Dorsolateral prefrontal neurons mediate subjective decisions and their variation in humans

Abstract

Subjective decisions play a vital role in human behavior because, while often grounded in fact, they are inherently based on personal beliefs that can vary broadly within and between individuals. While these properties set subjective decisions apart from many other sensorimotor processes and are of wide sociological impact, their single-neuronal basis in humans is unknown. Here we find cells in the dorsolateral prefrontal cortex (dlPFC) that reflect variations in the subjective decisions of humans when performing opinion-based tasks. These neurons changed their activities gradually as the participants transitioned between choice options but also reflected their unique point of conversion at equipoise. Focal disruption of the dlPFC, by contrast, diminished gradation between opposing decisions but had little effect on sensory perceptual choices or their motor report. These findings suggest that the human dlPFC plays an important role in subjective decisions and propose a mechanism for mediating their variation during opinion formation.

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Fig. 1: Single-neuronal recordings from the dlPFC during performance of a naturalistic situational assessment task.
Fig. 2: Trial structure, psychometric performances and variation in subjective decisions within and between the participants.
Fig. 3: Neuronal responses in the dlPFC correlate with variations in the participant’s subjective decisions.
Fig. 4: Neuronal modulation in relation to equipoise and voting profile.
Fig. 5: Neuronal responses and the effect of dlPFC lesions on sensory perceptual decisions.
Fig. 6: Temporal dynamic of neuronal response.
Fig. 7: Changes in neuronal activity in the dlPFC correlate with interpersonal variations in opinion.
Fig. 8: Focal dysfunction of the dlPFC leads to a loss of gradation between opposing decisions but does not influence the participant’s motor report.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The primary codes used to analyze the data are available from the corresponding author upon reasonable request.

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Acknowledgements

Z.M.W. is supported by NIH grant nos. R01HD059852 and R01NS091390, the Presidential Early Career Award for Scientists and Engineers and the Whitehall Foundation. M.J. is supported by the Banting Foundation, B.G. is supported by the NREF and Z.B.M. is supported by the NREF and NIH NRSA. K.H. is supported by the Simmons’s foundation. E.N.E. is supported by grant nos. NIH R01NS086422 and NIH UH3NS100548.

Author information

M.J. performed the analyses, helped obtain neuronal recordings and co-wrote the manuscript. B.G., Z.B.M., K.H., S.P., T.H. and E.N.E. helped perform the neuronal recordings and task set up. S.P. and T.H. helped program the task presentation. Z.M.W. conceived and designed the study, performed the neuronal recordings and wrote the manuscript.

Correspondence to Ziv M. Williams.

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The authors declare no competing interests.

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Journal peer review information: Nature Neuroscience thanks Rony Paz, Philip Starr, and other anonymous reviewer(s) for their contribution to the peer review of this work.

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Integrated supplementary information

Supplementary Figure 1 Recording stability, waveform morphology and single-unit isolation.

(a) The first principal component of the waveform morphology for a single-unit as a function of time. The lack of angular distortion in the centroid (red oval) indicates that there is no drift in waveform morphology over the course of recordings. This procedure was performed for all recorded neurons, and only neurons with stable waveform morphology over time were included. (b) Example of waveform morphologies and their isolations. Waveform morphologies and associated principal component distributions are displayed on the left and right, respectively. In the top panel, a single unit’s activity (red) was isolated from the electrode (number waveforms: 2,882). In the bottom panel, two units (green and red) were isolated (number waveforms: 7,876 green, 432 red). Light lines and dots denote individual waveforms. Dark lines represent average waveform. The horizontal bar indicates a 500 μs interval for scale. The gray areas in the PC space represent noise.

Supplementary Figure 2 Example neurons modulated by safe or unsafe choice selections during scene presentation in the situational assessment task.

Peri-stimulus spike histogram and trial heatmap of three example neurons during task performance. The shaded bands represent standard error of the mean. The first two neurons modulate more during unsafe scenes whereas the activity of the last neuron increases during safe trials. The first vertical dashed line (0 s) represents scene presentation onset and the second vertical dashed line (3 s) represents the choice option presentation. The grayed area represents the time period considered for neuronal analysis, and was chosen to reflect the corresponding stimulus-response delay and scene presentation times.

Supplementary Figure 3 Correlation between neuronal activity and choice.

The average (a) and the distribution (b) of correlation coefficients between neural activity and single-trial choice responses.

Supplementary Figure 4 Neuronal modulation in relation to equipoise and choice report.

Distribution of population firing rates near equipoise (that is, ordinal positions −1/+1) compared to firing rates far from equipoise (that is, ordinal positions −4/+4). Inset: average population firing activity when choices were made near equipoise compared to when they were not.

Supplementary Figure 5 Evaluating the relation between neural activity and motor response reaction time.

Scatter plot displaying the mean firing activity of all cells across all trials and their relation to the participant’s reaction times (the participants were given up to 10 s to make their choice). The red line indicates the degree of correlation and is non-significant (Pearson’s correlation r = 0.004, p = 0.78). Similar results were obtained if examining only activity between 100 and 101 (note that given the sparsity of spiking on individual trials, some data points reflect very low rates). Here, the negative log scale (for example, 10−8) indicate that the firing rate approaches zero for those individual trials.

Supplementary Figure 6 Example of a neuron modulated by safe versus unsafe choice selections during object or item presentation.

Peri-stimulus spike histogram and trial heatmap of an example neuron responding to the items (left) but not the scenes (right). The shaded bands represent s.e.m. The first vertical dashed line (0 s) represents scene presentation onset and the second vertical dashed line (3 s) represents the choice option presentation. The grayed area represents the time period considered for neuronal analysis, and was chosen to reflect the corresponding stimulus-response delay and scene presentation times.

Supplementary Figure 7 Psychometric profiles on the subjective situational assessment task for participants with dlPFC lesions and control.

(a) Psychometric profile and the corresponding raw data points for participants with dlPFC lesions (n = 4). (b) Psychometric profile and the corresponding raw data points for control participants who underwent surgery (n = 11). Here, the individual participants are additionally color coded.

Supplementary Figure 8 Psychometric profiles on the subjective trustworthiness assessment task for participants with dlPFC lesions and control based on pathology.

(a) Examples of faces generated by a data-driven computational model for the subjective assessment of trustworthiness. (b) Averaged voting profiles of participants with dlPFC lesions (n = 5), control participants (that is, patients which have undergone neuronal recording from the dlPFC but no lesioning; n = 2) and healthy participants (n = 12). Overall, the participants with dlPFC lesions displayed a significant increase in slope of their psychometric curves when compared to either the healthy participants (* p = 0.04; one-sided Wilcoxon test) or the control participants that had undergone neuronal recordings (** p = 0.01; one-sided Wilcoxon test), whereas the latter groups display no difference in their slopes of psychometric curves (p = 0.25; two-sided Wilcoxon test). The boxplots represent the median (thick lines), quartiles (boxes) and the range (whiskers) of the slopes for different subject groups.

Supplementary Figure 9 Evaluating the relation between neuronal response and underlying pathology.

The participants were divided into those that had PD (n = 2), ET (n = 7) or other disorders (n = 2; dystonia and pain). Overall, the participants appeared to possess a similar proportion of neurons that responded to opinion across the three different disease states. The boxplots represent the median (red lines), quartiles (boxes) and the range (whiskers) of the percentage of significant neurons across patients with different underlying pathology.

Supplementary Figure 10 Psychometric profiles on the subjective situational assessment task for participants with dlPFC lesions and control based on pathology.

Psychometric profiles for participants with gliomas (n = 6) non-glioma (n = 3) lesions, and control subjects (n = 11) are displayed in red, green, and gray respectively. Overall, subjects with non-glioma lesions displayed a similar slope compared to glioma patients (mean ± s.e.m.: 109 ± 9%Δ versus 115 ± 4%Δ respectively; two-sided Wilcoxon test, p = 0.99) and a significantly higher slope compared to control. Furthermore, both the glial (one-sided Wilcoxon test, p = 0.026) and non-glial (one-sided Wilcoxon test, p = 0.028) lesion participants displayed a significantly steeper voting profile slope compared to control. The boxplots represent the median (thick lines), quartiles (boxes) and the range (whiskers) of the slopes for different subject groups.

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Supplementary Figs. 1–10 and Supplementary Tables 1–4.

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