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Activation and disruption of a neural mechanism for novel choice in monkeys

Nature volume 591, pages 270–274 (2021)Cite this article

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Abstract

Neural mechanisms that mediate the ability to make value-guided decisions have received substantial attention in humans and animals1,2,3,4,5,6. Experiments in animals typically involve long training periods. By contrast, choices in the real world often need to be made between new options spontaneously. It is therefore possible that the neural mechanisms targeted in animal studies differ from those required for new decisions, which are typical of human imaging studies. Here we show that the primate medial frontal cortex (MFC)7 is involved in making new inferential choices when the options have not been previously experienced. Macaques spontaneously inferred the values of new options via similarities with the component parts of previously encountered options. Functional magnetic resonance imaging (fMRI) suggested that this ability was mediated by the MFC, which is rarely investigated in monkeys3; MFC activity reflected different processes of comparison for unfamiliar and familiar options. Multidimensional representations of options in the MFC used a coding scheme resembling that of grid cells, which is well known in spatial navigation8,9, to integrate dimensions in this non-physical space10 during novel decision-making. By contrast, the orbitofrontal cortex held specific object-based value representations1,11. In addition, minimally invasive ultrasonic disruption12 of MFC, but not adjacent tissue, altered the estimation of novel choice values.

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Fig. 1: Experimental design.
Fig. 2: Experiment 1, neural correlates of familiar and novel decisions.
Fig. 3: Experiment 2, stimulus and value space neural representations.
Fig. 4: Experiment 3, behavioural effect of neurostimulation.

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Data availability

The behavioural data supporting this study are available in the OSF repository, https://osf.io/kzhaq/. The raw fMRI data supporting this study are available from the corresponding author upon reasonable request. The unthresholded statistical fMRI maps associated with this study are available in the NeuroVault repository, https://neurovault.org/collections/8491/. Source data are provided with this paper.

Code availability

The MATLAB custom code supporting the behavioural results of this study is available in the same OSF repository, https://osf.io/kzhaq/. Any remaining code that supports the findings of this study is available from the corresponding author upon reasonable request.

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Acknowledgements

We thank the Biomedical Services staff at the University of Oxford for their care of the macaques. This work was funded by the Medical Research Council (MR/P024955/1), the Wellcome Trust (WT100973AIA, WT101092MA, 105238/Z/14/Z, 103184/Z/13/Z, 203139/Z/16/Z, 105651/Z/14/Z) and the Economic and Social Research Council (ES/J500112/1).

Author information

Author notes

  1. Lennart Verhagen

    Present address: Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands

  2. These authors jointly supervised this work: Miriam C. Klein-Flügge, Matthew F. S. Rushworth

Authors and Affiliations

  1. Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, UK

    Alessandro Bongioanni, Davide Folloni, Lennart Verhagen, Jérôme Sallet, Miriam C. Klein-Flügge & Matthew F. S. Rushworth

  2. Stem Cell and Brain Research Institute, Université Lyon 1, INSERM, Bron, France

    Jérôme Sallet

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Contributions

A.B., M.C.K.-F. and M.F.S.R. designed the research; A.B. (experiments 1–3) and D.F. (experiment 3) collected the data; J.S. developed the experimental set-up and tools; L.V. developed the pre-processing code (experiments 1 and 2) and A.B. wrote all other analysis pipelines; A.B., M.C.K.-F. and M.F.S.R. analysed the data; A.B., M.C.K.-F. and M.F.S.R. wrote the manuscript.

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Correspondence to Alessandro Bongioanni.

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Peer review information Nature thanks Birte Forstmann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Experiment 1 behaviour.

a, b, Number of times each stimulus was chosen within experiments 1 (a) and 2 (b). White: familiar options; grey: novel options (shading corresponds to relative frequency). Note that the same training trials were added to the familiar test trials in both a and b. c, Response time (RT) distribution. The x axis is on a logarithmic scale. Mean familiar and novel RT = 3.082 and 3.060 s respectively; median familiar and novel RTs = 0.931 s and 0.969 s respectively. d, Response times by subject and condition. We found no difference between familiar and novel response times (two-tailed paired t-test for mean: t11 = 0.1, P = 0.92 and median: t11 = 1.4, P = 0.19). Each dot in the graphs represents one subject and one level of dimensionality. A Bayesian ANOVA over all trials (n = 8,511) with subject as mixed effect and familiarity as fixed effect supported the null hypothesis—no difference between familiar and novel response times—with a posterior likelihood of 0.91. e, Logistic fit of the probability of choosing the left-hand option as a function of left minus right subjective value, as in Fig. 2c for objective performance. f, Subjective performance across conditions, as in Fig. 2d. Performance is expressed as the proportion of choices in favour of the option with highest subjective value, as estimated by our model. Bars represent the mean of four subjects. In all panels n = 4 subjects (55–75 training sessions each, merged; 12 test sessions each, merged).

Source data

Extended Data Fig. 2 Additional fMRI results of experiment 1.

a, The parametric modulation of BOLD signal by the difference in value between the unchosen and chosen options, estimated with GLM 1, activated other clusters beyond the OFC depicted in Fig. 2e. One cluster encompassed numerous areas including ACC, dlPFC, insula (cluster correction for multiple comparisons, with a threshold of Z > 2.3, P = 10−5, peak Z = 4.66 [11.5 2.5 17.5]); others were located in the visual cortex (not illustrated). In a–d, group-level mixed effects were carried out by FSL’s FLAME 1+2. b, We also report an observation highlighting an area likely involved in our task in the midbrain in the vicinity of the ventral tegmental area, (P < 0.001, uncorrected). A similar observation has been made in a task resembling our familiar task in analyses of individual neuron firing rates in the ventral tegmental area (M. Matsumoto, personal communication). c, The contrast of BOLD modulation by the value comparison signal (chosen minus unchosen value) in novel and familiar trials, estimated with GLM 2, which activated MFC as depicted in Fig. 2i, is also visible in the ventral striatum (P < 0.001, uncorrected). A similar observation has been made in a task resembling our familiar task in analyses of individual neuron firing rates in the ventral striatum47. d, fMRI effect of value comparison as in Fig. 2i, but using a subjective estimate of value (whole-brain FWE corrected at Z > 2.3, P = 0.003, peak Z = 3.88 [0 24.5 7.5]). e, Time course of the value comparison signal from GLM2 in OFC (top) in familiar and novel trials separately, in analogy with what reported for MFC in Fig. 2j, replicated here (bottom). All conventions as in the main figures, in particular the coloured shades represent the s.e.m. across sessions. f, Time courses of the component effects (value of chosen option; value of unchosen option) as in Fig. 2g, k but separately for familiar, novel, OFC and MFC. Top row, orbitofrontal ROI; bottom row, medial frontal ROI. g, Time course analysis of the value comparison signal, as in Fig. 2j, but using a subjective definition of value. h, i, Time courses of the parametric modulation of the BOLD signal for the same contrasts as in e and f and ROIs defined in the same way, but time-locked to the stimulus presentation and removing trials with RT > 3 s (coloured shades represent s.e.m.). j, Schema of the relationship between network dynamic and BOLD response. This shows that in a drift-diffusion or an attractor model, if activity falls immediately after the threshold is reached (thick lines in the top panel and bright colours in the bottom panel), the BOLD response will be negatively correlated with value difference, as in macaques; but if the activity is maintained for longer (dotted lines in the top panel and shaded colours in the bottom panel), the BOLD response will be positively correlated with value difference, as in humans (Supplementary Information). Illustration reproduced with permission3. In all panels n = 4 subjects × n = 12 sessions.

Source data

Extended Data Fig. 3 fMRI design matrices.

Correlations among task-related regressors (averaged across sessions) in all GLMs. In addition, for GLM 1 (a) the 13 motion regressors and the first 10 low-quality volume regressors are also illustrated. For all other GLMs, these regressors are not illustrated. Colours represent the average across subjects (n = 4) and sessions (n = 12, 12, 3, 5 in a–d, respectively). Black contour: regressors of interest. a, GLM 1: experiment 1 decision task, value comparison analysis, all trials. b, GLM 2: experiment 1 decision task, value comparison analysis, trials divided by condition depending on familiarity and dimensionality. There are six copies of each decision-related regressor. c, GLM 3: experiment 2 single-option task, repetition suppression analysis. There are six copies of each decision-related regressor, one per condition depending on the preceding stimulus. d, GLM 4: experiment 2 single-option task, grid-code analysis. There are two copies of each decision-related regressor: valid and invalid trials. Correlations here shown for sixfold modulations of the BOLD signal; they were similar for other periodicities (fourfold to eightfold), as well as for the two halves of the phase consistency design.

Source data

Extended Data Fig. 4 Additional fMRI results of experiment 2.

a, Using whole-brain FWE cluster correction, no significant repetition suppression effects were found elsewhere in the brain. However, for the sake of completeness, we illustrate here the distribution of uncorrected effects throughout the brain indicating the locations of potential representations of specific identities of stimuli; contrast of BOLD activity elicited by stimuli preceded by identical stimuli vs stimuli preceded by different stimuli (ID − DV, GLM 3). Outside the prefrontal mask (Extended Data Fig. 9d), repetition suppression effects analogous with those reported in the main text for anterior OFC (Fig. 3e), were observed in the anterior temporal lobe or perirhinal cortex, in entorhinal cortex and the perigenual ACC, in the principal sulcus and in medial frontal pole (Z > 2.3, uncorrected). The brighter anterior medial effect is part of the anterior OFC (aOFC) cluster reported in the main text. In a–c, n = 4 subjects × n = 3 sessions. b, After Bonferroni correction for multiple comparisons (one-sided t-tests, all t11 < 1.42, all adjusted P > 0.28) there were no significant repetition suppression effects in aOFC when successive stimuli had the same value but different probability and magnitude components (SV) compared to successive stimuli with different values (DV), nor when successive stimuli sharing just the same magnitude component (SM) or the same probability component (SP) were compared to DV stimuli. Error bars represent s.e.m. across sessions. As in Fig. 3e, this test is restricted to sessions 3–5 of experiment 2. c, No significant effect of repetition suppression was observed in MFC (ROI identified in experiment 1) after Bonferroni correction (one-sided t-tests, all t11 < 2.1, all adjusted P > 0.12) for the same contrasts as shown in b, plus the effect of identical stimuli versus different stimuli (ID − DV). Error bars represent s.e.m. d, Temporal signal to noise ratio; the tSNR was very good in the anterior part of brain, but slightly lower at the edges, which may potentially explain the weak grid effect in entorhinal cortex. e, Illustration of the relationship between trajectory angle and response of grid cells. In grey, schema of the receptive field of an ideal grid cell (multiple neighbouring cells tend to align with each other). In red, trajectory from one location to the next one in a two-dimensional space. The amount of activation of the cell’s receptive field varies continuously with the trajectory angle and repeats itself every 60°. In this example, it is maximum at 0° and 60° and minimum at 30°, but in the general case the orientation of the grid is not known a priori. f, Additional sections of the whole-brain distribution of one-sided quadrature test scores for a hexagonal grid code, also reported in Fig. 3f, averaged across 20 sessions and thresholded at F > 1.74 (P < 0.001 uncorrected). Consistent with previous results in humans, we observed a sixfold modulation of the BOLD signal in entorhinal cortex (ERH), visual cortex (VC), anterior cingulate cortex (ACC), frontal eye field (FEF), temporal pole (TP), and posterior intraparietal lobule pIPL. These results are presented without cluster correction (because of the non-normal distribution of the F-statistics, standard cluster correction could not be performed). In d, f–i, n = 4 subjects × n = 5 sessions. g, Separate null distributions for each periodicity. Each distribution is based on all sessions and locations within the brain, for a given GLM. One-sided non-parametric tests of each periodicity based on these distributions gave the same results as those based on the joint distribution. Bonferroni-corrected P values are reported in the table. h, i, Statistical results from the non-parametric one-sided grid code test at different periodicities. No significant effect after Bonferroni correction for multiple comparisons (factor 5) in OFC (h), nor entorhinal cortex (ERH) (i). The results reported in i refer to bilateral ERH; they were qualitatively the same in each hemisphere. The shuffled null distribution is shown to the right and the F values indexing grid effects for each periodicity are shown to the left in each panel. None of the periodicities reached significance in either region, even if some voxels in entorhinal cortex exceeded the P < 0.001 threshold for the sixfold symmetry, as reported in f.

Source data

Extended Data Fig. 5 Additional data related to experiment 3.

a, TUS locations recorded at end of each session. b, Logistic fits of choices in experiment 3, divided by subject and condition. Each circle represents 22 choices. There was no effect of neurostimulation on the slope of the psychometric curve (repeated-measures ANOVA: F2,4 = 0.05, P = 0.95). c, Parameter estimates of all the subjective model parameters. Error bars represent s.e.m. across the four sessions per subject per condition, fitted separately. A systematic effect of stimulation is present for the integration coefficient after Bonferroni correction (F2,4 = 24.2, uncorrected P = 0.006, corrected P = 0.024), but no effects of magnitude vs probability (F2,4 = 1.5, uncorrected P = 0.32); inverse temperature (F2,4 = 0.2, uncorrected P = 0.83) or side bias (F2,4 = 0.9, uncorrected P = 0.48). Post hoc one-tailed paired t-tests on the integration coefficient confirmed a significant effect of ultrasound stimulation in novel trials specific to the MFC condition. MFC vs sham, t2 = −10.1, Cohen’s d = −5.8, P = 0.005; MFC vs control site t2 = −4.4, Cohen’s d = −2.6, P = 0.024. Note that the large error margins in the ‘magnitude vs probability’ parameter β fits in monkey B are due to the fact that this parameter only affects behaviour when the integration coefficient η is lower than 1. In all panels, n = 3 subjects × n = 4 sessions per condition × 3 conditions.

Source data

Extended Data Fig. 6 Subjective value modelling.

a, Results of the full model comparison based on experiment 1 data (Methods). 512 models were tested, each one including or excluding 9 possible parameters corresponding to decision biases. The winning model includes 4 parameters: integration coefficient η, magnitude vs probability weight β, inverse temperature θ, fixed side bias ζ1, with a BIC difference of 15.9 relative to the second-best model, corresponding to a Schwarz weight = 0.999. N = 4 subjects (12 sessions each, merged). b, Equation defining the winning model (Methods, equation (1)). c, Parameter estimates for the four parameters of the winning subjective value model in experiment 1. d, Simulation of a reduced model comparison, created to test whether the way individual dimensions (magnitude, probability) are represented by linear or nonlinear functions can be dissociated from the way the dimensions are combined together. Nine (three by three) models were used to generate artificial decisions. They differed in two respects, dimension combination: (i) MUL, multiplicative; (ii) MIX, mixed multiplicative and additive; (iii) ADD, additive; and basis functions: (i) LIN, linear; (ii) PT, nonlinear distortions defined as in prospect theory; (iii) LOG, logarithmic. Each panel represents a separate model comparison based on artificial choices generated according to the model described in the panel title. For each model, 100 repetitions were run with 3 simulated agents performing 440 decisions each; in all cases the total Akaike weight indicated a strong conditional probability in favour of the true model; onsets refer to the proportion of repetitions in which the true model won the model comparison. e, Results of the model comparison based on the real data from experiment 3, in which the same 9 models from the previous analysis (d) were compared. In the baseline condition and also in the ultrasound stimulation conditions, choices were best represented by a linear model (no saturating basis functions) with mixed multiplicative and additive dimension combination, which is consistent with the winning model in experiment 1 (a, b). N = 4 subjects (4 sessions each per condition, merged). f, Results of a simulation using the winning model. This shows good recovery of all model parameters and most importantly the integration coefficient η: correlation between true and fitted value r = 0.89. Recovery based on 440 artificially generated choices; the red lines indicate ground-truth values (an equivalent result was obtained based on experiment 1 stimulus schedules). g, Parameter recovery from a further simulation of experiment 3 with the same model: three parameters were fixed at realistic values and only one was varied at any time. Recovery based on 440 artificially generated choices; the red lines indicate ground-truth values. On-diagonal panels show recovery of each parameter as a function of itself (high accuracy), off-diagonal panels show recovery of each parameter as a function of the irrelevant ones (no cross-correlations).

Source data

Extended Data Fig. 7 Human decision-making data.

Both humans and macaques perform binary value-based decision-making in a similar manner. These human data were previously published4, but here they have been re-analysed (with permission) using the same approach as the one applied to the data obtained from macaques in experiment 1. a, 21 subjects performed 300 choices each; a model comparison identified the same subjective value model (ηβθ) described above as the one best explaining their behaviour, with a Schwarz weight >0.999. Side bias ζ was not tested. b, The BIC score difference with the second-best model was 75. c, The human subjects displayed a range of different behaviours, as revealed by the histograms of the integration, magnitude versus probability, and inverse temperature parameter fits. In particular, we noted how some subjects were fully additive (η = 0) but others were very good at integrating the two dimensions of value (η approaching 1).

Source data

Extended Data Fig. 8 fMRI effects split by subject and session.

a, Illustration of effect sizes in each session for key analyses shown separately by subject and GLM. Mean and s.e.m. across sessions. No statistical test was performed. N = 12 sessions for GLMs 1–2, N = 3 sessions for GLM 3, N = 5 sessions for GLM4. b, Key effects plotted session by session (mean and s.e.m. across subjects). The continuous horizontal line represents the absence of an effect. The dotted line is a linear fit. N = 4 subjects. No trend was statistically significant (two-sided t-test GLM 1: t10 = −1.30, P = 0.22; GLM 2: t10 = 0.63, P = 0.54; GLM 4: t3 = 1.57, P = 0.21). Note that the selection of the aOFC ROI for GLM3 (third graph in b) was biased towards finding a temporal trend, because it was based on the effect of the last three sessions of each subject. It was merely included here for completeness.

Source data

Extended Data Fig. 9 Masks and ROIs.

a, Spherical (5 mm diameter) OFC ROI used for time course analysis of experiment 1 (Fig. 2f–h, Extended Data Fig. 2e–i, top row), centred on the peak of the activation for GLM 1, reported in Fig. 2e, with a leave-one-out procedure. b, Spherical (5 mm diameter) MFC ROI used for experiment 1 time course analysis and repetition suppression analysis (Fig. 2j–l, Extended Data Figs. 2e–i, 4c), centred on the peak of the activation for GLM 2, reported in Fig. 2i, with a leave-one-out procedure. c, Spherical (5 mm diameter) aOFC ROI used for further repetition suppression analyses (Extended Data Fig. 4b), centred on the peak of the activation for GLM 3, reported in Fig. 3e, with a leave-one-out procedure. d, Frontal cortex mask used in the repetition suppression analysis (Fig. 3e) defined as cortical grey matter anterior to y = 4.5. e, 3 × 3× 3 voxel OFC ROI defined in acquisition space for the grid-code signature analysis (Extended Data Fig. 4h), centred on the peak of the activation for GLM 1. f, 3 × 3 ×3 voxel MFC ROI defined in acquisition space for the grid-code signature analysis (Fig. 3g–i), centred on the peak of the activation for GLM 2. The locations in e and f are as in a and b (bar the leave-one-out procedure) and are therefore independent with respect to experiment 2. g, Bilateral 3 × 3 × 3 voxel entorhinal cortex ROI used for grid-code signature analysis (Extended Data Fig. 4i) defined a priori.

Extended Data Table 1 Loci of fMRI activations
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Supplementary information

Supplementary Information

This file contains Supplementary Table 1 which shows the number of planned and completed trials in each session of Experiment 2, and a Supplementary Discussion about the sign of the fMRI comparison signal across species.

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Bongioanni, A., Folloni, D., Verhagen, L. et al. Activation and disruption of a neural mechanism for novel choice in monkeys. Nature 591, 270–274 (2021). https://doi.org/10.1038/s41586-020-03115-5

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