vmPFC and lOFC high-frequency activity, plus several other brain regions, including the hippocampus. We then demonstrated that subjective value could be decoded (1) in pre-stimulus activity, (2) for various categories of items, (3) even during a distractive task and (4) as both linear and quadratic signals (encoding both value and confidence). Thus, our findings specify key functional properties of neural value signals (anticipation, generality, automaticity, quadraticity), which might provide insights into human irrational choice behaviors.

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Fig. 1: Behavioral tasks and results.
Fig. 2: Anatomical locations of recording sites in the whole dataset.
Fig. 3: Time–frequency investigation of the BVS-evoked response.
Fig. 4: Anticipation of value signaling during the pre-stimulus period in the BVS.
Fig. 5: Generality of value signaling in the BVS.
Fig. 6: Automaticity of value signaling in the BVS.
Fig. 7: Quadraticity of value signaling in the BVS.

Data availability

The data that support the findings of this study are available from the corresponding author (A.L.-P.) upon request.

Code availability

The custom code used to generate the figures and statistics are available from the corresponding author (A.L.-P.) upon request.

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Acknowledgements

We thank J.-P. Lachaux, M. El Zein, S. Bouret and E. Procyk for their helpful comments on the analysis of iEEG data, and Institut du Cerveau et de la Moelle épinière for purchasing the acquisition material. This work benefited from the program ‘Investissements d’Avenir’ (ANR-10-IAIHU-06), from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 720270 (HBP SGA1) and no. 785907 (HBP SGA2), from the LABEX CORTEX (ANR-11-LABX-0042) of Université de Lyon, within the program ‘Investissements d’Avenir’ (ANR-11-IDEX-0007), from IHU CESAME, within the program ‘Investissements d’Avenir’ (ANR-10-IBHU-0003), and from University Grenoble Alpes, within the program ‘Investissements d’Avenir’ (ANR-17-CE37-0018 and ANR-18-CE28-0016). A.L-P. received a PhD fellowship from Direction Générale de l’Armement and a grant from LabEx Bio-Psy. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Motivation, Brain and Behavior Team, Institut du Cerveau, Paris, France

    Alizée Lopez-Persem, Raphaëlle Abitbol & Mathias Pessiglione

  2. Institut du Cerveau (ICM; INSERM UMRS 1127, CNRS UMR 7225, Sorbonne Université), Paris, France

    Alizée Lopez-Persem, Raphaëlle Abitbol, Katia Lehongre, Claude Adam, Vincent Navarro, Philippe Domenech & Mathias Pessiglione

  3. Grenoble Institut Neuroscience (GIN), INSERM U1216, Université Grenoble Alpes, Grenoble, France

    Julien Bastin & Philippe Kahane

  4. Université Claude Bernard Lyon 1, Villeurbanne, France

    Mathilde Petton & Sylvain Rheims

  5. AP-HP, Epilepsy Unit, INSERM U1028, CNRS UMR5292, Paris, France

    Mathilde Petton, Claude Adam & Vincent Navarro

  6. Lyon Neuroscience Research Center, Pitié-Salpêtrière Hospital, Lyon-Bron, France

    Sylvain Rheims

  7. Department of Functional Neurology and Epileptology, Hospices Civils de Lyon and Université de Lyon, Lyon, France

    Sylvain Rheims

  8. CHU Grenoble Alpes, Grenoble, France

    Philippe Kahane

  9. Neurophysiology of Repetitive Behavior team, Institut du Cerveau, Paris, France

    Philippe Domenech

  10. Université Paris-Est Créteil, Créteil, France

    Philippe Domenech

  11. APHP, GHU Henri Mondor, DMU Psychiatrie et d’Addictologie, Créteil, France

    Philippe Domenech

Authors
  1. Alizée Lopez-Persem
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Contributions

M. Pessiglione designed all the experiments. A.L.-P., J.B., M. Petton and R.A. collected the data. J.B. provided preprocessing scripts. A.L.-P. performed the data analysis. C.A., V.N., S.R. and P.K. did the intracranial investigation and allowed the collection of iEEG data. K.L. supervised the access to patients in Paris. P.D. provided tools for the time–frequency analysis. A.L-P. and M. Pessiglione wrote the manuscript. All the authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Alizée Lopez-Persem or Mathias Pessiglione.

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Extended data

Extended Data Fig. 1 Distribution of ratings.

The different plots show distributions across rating types (first-order and second-order), tasks (age and likeability rating), stimuli (food, faces and paintings). Different colors correspond to different patients. Age ratings were restricted to the range 20-50 years old for faces and to the dates 1400-2000 for paintings.

Extended Data Fig. 2 Distribution of response times.

Two times were considered: response onset (first key press made to move the cursor and response validation (press of the validation key). Note that response onset for confidence rating was not recorded. The different plots show distributions across rating types (first-order and second-order), tasks (age and likeability rating), stimuli (food, faces and paintings). Different colors correspond to different patients.

Extended Data Fig. 3 Anatomical locations of vmPFC, lOFC, hippocampus and PHC recording sites in the whole and restricted datasets.

Top. Axial (top) and sagittal (bottom) slices of a brain template on which were superimposed the approximate locations of vmPFC (red), lOFC (blue), hippocampus (green) and PHC (brown) recording sites in all patients. Each dot represents one recording site. Bottom. Axial (top) and sagittal (bottom) slices of a brain template on which were superimposed the approximate locations of vmPFC, lOFC, hippocampus and PHC recording sites in patients who completed the long version of the task. Each dot represents one recording site. Different colors correspond to different patients.

Extended Data Fig. 4 Anatomical dissociation of the vmPFC and lOFC.

Left: Coronal view of the BVS, formed by the lOFC (blue) and the vmPFC (red). Right: Regression estimates of gamma and high gamma binned in function of the x-MNI location of each recording site. Dots (and error bars) indicate mean (and SEM) across n = 450 power time series. Solid curves represent a second-order polynomial fit across regression estimates, applied separately to each subpart of the BVS. Regression estimates varied with laterality (x-MNI coordinate), with a maximum at the center of each ROI (around x = −10 and 10 for vmPFC, and x=−30 and 30 for lOFC), supporting the anatomical position of the boundaries between ROIs.

Extended Data Fig. 5 Time-frequency investigation of the evoked response in supplementary BVS regions.

a, Anatomical localization of the inferior temporal cortex, fusiform anterior gyrus, anterior cingulate gyrus and opercular part of the inferior frontal gyrus (one line per region). All recording sites located in those (bilateral) areas were included in the ROI analysis. N indicates the number of recording sites in each ROI. b, Time-frequency analysis of the evoked response following visual item onset (dashed vertical line) averaged over all recording sites and all food likeability rating trials. Hotter colors indicate higher power. Horizontal dashed lines indicate boundaries between frequency bands that are investigated in panels c and d. c, Regression estimates of power against food likeability rating, averaged over the 0.5-1 s window, for each frequency band defined in b. Center lines, center circles, box limits, whiskers and points of the box plots respectively represent median, mean, interquartile range, extreme data points and outliers of the data distribution. Black stars indicate significance of regression estimates (two-sided one-sample t-test, p<0.05). d, Time course of regression estimates for the gamma and high-gamma frequency band. Stars indicate significant regression estimate (two-sided one-sample t-test, p<0.05, uncorrected) at each time point. Shaded areas highlight the time window of interest used in c and in following analyses. n indicates the number of power time series included in the analysis. Solid (dashed) lines indicates mean (SEM) across power time series.

Extended Data Fig. 6 Decoding food likeability rating from high-frequency OFC (top, purple) and (P)HC (bottom, yellow) activity.

Left: Time course of decoding accuracy from OFC (top, pooling vmPFC and lOFC) and (P)HC (bottom, pooling hippocampus and PHC) signals. Solid lines represent mean accuracy across 100 testing folds from 225 (and 201) recording sites in the OFC (and (P)HC) in the high frequencies (gamma and high gamma) and dashed lines represent SEM (across tested folds). The accuracy of the classifier was tested against the accuracy of a classifier trained on shuffled ratings (grey line). Stars indicate significant difference (two-sided one-sample t-test, p<0.05, cluster-wise corrected across time points). Right: Regions of interest used in each time course. Red: vmPFC, blue: lOFC, green: hippocampus, brown: PHC.

Extended Data Fig. 7 High-frequency activity in the BVS sorted by likeability rating tertiles (high and low).

High-gamma activity in each ROI (vmPFC in red, lOFC in blue, hippocampus in green and PHC in orange/brown) was averaged separately for first (low, dark) and third (high, light) tertiles of likeability ratings, during both food likeability rating task and non-food age rating task. Solid lines indicate mean and dashed lines indicate SEM across all trials, independently of patients and recording sites. nt indicates the number of power time series (trials) included in the analysis.

Extended Data Fig. 8 Linear and quadratic links with food likeability in the BVS.

BVS: comparison between short and long versions of the task, and between squared rating and confidence judgment. a,b, Time courses of regression estimates obtained for linear (top) and squared (bottom) food likeability, locked on stimulus onset (left) or first button press (right), during the short (a) or long (b) version of the task. c, Time courses of regression estimates for squared first-order judgment (top) and confidence on these judgments (bottom), locked on stimulus onset (left) or first button press (right). First-order judgments mean age and likeability ratings pooled together. Solid lines indicate mean and dashed lines indicate SEM across recording sites. Stars indicate significant regression estimate (two-sided one-sample t-test, p<0.05, uncorrected) at each time point. Statistics: a. (post-stimulus: OFC: t(323)=4.43, p=1.10-5, (P)HC: t(255)=4.54, p=9.10-6; pre-response: OFC: t(323)=5.36, p=2.10-7, (P)HC: t(255)=6.43, p=6.10-10; two-sided one-sample t-tests). c. Regression against actual confidence judgments in OFC: squared first-order ratings, post-stimulus: t(125)=5.87, p=4.10-8; pre-response: t(125)=5.88, p=3.10-8; confidence ratings, post-stimulus: t(125)=5.05, p=1.10-6 ; pre-response: t(125)=3.57, p=5.10-4.

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Lopez-Persem, A., Bastin, J., Petton, M. et al. Four core properties of the human brain valuation system demonstrated in intracranial signals. Nat Neurosci 23, 664–675 (2020). https://doi.org/10.1038/s41593-020-0615-9

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