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Independent inhibitory control mechanisms for aggressive motivation and action

Abstract

Social behaviors often consist of a motivational phase followed by action. Here we show that neurons in the ventromedial hypothalamus ventrolateral area (VMHvl) of mice encode the temporal sequence of aggressive motivation to action. The VMHvl receives local inhibitory input (VMHvl shell) and long-range input from the medial preoptic area (MPO) with functional coupling to neurons with specific temporal profiles. Encoding models reveal that during aggression, VMHvl shellvgat+ activity peaks at the start of an attack, whereas activity from the MPO–VMHvlvgat+ input peaks at specific interaction endpoints. Activation of the MPO–VMHvlvgat+ input promotes and prolongs a low motivation state, whereas activation of VMHvl shellvgat+ results in action-related deficits, acutely terminating attack. Moreover, stimulation of MPO–VMHvlvgat+ input is positively valenced and anxiolytic. Together, these data demonstrate how distinct inhibitory inputs to the hypothalamus can independently gate the motivational and action phases of aggression through a single locus of control.

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Fig. 1: VMHvl neurons represent the sequence of motivation to action in free and constrained aggression.
Fig. 2: Local and long-range inhibitory populations target heterogeneous VMHvl neurons.
Fig. 3: Optogenetic identity-tagged neurons show distinct temporal profiles.
Fig. 4: Local and long-range inhibitory inputs to the VMHvl have unique activity profiles during social behaviors.
Fig. 5: Encoding models reveal differential responses to aggressive-action and aggression-ending events.
Fig. 6: Activation of long-range but not local inhibitory inputs to the VMHvl delays aggressive motivation.
Fig. 7: Long range and local inhibition differentially modifies instantaneous behavior and aggressive state.

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Data availability

The data that support the findings of this study are available on FigShare https://figshare.com/projects/Independent_inhibitory_control_mechanisms_for_aggressive_motivation_and_action/187881.

Code availability

Code associated with this publication can be found in our GitHub repository at https://github.com/FalknerLab/InhibitoryControl.

References

  1. Ghossoub, E., Cherro, M., Akil, C. & Gharzeddine, Y. Mental illness and the risk of self- and other-directed aggression: results from the National Survey on Drug Use and Health. J. Psychiatr. Res. 132, 161–166 (2021).

    Article  PubMed  Google Scholar 

  2. Correll, C. U., Yu, X., Xiang, Y., Kane, J. M. & Masand, P. Biological treatment of acute agitation or aggression with schizophrenia or bipolar disorder in the inpatient setting. Ann. Clin. Psychiatry 29, 92–107 (2017).

    PubMed  Google Scholar 

  3. Wong, L. C. et al. Effective modulation of male aggression through lateral septum to medial hypothalamus projection. Curr. Biol. 26, 593–604 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Flanigan, M. E. et al. Orexin signaling in GABAergic lateral habenula neurons modulates aggressive behavior in male mice. Nat. Neurosci. 23, 638–650 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Yang, B., Karigo, T. & Anderson, D. J. Transformations of neural representations in a social behaviour network. Nature 608, 741–749 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hong, W., Kim, D.-W. & Anderson, D. J. Antagonistic control of social versus repetitive self-grooming behaviors by separable amygdala neuronal subsets. Cell 158, 1348–1361 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Bayless, D. W. et al. Limbic neurons shape sex recognition and social behavior in sexually naive males. Cell 176, 1190–1205 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Takahashi, A., Nagayasu, K., Nishitani, N., Kaneko, S. & Koide, T. Control of intermale aggression by medial prefrontal cortex activation in the mouse. PLoS ONE 9, e94657 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Wei, D. et al. A hypothalamic pathway that suppresses aggression toward superior opponents. Nat. Neurosci. 26, 774–787 (2023).

    Article  CAS  PubMed  Google Scholar 

  10. Hashikawa, K., Hashikawa, Y., Lischinsky, J. & Lin, D. The neural mechanisms of sexually dimorphic aggressive behaviors. Trends Genet. 34, 755–776 (2018).

    Article  CAS  PubMed  Google Scholar 

  11. Covington, H. E. 3rd et al. The urge to fight: persistent escalation by alcohol and role of NMDA receptors in mice. Front. Behav. Neurosci. 12, 206 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Golden, S. A. et al. Compulsive addiction-like aggressive behavior in mice. Biol. Psychiatry 82, 239–248 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Fish, E. W., De Bold, J. F. & Miczek, K. A. Aggressive behavior as a reinforcer in mice: activation by allopregnanolone. Psychopharmacology 163, 459–466 (2002).

    Article  CAS  PubMed  Google Scholar 

  14. Falkner, A. L., Grosenick, L., Davidson, T. J., Deisseroth, K. & Lin, D. Hypothalamic control of male aggression-seeking behavior. Nat. Neurosci. 19, 596–604 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Falkner, A. L., Dollar, P., Perona, P., Anderson, D. J. & Lin, D. Decoding ventromedial hypothalamic neural activity during male mouse aggression. J. Neurosci. 34, 5971–5984 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Lin, D. et al. Functional identification of an aggression locus in the mouse hypothalamus. Nature 470, 221–226 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Lee, H. et al. Scalable control of mounting and attack by Esr1+ neurons in the ventromedial hypothalamus. Nature 509, 627–632 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hashikawa, K. et al. Esr1+ cells in the ventromedial hypothalamus control female aggression. Nat. Neurosci. 20, 1580–1590 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Shao, Y.-Q., Fan, L., Wu, W.-Y., Zhu, Y.-J. & Xu, H.-T. A developmental switch between electrical and neuropeptide communication in the ventromedial hypothalamus. Curr. Biol. 32, 3137–3145 (2022).

    Article  CAS  PubMed  Google Scholar 

  20. Lo, L. et al. Connectional architecture of a mouse hypothalamic circuit node controlling social behavior. Proc. Natl Acad. Sci. USA 116, 7503–7512 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Newman, S. W. The medial extended amygdala in male reproductive behavior. A node in the mammalian social behavior network. Ann. NY Acad. Sci. 877, 242–257 (1999).

    Article  CAS  PubMed  Google Scholar 

  22. Wu, Z., Autry, A. E., Bergan, J. F., Watabe-Uchida, M. & Dulac, C. G. Galanin neurons in the medial preoptic area govern parental behaviour. Nature 509, 325–330 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Karigo, T. et al. Distinct hypothalamic control of same- and opposite-sex mounting behaviour in mice. Nature 589, 258–263 (2021).

    Article  CAS  PubMed  Google Scholar 

  24. Todd, W. D. et al. A hypothalamic circuit for the circadian control of aggression. Nat. Neurosci. 21, 717–724 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Yamamoto, R., Ahmed, N., Ito, T., Gungor, N. Z. & Pare, D. Optogenetic study of anterior BNST and basomedial amygdala projections to the ventromedial hypothalamus. eNeuro https://doi.org/10.1523/ENEURO.0204-18.2018 (2018).

  26. Pereira, T. D. et al. SLEAP: A deep learning system for multi-animal pose tracking. Nat. Methods 19, 486–495 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).

    Article  CAS  PubMed  Google Scholar 

  28. Remedios, R. et al. Social behaviour shapes hypothalamic neural ensemble representations of conspecific sex. Nature 550, 388–392 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Schmitt, L. I. et al. Thalamic amplification of cortical connectivity sustains attentional control. Nature 545, 219–223 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kim, D.-W. et al. Multimodal analysis of cell types in a hypothalamic node controlling social behavior. Cell 179, 713–728 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Knoedler, J. R. et al. A functional cellular framework for sex and estrous cycle-dependent gene expression and behavior. Cell 185, 654–671 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Willmore, L., Cameron, C., Yang, J., Witten, I. B. & Falkner, A. L. Behavioural and dopaminergic signatures of resilience. Nature 611, 124–132 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Arakawa, T. et al. Automated estimation of mouse social behaviors based on a hidden markov model. Methods Mol. Biol. 1552, 185–197 (2017).

    Article  CAS  PubMed  Google Scholar 

  34. Goodwin, N. L., Nilsson, S. R. O. & Golden, S. A. Rage against the machine: advancing the study of aggression ethology via machine learning. Psychopharmacology 237, 2569–2588 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Andermann, M. L. & Lowell, B. B. Toward a wiring diagram understanding of appetite control. Neuron 95, 757–778 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Isaacson, J. S. & Scanziani, M. How inhibition shapes cortical activity. Neuron 72, 231–243 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Vallentin, D., Kosche, G., Lipkind, D. & Long, M. A. Neural circuits. Inhibition protects acquired song segments during vocal learning in zebra finches. Science 351, 267–271 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Sohal, V. S. & Rubenstein, J. L. R. Excitation–inhibition balance as a framework for investigating mechanisms in neuropsychiatric disorders. Mol. Psychiatry 24, 1248–1257 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Stagkourakis, S. et al. A neural network for intermale aggression to establish social hierarchy. Nat. Neurosci. 21, 834–842 (2018).

    Article  CAS  PubMed  Google Scholar 

  40. Chen, A.-X. et al. Specific hypothalamic neurons required for sensing conspecific male cues relevant to inter-male aggression. Neuron 108, 763–774 (2020).

    Article  CAS  PubMed  Google Scholar 

  41. Falkner, A. L. et al. Hierarchical representations of aggression in a hypothalamic-midbrain circuit. Neuron 106, 637–648 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Chen, P. B. et al. Sexually dimorphic control of parenting behavior by the medial amygdala. Cell 176, 1206–1221 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Tsuneoka, Y. et al. Distinct preoptic-BST nuclei dissociate paternal and infanticidal behavior in mice. EMBO J. 34, 2652–2670 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Tripp, J. A., Salas-Allende, I., Makowski, A. & Bass, A. H. Mating behavioral function of preoptic galanin neurons is shared between fish with alternative male reproductive tactics and tetrapods. J. Neurosci. 40, 1549–1559 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Bergan, J. F., Ben-Shaul, Y. & Dulac, C. Sex-specific processing of social cues in the medial amygdala. eLife 3, e02743 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Zhang, G.-W. et al. Medial preoptic area antagonistically mediates stress-induced anxiety and parental behavior. Nat. Neurosci. 24, 516–528 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Fang, Y.-Y., Yamaguchi, T., Song, S. C., Tritsch, N. X. & Lin, D. A hypothalamic midbrain pathway essential for driving maternal behaviors. Neuron 98, 192–207 (2018).

  48. Moffitt, J. R. et al. Molecular, spatial and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Kennedy, A. et al. Stimulus-specific hypothalamic encoding of a persistent defensive state. Nature 586, 730–734 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Nair, A. et al. An approximate line attractor in the hypothalamus encodes an aggressive state. Cell 186, 178–193 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Mei, L., Yan, R., Yin, L., Sullivan, R. M. & Lin, D. Antagonistic circuits mediating infanticide and maternal care in female mice. Nature 618, 1006–1016 (2023).

    Article  CAS  PubMed  Google Scholar 

  52. Guthman, E. M. & Falkner, A. L. Neural mechanisms of persistent aggression. Curr. Opin. Neurobiol. 73, 102526 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Stagkourakis, S., Spigolon, G., Liu, G. & Anderson, D. J. Experience-dependent plasticity in an innate social behavior is mediated by hypothalamic LTP. Proc. Natl Acad. Sci. USA 117, 25789–25799 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Calhoun, A. J., Pillow, J. W. & Murthy, M. Unsupervised identification of the internal states that shape natural behavior. Nat. Neurosci. 22, 2040–2049 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Friard, O. & Gamba, M. BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330 (2016).

    Article  Google Scholar 

  56. Renier, N., Wu, Z., Simon, D. J., Yang, J., Ariel, P. & Tessier-Lavigne, M. iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging. Cell 159, 896–910 (2014).

    Article  CAS  PubMed  Google Scholar 

  57. Princeton Mouse Brain Atlas. Princeton Neuroscience Institute https://brainmaps.princeton.edu/2020/09/princeton-mouse-brain-atlas-links/ (2023).

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Acknowledgements

We thank A. Le, L. Sirrs, O. Timmermans and P. Wonnenberg for technical assistance on this project and members of the Falkner lab for useful discussion. We thank M. Murthy and C. Brody for constructive feedback on this project. Funding was from NIMH DP2MH126375 (A.L.F.), NIMH R00MH109674 (A.L.F.), NIH R01MH126035 (A.L.F.), NIH F32MH126562 (E.M.G.) New York Stem Cell Foundation (A.L.F.), SCGB (A.L.F.), Klingenstein Foundation (A.L.F.), Alfred P. Sloan Fellowship (A.L.F.) and the Nakajima Foundation (T.M.). A.L.F. is an New York Stem Cell Foundation Robertson investigator.

Author information

Authors and Affiliations

Authors

Contributions

A.L.F. conceived of and designed this study. T.M. performed all photometry and optogenetics experiments. S.N.O. performed all extracellular in vivo physiology experiments. E.M.G. performed all slice physiology experiments. A.L.F., T.M., S.N.O. and E.M.G. analyzed data. W.F. and I.B.W. contributed to the slice physiology experiments. P.A. and J.H. contributed to animal training and behavioral analysis. T.M., E.M.G., S.N.O. and A.L.F. wrote the paper.

Corresponding author

Correspondence to Annegret L. Falkner.

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The authors declare no competing interests.

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Nature Neuroscience thanks Felix Leroy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Characterization of behavior during SOAR training.

a, Learning curve for the SOAR task across 7 days of training showing mean poke rates for social port (red) and null port (back). n = 27 animals. b, Poke rates were significantly increased across task training. The poke rate for the social and null port was significantly higher on the last day than on the first day (social port: t(26) = −8.18, p = 1.15 × 10−8; null port: t(26) = −2.39, p = 0.0243, n = 27 animals, paired t-test). c, The percentage of social pokes out of total pokes across training days. The percentage of social pokes was significantly higher on the last day than on the first day (t(26) = −5.48, p = 9.41 × 10−6, n = 27 animals, paired t-test). d, The percentage of trials with observed attack in total pokes to social port. The trained animals attacked the reward animal significantly more on the last day than on the first day (t(17) = −5.82, p = 2.05 × 10−5, n = 18 animals, paired t-test). e, Learning curve across task training for a task version where reward is a novel object. Individuals did not increase poking to the novel-object paired port (red) or to the null port (black) n = 8 animals. f, The poke rate for the object port and null port was not significantly different between the first and the last day of the training periods (object port: t(7) = 1.46, p = 0.185; null port: t(7) = 1.13, p = 0.296; n = 8 animals, paired t-test).For all panels *p < 0.05, **p < 0.01, ***p < 0.001, NS=non-significant. Plots show mean ± SEM.

Extended Data Fig. 2 Histology for chronic physiology during free aggression and SOAR task.

a-b, The VMHvl shell is defined by as the vgat+ rich cell population surrounding the lateral, anterior, and posterior edges of the VMH (vgat+ poor population). In situ showing extent of vgat+ neurons along the lateral edge of VMHvl (a, coronal) and on the anterior and posterior edges of VMHvl (b, sagittal). Images from Allen Mouse Brain Atlas, Slc32a1, https://mouse.brain-map.org/experiment/siv?id=72081554&imageId=71975444&initImage=ish&coordSystem=pixel&x=5132&y=5412&z=3 (a) and. https://mouse.brain-map.org/experiment/siv?id=79677349&imageId=79683718&initImage=ish&coordSystem=pixel&x=6168&y=5784&z=2 (b). c, Schematic of shuttle drive with tetrodes and fibers targeting MPO and VMHvl shell. d, Close photo with ruler (mm) showing adjustable-depth tetrodes seated inside polyamide guide tubes. e, Image of an implanted mouse. f, Reconstruction of tetrode tip locations using iDISCO, example coronal plane of VMHvl (left) and zoomed in reconstruction of a single tetrode track (right). Tetrodes were fluorescentlylabeled with CM-DiI dye, and the 3-dimensional location was determined using in-house visualization tools (Neuroglancer, see Methods) by evaluating the fluorescence from multiple coronal, sagittal, and horizontal perspectives. g, Assessment of tetrode and fiber locations for identity-tagging and connectivity experiments for all 4 implanted animals implanted with shuttleDrives. Mouse 2357’s tetrodes were not labeled with CM-DiI dye, and clearly-visible tetrode track lesions were used instead. Green dotted lines represent reconstructed fiber tracks and red dotted lines represent reconstructed tetrode tracks.

Extended Data Fig. 3 Additional characterization of electrophysiological responses during SOAR task.

a-b, Comparison of response during interaction with a novel object and interaction with a male. Action selective neurons are increased during male relative to novel object (a, ***p = 0.0002, Wilcoxon signed-rank test, n = 236 neurons), but motivation-selective neurons do not show a significant difference (b, p = 0.958, Wilcoxon signed-rank test, n = 117 neurons). c-d, Neural responses are increased when the actuator is moving down relative to up. PETH aligned to actuator start (c) and comparison of responses (d, *p = 0.0189, Wilcoxon signed-rank test, n = 253 neurons). e-f, Neural responses are significantly increased aligned to actuator down (bringing male in) when trial contains future attack (e, red), compared to no future attack (e, blue). PETH aligned to actuator start, male entering (c) and comparison of responses (d, ***p = 0.00031, Wilcoxon signed-rank test, n = 253 neurons). g-h, Neural responses are not significantly different aligned to actuator up (bringing male out) if trial contained a previous attack (e, red), compared to no previous attack (e, blue). PETH aligned to actuator start, male leaving (c) and comparison of responses (d, p = 0.837, Wilcoxon signed-rank test, n = 253 neurons). For all panels *p < 0.05, **p < 0.01, ***p < 0.001, NS=non-significant. All tests are two-sided. Plots show mean ± SEM.

Extended Data Fig. 4 Fiber placement, controls, and additional characterization of fiber photometry experiments.

a-b, Locations of fiber tips for the fiber photometry recording for VMHvl shellvgat+ (a) and MPO-VMHvlvgat+ (b) scale bars = 1 mm. c-e, No behavioral or task events modulated control signal. Average z-scored signal from control animals injected with GFP aligned to behavioral events in free interactions (c-d) and SOAR task (e). NS comparisons shown using paired t-test. f, Average neural activity aligned to fleeing behaviors within the window around attack offset (−0.5 s to 0.5 s) for VMHvl shellvgat+ (green, top) and MPO-VMHvlvgat+(purple, bottom). Shaded regions represent windows for statistical comparison. VMHvl shellvgat+: t(6) = 3.568, p = 0.0118, n = 7, paired t-test. MPO-VMHvlvgat+: t(5) = −2.808, p = 0.0376, n = 6, paired t-test. g-i, Comparison of first trial responses with last trial responses for VMHvl shellvgat+ (green, top) and MPO-VMHvlvgat+(purple, bottom) for free social behaviors. n = 8 animals for all comparisons, paired t-test. g, Attack VMHvl shellvgat+ t(7) = 3.489, p = 0.010, Attack MPO-VMHvlvgat+ t(7) = 1.328, p = 0.226. h, Female investigation VMHvl shellvgat+ t(7) = 3.99, p = 0.0058, Female investigation MPO-VMHvlvgat+ t(7) = 3.430, p = 0.0139,, paired t-test. i, Pup investigation VMHvl shellvgat+ t(7) = 3.912, p = 0.0068, Pup investigation MPO-VMHvlvgat+t(7) = 1.088, p = 0.318, paired t-test. j-k, The weights for continuous variables in the encoding models for free aggression (j) and SOAR task (k). n = 8 animals, paired t-test, uncorrected. j, VMHvl shellvgat+: (t(7) = −4.144, 2.992, 4.115, 4.180, p = 0.006, 0.024, 0.006, 0.005). MPO-VMHvlvgat+: (t(7) = 0.295, 0.406, 1.68, 1.16, p = 0.776, 0.700, 0.137, 0.283). k, VMHvl shellvgat+: (t(7) = −0.614, −0.269, 1.317, 3.03, −1.34; p = 0.558, 0.795, 0.229, 0.0190, 0.221). MPO-VMHvlvgat+: (t(7) = −0.803, −0.635, 0.0155, 1.97, 2.97,; p = 0.448, 0.545, 0.988, 0.088, 0.0209). Data are presented as mean values ± SEM. Statistical tests are two-sided.

Extended Data Fig. 5 Fiber placement and control experiments for optogenetic manipulations.

a-b, Locations of fiber tips shown in green and purple lines for the optogenetic stimulation for VMHvl shellvgat+ (a) and MPO-VMHvlvgat+ (b), respectively. Scale bars = 1 mm. c-e, Optogenetic stimulation in control animals injected with GFP did not result in motivational change during any stimulation regime. c, Trial-to-trial poke latencies in representative sessions for each stimulation regime. The blue dots represent the poke latencies for stimulation trials and the gray dots represent the poke latencies for sham trials. d, Cumulative fractions of poke latencies during sham (gray) and stim trials (blue). IPI dwell: p = 0.390, KS statistics = 0.171, (n = 53 stim trials, 49 sham trials), IPI5: p = 0.857, KS statistics = 0.109 (n = 55 stim trials, 52 sham trials). IPI20:, p = 0.130, KS statistics = 0.220, (n = 51 stim trials, 54 sham trials); IPIfull: p = 0.827, KS statistics = 0.114, (n = 53 stim trials, 51 sham trials), Kolmogorov–Smirnov test. e, IPI dwell: t(5) = −1.747, p = 0.141; IPI5: t(6) = 1.069, p = 0.326; IPI20: t(6) = −0.731, p = 0.492, IPIfull: t(6) = 0.740, p = 0.487, n = 6 (IPI dwell) or 7 animals (IPI5,20, full). Paired t-test. f, The poke latencies were significantly different between the VMHvl shell stimulated and MPO-VMHvl stimulated animals for IPI 5 and IPI 20, but not for IPI full regimen. 2-way ANOVA with two factors, the stimulation region and the stim v.s. sham. The region effect was significant for IPI 5 (F1,1 = 11.04, p = 0.0010), IPI 20 (F1,1 = 14.56, p = 0.00018), but not for IPI full (F1,1 = 0.0569, p = 0.81).

Extended Data Fig. 6 Additional characterization of behavioral effects of optogenetic stimulation.

a, Example behavior rasters during untimed optogenetic stimulation during free aggression of VMHvl shellvgat+ (top) and MPO-VMHvlvgat+ (bottom). b, Optogenetic stimulation during real-time place preference assay (RTPP). Example traces of animal head position during stimulation sessions for VMHvl shellvgat+ (top) and MPO-VMHvlvgat+ (bottom) stimulation. c, Comparison of the time spent on the stimulated side during baseline day (no stimulation) and the stimulation day. VMHvl shellvgat+ (top): (t(7) = 0.729, p = 0.489). MPO-VMHvlvgat+ (bottom): (t(7) = −3.981, p = 0.00532) all groups n = 8, paired t-test. d, Comparison of the mean velocity on the sham and stimulation sides of the chamber during baseline(left) and stimulation (right). For baseline, VMHvl shellvgat+: t(7) = 0.863, p = 0.416, n = 8); MPO-VMHvlvgat+ (bottom): t(7) = 0.245, p = 0.813, n = 8). During stimulation, VMHvl shellvgat+: t(7) = −0.543, p = 0.603), MPO-VMHvlvgat+ t(7) = −4.598, p = 0.00249); all groups n = 8, paired t-test. e, Time spent in the open arm during elevated plus maze (left) during interleaved stimulation and sham stimulation (right). VMHvl shellvgat+: t(7) = 0.684, p = 0.516. MPO-VMHvlvgat+: (t(7) = −3.03, p = 0.019); all groups n = 8, paired t-test. f, Effects of stimulation on non-social motivated behavior (feeding). Schematic and stimulation regime (left). Time spent feeding, MPO-VMHvlvgat+ (t(7) = 0.4634, p = 0.657) n = 8, paired t-test). Normalized feeding attempts, MPO-VMHvlvgat+ (t(7) = 1.87, p = 0.103, n = 8, paired t-test). For all panels *p < 0.05, **p < 0.01, ***p < 0.001, NS=non-significant. Statistical tests are two-sided.

Extended Data Fig. 7 HHM-defined transition probabilities for optogenetic stimulation.

a, BIC from models with different numbers of states using 10-fold cross validation. BICs were significantly smaller for the 3-state model than for the 2-state model (t(18) = 4.247, p = 0.000485), but were not significantly different between the 4-state model and 3-state model (t(18) = 0.6355, p = 0.533), n = 10 for each model, unpaired two-sided t-test. Data are presented as mean values ± SEM. b, Differences of the state-transition probabilities from the 3-state model between sham and stim trials for the dwell, IPI 5, IPI 20, and IPI full regimens. The rows represent the current states and the columns represent the next states. The positive or negative numbers in each cell of the heatmap indicate that the corresponding transition probability was higher or lower for the stim trials than for the sham trials, respectively. The state #1, #2, #3 are the same as the three states described in Fig. 7, and correspond to putative low motivation, high motivation, and attack states, respectively. c, Comparison of transitions between sham and stim trials. Black lines represent individual animals and the green or purple lines represent the mean across animals. t-statistics and p-values for individual comparisons are shown in Supplementary Table 2.

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Minakuchi, T., Guthman, E.M., Acharya, P. et al. Independent inhibitory control mechanisms for aggressive motivation and action. Nat Neurosci 27, 702–715 (2024). https://doi.org/10.1038/s41593-023-01563-6

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