Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

A neural substrate of sex-dependent modulation of motivation

Abstract

While there is emerging evidence of sex differences in decision-making behavior, the neural substrates that underlie such differences remain largely unknown. Here we demonstrate that in mice performing a value-based decision-making task, while choices are similar between the sexes, motivation to engage in the task is modulated by action value more strongly in females than in males. Inhibition of activity in anterior cingulate cortex (ACC) neurons that project to the dorsomedial striatum (DMS) preferentially disrupts this relationship between value and motivation in females, without affecting choice in either sex. In line with these effects, in females compared to males, ACC–DMS neurons have stronger representations of negative outcomes and more neurons are active when the value of the chosen option is low. By contrast, the representation of each choice is similar between the sexes. Thus, we identify a neural substrate that contributes to sex-specific modulation of motivation by value.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Sex-dependent modulation of motivation, but not choices, by previous outcome and value.
Fig. 2: ACC–DMS inhibition increases motivation in female mice, especially on trials with low relative chosen value, without affecting choice in either sex.
Fig. 3: Similar excitatory synaptic strength of ACC–DMS neurons on D1R versus D2R MSNs in males and females.
Fig. 4: More ACC–DMS neurons respond during unrewarded outcomes in female than in male mice.
Fig. 5: Similar correlates of contralateral versus ipsilateral actions in ACC–DMS neurons of females and males.
Fig. 6: More relative chosen value encoding, but similar relative side value encoding, in females compared to males.

Similar content being viewed by others

Data availability

Data used in this study are publicly available at https://doi.org/10.6084/m9.figshare.21424824. Source data are provided with this paper.

Code availability

Code used to analyze data and generate figures is available at https://github.com/juliamcox/ACC-sex-diffs.

References

  1. Grissom, N. M. & Reyes, T. M. Let’s call the whole thing off: evaluating gender and sex differences in executive function. Neuropsychopharmacology 44, 86–96 (2019).

    Article  Google Scholar 

  2. van den Bos, R., Homberg, J. & de Visser, L. A critical review of sex differences in decision-making tasks: focus on the Iowa gambling task. Behav. Brain Res. 238, 95–108 (2013).

    Article  Google Scholar 

  3. Orsini, C. A. & Setlow, B. Sex differences in animal models of decision making. J. Neurosci. Res. 95, 260–269 (2017).

    Article  CAS  Google Scholar 

  4. Shansky, R. M. & Murphy, A. Z. Considering sex as a biological variable will require a global shift in science culture. Nat. Neurosci. 24, 457–464 (2021).

    Article  CAS  Google Scholar 

  5. Chowdhury, T. et al. Sex differences in reward- and punishment-guided actions. Biol. Psychiatry 87, S62 (2020).

    Article  Google Scholar 

  6. Pellman, B. A., Schuessler, B. P., Tellakat, M. & Kim, J. J. Sexually dimorphic risk mitigation strategies in rats. eNeuro 4, ENEURO.0288-16.2017 (2017).

  7. World Health Organization. Depression and Other Common Mental Disorders: Global Health Estimates (Wolrd Health Organization, 2017).

  8. Bishop, S. J. & Gagne, C. Anxiety, depression, and decision making: a computational perspective. Annu. Rev. Neurosci. 41, 371–388 (2018).

    Article  CAS  Google Scholar 

  9. Will, T. R. et al. Problems and progress regarding sex bias and omission in neuroscience research. eNeuro 4, ENEURO.0278-17.2017 (2017).

  10. Heilbronner, S. R. & Hayden, B. Y. Dorsal anterior cingulate cortex: a bottom-up view. Annu. Rev. Neurosci. 39, 149–170 (2016).

    Article  CAS  Google Scholar 

  11. Rushworth, M. F. S. & Behrens, T. E. J. Choice, uncertainty and value in prefrontal and cingulate cortex. Nat. Neurosci. 11, 389–397 (2008).

    Article  CAS  Google Scholar 

  12. Cox, J. & Witten, I. B. Striatal circuits for reward learning and decision-making. Nat. Rev. Neurosci. 20, 482–494 (2019).

    Article  CAS  Google Scholar 

  13. Balleine, B. W., Delgado, M. R. & Hikosaka, O. The role of the dorsal striatum in reward and decision-making. J. Neurosci. 27, 8161–8165 (2007).

    Article  CAS  Google Scholar 

  14. Wang, A. Y., Miura, K. & Uchida, N. The dorsomedial striatum encodes net expected return, critical for energizing performance vigor. Nat. Neurosci. 16, 639–647 (2013).

    Article  CAS  Google Scholar 

  15. Hamid, A. A. et al. Mesolimbic dopamine signals the value of work. Nat. Neurosci. 19, 117–126 (2016).

    Article  CAS  Google Scholar 

  16. Bari, B. A. et al. Stable representations of decision variables for flexible behavior. Neuron 103, 922–933 (2019).

    Article  CAS  Google Scholar 

  17. Bryden, D. W., Johnson, E. E., Tobia, S. C., Kashtelyan, V. & Roesch, M. R. Attention for learning signals in anterior cingulate cortex. J. Neurosci. 31, 18266–18274 (2011).

    Article  CAS  Google Scholar 

  18. Carvalho Poyraz, F. et al. Decreasing striatopallidal pathway function enhances motivation by energizing the initiation of goal-directed action. J. Neurosci. 36, 5988–6001 (2016).

    Article  Google Scholar 

  19. Parker, N. F. et al. Choice-selective sequences dominate in cortical relative to thalamic inputs to NAc to support reinforcement learning. Cell Rep. 39, 110756 (2022).

    Article  CAS  Google Scholar 

  20. Beierholm, U. et al. Dopamine modulates reward-related vigor. Neuropsychopharmacology 38, 1495–1503 (2013).

    Article  CAS  Google Scholar 

  21. Verharen, J. P. H., Kentrop, J., Vanderschuren, L. J. M. J. & Adan, R. A. H. Reinforcement learning across the rat estrous cycle. Psychoneuroendocrinology 100, 27–31 (2019).

    Article  Google Scholar 

  22. Akam, T. et al. The anterior cingulate cortex predicts future states to mediate model-based action selection. Neuron 109, 149–163 (2021).

    Article  CAS  Google Scholar 

  23. Friedman, A. et al. A corticostriatal path targeting striosomes controls decision-making under conflict. Cell 161, 1320–1333 (2015).

    Article  CAS  Google Scholar 

  24. Walton, M. E., Bannerman, D. M., Alterescu, K. & Rushworth, M. F. S. Functional specialization within medial frontal cortex of the anterior cingulate for evaluating effort-related decisions. J. Neurosci. 23, 6475–6479 (2003).

    Article  CAS  Google Scholar 

  25. Tai, L.-H., Lee, A. M., Benavidez, N., Bonci, A. & Wilbrecht, L. Transient stimulation of distinct subpopulations of striatal neurons mimics changes in action value. Nat. Neurosci. 15, 1281–1289 (2012).

    Article  CAS  Google Scholar 

  26. Bloem, B. et al. Multiplexed action-outcome representation by striatal striosome-matrix compartments detected with a mouse cost–benefit foraging task. Nat. Commun. 13, 1541 (2022).

    Article  CAS  Google Scholar 

  27. Collins, A. G. E. & Frank, M. J. Opponent actor learning (OpAL): modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive. Psychol. Rev. 121, 337–366 (2014).

    Article  Google Scholar 

  28. Tervo, D. G. R. et al. The anterior cingulate cortex directs exploration of alternative strategies. Neuron 109, 1876–1887 (2021).

    Article  CAS  Google Scholar 

  29. Chen, C. S., Knep, E., Han, A., Ebitz, R. B. & Grissom, N. M. Sex differences in learning from exploration. eLife 10, e69748 (2021).

    Article  Google Scholar 

  30. Quilodran, R., Rothé, M. & Procyk, E. Behavioral shifts and action valuation in the anterior cingulate cortex. Neuron 57, 314–325 (2008).

    Article  CAS  Google Scholar 

  31. Delevich, K., Hall, C. D. & Wilbrecht, L. Prepubertal ovariectomy alters dorsomedial striatum indirect pathway neuron excitability and explore/exploit balance in female mice. Preprint at bioRxiv https://doi.org/10.1101/2021.06.01.446609. (2021).

  32. Kolb, B. & Stewart, J. Sex-related differences in dendritic branching of cells in the prefrontal cortex of rats. J. Neuroendocrinol. 3, 95–99 (1991).

    Article  CAS  Google Scholar 

  33. Delevich, K. et al. Sex and pubertal status influence dendritic spine density on frontal corticostriatal projection neurons in mice. Cereb. Cortex 30, 3543–3557 (2020).

    Article  Google Scholar 

  34. Liu, J., Zubieta, J.-K. & Heitzeg, M. Sex differences in anterior cingulate cortex activation during impulse inhibition and behavioral correlates. Psychiatry Res. 201, 54–62 (2012).

  35. Joel, D. & McCarthy, M. M. Incorporating sex as a biological variable in neuropsychiatric research: where are we now and where should we be? Neuropsychopharmacology 42, 379–385 (2016).

    Article  Google Scholar 

  36. de Vries, G. J. & Forger, N. G. Sex differences in the brain: a whole body perspective. Biol. Sex Differ. 6, 15 (2015).

    Article  Google Scholar 

  37. Maney, D. L. Perils and pitfalls of reporting sex differences. Philos. Trans. R. Soc. Lond. B 371, 20150119 (2016).

    Article  Google Scholar 

  38. Narayanan, N. S., Cavanagh, J. F., Frank, M. J. & Laubach, M. Common medial frontal mechanisms of adaptive control in humans and rodents. Nat. Neurosci. 16, 1888–1895 (2013).

    Article  CAS  Google Scholar 

  39. Vertechi, P. et al. Inference-based decisions in a hidden state foraging task: differential contributions of prefrontal cortical areas. Neuron 106, 166–176 (2020).

    Article  CAS  Google Scholar 

  40. Khalighinejad, N. A basal forebrain–cingulate circuit in macaques decides it is time to act. Neuron 105, 370–384 (2020).

    Article  CAS  Google Scholar 

  41. Peters, A. J., Fabre, J. M. J., Steinmetz, N. A., Harris, K. D. & Carandini, M. Striatal activity topographically reflects cortical activity. Nature 591, 420–425 (2021).

    Article  CAS  Google Scholar 

  42. Hunnicutt, B. J. et al. A comprehensive excitatory input map of the striatum reveals novel functional organization. eLife 5, e19103 (2016).

    Article  Google Scholar 

  43. Heilbronner, S. R., Rodriguez-Romaguera, J., Quirk, G. J., Groenewegen, H. J. & Haber, S. N. Circuit-based corticostriatal homologies between rat and primate. Biol. Psychiatry 80, 509–521 (2016).

    Article  Google Scholar 

  44. Kim, J.-H., Ma, D.-H., Jung, E., Choi, I. & Lee, S.-H. Gated feedforward inhibition in the frontal cortex releases goal-directed action. Nat. Neurosci. https://doi.org/10.1038/s41593-021-00910-9 (2021).

    Article  Google Scholar 

  45. Enel, P., Wallis, J. D. & Rich, E. L. Stable and dynamic representations of value in the prefrontal cortex. eLife 9, e54313 (2020).

    Article  CAS  Google Scholar 

  46. Shin, E. J. et al. Robust and distributed neural representation of action values. eLife 10, e53045 (2021).

    Article  CAS  Google Scholar 

  47. Choi, K. et al. Distributed processing for action control by prelimbic circuits targeting anterior–posterior dorsal striatal subregions. Preprint at bioRxiv https://doi.org/10.1101/2021.12.01.469698 (2021).

  48. Orsini, C. A. et al. Optogenetic inhibition reveals distinct roles for basolateral amygdala activity at discrete time points during risky decision making. J. Neurosci. 37, 11537–11548 (2017).

    Article  CAS  Google Scholar 

  49. Parker, N. F. et al. Reward and choice encoding in terminals of midbrain dopamine neurons depends on striatal target. Nat. Neurosci. 19, 845–854 (2016).

    Article  CAS  Google Scholar 

  50. Litman, L., Robinson, J. & Abberbock, T. TurkPrime.com: a versatile crowdsourcing data acquisition platform for the behavioral sciences. Behav. Res. Methods 49, 433–442 (2017).

    Article  Google Scholar 

  51. Paxinos, G. & Franklin, K. B. J. The Mouse Brain in Stereotaxic Coordinates (Gulf Professional Publishing, 2004).

  52. Pinto, L. et al. Task-dependent changes in the large-scale dynamics and necessity of cortical regions. Neuron 104, 810–824 (2019).

    Article  CAS  Google Scholar 

  53. Guo, Z. V. et al. Flow of cortical activity underlying a tactile decision in mice. Neuron 81, 179–194 (2014).

    Article  CAS  Google Scholar 

  54. Ghosh, K. K. et al. Miniaturized integration of a fluorescence microscope. Nat. Methods 8, 871–878 (2011).

    Article  CAS  Google Scholar 

  55. McLean, A. C., Valenzuela, N., Fai, S. & Bennett, S. A. L. Performing vaginal lavage, crystal violet staining, and vaginal cytological evaluation for mouse estrous cycle staging identification. J. Vis. Exp. https://doi.org/10.3791/4389 (2012).

    Article  Google Scholar 

  56. Byers, S. L., Wiles, M. V., Dunn, S. L. & Taft, R. A. Mouse estrous cycle identification tool and images. PLoS ONE 7, e35538 (2012).

    Article  CAS  Google Scholar 

  57. Anders, R., Alario, F.-X. & Van Maanen, L. The shifted Wald distribution for response time data analysis. Psychol. Methods 21, 309–327 (2016).

    Article  Google Scholar 

  58. Lee, R. S., Mattar, M. G., Parker, N. F., Witten, I. B. & Daw, N. D. Reward prediction error does not explain movement selectivity in DMS-projecting dopamine neurons. eLife 8, e42992 (2019).

  59. Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. https://doi.org/10.18637/jss.v076.i01 (2017).

    Article  Google Scholar 

  60. Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).

    Article  Google Scholar 

  61. Zhou, P. et al. Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data. eLife 7, e28728 (2018).

    Article  Google Scholar 

  62. Pinto, L. & Dan, Y. Cell-type-specific activity in prefrontal cortex during goal-directed behavior. Neuron 87, 437–450 (2015).

    Article  CAS  Google Scholar 

  63. Engelhard, B. et al. Specialized coding of sensory, motor and cognitive variables in VTA dopamine neurons. Nature 570, 509–513 (2019).

    Article  CAS  Google Scholar 

  64. Sabatini, B. L. The impact of reporter kinetics on the interpretation of data gathered with fluorescent reporters. Preprint at bioRxiv https://doi.org/10.1101/834895 (2019).

  65. Steinmetz, N. A., Zatka-Haas, P., Carandini, M. & Harris, K. D. Distributed coding of choice, action and engagement across the mouse brain. Nature 576, 266–273 (2019).

    Article  CAS  Google Scholar 

  66. Pillow, J. W. et al. Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature 454, 995–999 (2008).

    Article  CAS  Google Scholar 

  67. Elber-Dorozko, L. & Loewenstein, Y. Striatal action-value neurons reconsidered. eLife 7, e34248 (2018).

    Article  Google Scholar 

  68. Park, I. M., Meister, M. L. R., Huk, A. C. & Pillow, J. W. Encoding and decoding in parietal cortex during sensorimotor decision-making. Nat. Neurosci. 17, 1395–1403 (2014).

    Article  CAS  Google Scholar 

  69. Lak, A. et al. Dopaminergic and prefrontal basis of learning from sensory confidence and reward value. Neuron 105, 700–711 (2020).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We would like to thank members of the Witten laboratory as well as N.D. Daw for comments and advice on this work. This research was funded by an NYSCF and SCGB grant to I.B.W. as well as ARO W911NF1710554 to I.B.W. and the following NIH grants: U19NS104648-01, R01DA047869 and 5R01MH106689-02 to I.B.W. and F32MH112320-02 to J.C. I.B.W. is a New York Stem Cell Foundation–Robertson Investigator. The funders had no role in study design, data collection, analysis, the decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

J.C. and I.B.W. conceived the project and designed the experiments. J.C., A.R.M., W.T.F., C.H., A.B., S.O., B.M. and S.Z. collected the data. J.C. analyzed the data with support from C.A.Z., N.F.P. and S.Z. J.C. and I.B.W. wrote the manuscript.

Corresponding authors

Correspondence to Julia Cox or Ilana B. Witten.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Neuroscience thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Sex differences in motivation to perform a value-based decision-making task in mice.

a. To quantify sex differences in the distribution of trial initiation latencies, we fit each animal’s data with a shifted inverse gaussian distribution. Top: probability distribution function for the shifted inverse gaussian. Middle: Cumulative distribution of trial initiation latencies (shaded line) and fit (dashed line) for example females (orange) and males (green). Bottom: Probability distribution function of trial initiation latencies (shading) and fit (dashed line) for example females (orange) and males (green) for 1–10 seconds. b. Parameter estimates for each mouse. Translucent circles are individual animals, crosses are mean and SEM across males or females. Comparisons between males and females were performed with 2-sided Wilcoxon rank sum tests. μ: Z = −4.44, p = 9.04 × 10−6; λ: Z = −5.46, p = 4.65 × 10−8; θ: Z = 5.19, p = 2.06 × 10−7. c. Histograms of trial initiation latencies binned in log-space for males and females following rewarded and unrewarded trials. Top panels are the mean and SEM across animals, and the bottom panels show the histograms separately for each animal. Trials preceded by reward or no reward are plotted separately. d. Trial initiation latencies binned as short (<1 s), medium (1–10 s) and long (>10 s) for previously rewarded and unrewarded trials for males and females. Males had more short trials than females (2-sided Wilcoxon rank sum tests, reward: Z = −3.90, p = 9.7 × 10−5; unrewarded: Z = −6.01, p = 1.9 × 10−9). Females had more medium trials than males (2-sided Wilcoxon rank sum tests, rewarded: Z = 4.20, p = 2.6 × 10−5; unrewarded: Z = 5.62, p = 1.9 × 10−8). Females had more long trials only following unrewarded outcomes (2-sided Wilcoxon rank sum tests, rewarded: Z = 1.37, p = 0.17; unrewarded: Z = 4.21, p = 2.5 × 10−5). e. Daily fluctuations in weight affected value modulation of trial initiation latencies similarly in males and females (mixed effects regression of latency with sex, weight, relative chosen value, trial number and their interactions as fixed effects; see Supplementary Table 5 for details. Significance of coefficients was assessed with F tests: weight: F(1,55.95) = 8.87, p = 0.004; sex:weight: F(1,55.95) = 13.03, p = 6.55 × 10−4; relative_chosen_value:weight: F(3,135.8)=28.57, p = 2.20 × 10−14; sex:relative_chosen_value:weight: F(3,135.8) = 1.60, p = 0.19. For each mouse, sessions were binned in terciles of weight and trials were divided into quantile bins of relative chosen value. Trial initiation latencies were then averaged for each bin. n = 35 females and 36 males. One male maintained constant weight throughout the experiment and was excluded from this plot. f. Sessions were divided based on weight, and trial initiation latencies were binned in 4 quantile bins of relative chosen value and averaged for each animal. g. Average trial initiation latency versus relative chosen value in 30 min bins for males and females. Trials were binned based on time in session as well as in quantiles of relative chosen value and averaged for each mouse. Trial number significantly affected trial initiation latencies but there was no effect of trial on the interaction between relative chosen value and sex (see Supplementary Table 5 for details; trial: F(1,66.20) = 392.34, p = 1.60 × 10−29; sex:trial: F(1,66.20) = 4.08, p = 0.05; sex:trial:relative_chosen_value: F(3,100.02) = 1.45, p = 0.23). h. Following unrewarded trials, only females modulated their trial initiation latencies based on the upcoming stay versus switch decision. There was a significant effect of sex, stay/switch and a significant interaction between sex and stay/switch, reflecting greater modulation of trial initiation latency by whether or not the upcoming trial was a stay or switch trial in females compared to males (mixed-effects regression: latency ~ sex + stay + sex:stay + (1 + stay|subject); sex: F(1,424000) = 857.46, p = 6.67 × 10−188; stay: F(1,37.27) = 13.02, p = 8.99 × 10−4; sex:stay: F(1,23373) = 9.54, p = 0.002). Females were significantly slower than males to initiate stay and switch trials (2-sided Wilcoxon rank sum tests; switch male vs. female: Z = 4.55, p = 5.3 × 10−6; stay male vs. female: Z = 4.83, p = 1.34 × 10−6). In females, trial initiation latencies for switch trials were slower than stay trials. There was no difference between trial types in males (2-sided Wilcoxon signed rank tests. Female: Z = 3.52, p = 4.29 × 10−4; Male: Z = 1.26, p = 0.21). ** p < 0.01, *** p < 0.001; n = 37 males, 35 females. b–h. Error bars or shading are standard error of the mean.

Source data

Extended Data Fig. 2 Trial initiation latency was modulated by total value.

a. Trial-by-trial total value (QCh + QU, where QCh and QU are the action values for the chosen and unchosen lever, respectively) was divided into quartiles for each animal, and trial initiation latencies were averaged in quartile bins. To compare how total value influenced trial initiation latency in males and females we fit the following mixed effects regression: trial_initiation_latency ~ Sex + Total_value + Trial + Weight + Sex:Total_value + Trial:Total_value + Trial:Sex + Weight:Total_value + Weight:Sex + Weight:Sex:Total_value + Trial:Sex:Total_value + (1 + Total_value + Weight + Trial + Weight:Total_value + Trial:Total_value|subject) + (1 + Total_value + Trial + Total_value:Trial|session:subject). Model was fit with effects coding and significance was determined with F tests using the Satterthwaite method to estimate degrees of freedom. Total value and the interaction between sex and total value significantly affected trial initiation latency (Sex: F(1,56.35) = 4.13, p = 0.05; Trial: F(1,66.31) = 355.60, p < 0.001; Total_value: F(3,108.22) = 84.79, p = 2.71 × 10−28; Weight: F(1,57.03) = 12.21, p = 9.27 × 10−4; Trial:Sex: F(1, 66.31) = 2.42 p = 0.13; Trial:Total_value: F(3,97.95) = 49.40, p = 1.56 × 10−19; Sex:Total_value: F(3, 108.22) = 19.03, p = 5.52 × 10−10; Sex:Weight: F(1, 57.03) = 11.97, p = 0.001; Total_Value:Weight: F(1,144.71) = 17.59, p = 8.66 × 10−10; Trial:Sex:Total_value: F(3, 97.95) = 0.10, p = 0.96; Weight:Sex:Total_value: F(3, 144.71) = 1.52, p = 0.21). Specifically, females were slower than males to initiate low total value trials (2-sided Wilcoxon rank sum tests between males and females: bin 1: Z = 4.06, p = 4.99 × 10−5; bin 2: Z = 3.46, p = 5.42 × 10−4; bin 3: Z = 3.17, p = 0.002; bin 4: Z = 1.75 p = 0.08). n = 37 females, 35 males. b. Inhibition of ACC-DMS neurons decreased the influence of total value on trial initiation latency in females. To determine how the effect of inhibition on trial initiation latencies was influenced by sex and opsin, we divided trials into quartile bins of total value and averaged trial initiation latencies for laser and non-laser trials in each bin. We then fit a linear mixed-effects model to the difference between laser and non-laser trials with sex, total value, opsin and their interactions as fixed effects and random intercepts for each subject. The effect of inhibition depended on sex and and its interaction with opsin (2-sided F-tests: sex:opsin: F(1,29) = 4.95, p = 0.03, sex: F(1,29) = 14.48, p = 6.77 × 10−4; opsin: F(1,29) = 3.25, p = 0.08; total value: F(1,29) = 4.13, p = 0.01, sex:total value quantile: F(3,29) = 1.51, p = 0.23; opsin:total value quantile: F(3,29) = 0.36, p = 0.78, sex:opsin:total value quantile: F(3,29) = 0.55, p = 0.65). Specifically, females expressing NpHR were significantly faster to initiate trials following inhibition compared to control trials (2-sided Wilcoxon rank sum tests, bin 1: W = 35, p = 0.02; bin 2: W = 35, p = 0.02; bin 3: W = 36, p = 0.008, bin 4: W = 36, p = 0.008). In males, trial initiation latencies for laser and non-laser trials did not differ in any bin (2-sided Wilcoxon signed rank tests, p > 0.1 for all comparisons). n = 8 females, 9 males. c. Significantly more neurons encoded upcoming total value during the outcome in females than males (χ2-test, χ2(1,N = 756) = 13.58, p = 2.3 × 10−4). Proportions of neurons encoding current trial total value did not differ between males and females for any task event (χ2-tests, all p > 0.3). d. Outcome activity was averaged across time (8 seconds from presentation of the outcome) and averaged in 3 quantile bins of total value. The left plots show the value modulation for all total value encoding neurons in females and males and the right-most plot shows the mean and SEM across neurons for males and females. (a, b) Circles are data from individual mice and lines show across animal averages and error bars standard error of the mean. * p < 0.05, ** p < 0.01, ***p < 0.001.

Source data

Extended Data Fig. 3 Estrous cycle modulates trial initiation latencies.

a. Representative images of vaginal cells across the estrous cycle. Scale bar is 50 μm. b. Estrous cycle significantly modulated the relationship between relative chosen value and trial initiation latency. (Mixed effects regression, significance assessed with one-sided F tests using the Satterthwaite method to estimate degrees of freedom: trial initiation latency ~ relative_chosen_value + estrous_stage + relative_chosen_value:estrous_stage + (1 + relative_chosen_value|subject)). Relative_chosen_value: F(3,13.54) = 95.70, p = 2.32 × 10−9; Estrous_stage: F(1,164410) = 0.72, p = 0.40; Relative_chosen_value:Estrous_stage: F(3,8481) = 6.07, p = 4.04 × 10−4. Post-hoc comparisons of trial initiation latencies during proestrus/estrus and metestrus/diestrus for each value bin were performed with F tests for each contrast using the Satterthwaite method to estimate degrees of freedom (bin 1: F(1,69433) = 13.35, p = 2.59 × 10−4; bin 2: F(1,34944) = 0.48, p = 0.45; bin 3: F(1,10493) = 1.46, p = 0.23; bin 4: F(1,4571) = 2.82, p = 0.09). *** p < 0.001.

Source data

Extended Data Fig. 4 Gender differences in human subjects performing the self-initiated probabilistic reversal learning task.

a. Schematic of the online task. After an intertrial interval (ITI), trial start was cued with the appearance of a plus sign (‘+’) on the screen. Subjects initiated a trial by pressing the spacebar which led to the presentation of 2 colored circles on either side of the screen. Subjects indicated their choice by pressing the ‘a’ key for a left choice and the ‘l’ key for a right choice. The outcome was then presented, indicating the number of points received for that choice (rewarded: +10, unrewarded +0) accompanied by auditory cues indicating reward (bell-like sound) or no reward (buzzer). High probability choices were rewarded 60% of the time and low probability choices 10% of the time and the identity of the high and low choice alternated as in the mouse task (see Methods for details). b. Box-plots of the estimates of Q-learning parameters fit with the same hierarchical model used for the mice. The centers are median, the bottom and top of the boxes indicate 25th and 75th percentiles, respectively. Whiskers are the minimum and maximum values that are not outliers (outliers defined as greater (or less) than the 75th percentile (or 25th percentile) plus (or minus) 1.5x the interquartile range. c-d) Choice was similar between men and women. There was a significant main effect of relative side value, but no effect of gender on how relative side value modulated the probability of choosing the right option (mixed-effects, logistic regression: choice ~ relative_side_value_quantile + gender + age + sex:relative_side_value_quantile + sex:age + relative_side_value_quantile:age + sex*relative_side_value_quantile:age + (1 + relative_side_value_quantile|subject). relative side value: F(1,142180) = 100.5, p = 8.58 × 10−209; gender: F(1,142180) = 1.20, p = 0.27; age: F(1,142180) = 0.98, p = 0.32; relative_side_value:gender: F(1,142180) = 0.72, p = 0.71; gender:age: F(1,142180) = 1.26, p = 0.26; relative_side_value:age: F(10,142180) = 0.73, p = 0.70; relative_side_value:gender:age: F(1,142180) = 0.62, p = 0.80, gender was a categorical variable and age was z-scored. Significance was assessed with 2-sided F tests). c. Trials were divided into 11 quantile bins of relative side value and the probability of making a right choice was averaged by bin for each subject younger than or equal to the median age (between 19 and 39 years old). Error bars are standard error of the mean. d. Same as c, except for subjects 40–70 years old. e. Trial initiation latencies were significantly affected by age in men, but not women (linear correlation, males: r = 0.16, p = 0.02, females: r = −0.006, p = 0.95). f-g) The modulation of trial initiation latency by value differed in males and females, and this effect was modulated by age (mixed-effects regression with relative chosen value quantile, gender, age and their interactions as fixed effects and random effects of subject. relative_chosen_value: F(4,1754) = 3.46, p = 0.008: gender: F(1,1754) = 1.68 × 10−4, p = 0.99; age: F(1,1754) = 1.62 × 10−4, p = 0.99; relative_chosen_value:gender: F(4,1754) = 2.49, p = 0.04; relative_chosen_value:age: F(4,1754) = 6.71, p = 2.36 × 10−5; gender:age: F(1,1754) = 1.16 × 10−4, p = 0.99; relative_chosen_value:gender:age: F(4,1754) = 2.46, p = 0.04; n = 141 women, 209 men). f. Trials were divided into 5 quantile bins of relative chosen value for males and females 19–39 years old and trial initiation latencies were averaged by bin. Error bars are standard error of the mean. Two-sided Wilcoxon rank sum tests showed that women were significantly slower than men to initiate trials in the lowest quantile bin of relative chosen value (bin 1: Z = −2.01, p = 0.04, bin 2: Z = –0.10, p = 0.92, bin 3: Z = 0.79, p = 0.43, bin 4: Z = 1.35, p = 0.18, bin 5: Z = 0.54, p = 0.59) g. Same as f except for subjects aged between 40 and 70. There were no significant differences in trial initiation latencies between males and females older than 39 (2-sided Wilcoxon rank sum tests, p > 0.07). QCh: action value for the chosen lever; QU: action value for the unchosen lever; QR: action value for the right lever; QL: action value for the left lever; * p < 0.05.

Source data

Extended Data Fig. 5 Location of fiber tips for optogenetic inhibition.

Circles indicate animals expressing NpHR and squares indicate animals expressing EYFP. Orange: females, green: males.

Extended Data Fig. 6 Laser during outcome presentation had no effect on trial initiation latencies or choice in mice expressing EYFP in ACC-DMS neurons.

a. Comparisons between shifted inverse gaussian fits from laser and non-laser trials revealed no differences in EYFP-expressing males or females. (2-sided Wilcoxon signed rank tests, females: scale: W = 19, p = 0.09, shape: W = 4, p = 0.22 shift: W = 12, p = 0.84, males: shape: W = 15, p = 0.44, scale: W = 12, p = 0.84, shift: W = 15, p = 0.44, n = 6 females, 6 males; Data is presented as mean ± SEM). b. Trial initiation latencies averaged in quartile bins of relative chosen value (QChQU, where QCh and QU are the action values for the chosen and unchosen lever, respectively) for mice expressing EYFP in ACC-DMS neurons. Paired comparisons between laser and non-laser trials were performed for each bin with 2-sided Wilcoxon signed rank tests (Female laser v. no laser bin 1: W = 17, p = 0.22, bin 2: W = 20, p = 0.06, bin 3: W = 6, p = 0.31, bin 4: W = 20, p = 0.06; Male bin 1: W = 16, p = 0.31, bin 2: W = 10, p = 1, bin 3: W = 19, p = 0.09, bin 4: W = 13, p = 0.69; Data is presented as mean ± SEM). c. The probability of making a right choice was averaged in quantile bins of relative side value (QR−QL, where QR and QL are the action values for the right and left lever, respectively) for each animal and averaged. Error bars are SEM.

Source data

Extended Data Fig. 7 Similar excitatory postsynaptic potentials evoked by ACC-DMS stimulation in D1R and D2R MSNs in males.

a. Schematic of viral strategy to express ChR2 in ACC neurons (top), and record optogenetically-evoked excitatory postsynaptic potentials (EPSPs) in DMS (bottom). b. Schematic of paired, sequential recordings of neighboring MSNs, where MSNs were visually identified as D1R MSNs (tdTomato+) or D2R MSNs (tdTomato-). Brief light pulses elicited EPSPs from ChR2-expressing ACC terminals. c. Example EPSPs measured in pairs from a D1R MSN (red) and D2R MSN (grey). Traces are mean responses across trials from a single cell. Shading is SEM. Blue line indicates the time of light stimulation. d. Summary of EPSP amplitudes. Each line is data from a pair of MSNs. A mixed effects regression revealed no effect of cell-type on EPSP. The model was EPSP ~ msn_type + (1|subject) + (1|subject:pair). Significance was assessed with a 2-sided F-test using the Satterthwaite method to estimate degrees of freedom; msn_type: F(1,9) = 0.67, p = 0.43. N = 3 mice, 9 pairs.

Source data

Extended Data Fig. 8 Center of imaging fields in ACC.

Color indicates whether the subject was male (green) or female (orange). Circles indicate lens implants and squares are prism implants.

Extended Data Fig. 9 Task-related activity of ACC-DMS neurons.

a. Trial-averaged, time-locked fluorescence to each task event for all imaged neurons. Neurons are sorted by the time of their peak fluorescence. b. Histograms showing the distribution of all imaged neurons, of the approximate numerical integral of the response kernel for the ‘no reward’ temporal kernel from the regression used to assess significant outcome encoding. The histograms are plotted separately for male and female neurons. The sign of the integral of the ‘no reward’ response kernel was used to classify neurons as reward-preferring (negative) or no reward-preferring (positive). c. Same as b for the ‘ipsilateral lever press’ event in the regression used to assess significant choice encoding. The sign of the integral was used to classify neurons as contra-preferring (negative) or ipsi-preferring (positive). d. Same as b for the ‘reward to stay’ event (left) and the ‘no reward to stay’ event (right) in the regression used to assess significant stay versus switch encoding. The sign of the integral was used to classify neurons as stay-preferring (positive) or switch-preferring (negative) for rewarded (left) and unrewarded (right) trials. n = 307 female neurons, 449 male neurons.

Source data

Extended Data Fig. 10 Bilinear encoding models allow value to multiplicatively modulate all event-related temporal kernels.

a. Schematic of the encoding model used to estimate how trial-by-trial fluctuations in relative side value (QIQL, where QI and QC are the action values for the lever ipsilateral and contralateral to the imaging field, respectively) or relative chosen value (QCh−QU, where QCh and QU are the action values for the chosen and unchosen lever, respectively) influences responses to each task event. For each neuron, GCaMP6f fluorescence was estimated as the sum of all event kernels multiplied by the gain coefficients (an offset gain as well as gains for relative chosen value (Fig. 6a–c) or relative side value (Fig. 6d–f)). b. Fluorescence (gray) and estimated fluorescence (green) from the same neuron during the outcome period on 3 trials with different upcoming relative chosen values (labeled on bottom). c. Schematic of the 2-step iterative fitting procedure (see Methods for more details). d. The proportion of neurons significantly encoding current relative chosen value during all task events for males and females. Significantly more neurons encoded current trial relative chosen values in females compared to males during the nose poke event (χ2-test, χ2(1,N = 756) = 4.04, p = 0.04) and significantly more neurons encoded current trial relative chosen value in males compared to females during the outcome event (χ2-test, χ2(1,N = 756) = 5.30, p = 0.02). There were no sex differences in the encoding of relative chosen value for any other task events (χ2-test, p > 0.05). e. The proportion of neurons significantly encoding current relative side value during the nose poke, lever presentation, and outcome periods and the upcoming relative side value during rewarded and unrewarded outcomes. Significantly more neurons encoded upcoming relative side value in males compared to females during rewarded outcomes (χ2-test, χ2(1,N = 756) = 5.35, p = 0.02).

Source data

Supplementary information

Supplementary Information

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

Reporting Summary

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Source Data Extended Data Fig. 10

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cox, J., Minerva, A.R., Fleming, W.T. et al. A neural substrate of sex-dependent modulation of motivation. Nat Neurosci 26, 274–284 (2023). https://doi.org/10.1038/s41593-022-01229-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-022-01229-9

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing