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Adolescent thalamic inhibition leads to long-lasting impairments in prefrontal cortex function

Abstract

Impaired cortical maturation is a postulated mechanism in the etiology of neurodevelopmental disorders, including schizophrenia. In the sensory cortex, activity relayed by the thalamus during a postnatal sensitive period is essential for proper cortical maturation. Whether thalamic activity also shapes prefrontal cortical maturation is unknown. We show that inhibiting the mediodorsal and midline thalamus in mice during adolescence leads to a long-lasting decrease in thalamo–prefrontal projection density and reduced excitatory drive to prefrontal neurons. It also caused prefrontal-dependent cognitive deficits during adulthood associated with disrupted prefrontal cross-correlations and task outcome encoding. Thalamic inhibition during adulthood had no long-lasting consequences. Exciting the thalamus in adulthood during a cognitive task rescued prefrontal cross-correlations, task outcome encoding and cognitive deficits. These data point to adolescence as a sensitive window of thalamocortical circuit maturation. Furthermore, by supporting prefrontal network activity, boosting thalamic activity provides a potential therapeutic strategy for rescuing cognitive deficits in neurodevelopmental disorders.

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Fig. 1: Adolescent, but not adult, thalamic inhibition leads to persistent cognitive impairments in adulthood.
Fig. 2: Adolescent, but not adult, thalamic inhibition leads to a persistent reduction in excitatory drive onto mPFC pyramidal neurons.
Fig. 3: Adolescent thalamic inhibition leads to a persistent reduction in the density of thalamo–prefrontal, but not BLA–prefrontal, projections.
Fig. 4: Acute thalamic activity enhancement rescues the ASST behavioral deficit following adolescent thalamic inhibition.
Fig. 5: Adolescent thalamic inhibition does not affect mPFC gamma power in adult mice performing the EDSS task.
Fig. 6: Adolescent thalamic inhibition leads to loss of mPFC cellular encoding of ASST trial outcome in adult mice.

Data availability

The source data that support the findings of this study are available on figshare at https://figshare.com/projects/Benoit_Kellendonk_NN-A76458A/135581 or from the corresponding author upon reasonable request.

Code availability

Med-PC V, MATLAB and Python code used for administering the behavior and analysis of the data that support the findings of this study is available on figshare at https://figshare.com/projects/Benoit_Kellendonk_NN-A76458A/135581 or from the corresponding author upon reasonable request.

References

  1. Takesian, A. E. & Hensch, T. K. Balancing plasticity/stability across brain development. Prog. Brain Res. 207, 3–34 (2013).

    Article  PubMed  Google Scholar 

  2. Wiesel, T. N. & Hubel, D. H. Single-cell responses in striate cortex of kittens deprived of vision in one eye. J. Neurophysiol. 26, 1003–1017 (1963).

    CAS  Article  PubMed  Google Scholar 

  3. de Villers-Sidani, E., Chang, E. F., Bao, S. & Merzenich, M. M. Critical period window for spectral tuning defined in the primary auditory cortex (A1) in the rat. J. Neurosci. 27, 180–189 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Caras, M. L. & Sanes, D. H. Sustained perceptual deficits from transient sensory deprivation. J. Neurosci. 35, 10831–10842 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. Sun, Y. J., Espinosa, J. S., Hoseini, M. S. & Stryker, M. P. Experience-dependent structural plasticity at pre- and postsynaptic sites of layer 2/3 cells in developing visual cortex. Proc. Natl Acad. Sci. USA 116, 21812–21820 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. Canetta, S. E. et al. Mature parvalbumin interneuron function in prefrontal cortex requires activity during a postnatal sensitive period. Preprint at bioRxiv https://doi.org/10.1101/2021.03.04.433943 (2021).

  7. Bitzenhofer, S. H., Pöpplau, J. A., Chini, M., Marquardt, A. & Hanganu-Opatz, I. L. A transient developmental increase in prefrontal activity alters network maturation and causes cognitive dysfunction in adult mice. Neuron 109, 1350–1364 (2021).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. Larsen, B. & Luna, B. Adolescence as a neurobiological critical period for the development of higher-order cognition. Neurosci. Biobehav. Rev. 94, 179–195 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Bicks, L. K. et al. Prefrontal parvalbumin interneurons require juvenile social experience to establish adult social behavior. Nat. Commun. 11, 1003 (2020).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. Nabel, E. M. et al. Adolescent frontal top–down neurons receive heightened local drive to establish adult attentional behavior in mice. Nat. Commun. 11, 3983 (2020).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. Weinberger, D. R. & Berman, K. F. Prefrontal function in schizophrenia: confounds and controversies. Philos. Trans. R. Soc. Lond. B Biol. Sci. 351, 1495–1503 (1996).

    CAS  Article  PubMed  Google Scholar 

  12. Sakurai, T. & Gamo, N. J. Cognitive functions associated with developing prefrontal cortex during adolescence and developmental neuropsychiatric disorders. Neurobiol. Dis. 131, 104322 (2019).

    Article  PubMed  Google Scholar 

  13. Feinberg, I. Schizophrenia: caused by a fault in programmed synaptic elimination during adolescence? J. Psychiatr. Res. 17, 319–334 (1982).

    Article  PubMed  Google Scholar 

  14. Marenco, S. et al. Investigation of anatomical thalamo-cortical connectivity and FMRI activation in schizophrenia. Neuropsychopharmacology 37, 499–507 (2012).

    Article  PubMed  Google Scholar 

  15. Woodward, N. D., Karbasforoushan, H. & Heckers, S. Thalamocortical dysconnectivity in schizophrenia. Am. J. Psychiatry 169, 1092–1099 (2012).

    Article  PubMed  Google Scholar 

  16. Mitelman, S. A., Byne, W., Kemether, E. M., Hazlett, E. A. & Buchsbaum, M. S. Metabolic disconnection between the mediodorsal nucleus of the thalamus and cortical Brodmann’s areas of the left hemisphere in schizophrenia. Am. J. Psychiatry 162, 1733–1735 (2005).

    Article  PubMed  Google Scholar 

  17. Giraldo-Chica, M., Rogers, B. P., Damon, S. M., Landman, B. A. & Woodward, N. D. Prefrontal–thalamic anatomical connectivity and executive cognitive function in schizophrenia. Biol. Psychiatry 83, 509–517 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Woodward, N. D. & Heckers, S. Mapping thalamocortical functional connectivity in chronic and early stages of psychotic disorders. Biol. Psychiatry 79, 1016–1025 (2016).

    Article  PubMed  Google Scholar 

  19. Anticevic, A. et al. Association of thalamic dysconnectivity and conversion to psychosis in youth and young adults at elevated clinical risk. JAMA Psychiatry 72, 882–891 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Cho, K. I. et al. Altered thalamo-cortical white matter connectivity: probabilistic tractography study in clinical-high risk for psychosis and first-episode psychosis. Schizophrenia Bull. 42, 723–731 (2016).

    Article  Google Scholar 

  21. Bolkan, S. S. et al. Thalamic projections sustain prefrontal activity during working memory maintenance. Nat. Neurosci. 20, 987–996 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. Parnaudeau, S., Bolkan, S. S. & Kellendonk, C. The mediodorsal thalamus: an essential partner of the prefrontal cortex for cognition. Biol. Psychiatry 83, 648–656 (2018).

    Article  PubMed  Google Scholar 

  23. Rikhye, R. V., Gilra, A. & Halassa, M. M. Thalamic regulation of switching between cortical representations enables cognitive flexibility. Nat. Neurosci. 21, 1753–1763 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. Parnaudeau, S. et al. Inhibition of mediodorsal thalamus disrupts thalamofrontal connectivity and cognition. Neuron 77, 1151–1162 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. Benoit, L. J. et al. Medial prefrontal lesions impair performance in an operant delayed nonmatch to sample working memory task. Behav. Neurosci. 134, 187–197 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Bissonette, G. B. et al. Double dissociation of the effects of medial and orbital prefrontal cortical lesions on attentional and affective shifts in mice. J. Neurosci. 28, 11124–11130 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. Guirado, R., Umemori, J., Sipila, P. & Castren, E. Evidence for competition for target innervation in the medial prefrontal cortex. Cereb. Cortex 26, 1287–1294 (2016).

    Article  PubMed  Google Scholar 

  29. Stefanini, F. et al. A distributed neural code in the dentate gyrus and in CA1. Neuron 107, 703–716 (2020).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. Yuste, R. From the neuron doctrine to neural networks. Nat. Rev. Neurosci. 16, 487–497 (2015).

    CAS  Article  PubMed  Google Scholar 

  31. Narayanan, N. S. & Laubach, M. Methods for studying functional interactions among neuronal populations. Methods Mol. Biol. 489, 135–165 (2009).

    Article  PubMed  Google Scholar 

  32. Bernardi, S. et al. The geometry of abstraction in the hippocampus and prefrontal cortex. Cell 183, 954–967 (2020).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. Hill, R. A., Wu, Y. W., Kwek, P. & van den Buuse, M. Modulatory effects of sex steroid hormones on brain-derived neurotrophic factor-tyrosine kinase B expression during adolescent development in C57BL/6 mice. J. Neuroendocrinol. 24, 774–788 (2012).

    CAS  Article  PubMed  Google Scholar 

  34. Flores-Barrera, E. et al. Late adolescent expression of GluN2B transmission in the prefrontal cortex is input-specific and requires postsynaptic protein kinase A and D1 dopamine receptor signaling. Biol. Psychiatry 75, 508–516 (2014).

    CAS  Article  PubMed  Google Scholar 

  35. Baker, K. D., Gray, A. R. & Richardson, R. The development of perineuronal nets around parvalbumin GABAergic neurons in the medial prefrontal cortex and basolateral amygdala of rats. Behav. Neurosci. 131, 289–303 (2017).

    CAS  Article  PubMed  Google Scholar 

  36. Fagiolini, M. & Hensch, T. K. Inhibitory threshold for critical-period activation in primary visual cortex. Nature 404, 183–186 (2000).

    CAS  Article  PubMed  Google Scholar 

  37. Paus, T., Keshavan, M. & Giedd, J. N. Why do many psychiatric disorders emerge during adolescence. Nat. Rev. Neurosci. 9, 947–957 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. Caballero, A., Flores-Barrera, E., Cass, D. K. & Tseng, K. Y. Differential regulation of parvalbumin and calretinin interneurons in the prefrontal cortex during adolescence. Brain Struct. Funct. 219, 395–406 (2014).

    CAS  Article  PubMed  Google Scholar 

  39. Chini, M. & Hanganu-Opatz, I. L. Prefrontal cortex development in health and disease: lessons from rodents and humans. Trends Neurosci. 44, 227–240 (2021).

    CAS  Article  PubMed  Google Scholar 

  40. Konstantoudaki, X. et al. Prefrontal cortical-specific differences in behavior and synaptic plasticity between adolescent and adult mice. J. Neurophysiol. 119, 822–833 (2018).

    CAS  Article  PubMed  Google Scholar 

  41. Miyamae, T., Chen, K., Lewis, D. A. & Gonzalez-Burgos, G. Distinct physiological maturation of parvalbumin-positive neuron subtypes in mouse prefrontal cortex. J. Neurosci. 37, 4883–4902 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. Yang, J. M., Zhang, J., Yu, Y. Q., Duan, S. & Li, X. M. Postnatal development of 2 microcircuits involving fast-spiking interneurons in the mouse prefrontal cortex. Cereb. Cortex 24, 98–109 (2014).

    Article  PubMed  Google Scholar 

  43. Bitzenhofer, S. H., Popplau, J. A. & Hanganu-Opatz, I. Gamma activity accelerates during prefrontal development. eLife 9, e56795 (2020).

  44. Delevich, K., Klinger, M., Okada, N. J. & Wilbrecht, L. Coming of age in the frontal cortex: the role of puberty in cortical maturation. Semin. Cell Dev. Biol. 118, 64–72 (2021).

    Article  PubMed  Google Scholar 

  45. Zuo, Y., Lin, A., Chang, P. & Gan, W.-B. Development of long-term dendritic spine stability in diverse regions of cerebral cortex. Neuron 46, 181–189 (2005).

    CAS  Article  PubMed  Google Scholar 

  46. Pattwell, S. S. et al. Dynamic changes in neural circuitry during adolescence are associated with persistent attenuation of fear memories. Nat. Commun. 7, 11475 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  47. Van Eden, C. G. & Uylings, H. B. Postnatal volumetric development of the prefrontal cortex in the rat. J. Comp. Neurol. 241, 268–274 (1985).

    Article  PubMed  Google Scholar 

  48. Ferguson, B. R. & Gao, W. J. Development of thalamocortical connections between the mediodorsal thalamus and the prefrontal cortex and its implication in cognition. Front. Hum. Neurosci. 8, 1027 (2014).

    PubMed  Google Scholar 

  49. Rios, O. & Villalobos, J. Postnatal development of the afferent projections from the dorsomedial thalamic nucleus to the frontal cortex in mice. Brain Res. Dev. Brain Res. 150, 47–50 (2004).

    CAS  Article  PubMed  Google Scholar 

  50. Uhlhaas, P. J. & Singer, W. The development of neural synchrony and large-scale cortical networks during adolescence: relevance for the pathophysiology of schizophrenia and neurodevelopmental hypothesis. Schizophrenia Bull. 37, 514–523 (2011).

    Article  Google Scholar 

  51. Wen, W. & Turrigiano, G. G. Developmental regulation of homeostatic plasticity in mouse primary visual cortex. J. Neurosci. 41, 9891–9905 (2021).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. Guo, Z. V. et al. Maintenance of persistent activity in a frontal thalamocortical loop. Nature 545, 181–186 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  53. Kamigaki, T. & Dan, Y. Delay activity of specific prefrontal interneuron subtypes modulates memory-guided behavior. Nat. Neurosci. 20, 854–863 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. Tanaka, S. Dysfunctional GABAergic inhibition in the prefrontal cortex leading to “psychotic” hyperactivation. BMC Neurosci. 9, 41 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Taylor, S. F., Welsh, R. C., Chen, A. C., Velander, A. J. & Liberzon, I. Medial frontal hyperactivity in reality distortion. Biol. Psychiatry 61, 1171–1178 (2007).

    Article  PubMed  Google Scholar 

  56. Spellman, T., Svei, M., Kaminsky, J., Manzano-Nieves, G. & Liston, C. Prefrontal deep projection neurons enable cognitive flexibility via persistent feedback monitoring. Cell 184, 2750–2766 (2021).

    CAS  Article  PubMed  Google Scholar 

  57. Park, A. J. et al. Reset of hippocampal–prefrontal circuitry facilitates learning. Nature 591, 615–619 (2021).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  58. Bonaventura, J. et al. High-potency ligands for DREADD imaging and activation in rodents and monkeys. Nat. Commun. 10, 4627 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Canetta, S. et al. Maternal immune activation leads to selective functional deficits in offspring parvalbumin interneurons. Mol. Psychiatry 21, 956–968 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  60. Vinck, M., van Wingerden, M., Womelsdorf, T., Fries, P. & Pennartz, C. M. The pairwise phase consistency: a bias-free measure of rhythmic neuronal synchronization. Neuroimage 51, 112–122 (2010).

    Article  PubMed  Google Scholar 

  61. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

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Acknowledgements

We thank members of the C.K., S.C. and A.Z.H. labs for technical assistance and discussions. This work was supported by grants from the NIMH (K01MH107760 to S.C., R21 MH121334 and R21 MH117454 to C.K., F31 MH119691 to L.J.B., R01MH124998 to A.Z.H. and NSF Neuronex to L.P. and S.F.).

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Authors

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L.J.B., S.C. and C.K. designed the experiments. L.J.B. performed the experiments and analyzed the data. E.S.H. assisted in the performance and analysis of the experiments. A.Z.H. assisted in the design, performance, analysis and interpretation of experiments. L.P. and S.F. assisted in the analysis of experiments. L.J.B., S.C. and C.K. interpreted the results and wrote the paper.

Corresponding author

Correspondence to Christoph Kellendonk.

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The authors declare no competing interests.

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

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Extended data

Extended Data Fig. 1 A chemogenetic approach to reversibly and chronically inhibit thalamic cells during development or adulthood.

(a) Experimental design and timeline. Mice were injected with virus at P13, and whole cell patch clamp recordings were made at P35, P50, or P105 in cells expressing hM4D-mCherry or control cells at baseline and in response to bath application of 10 µM. Animals expressing hM4D were given twice daily 1 mg/kg CNO i.p. injections for 15 days (P20-35 and P90-105) or 30 days (P20-50). Created with Biorender.com. (b) Example images illustrating hM4D-mCherry expression in the midline thalamus in adolescent and adult animals. Histology images were collected from each animal for each cohort, with at least 6 slices taken for each animal and at least 8 adolescent and 5 adult animals. (c) Superimposed traces of hM4D-mCherry viral spread (red shading) relative to mediodorsal and midline thalamic nuclei (dashed black lines) in coronal slices. Distance from bregma listed beside each coronal slice. (d) Quantification of CNO-induced hyperpolarization. Control cells at P35 and P105 were pooled because CNO did not show an effect at either age. CNO induced a significant hyperpolarization in P35, P50, and P105 cells expressing hM4D relative to control cells. Dots indicate individual cell responses and bars indicate mean ± SEM. Control: n = 15 cells, 5 animalshM4D, P35 (CNO P20-35, 15 days): n = 8 cells, 3 animals; hM4D, P50 (CNO P20-50, 30 days): n = 5 cells, 2 animals; hM4D, P105 (CNO P90-105, 15 days): n = 5 cells, 3 animals; 1-way ANOVA, effect of treatment F(3, 29)=4.573, p = 0.0097; Holm-Sidak post-hoc, Control vs. hM4D P35 *p = 0.0460, Control vs. hM4D P50 **p = 0.0095, Control vs. hM4D P105 *p = 0.0459. *p < 0.05.

Extended Data Fig. 2

(a) Adolescent-inhibited hM4D animals have a significantly worse performance during the first 8 days of the NMS task, particularly at day 4. Control: n = 17 animals; hM4D = 21 animals. 2-way rmANOVA, effect of time F(3.129,112.7)=87.66, p < 0.0001, effect of group F(1,36)=4.575, *p = 0.0358, effect of group x time F(7,252)=1.546, p = 0.1523; Holm-Sidak post-hoc analysis day 4, *p = 0.0456. Learning curves depict mean performance ± SEM each day. b) In the NMS task, mobility and motivation were unaffected by adolescent (top) or adult (bottom) thalamic inhibition: mean trial length, adolescent: Control: n = 17 animals, 18.94 ± 1.61 s, hM4D: n = 21 animals, 19.03 ± 1.26 s; two-sided unpaired t-test: t = 0.04228, df=36, p = 0.9665; adult: Control: n = 6 animals, 19.41 ± 4.19 s, hM4D: n = 10 animals, 19.05 ± 1.50 s; two-sided unpaired t-test: t = 0.09631, df=14, p = 0.9246, (c) sample lever-choice lever press latency, adolescent: Control: 4.932 ± 0.199 s, hM4D: 5.372 ± 0.252 s; two-sided unpaired t-test: t = 1.321, df=36, p = 0.1949; adult: Control: 5.304 ± 0.416 s, hM4D: 4.999 ± 0.102 s; two-sided unpaired t-test: t = 0.8949, df=14, p = 0.3875, (d) latency to collect reward, adolescent: Control: 0.5781 ± 0.0143 s, hM4D: 0.5801 ± 0.0125 s; two-sided unpaired t-test: t = 0.1086, df=36, p = 0.9141; adult: Control: 0.6091 ± 0.0396 s, hM4D: 0.6137 ± 0.0233 s; two-sided unpaired t-test: t = 0.1064, df=14, p = 0.9168, and (e) percentage of rewards retrieved, adolescent: Control: 99.78 ± 0.05%, hM4D: 99.71 ± 0.07%; two-sided unpaired t-test: t = 0.7668, df=36, p = 0.4482; adult: Control: 99.36 ± 0.21 s, hM4D: 99.62 ± 0.10 s; two-sided unpaired t-test: t = 1.199, df=14, p = 0.2503. f) In the ASST, mobility and motivation were unaffected by adolescent (top) or adult (bottom) thalamic inhibition: median latency to dig during IA (adolescent: Control: n = 14 animals, 24.29 ± 5.84 s, hM4D: n = 16 animals, 32.47 ± 8.91 s; two-sided unpaired t-test: t = 0.7448, df=28, p = 0.4626; adult: Control: n = 20 animals, 53.50 ± 9.41 s, hM4D: n = 17 animals, 45.44 ± 6.95 s; two-sided unpaired t-test: t = 0.6682, df=35, p = 0.5084) or (g) SS (adolescent: Control: n = 14 animals, 34.57 ± 8.39 s, hM4D: n = 15 animals, 32.07 ± 4.32 s; two-sided unpaired t-test: t = 0.2708, df=27, p = 0.7886; adult: Control: n = 20 animals, 90.20 ± 12.58 s, hM4D: n = 17 animals, 78.44 ± 13.70 s; two-sided unpaired t-test: t = 0.6323, df=35, p = 0.5313). (h) EDSS error type breakdown, perseverative (P) and random (R), was unaffected. Adolescent inhibition (top) caused increased P and R errors (Control: n = 14 animals, hM4D: n = 15 animals; 2-way rmANOVA, effect of group F(1,27)=4.215, *p = 0.0499). Adult inhibition (bottom) caused no change in error type (Control: n = 20 animals, hM4D: n = 17 animals; 2-way rmANOVA, effect of group F(1,35)=1.369, p = 0.2499). Dots represent individual animals; lines represent mean ± SEM. *p < 0.05.

Extended Data Fig. 3 Thalamo-mPFC projection activity during adolescence is required for adult cognitive flexibility.

(a) Schematic for viral injections to target thalamo-mPFC projections. At P13, one virus was injected into the mPFC containing a retrogradely transported Cre driver, and another virus was injected into the thalamus containing floxed, Cre-dependent hM4D or the control GFP. Created with Biorender.com. (b) Example image illustrating hM4D-mCherry expression in the thalamo-mPFC projections. Histology images were collected from each animal, with at least 6 slices taken for each animal (n = 14 animals). (c) Superimposed traces of hM4D-mCherry viral spread (red shading) relative to mediodorsal and midline thalamic nuclei (dashed black lines) in coronal slices. Distance from bregma listed beside each coronal slice. (d) Adolescent-inhibited hM4D animals are no different than controls in the IA portion of the ASST. Control: n = 12 animals, 9.75 ± 0.70 trials; hM4D: n = 14 animals, 10.00 ± 0.70 trials; two-sided unpaired t-test, t = 0.2507, df=24, p = 0.8042. Dots represent individual animals; lines represent mean ± SEM. (e) Adolescent-inhibited hM4D animals take significantly more trials in the EDSS to reach criterion than controls. Control: n = 12 animals, 10.25 ± 0.37 trials; hM4D: n = 14 animals, 13.00 ± 1.02 trials; two-sided unpaired t-test, t = 2.385, df=24, *p = 0.0254. Dots represent individual animals; lines represent mean ± SEM. *p < 0.05.

Extended Data Fig. 4 Thalamic activity in adolescence is not required for thalamic cell activity in adulthood.

(a) Adolescent experimental timeline and schematic. Whole cell patch clamp recordings were made from thalamic cells from control and hM4D mice. These cells receive inputs from the mPFC and express the control or hM4D virus. Created with Biorender.com. (b) Representative traces showing spontaneous excitatory post-synaptic currents (sEPSCs). sEPSC (c) frequency and (d) amplitude are unchanged following adolescent thalamic inhibition relative to control mice. Control: n = 9 cells, 3 animals; hM4D: n = 8 cells, 2 animals; frequency: Control: 5.131 ± 1.234 Hz, hM4D: 3.710 ± 1.318 Hz; two-sided unpaired t-test, t = 0.7874, df=15, p = 0.4433; amplitude: Control: 21.70 ± 0.98 pA, hM4D: 20.32 ± 0.89 pA; two-sided unpaired t-test, t = 1.023, df=15, p = 0.3227. (e) Representative traces showing events in current clamp recordings. (f) Rheobase is unchanged following adolescent thalamic inhibition, as is (g) firing frequency in response to input currents. Control: n = 8 cells, 3 animals; hM4D: n = 8 cells, 2 animals; rheobase: Control: 21.25 ± 4.41 pA, hM4D: 21.25 ± 8.33 pA; two-sided unpaired t-test, t = 0.000, df=14, p > 0.9999; input current vs. firing frequency: 2-way rmANOVA, effect of input current F(2.484,34.59)=48.33, p < 0.0001, effect of group F(1,14)=0.08940, p = 0.7693, effect of group x input current F(14,195)=0.4870, p = 0.9383. Curves depict mean firing frequency ± SEM for each input current. For all other plots, dots represent individual animals; lines represent mean ± SEM.

Extended Data Fig. 5 Density of thalamic projections to the mPFC is already decreased at P35.

(a) After twice daily CNO injections from P20-35, stereological estimates of thalamo-mPFC projections at P35 showed a significant decrease in density in adolescent-inhibited hM4D animals compared to controls (Control: n = 9 animals, 762.3 ± 110.9 cells/mm2, hM4D: n = 10 animals, 462.1 ± 87.21 cells/mm2; two-sided unpaired t-test, t = 2.149, df=17, *p = 0.0463). (b) Meanwhile, there were no differences in overall thalamic area (Control: n = 9 animals, 6.262 ± 0.176 mm2, hM4D: n = 10 animals, 6.231 ± 0.072 mm2; two-sided unpaired t-test, t = 0.1712, df=17, p = 0.8661). (c) Moreover, there is a higher density of thalamo-mPFC projections at P35 than at P90 (P35: Control: n = 9 animals, hM4D: n = 10 animals; P90: Control: n = 6 animals, hM4D: n = 7 animals; 2-way rmANOVA, effect of age F(1,28)=7.731, **p = 0.0096, effect of group F(1,28)=7.205, *p = 0.0121, effect of age x group F(1,28)=0.3405, p = 0.5642). Dots represent individual animals; lines represent mean ± SEM. *p < 0.05, **p < 0.01.

Extended Data Fig. 6

Mean thalamic beta (12-30 Hz) power is specifically enhanced during the EDSS trials compared to the ITI for (a) control (n = 10 animals, ITI: 0.1822 ± 0.0042; Trial: 0.2044 ± 0.0036; linear mixed effects model (power~trial + (1|animal)): fixed effect (Trial), ****p = 3.2642e-16) and (b) hM4D (n = 15 animals, ITI: 0.1866 ± 0.0018; Trial: 0.2124 ± 0.0022; linear mixed effects model (power~trial + (1|animal)): fixed effect (Trial), ****p = 2.0872e-41) animals. (c) As in (a) except for mean beta (12-30 Hz) mPFC-thalamic coherence (n = 10 animals, ITI: 0.3890 ± 0.0090; Trial: 0.4146 ± 0.0080; linear mixed effects model (coherence~trial + (1|animal)): fixed effect (Trial), ****p = 2.0137e-07). (d) As in (b) except for mean beta mPFC-thalamic coherence (n = 15 animals, ITI: 0.3968 ± 0.0041; Trial: 0.4331 ± 0.0044; linear mixed effects model (coherence~trial + (1|animal)): fixed effect (Trial), ****p = 6.7099e-19). (e) Mean thalamic beta power (n = 10 animals, 88 correct trials, 23 incorrect trials, Correct: 0.2052 ± 0.0041; Incorrect: 0.2011 ± 0.0075; linear mixed effects model (power~trial type + (1|animal)): fixed effect (Trial Type), p = 0.18827) and (f) beta mPFC-thalamic coherence (n = 10 animals, Correct: 0.4188 ± 0.0092; Incorrect: 0.3984 ± 0.0152; linear mixed effects model (coherence~trial type + (1|animal)): fixed effect (Trial Type), p = 0.72808) are unchanged across trial types in controls. Dots represent individual trials for each animal (colors of the dots). Lines and error represent mean ± SEM. g) Pairwise phase consistency (PPC) values show no differences between phase-locking of mPFC cell firing and thalamic beta oscillatory activity in control (n = 6 animals, 27 cells, Correct PPC: 0.01575 ± 0.00420; Incorrect: 0.01904 ± 0.00642; two-sided paired t-test: t = 0.4114, df=26, p = 0.6841) or (h) hM4D (n = 7 animals, 22 cells, Correct: 0.01623 ± 0.00441; Incorrect: 0.01205 ± 0.00561; two-sided paired t-test: t = 0.7443, df=21, p = 0.4649) animals. Dots represent individual cells, with lines connecting each cell’s correct and incorrect PPC value. ****p < 0.0001.

Extended Data Fig. 7

Breakdown of cells by behavior during the trial compared to the inter-trial interval (ITI), with cells that have a significantly increased firing rate during the trial (increasers), decreased firing rate (decreasers), or unchanged firing rate compared to the ITI (non-changers). This shows a majority of cells modulated during EDSS trials, with (a) 80% modulated in control animals, (b) 81.82% modulated in adolescent-inhibited hM4D animals, and (c) 71.83% modulated in hM4D animals during EDSS thalamic activation. d) Raw firing rates show no differences between groups across different epochs of the EDSS: during the ITI (Control Light OFF: n = 8 animals, 75 cells, 3.503 ± 0.561 Hz; hM4D Light OFF: n = 12 animals, 55 cells, 2.619 ± 0.645 Hz; hM4D Light ON: n = 13 animals, 71 cells, 2.877 ± 0.570 Hz; 1-way ANOVA, F(2,194)=0.5002, p = 0.6072, dots represent individual cells; lines represent mean ± SEM), (e) over the course of the trial (overlapping 2 s bins, with x-axis labels depicting the middle of each bin; dots represent mean FR for each bin, error bars represent SEM); Control Light OFF: n = 8 animals, 75 cells; hM4D Light OFF: n = 12 animals, 55 cells; hM4D Light ON: n = 13 animals, 71 cells; 2-way rmANOVA, effect of group, F(2,194)=0.2743, p = 0.7604), during the (f) pre-decision (all trials: Control Light OFF: n = 8 animals, 75 cells, 3.717 ± 0.634 Hz; hM4D Light OFF: n = 12 animals, 55 cells, 3.482 ± 0.765 Hz; hM4D Light ON: n = 13 animals, 71 cells, 3.097 ± 0.533 Hz; 1-way ANOVA, F(2,194)=0.2492, p = 0.7797; correct vs. incorrect: 2-way rmANOVA, Holm-Sidak post-hoc, Control Light OFF: p = 0.9962, hM4D Light OFF: p > 0.9999, hM4D Light ON: p = 0.9707) and (g) post-decision (all trials: Control Light OFF: n = 8 animals, 75 cells, 3.585 ± 0.650 Hz; hM4D Light OFF: n = 12 animals, 55 cells, 3.493 ± 0.795 Hz; hM4D Light ON: n = 13 animals, 71 cells, 3.014 ± 0.500 Hz; 1-way ANOVA, F(2,194)=0.2693, p = 0.7642; correct vs. incorrect: 2-way rmANOVA, Holm-Sidak post-hoc, Control Light OFF: p = 0.6988, hM4D Light OFF: p = 0.9761, hM4D Light ON: p = 0.9475) periods, both across trial types (left; dots represent individual cells; lines represent mean ± SEM) and between correct and incorrect trials (right; dots represent individual cells, lines connecting FR for correct and incorrect trials).

Extended Data Fig. 8

Firing rates normalized to the ITI show no differences between groups across different EDSS epochs (for a-f: Control OFF: n = 8 animals, 75 cells; hM4D OFF: n = 12 animals, 55 cells; hM4D ON: n = 13 animals, 71 cells): (a) during the trial (Control OFF:7.250 ± 3.907; hM4D OFF:6.442 ± 2.351; hM4D ON:2.353 ± 1.556; 1-way ANOVA, F(2,194)=1.346, p = 0.2628; dots represent individual cells; lines represent mean ± SEM), (b) between trial types (2-way rmANOVA, Holm-Sidak post-hoc, Control OFF: p = 0.2067, hM4D OFF: p = 0.9981, hM4D ON: p = 0.9848; dots represent individual cells, lines connecting FR for correct and incorrect trials), (c) over the course of the trial (overlapping 2 s bins, with x-axis labels depicting the middle of each bin; dots represent mean FR, error bars represent SEM; 2-way rmANOVA, effect of group, F(2,194)=0.9097, p = 0.4044), (d) during the ITI (Control OFF:−0.7287 ± 0.2749; hM4D OFF:−1.508 ± 0.2542; hM4D ON:−0.7053 ± 0.2727; 1-way ANOVA, F(2,194)=0.5465, p = 0.5798; dots represent individual cells; lines represent mean ± SEM), (e) pre-decision (all trials: Control OFF:7.898 ± 4.166; hM4D OFF:6.356 ± 2.273; hM4D ON:2.716 ± 1.722; 1-way ANOVA, F(2,194)=1.241, p = 0.2914; correct vs. incorrect: 2-way rmANOVA, Holm-Sidak post-hoc, Control OFF:p = 0.5151, hM4D OFF:p = 0.9983, hM4D ON:p > 0.9999) and (f) post-decision (all trials: Control OFF:6.608 ± 3.720; hM4D OFF:6.521 ± 2.443; hM4D ON:1.993 ± 1.440; 1-way ANOVA, F(2,194)=1.441, p = 0.2391; correct vs. incorrect: 2-way rmANOVA, Holm-Sidak post-hoc, Control OFF:p = 0.2171, hM4D OFF:p = 0.9994, hM4D ON:p = 0.9347), across trial types (left; dots represent individual cells; lines represent mean ± SEM) and between trial types (right; dots represent individual cells, lines connecting FR for correct and incorrect trials). (g) Un-truncated plot from Fig. 6d. Control OFF: n = 6 animals, 73 cells, 507 cellpairs, 0.0177 ± 0.0430; hM4D OFF: n = 9 animals, 52 cells, 181 cellpairs, 0.0124 ± 0.0212; hM4D ON: n = 11 animals, 69 cells, 327 cellpairs, 0.0201 ± 0.0414; linear mixed effects model (peak cross-correlation~group+outcome + (1|cellpair)+(1|animal)+(1 | cell1)+(1 | cell2)), fixed effect of group: Control OFF vs. hM4D OFF:*p = 0.041622; hM4D OFF vs. hM4D ON:**p = 0.0090838. Bars with error represent mean ± SEM. Individual dots represent cell pair correlations for each trial type. (h) FRs of cells with peak cross-correlation above 0.08 show no FR pattern. Dots represent cells, lines connecting each cell’s FR for correct and incorrect trials. Control OFF: n = 6 animals, 33 cells; hM4D OFF: n = 3 animals, 6 cells; hM4D ON: n = 5 animals, 26 cells.

Extended Data Fig. 9

Mean thalamic epsilon firing rates during EDSS trials for each animal show no significant differences in thalamic activity for control or adolescent-inhibited hM4D animals. Control: n = 10 animals, 8.606 ± 2.114 Hz; hM4D: n = 15 animals, 5.726 ± 1.499 Hz; two-sided unpaired t-test, t = 1.144, df=23, p = 0.2643. Dots represent individual animals; lines represent mean ± SEM.

Extended Data Fig. 10

(a) Peak cross-correlation values for pairs of mPFC single units during the delay of a working memory T-maze task for correct (green) and incorrect (orange) trials, as described in Bolkan et al 201721. Acute thalamo-mPFC inhibition (Light ON) during the delay shows decreased cross-correlations compared with baseline (Light OFF). n = 9 animals, 891 cells, 5254 cell pairs; Light OFF: 0.0048 ± 0.0002; Light ON: 0.0043 ± 0.0002; linear mixed effects model (peak cross-correlation~group+trial type + (1|cellpair)+(1|animal)+(1 | cell1)+(1 | cell2)), fixed effect of group: Light OFF vs. ON: ****p = 3.587e-17. Bars with error represent mean ± SEM. Individual dots represent cell pairs. This graph has been truncated along the y-axis. (b) Histogram of control (blue), hM4D (red), and hM4D Light ON (gold) cell decoding weights show the distribution of contributions across cells is unchanged across groups. (c) Firing rates of cells with a significantly elevated decoding weight relative to shuffled data. Very few cells (<10% for each group) contribute significantly more than when shuffled, and these cells do not have different FRs across correct and incorrect trials. Dots represent cell FRs for each trial type, lines connecting each cell’s correct and incorrect trials. Control: n = 3 animals, 7 cells; hM4D: n = 1 animal, 1 cell; hM4D Light ON: n = 2 animals, 5 cells. (d) Decoder performance was calculated using random subgroups of neurons, repeated 25 times for each multiple of 5 neurons. Significant separation between hM4D and both Control and hM4D Light ON groups is seen with 5 neurons. Dashed line represents chance performance (50%). Dots represent mean performance for the repetitions, error bars represent standard deviation. Control Light OFF: n = 4 animals, 60 cells; hM4D Light OFF: n = 7 animals, 45 cells; hM4D Light ON: n = 9 animals, 61 cells; 2-way rmANOVA Holm-Sidak post-hoc analysis, with 5 neurons: Control vs. hM4D: p < 0.0001, Control vs. hM4D Light ON: p = 0.1557, hM4D vs. hM4D Light ON: p < 0.0001. (e) Decoding performance (blue diamond) is no better than chance for control animals during IA. Shuffled trial outcomes show chance decoder performance, mean ± standard deviation (black circles and error bars) and individual shuffles (grey circles). n = 3 animals, 47 cells, 1000 shuffles; actual: 50.35%, shuffled: 50.13 ± 3.14%, p = 0.9438.

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Benoit, L.J., Holt, E.S., Posani, L. et al. Adolescent thalamic inhibition leads to long-lasting impairments in prefrontal cortex function. Nat Neurosci 25, 714–725 (2022). https://doi.org/10.1038/s41593-022-01072-y

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