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Using temperature to analyze the neural basis of a time-based decision

Nature Neuroscience volume 26, pages 1407–1416 (2023)Cite this article

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Abstract

The basal ganglia are thought to contribute to decision-making and motor control. These functions are critically dependent on timing information, which can be extracted from the evolving state of neural populations in their main input structure, the striatum. However, it is debated whether striatal activity underlies latent, dynamic decision processes or kinematics of overt movement. Here, we measured the impact of temperature on striatal population activity and the behavior of rats, and compared the observed effects with neural activity and behavior collected in multiple versions of a temporal categorization task. Cooling caused dilation, and warming contraction, of both neural activity and patterns of judgment in time, mimicking endogenous decision-related variability in striatal activity. However, temperature did not similarly affect movement kinematics. These data provide compelling evidence that the timecourse of evolving striatal activity dictates the speed of a latent process that is used to guide choices, but not continuous motor control. More broadly, they establish temporal scaling of population activity as a likely neural basis for variability in timing behavior.

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Fig. 1: Temperature rescaled single striatal neuron responses monotonically in time.
Fig. 2: Temperature rescaled striatal population activity in time leading to graded monotonic modulation of decoded time estimates.
Fig. 3: Conditioning striatal activity on behavior produced monotonic modulation of population speed and decoded estimates of elapsed time.
Fig. 4: Conditioning behavior on striatal population speed or temperature produced monotonic modulation of temporal judgments.
Fig. 5: Striatal temperature affected the kinematics of movement nonmonotonically.

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Data availability

Raw data are available in a Figshare public repository66.

Code availability

Analysis code that supports the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

We thank B. Atallah and C. Haimerl for comments on versions of the manuscript and the entire Paton laboratory, past and present, for feedback during the course of this project. We would also like to thank the ABBE Facility and the Scientific Hardware, Histopathology and Rodent Champalimaud Research Platforms for unparalleled technical assistance. Special thanks to A. Silva, P. Carriço, D. Bento and F. Carvalho for tireless support in designing and troubleshooting our closed-loop temperature control system. We thank F. Fernandes and D. Nunes for acquiring the MRI scans and M. Toro and R. Sousa for help with animal training. This work was developed with the support from the research infrastructure Congento, cofinanced by Lisboa Regional Operational Programme (Lisboa2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF) and Fundação para a Ciência e Tecnologia (FCT, Portugal) under the project LISBOA-01-0145-FEDER-022170. The work was funded by an HHMI International Research Scholar Award to J.J.P. (55008745), a European Research Council Consolidator grant (DYCOCIRC - REP-772339-1) to J.J.P., a Bial bursary for scientific research to J.J.P. (193/2016), internal support from the Champalimaud Foundation, and PhD fellowships from FCT to F.S.R. (SFRH/BD/130037/2017), B.F.C. (PD/BD/105945/2014) and A.I.G. (PD/BD/128291/2017). We thank the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Author notes

  1. Tiago Monteiro

    Present address: Department of Biology, University of Oxford, Oxford, UK

  2. Margarida Pexirra

    Present address: Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK

  3. Bruno F. Cruz

    Present address: NeuroGEARS Ltd., London, UK

  4. These authors contributed equally: Tiago Monteiro, Filipe S. Rodrigues, Margarida Pexirra.

Authors and Affiliations

  1. Neuroscience Programme, Champalimaud Foundation, Lisbon, Portugal

    Tiago Monteiro, Filipe S. Rodrigues, Margarida Pexirra, Bruno F. Cruz, Ana I. Gonçalves & Joseph J. Paton

  2. Institute of Neurobiology, UNAM Juriquilla, Juriquilla, Mexico

    Pavel E. Rueda-Orozco

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Contributions

T.M., F.S.R., M.P. and J.J.P. devised the experiments. T.M., F.S.R. and M.P. performed all experiments, analyzed the data and drafted and edited the manuscript. B.F.C. helped design and perform the acute electrophysiology experiments and reviewed the manuscript. A.I.G. performed a subset of temperature manipulation experiments during behavior and reviewed the manuscript. P.E.R.-O. devised and assisted in implementing the method of optogenetically stimulating reproducible striatal dynamics and reviewed the manuscript. J.J.P. supervised all aspects of the project and drafted and edited the manuscript.

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Correspondence to Joseph J. Paton.

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Extended data

Extended Data Fig. 1 Spatiotemporal characterization of thermoelectric device (TED).

(a) Schematic of the preparation in which we set our TED to one of several manipulation temperatures (T = {5, 15, 20, 25, 30, 45} °C) while measuring temperature at its lower plate and probe tip simultaneously. (b) Temperature measured at the TED plate and probe tip thermistors. (c) Temperature traces measured at the probe tip thermistor during manipulation blocks aligned to block transitions. Solid lines represent model fits. (d) Schematic of the preparation in which we set our TED to one of several manipulation temperatures (T = {15, 25, 42, 45} °C) while measuring temperature at its lower plate a movable temperature probe simultaneously. (e) Decay parameters for models fit to manipulation temperatures (as shown in (C)) across the 2 experiments (manipulation temperatures that are common to both experiments are connected with solid colored lines). (f) Same as (E), but for the gain / offset parameter across all model fits.

Extended Data Fig. 2 Histological reconstruction of TED, optical fiber and recordings probe placements for acute and chronic experiments.

(a) Intermediate medial-lateral (ML) locations of TED probes (black markers), optical fibers (blue markers) and Neuropixels probes (white triangles) projected onto a reference sagittal slice (ML = 2.62 mm from Bregma). (b) Distributions of relative recording depths for all animals (N = 4) and recorded units (N = 335, before enforcing the minimum firing rate selection criterion, see methods). Horizontal dashed line depicts corpus callosum. Putative motor cortical and striatal neurons in gray and black, respectively. Histograms’ relative depth is overlaid in (A) using the same color scheme. We were unable to clearly identify the Neuropixels tract for animal I. (c–e) Intermediate anterior posterior (AP) location of microwire recording bundles in the no-fixation (C, orange, N = 3 animals implanted unilaterally), fixation version (D, petrol blue, N = 3 animals implanted bilaterally) and TED (E) probes for striatal (black markers, N = 6) and cortical (gray markers, N = 4) targets projected onto target coronal slice (AP = +0.84 mm from Bregma). White markers show implant locations for the no-fixation cohort (N = 5).

Extended Data Fig. 3 Temporal scaling as opposed to shifting provided a better account of temperature’s effect on neural activity.

(a) Top: Simulated spike density functions exhibiting bidirectional and dose-dependent temporal scaling with temperature. Middle: Templates built by warping a control spike density function (thicker black line) in time by scale factors ranging from 0.625 (maximum contraction) to 1.75 (maximum dilation). Note that when applying this method to data, this control response is not the same as the one shown in the top panel, as the two are built using two non-overlapping random sets of control trials. Bottom: Thick lines represent the coefficient of determination (R2) for all scaled templates in the middle panel regressed against each of the target spike density functions shown at the top. We computed this objective function for each neuron-temperature condition pair and took its global maximum as the corresponding temporal scaling factor, highlighted here by the larger markers. Thinner lines and smaller markers depict R2 values for a similar regression procedure applied to a series of shifted, as opposed to scaled, templates. (b) Same as (A), except that for artificially temporally shifted responses relative to control (top), temporally shifted templates (middle), and their regression outcomes (bottom). The thinner lines and smaller markers respectively represent the R2 curves and maxima resulting from regressing the scaled templates from A (middle) against the shifted targets in B (top). Conversely, the result of regressing shifted templates against scaled targets is plotted in the same manner in (A, bottom). (c) To assess whether the effects of temperature on individual striatal responses were better accounted for by temporal scaling or shifting, we built two separate spiking models in which we either injected one effect or the other. Briefly, we modeled 500 control firing rate functions as gaussian bumps defined over 1.5 s with means spanning the interval from 150 ms to 750 ms (Fig. 1e) and a standard deviation of 50 ms. The amplitudes of the resulting probability density functions were rescaled so that their distribution of mean firing rates matched that of striatal data. Next, we created one additional rate function per neuron per manipulation condition by either shifting or scaling its control response in time. Again, the distribution of generative temporal scaling factors and shifts used was informed by the empirical distributions of these metrics extracted from striatal data. We then generated 150 spike trains of each condition per neuron by sampling spike times from inhomogeneous Poisson point processes with the aforementioned condition-specific responses as their time-dependent rate parameters. From this point on, we proceeded to analyze the resulting surrogate spike data in the exact same way we did for the striatal data, by first averaging trials within condition, generating libraries of templates and then computing temporal scaling factors and shifts. Finally, for each ‘neuron’-condition pair within each model, we stored the R2 values corresponding to the best-matching scaled and shifted templates and subtracted the former from the latter to build the distributions shown here at the top (scaling model) and middle (shifting model) panels. Thick solid sigmoidal lines represent the CDFs of each condition’s R2 difference. Thin vertical black lines denote control mean differences. Small horizontal colored lines link the respective means of the corresponding manipulation and control distributions. (d) Same as (C), but for striatal data.

Extended Data Fig. 4 Discrimination performance in both task variants was qualitatively similar before and after TED implantation.

(a) Discrimination performance of rats trained on the no-fixation variant of the interval discrimination task and implanted with the initial version of our custom TED targeting the DS (N = 4) on the last day of training before surgery. Gray lines are psychometric fits to individual animals. The black line is a fit to the average across animals. The underlying choice data is shown for the cohort average (± s.e.m.). (b) Same as (A), but for rats that trained on the fixation task variant and implanted with the initial (N = 1) or final version of our custom TED targeting the DS (N = 6). (c) Same as (B), but for rats implanted with the final version of our custom TED targeting M1 (N = 4). (d) Schematized timeline for a typical experimental rat. Elongated bars illustrate long periods of time, whereas squares represent individual daily sessions, which, with the exception of surgeries, lasted for 2 hours. (e-g) Same as (A-C), but for the last post-surgery training session in advance of starting temperature manipulation sessions.

Extended Data Fig. 5 An initial TED capable of a single mild cooling temperature produced qualitatively similar effects on timing judgments in both variants of the interval discrimination task.

(a) Performance in the no-fixation version of the interval discrimination task conditioned on neural population speed (N = 3). Psychometric curves split by whether activity progressed more slowly (blue) or at a typical speed (black, see methods). Bottom right inset: Differences in proportion of long choices from the slow speed condition to the typical speed condition (mean ± propagated s.e.m.). Top-left inset: Marginal posterior distributions of the threshold parameter for each speed condition’s psychometric fit. Solid black lines represent the M.A.P. point estimates. (b) Analogous to (A), but conditioned on whether striatal temperature was set to control (black) or a mild cooling (teal) dose (N = 4). Psychometric functions fit to cross-animal averages (± s.e.m.) of temperature-split psychophysical data, respectively shown as solid lines and markers of matching color. Bottom right inset: Average differences in proportion of long choices from the mild cooling condition to control (± propagated s.e.m.). Top-left inset: Marginal posterior distributions of the threshold parameter for each condition’s psychometric fit. Solid black lines represent the M.A.P. point estimates. (C-D) Effect of temperature on psychophysical thresholds. (c) Markers represent M.A.P. estimates and transparent patches the corresponding 95% confidence intervals of threshold parameters fit to individual animals’ performance on control (vertical axis) versus mild cooling blocks (horizontal axis). Single animals contribute one data point of each color. Top-right inset: Distribution of threshold differences between the mild cooling and control conditions (mean ± s.e.m.). (d) Left: Distributions of threshold dilation as a function of induced temperature changes (one-sample two-tailed t-test, t(3) = 5.67, P = 0.01). Markers linked by solid black lines represent individual animal threshold dilations. Boxplots show animal means (horizontal thick lines) and s.e.m. (colored bars). Right: Distribution of threshold stretch (one-sample two-tailed t-test, t(3) = −5.67, P = 0.01). Markers represent individual animals, and their size and color denote bootstrapped significance. Boxplots show animal means (horizontal thick lines) and s.e.m. (colored bars). (e) Same as (A), but in the fixation task variant. (f-h) Analogous to (B-D), but in the fixation task variant (N = 1 animal, mean ± propagated s.e.m. across trials instead of animals in (F)).

Extended Data Fig. 6 Manipulating M1 temperature did not produce discernible effects on timing judgments.

(a) Average discrimination performance in the fixation version of the interval discrimination task at the onset of M1 temperature manipulations. Psychometric functions fit to cross-animal averages (N = 4) of temperature-split psychophysical data, respectively shown as solid lines and markers of matching color (mean ± s.e.m.). Bottom right inset: Average differences in proportion of long choices from each manipulation condition to control (mean ± propagated s.e.m.). Top-left inset: Marginal posterior distributions of the threshold parameter for each condition’s psychometric fit. Solid black lines represent the M.A.P. point estimates implicit in the fits shown in the main axes. (b) Animal-split discrimination behavior. Markers represent M.A.P. estimates and transparent patches the corresponding 95% confidence intervals of threshold parameters fit to individual animals’ performance on control (vertical axis) versus manipulation blocks (horizontal axis). Inset: Distribution of threshold differences between manipulation and control conditions. Markers represent individual animal differences (mean ± s.e.m.). (c) Effect of motor cortical temperature manipulations on psychophysical threshold (all non-significant |ts(3)| = [0.93, 2.90], P = [0.06, 0.42]; repeated measures ANOVA, F(2, 9) = 1.93, P = 0.20). Markers represent individual threshold dilations, linked within animals by thin solid black lines. Boxplots show animal means (horizontal black lines) and s.e.m. (colored bars). (d) Threshold dynamics aligned to and across block transitions. Condition-split cross-animal average thresholds (mean ± s.e.m.) were computed using trials that fell into a sliding window lasting 90 s (half the block duration) that was swept from the preceding to the succeeding control blocks in increments of 9 s. Each marker corresponds to one sweep, and its color shading denotes the fraction of that sweep’s window that was inside a control block (with black markers corresponding to 100% control trials), and by extension its complement that fell in a manipulation block (with pure manipulation colors corresponding to 100% manipulation trials).

Extended Data Fig. 7 Striatal temperature did not produce monotonic effects on movement during stimulus presentation, latency to initiate, or time to execute choice movements.

(a) x (top) and y (bottom) coordinates of implant position aligned to stimulus onset (representative DS-implanted animal). Dashed (solid) lines correspond to short (long) choices with individual control trials ghosted in the background, and condition-split averages on top (mean ± s.e.m.). Filled and open markers show average position at reaction and choice, respectively. (b) Same data as in (A), combined into an overall speed metric (see Methods). (c) Left: Distributions of median implant speed dilation for DS animals (one-sample two-tailed t-tests, t(5) = [0.11, 0.87], P = [0.43, 0.92]; repeated measures ANOVA, F(2, 15) = 0.11, P = 0.89). Right: Distribution of median implant speed stretches for DS (N = 6) and M1 (N = 4) animals (one-sample two-tailed t-tests, tDS(5) = −0.07, P = 0.95; tM1(3) = −0.76, P = 0.51; two-sample two-tailed t-test, t(8) = 0.56, P = 0.59). Markers represent individual animals, and their size and color denote bootstrapped significance. (d) Schematics highlighting the task epoch in between stimulus offset and the initiation of the choice movement (reaction time). (e) Markers represent the median and transparent patches the corresponding i.q.r. of individual animals’ reaction times on control (vertical axis) versus manipulation blocks (horizontal axis). Single animals contribute one data point of each color. Top-right inset: Distribution of reaction time differences between manipulation and control condition. Markers represent individual animal differences, bars and error bars are animal means and s.e.m. (f) Same as (C), but for median reaction times (left: one-sample two-tailed t-tests, t(5) = [1.31, 2.01], P = [0.10, 0.25]; repeated measures ANOVA, F(2, 15) = 1.43, P = 0.27; right: one-sample two-tailed t-tests, tDS(5) = −1.84, P = 0.12; tM1(3) = −0.52, P = 0.64; two-sample two-tailed t-test, t(8) = −0.94, P = 0.37). (g-i) Same as (D-F), but for the task epoch in between the initiation of the choice movement and the moment that choice is registered (movement time; I-left: one-sample two-tailed t-tests, t(5) = [0.28, 1.04], P = [0.35, 0.79]; repeated measures ANOVA, F(2, 15) = 0.60, P = 0.56; I-right: one-sample two-tailed t-tests tDS(5) = −1.01, P = 0.36; tM1(3) = −1.04, P = 0.38; two-sample two-tailed t-test, t(8) = −0.30, P = 0.77). (j) Comparison between two one-parameter models of how DS temperature affected all movement-related metric dilations reported (speed profile of choice movements: one-sample two-tailed t-tests tβ-∆T(5) = 1.78, P = 0.13; tβ+|∆T|(5) = 3.72, P = 1.38e-2; paired-sample two-tailed t-test, t(5) = −0.25, P = 0.82; average speed of choice movements: one-sample two-tailed t-tests, tβ+∆T(5) = −1.19, P = 0.29; tβ-|∆T|(5) = 3.21, P = 2.36e-2; paired-sample two-tailed t-test, t(5) = −2.72, P = 4.16e-2; speed profile during stimulus period: one-sample two-tailed t-tests, tβ-∆T(5) = −0.77, P = 0.48; tβ+|∆T|(5) = 0.58, P = 0.59; paired-sample two-tailed t-test, t(5) = −1.27, P = 0.26; reaction time: one-sample two-tailed t-tests tβ-∆T(5) = 3.30, P = 2.14e-2; tβ+|∆T|(5) = 6.86, P = 1.21e-3; paired-sample two-tailed t-test, t(5) = −1.50, P = 0.19; movement time: one-sample two-tailed t-tests, tβ-∆T(5) = 0.64, P = 0.55; tβ+|∆T|(5) = 1.48, P = 0.20; paired-sample two-tailed t-test, t(5) = −1.46, P = 0.20). On each panel, the goodness of fit of the monotonic (left) and the non-monotonic (right) model, as measured by its coefficient of determination (R2), is shown for individual animals (open markers) and the population (mean ± s.e.m.). Note that the β coefficients were constrained to positive or negative values, as indicated by their subscripts. The highlighted panel (dashed gray rectangle) is reproduced from Fig. 5f (right panel).

Extended Data Fig. 8 Baseline DS firing rate correlates with reaction but not movement times in the fixation version of the interval discrimination task.

(a) Average TED implant speeds aligned to stimulus onset recorded during control blocks in the fixation version of the interval discrimination task (DS cohort, N = 6 animals). Solid black lines and shaded gray patches represent individual animal medians and interquartile ranges, respectively. (b) Distribution of correlation coefficients between baseline firing rates of individual striatal neurons (N = 483) and subsequent reaction (top; one-sample two-tailed t-test, t(482) = −8.56, P = 1.55e-16) or movement times (bottom; one-sample two-tailed t-test, t(482) = 0.15, P = 0.88) for the DS-recorded animals trained on the fixation variant of the interval discrimination task (N = 3).

Extended Data Fig. 9 Animals adapted their behavior to both temperature and category boundary manipulations.

(a) Threshold dilation across the first three temperature manipulation sessions for the striatal cohort shown in Figs. 4 and 5 (N = 6). Small markers and thin lines linking them refer to threshold dilations for individual animals. Larger markers correspond to cross-animal averages (mean ± s.e.m.), and their facecolor being any other than black indicates that the underlying dilation distribution was significantly shifted from zero (p < 0.05, one-sample two-tailed t-test). The gradient bar and arrow symbolize the uneven contribution of the last two sessions to the data pool shown in (D), with the last session contributing the most. (b) Time course of the boundary manipulation experiment. The thin horizontal dotted lines represent the four categorical boundaries animals experienced in these sessions (that is, boundary changes followed the same rules as the temperature manipulation experiments: a control-manipulation-control 3-min block design with boundaries drawn at random and without replacement from the set B = {1.32, 1.5, 1.68, 1.85} s until exhaustion, at which point the set was replenished and the sampling process resumed). The color scheme introduced in this panel is preserved throughout the figure. (c) Same as (A), but for the first five boundary manipulation sessions (N = 5). (d) Average discrimination performance on the last and second to last sessions of striatal temperature manipulations. Psychometric functions fit to cross-animal averages of temperature-split psychophysical data, respectively shown as solid lines and markers of matching color (mean ± s.e.m.). Bottom right inset: Average differences in proportion of long choices from each manipulation condition to control (mean ± propagated s.e.m.). Top-left inset: Marginal posterior distributions of the threshold parameter for each condition’s psychometric fit. Solid black lines represent the M.A.P. point estimates implicit in the fits shown in the main axes. (e) Distributions of percentage change in threshold relative to control (dilation) as a function of which categorical boundary was enforced (N = 5; one-sample two-tailed t-tests, significant ts(4) = [−7.21; 2.89], P = [0.04; 2e-3], non-significant t(4) = 2.61, P = 0.06; repeated measures ANOVA followed by post-hoc contrasts with Tukey correction for multiple comparisons, F(2, 12) = 15.64, P = 5e-4; smallest significant qs(8) = 5.18, P = 0.01, non-significant qs(8) = 2.43, P = 0.15). Markers represent individual threshold dilations, linked within animals by thin solid black lines. Boxplots show animal means (horizontal black lines) and s.e.m. (colored bars). (f) Same as (D), but for the last two days of boundary manipulations, with all boundaries in our manipulation set as dotted vertical dashed lines. (g) Threshold dynamics aligned to and across block transitions early and late during DS temperature manipulations (left and right, respectively), and late during boundary manipulations (middle). Condition-split cross-animal average thresholds (mean ± s.e.m.) were computed using trials that fell into a sliding window lasting 90 s (half the block duration) that was swept from the preceding to the succeeding control blocks in increments of 9 s. Each marker corresponds to one sweep, and its color shading denotes the fraction of that sweep’s window that was inside a control block (with black markers corresponding to 100% control trials), and by extension its complement that fell in a manipulation block (with pure manipulation colors corresponding to 100% manipulation trials).

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Monteiro, T., Rodrigues, F.S., Pexirra, M. et al. Using temperature to analyze the neural basis of a time-based decision. Nat Neurosci 26, 1407–1416 (2023). https://doi.org/10.1038/s41593-023-01378-5

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