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Cortical glutamatergic projection neuron types contribute to distinct functional subnetworks

Abstract

The cellular basis of cerebral cortex functional architecture remains not well understood. A major challenge is to monitor and decipher neural network dynamics across broad cortical areas yet with projection-neuron-type resolution in real time during behavior. Combining genetic targeting and wide-field imaging, we monitored activity dynamics of subcortical-projecting (PTFezf2) and intratelencephalic-projecting (ITPlxnD1) types across dorsal cortex of mice during different brain states and behaviors. ITPlxnD1 and PTFezf2 neurons showed distinct activation patterns during wakeful resting, during spontaneous movements and upon sensory stimulation. Distinct ITPlxnD1 and PTFezf2 subnetworks were dynamically tuned to different sensorimotor components of a naturalistic feeding behavior, and optogenetic inhibition of ITsPlxnD1 and PTsFezf2 in subnetwork nodes disrupted distinct components of this behavior. Lastly, ITPlxnD1 and PTFezf2 projection patterns are consistent with their subnetwork activation patterns. Our results show that, in addition to the concept of columnar organization, dynamic areal and projection-neuron-type specific subnetworks are a key feature of cortical functional architecture linking microcircuit components with global brain networks.

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Fig. 1: Distinct activity patterns of ITPlxnD1 and PTFezf2 neurons during wakeful resting and upon sensory input.
Fig. 2: Distinct PTFezf2 and ITPlxnD1 subnetworks tuned to different sensorimotor components of a feeding behavior.
Fig. 3: ITPlxnD1 and PTFezf2 neurons within frontolateral and parietofrontal nodes show distinct temporal dynamics during feeding behavior.
Fig. 4: Feeding without hand lift selectively occludes PTFezf2 activity in parietal node.
Fig. 5: Inhibiting ITPlxnD1 and PTFezf2 disrupts distinct components of feeding.
Fig. 6: Brain-wide projections of ITPlxnD1 and PTFezf2 from frontolateral and parietofrontal networks.

Data availability

Sample data are available at https://doi.org/10.6084/m9.figshare.21437604.v1. All additional data will be made available upon reasonable request. Source data are provided with this paper.

Code availability

All code used in the study is available at https://github.com/HemanthMohan/Mohan-et-al-NatNeuro-2022/blob/e8ee2459934dcff4d638146aa6d325f831e614f5/Mohan_NatNeruo_2022_AnalysisScripts.zip.

References

  1. Bota, M., Sporns, O. & Swanson, L. W. Architecture of the cerebral cortical association connectome underlying cognition. Proc. Natl Acad. Sci. USA 112, E2093–E2101 (2015).

    Article  CAS  Google Scholar 

  2. Van Essen, D. C. & Glasser, M. F. Parcellating cerebral cortex: how invasive animal studies inform noninvasive mapmaking in humans. Neuron 99, 640–663 (2018).

    Article  Google Scholar 

  3. Hubel, D. H. & Wiesel, T. N. Shape and arrangement of columns in cat’s striate cortex. J. Physiol. 165, 559–568 (1963).

    Article  CAS  Google Scholar 

  4. Mountcastle, V. B., Davies, P. W. & Berman, A. L. Response properties of neurons of cat’s somatic sensory cortex to peripheral stimuli. J. Neurophysiol. 20, 374–407 (1957).

    Article  CAS  Google Scholar 

  5. Hubel, D. H. & Wiesel, T. N. Brain and Visual Perception: the Story of a 25-Year Collaboration (Oxford University Press, 2004).

  6. Douglas, R. J. & Martin, K. A. Mapping the matrix: the ways of neocortex. Neuron 56, 226–238 (2007).

    Article  CAS  Google Scholar 

  7. Harris, K. D. & Shepherd, G. M. The neocortical circuit: themes and variations. Nat. Neurosci. 18, 170–181 (2015).

    Article  CAS  Google Scholar 

  8. Markram, H. et al. Reconstruction and simulation of neocortical microcircuitry. Cell 163, 456–492 (2015).

    Article  CAS  Google Scholar 

  9. Lerner, T. N., Ye, L. & Deisseroth, K. Communication in neural circuits: tools, opportunities, and challenges. Cell 164, 1136–1150 (2016).

    Article  CAS  Google Scholar 

  10. Engel, T. A., Scholvinck, M. L. & Lewis, C. M. The diversity and specificity of functional connectivity across spatial and temporal scales. Neuroimage 245, 118692 (2021).

    Article  Google Scholar 

  11. Huang, Z. J. Toward a genetic dissection of cortical circuits in the mouse. Neuron 83, 1284–1302 (2014).

    Article  CAS  Google Scholar 

  12. Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).

    Article  CAS  Google Scholar 

  13. Gamanut, R. et al. The mouse cortical connectome, characterized by an ultra-dense cortical graph, maintains specificity by distinct connectivity profiles. Neuron 97, 698–715 (2018).

    Article  CAS  Google Scholar 

  14. Zingg, B. et al. Neural networks of the mouse neocortex. Cell 156, 1096–1111 (2014).

    Article  CAS  Google Scholar 

  15. Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014).

    Article  CAS  Google Scholar 

  16. Harris, J. A. et al. Hierarchical organization of cortical and thalamic connectivity. Nature 575, 195–202 (2019).

    Article  CAS  Google Scholar 

  17. Munoz-Castaneda, R. et al. Cellular anatomy of the mouse primary motor cortex. Nature 598, 159–166 (2021).

    Article  CAS  Google Scholar 

  18. Gozzi, A. & Schwarz, A. J. Large-scale functional connectivity networks in the rodent brain. Neuroimage 127, 496–509 (2016).

    Article  Google Scholar 

  19. Smith, S. M. et al. Functional connectomics from resting-state fMRI. Trends Cogn. Sci. 17, 666–682 (2013).

    Article  Google Scholar 

  20. Steinmetz, N. A. et al. Neuropixels 2.0: a miniaturized high-density probe for stable, long-term brain recordings. Science 372, eabf4588 (2021).

  21. Svoboda, K., Helmchen, F., Denk, W. & Tank, D. W. Spread of dendritic excitation in layer 2/3 pyramidal neurons in rat barrel cortex in vivo. Nat. Neurosci. 2, 65–73 (1999).

    Article  CAS  Google Scholar 

  22. Cardin, J. A., Crair, M. C. & Higley, M. J. Mesoscopic imaging: shining a wide light on large-scale neural dynamics. Neuron 108, 33–43 (2020).

    Article  CAS  Google Scholar 

  23. Allen, W. E. et al. Global representations of goal-directed behavior in distinct cell types of mouse neocortex. Neuron 94, 891–907 (2017).

    Article  CAS  Google Scholar 

  24. Musall, S., Kaufman, M. T., Juavinett, A. L., Gluf, S. & Churchland, A. K. Single-trial neural dynamics are dominated by richly varied movements. Nat. Neurosci. 22, 1677–1686 (2019).

    Article  CAS  Google Scholar 

  25. Wekselblatt, J. B., Flister, E. D., Piscopo, D. M. & Niell, C. M. Large-scale imaging of cortical dynamics during sensory perception and behavior. J. Neurophysiol. 115, 2852–2866 (2016).

    Article  CAS  Google Scholar 

  26. Vanni, M. P. & Murphy, T. H. Mesoscale transcranial spontaneous activity mapping in GCaMP3 transgenic mice reveals extensive reciprocal connections between areas of somatomotor cortex. J. Neurosci. 34, 15931–15946 (2014).

    Article  Google Scholar 

  27. Chen, T.-W., Li, N., Daie, K. & Svoboda, K. A map of anticipatory activity in mouse motor cortex. Neuron 94, 866–879 (2017).

    Article  CAS  Google Scholar 

  28. 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 

  29. West, S. L. et al. Wide-field calcium imaging of dynamic cortical networks during locomotion. Cereb. Cortex 32, 2668–2687 (2022).

    Article  Google Scholar 

  30. Mohajerani, M. H. et al. Spontaneous cortical activity alternates between motifs defined by regional axonal projections. Nat. Neurosci. 16, 1426–1435 (2013).

    Article  CAS  Google Scholar 

  31. Ren, C. & Komiyama, T. Characterizing cortex-wide dynamics with wide-field calcium imaging. J. Neurosci. 41, 4160–4168 (2021).

    Article  CAS  Google Scholar 

  32. Gilad, A., Gallero-Salas, Y., Groos, D. & Helmchen, F. Behavioral strategy determines frontal or posterior location of short-term memory in neocortex. Neuron 99, 814–828 (2018).

    Article  CAS  Google Scholar 

  33. Kauvar, I. V. et al. Cortical observation by synchronous multifocal optical sampling reveals widespread population encoding of actions. Neuron 107, 351–367 (2020).

    Article  CAS  Google Scholar 

  34. Ma, Y. et al. Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons. Proc. Natl Acad. Sci. USA 113, E8463–E8471 (2016).

    Article  CAS  Google Scholar 

  35. Vesuna, S. et al. Deep posteromedial cortical rhythm in dissociation. Nature 586, 87–94 (2020).

    Article  CAS  Google Scholar 

  36. Xiao, D. et al. Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons. eLife. 6, e19976 (2017).

  37. Matho, K. S. et al. Genetic dissection of the glutamatergic neuron system in cerebral cortex. Nature 598, 182–187 (2021).

    Article  CAS  Google Scholar 

  38. Shepherd, G. M. G. & Yamawaki, N. Untangling the cortico-thalamo-cortical loop: cellular pieces of a knotty circuit puzzle. Nat. Rev. Neurosci. 22, 389–406 (2021).

    Article  CAS  Google Scholar 

  39. Hooks, B. M. Sensorimotor convergence in circuitry of the motor cortex. Neuroscientist 23, 251–263 (2017).

    Article  Google Scholar 

  40. Voigts, J. & Harnett, M. T. Somatic and dendritic encoding of spatial variables in retrosplenial cortex differs during 2D navigation. Neuron 105, 237–245 (2020).

    Article  CAS  Google Scholar 

  41. Xu, N.-l et al. Nonlinear dendritic integration of sensory and motor input during an active sensing task. Nature 492, 247–251 (2012).

    Article  CAS  Google Scholar 

  42. Lacefield, C. O., Pnevmatikakis, E. A., Paninski, L. & Bruno, R. M. Reinforcement learning recruits somata and apical dendrites across layers of primary sensory cortex. Cell Rep. 26, 2000–2008 (2019).

    Article  CAS  Google Scholar 

  43. Kerlin, A. et al. Functional clustering of dendritic activity during decision-making. eLife 8, e46966 (2019).

    Article  Google Scholar 

  44. Beaulieu-Laroche, L., Toloza, E. H., Brown, N. J. & Harnett, M. T. Widespread and highly correlated somato-dendritic activity in cortical layer 5 neurons. Neuron 103, 235–241 (2019).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  46. Ragan, T. et al. Serial two-photon tomography for automated ex vivo mouse brain imaging. Nat. Methods 9, 255–258 (2012).

    Article  CAS  Google Scholar 

  47. Tyson, A. L. et al. Accurate determination of marker location within whole-brain microscopy images. Sci. Rep. 12, 867 (2022).

    Article  CAS  Google Scholar 

  48. Niedworok, C. J. et al. aMAP is a validated pipeline for registration and segmentation of high-resolution mouse brain data. Nat. Commun. 7, 11879 (2016).

    Article  Google Scholar 

  49. Whitesell, J. D. et al. Regional, layer, and cell-type-specific connectivity of the mouse default mode network. Neuron 109, 545–559 (2021).

    Article  CAS  Google Scholar 

  50. Shepherd, G. M. Corticostriatal connectivity and its role in disease. Nat. Rev. Neurosci. 14, 278–291 (2013).

    Article  CAS  Google Scholar 

  51. Brodmann, K. Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues (Barth, 1909).

  52. Vogt, C. & Vogt, O. Allgemeine ergebnisse unserer hirnforschung. Vol. 21 (Barth, 1919).

  53. von Economo, C. F. & Koskinas, G. N. Die cytoarchitektonik der hirnrinde des erwachsenen menschen (Springer, 1925).

  54. Adams, D. L. & Horton, J. C. Capricious expression of cortical columns in the primate brain. Nat. Neurosci. 6, 113–114 (2003).

    Article  CAS  Google Scholar 

  55. da Costa, N. M. & Martin, K. A. Whose cortical column would that be? Front. Neuroanat. 4, 16 (2010).

    Google Scholar 

  56. Rakic, P. Confusing cortical columns. Proc. Natl Acad. Sci. USA 105, 12099–12100 (2008).

    Article  CAS  Google Scholar 

  57. Douglas, R. J. & Martin, K. A. A functional microcircuit for cat visual cortex. J. Physiol. 440, 735–769 (1991).

    Article  CAS  Google Scholar 

  58. Arlotta, P. et al. Neuronal subtype-specific genes that control corticospinal motor neuron development in vivo. Neuron 45, 207–221 (2005).

    Article  CAS  Google Scholar 

  59. Greig, L. C., Woodworth, M. B., Galazo, M. J., Padmanabhan, H. & Macklis, J. D. Molecular logic of neocortical projection neuron specification, development and diversity. Nat. Rev. Neurosci. 14, 755–769 (2013).

    Article  CAS  Google Scholar 

  60. Lur, G., Vinck, M. A., Tang, L., Cardin, J. A. & Higley, M. J. Projection-specific visual feature encoding by layer 5 cortical subnetworks. Cell Rep. 14, 2538–2545 (2016).

    Article  CAS  Google Scholar 

  61. Kim, E. J., Juavinett, A. L., Kyubwa, E. M., Jacobs, M. W. & Callaway, E. M. Three types of cortical layer 5 neurons that differ in brain-wide connectivity and function. Neuron 88, 1253–1267 (2015).

    Article  CAS  Google Scholar 

  62. Li, N., Chen, T. W., Guo, Z. V., Gerfen, C. R. & Svoboda, K. A motor cortex circuit for motor planning and movement. Nature 519, 51–56 (2015).

    Article  CAS  Google Scholar 

  63. Takahashi, N. et al. Active dendritic currents gate descending cortical outputs in perception. Nat. Neurosci. 23, 1277–1285 (2020).

    Article  CAS  Google Scholar 

  64. Tang, L. & Higley, M. J. Layer 5 circuits in V1 differentially control visuomotor behavior. Neuron 105, 346–354 (2020).

    Article  CAS  Google Scholar 

  65. Waters, J. Sources of widefield fluorescence from the brain. eLife. 9, e59841 (2020).

  66. Ma, Y. et al. Wide-field optical mapping of neural activity and brain haemodynamics: considerations and novel approaches. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150360 (2016).

    Article  Google Scholar 

  67. Huang, L. et al. Relationship between simultaneously recorded spiking activity and fluorescence signal in GCaMP6 transgenic mice. eLife 10, e51675 (2021).

  68. 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 

  69. Kvitsiani, D. et al. Distinct behavioural and network correlates of two interneuron types in prefrontal cortex. Nature 498, 363–366 (2013).

    Article  CAS  Google Scholar 

  70. Whitlock, J. R. Posterior parietal cortex. Curr. Biol. 27, R691–R695 (2017).

    Article  CAS  Google Scholar 

  71. Lyamzin, D. & Benucci, A. The mouse posterior parietal cortex: anatomy and functions. Neurosci. Res. 140, 14–22 (2019).

    Article  Google Scholar 

  72. Carandini, M. & Churchland, A. K. Probing perceptual decisions in rodents. Nat. Neurosci. 16, 824–831 (2013).

    Article  CAS  Google Scholar 

  73. Hovde, K., Gianatti, M., Witter, M. P. & Whitlock, J. R. Architecture and organization of mouse posterior parietal cortex relative to extrastriate areas. Eur. J. Neurosci. 49, 1313–1329 (2019).

    Google Scholar 

  74. Olsen, G. M. et al. Organization of posterior parietal-frontal connections in the rat. Front. Syst. Neurosci. 13, 38 (2019).

    Article  CAS  Google Scholar 

  75. Hwang, E. J. et al. Corticostriatal flow of action selection bias. Neuron 104, 1126–1140 (2019).

    Article  CAS  Google Scholar 

  76. Andersen, R. A. & Cui, H. Intention, action planning, and decision making in parietal-frontal circuits. Neuron 63, 568–583 (2009).

    Article  CAS  Google Scholar 

  77. Sherman, S. M. Thalamus plays a central role in ongoing cortical functioning. Nat. Neurosci. 19, 533–541 (2016).

    Article  CAS  Google Scholar 

  78. Yamawaki, N., Radulovic, J. & Shepherd, G. M. A corticocortical circuit directly links retrosplenial cortex to M2 in the mouse. J. Neurosci. 36, 9365–9374 (2016).

    Article  CAS  Google Scholar 

  79. Hooks, B. M. et al. Topographic precision in sensory and motor corticostriatal projections varies across cell type and cortical area. Nat. Commun. 9, 3549 (2018).

    Article  Google Scholar 

  80. Wang, Q. et al. The Allen Mouse Brain Common Coordinate Framework: a 3D reference atlas. Cell 181, 936–953 (2020).

    Article  CAS  Google Scholar 

  81. Choi, H. M. et al. Third-generation in situ hybridization chain reaction: multiplexed, quantitative, sensitive, versatile, robust. Development 145, dev165753 (2018).

    Article  Google Scholar 

  82. Stringer, C. et al. Spontaneous behaviors drive multidimensional, brainwide activity. Science 364, 255 (2019).

    Article  Google Scholar 

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Acknowledgements

We thank J. Hatfield for performing STP imaging of PN. We are grateful to A. Churchland for numerous discussions and to T. Engel and Y. Shi for discussions on data analysis. We thank S. Lisberger and L. Glickfeld for comments on the manuscript. This research was supported by NIH grant U19MH114823-01 to Z.J.H. Z.J.H is also supported by an NIH Director’s Pioneer Award (1DP1MH129954-01).

Author information

Authors and Affiliations

Authors

Contributions

Z.J.H. and H.M. conceived the project. H.M. built setups, performed experiments and analyzed data. Z.J.H. supervised the research. X.A. provided advice on the design of feeding behavior and on building feeding behavior setup and provided mice. X.H.X performed cell type inhibition experiments. P.M. provided advice for analyzing neural data. H.K. performed STP viral injections and surgeries. K.S.M. helped with analyzing STP data. B.W. performed two-photon imaging experiments. S.Z. performed in situ imaging experiments. S.M. offered advice on building wide-field imaging setup. Z.J.H. and H.M. wrote the manuscript.

Corresponding author

Correspondence to Z. Josh Huang.

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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 ITPlxnD1 and PTFezf2 activation patterns during wakeful resting state across mice.

a Variance maps for each mouse (in columns) during quiescent and active episodes averaged over two sessions. b Distribution of activity variance during quiescent (Q) versus active (A) episodes in ITPlxnD1 (blue) and PTFezf2 (green) (n = 12 sessions from 6 mice). c Difference between ITPlxnD1 and PTFezf2 average variance maps for quiescent and active episodes (n = 12 sessions from 6 mice). Only significantly different pixels are displayed (two-sided Wilcoxon rank sum test with p-value adjusted by False Discovery Rate (FDR) = 0.05). Blue pixels indicate values significantly larger in ITPlxnD1 compared to PTFezf2 and vice versa for green pixels. d Distribution of Pearson’s correlation coefficients between quiescent variance maps within ITPlxnD1 (blue), PTFezf2 (green) and between ITPlxnD1 & PTFezf2 (blue green) (66 pairs within ITPlxnD1 & PTFezf2 and 144 pairs between ITPlxnD1 & PTFezf2 in 12 sessions from 6 mice for each cell type). e Distribution of Pearson’s correlation coefficients between active variance maps within ITPlxnD1 (blue), PTFezf2 (green) and between ITPlxnD1 and PTFezf2 (blue green) (66 pairs within ITPlxnD1 & PTFezf2 and 144 pairs between ITPlxnD1 & PTFezf2 in 12 sessions from 6 mice for each cell type). f Distribution of ITPlxnD1 (blue) and PTFezf2 (green) active variance maps projected to the subspace spanned by the top two principal components. g Distribution of ITPlxnD1 (blue) and PTFezf2 (green) quiescent variance maps projected to the subspace spanned by the top two principal components. h Average maps of the 75th (top) and 95th (bottom) percentile df/f value during active and quiescent episodes for ITPlxnD1 and PTFezf2 (n = 12 sessions from 6 mice). *p < 0.05, **p < 0.005, ***p < 0.0005. For box plots, central mark indicates median, bottom and top edges indicate 25th and 75th percentiles and the whiskers extend to extreme points excluding outliers (1.5 times more or less than the interquartile range). All statistics in Supplementary table 1.

Source data

Extended Data Fig. 2 Spontaneous activity comparison and correlation of sensory response in ITPlxnD1 and PTFezf2 across mice.

a Probability distribution of df/f values from ITPlxnD1 (blue) and PTFezf2 (green) during wakeful resting state (average of 12 sessions from 6 mice each, shaded region indicates ±2 s.e.m). b Mean peak df/f maps of ITPlxnD1 and PTFezf2 during spontaneous behavior (average of 12 sessions from 6 mice for each cell type). c Difference between ITPlxnD1 and PTFezf2 mean peak df/f maps. Only significantly different pixels are displayed (two-sided Wilcoxon rank sum test with p-value adjusted by FDR = 0.05). Note that no pixels are significantly different. d Mean temporal dynamics of ITPlxnD1 and PTFezf2 activity with (colored) and without (black) hemodynamic correction from hindlimb sensory area during spontaneous behavior. Activity is aligned to the onset of spontaneous movements (ITPlxnD1: 367 and PTFezf2: 474 trials in 12 sessions from 6 mice each, shaded region indicates ±2 s.e.m). Left image with red dot indicates location used to extract signal. e Left: Distribution of difference between hemodynamic corrected and uncorrected peak df/f value between 0 to 1 sec after spontaneous movement onset for ITPlxnD1 (blue) and PTFezf2 (green) from panel d. Right: Distribution of Pearson’s correlation coefficient between hemodynamic corrected and uncorrected ITPlxnD1 and PTFezf2 activity from panel d. (ITPlxnD1: 367 and PTFezf2: 474 trials). f Mean peak normalized activity maps of ITPlxnD1 (top) and PTFezf2 (bottom) in response to corresponding unimodal sensory simulation (n = 12 sessions from 6 mice each). g Distribution of Pearson’s correlation coefficients between sensory activation maps within ITPlxnD1 (blue), PTFezf2 (green) and between ITPlxnD1 and PTFezf2 (blue green) (66 pairs within ITPlxnD1 & PTFezf2 and 144 pairs between ITPlxnD1 & PTFezf2 in 12 sessions from 6 mice each for all stimulations). *p < 0.05, **p < 0.005, ***p < 0.0005. For box plots, central mark indicates median, bottom and top edges indicate 25th and 75th percentiles and the whiskers extend to extreme points excluding outliers (1.5 times more or less than the interquartile range). All statistics in Supplementary table 1.

Source data

Extended Data Fig. 3 Calcium dynamics of ITPlxnD1 and PTFezf2 at cellular resolution reflect widefield responses.

a Schematic of the whisker stimulation paradigm and the cortical location for two photon imaging (blue circle). b Left: Example field of view (FOV) of ITPlxnD1 cell bodies and apical dendrites of PTFezf2 in the whisker barrel cortex. Right: Example traces from single ITPlxnD1 cell bodies and apical dendrites of PTFezf2. Numbers indicate the corresponding location on the FOV. Magenta bars indicate whisker stimulation events. c Heat map of average single neuron responses of ITPlxnD1 and PTFezf2 classified into 3 groups based on their activity during whisker stimulation from the example FOV. d Average responses across all ITPlxnD1 and PTFezf2 neurons within each group from the example FOV (shaded region indicates ±2 s.e.m). Magenta bars indicate duration of whisker stimulation. e Average responses across all ITPlxnD1 and PTFezf2 neurons from the example FOV (shaded region indicates ±2 s.e.m). f Heat map of average single neuron responses of ITPlxnD1 and PTFezf2 classified based on their activity during whisker stimulation across all mice and sessions (ITPlxnD1 42 FOV’s from n = 4 mice and PTFezf2 36 FOV’s from n = 3 mice). g Average responses across all ITPlxnD1 and PTFezf2 neurons within each group across all mice and sessions (ITPlxnD1 42 FOV’s from n = 4 mice and PTFezf2 36 FOV’s from n = 3 mice, shaded region indicates ±2 s.e.m). h Average responses across all ITPlxnD1 and PTFezf2 neurons from all mice and sessions combined (ITPlxnD1 42 FOV’s from n = 4 mice and PTFezf2 36 FOV’s from n = 3 mice, shaded region indicates ±2 s.e.m). i Proportion of neurons in each group from ITPlxnD1 and PTFezf2.

Extended Data Fig. 4 Temporal dynamics of ITPlxnD1 and PTFezf2 within parietofrontal and frontolateral networks centered to lick and hand lift onset.

a Difference between ITPlxnD1 and PTFezf2 mean activity map from Fig. 3a,d. Only significantly different pixels are displayed (two-sided Wilcoxon rank sum test with p-value adjusted by FDR = 0.05, n = 24 maps from ITPlxnD1 and 23 maps from PTFezf2). Blue pixels indicate values significantly larger in ITPlxnD1 compared to PTFezf2 and vice versa for green pixels. b Distribution of Pearson’s correlation coefficients within ITPlxnD1 (blue), PTFezf2 (green) and between ITPlxnD1 & PTFezf2 (blue green) mean feeding sequence activity maps (n = 253 pairs within ITPlxnD1, 276 pairs within PTFezf2 and 522 pairs between ITPlxnD1 & PTFezf2). c Distribution of ITPlxnD1 (blue) and PTFezf2 (green) mean feeding sequence activity maps projected to the subspace spanned by the top two principal components (n = 24 maps from ITPlxnD1 and 23 maps from PTFezf2). d Single trial heatmaps and mean activity of PTFezf2 and ITPlxnD1 from parietal, frontal, FLA and FLP centered to lick (top) and handlift onset (bottom, ITPlxnD1 - 23 sessions from 6 mice, PTFezf2 - 24 sessions from 5 mice, shaded region indicates ±2 s.e.m). ***p < 0.0005. For box plots, central mark indicates median, bottom and top edges indicate 25th and 75th percentiles and the whiskers extend to extreme points excluding outliers (1.5 times more or less than the interquartile range). All statistics in Supplementary table 1.

Source data

Extended Data Fig. 5 Temporal dynamics of ITPlxnD1 in frontolateral and PTFezf2 within parietofrontal nodes during feeding with and without hand lift across mice.

a Single trial heatmaps of PTFezf2 activity centered to pellet in mouth onset from parietal and frontal node and ITPlxnD1 activity in FLP and FLA from eating with (top) and without hand lift (bottom) from all mice and sessions (With handlift: ITPlxnD1 - 23 sessions from 6 mice, PTFezf2 - 24 sessions from 5 mice. Without hand lift: ITPlxnD1 - 15 sessions from 6 mice, PTFezf2 - 13 sessions from 5 mice). b Left: Mean PTFezf2 activity aligned to pellet in mouth onset from parietal node with (light brown) and without (light green) hand lift, frontal node with (dark brown) and without (dark green) hand lift. Right: ITPlxnD1 activity aligned to PIM onset from FLP with (orange) and without (cyan) hand lift and FLA with (magenta) and without (dark blue) hand lift (shaded region indicates ±2 s.e.m, sample size as in panel a).

Extended Data Fig. 6 Inhibition of parietofrontal and frontolateral regions differentially disrupts sensorimotor components of feeding behavior.

a Schematic of optogenetic laser scanning setup. b Single trial (translucent) and averaged (opaque) tongue trajectories centered to inhibition of frontal (dark brown), parietal (light brown) and frontolateral (magenta) nodes. Control (grey). Note that trajectories evolve from top to bottom with the mouth at top and pellet at bottom (Green schematic). Upward change in value indicates decrease in tongue length. c Distribution of total tongue length 0.5 s before and after inhibition onset of frontal (n = 76), parietal (n = 88) and frontolateral (n = 89 trials) nodes. During pellet retrieval, inhibition of frontal and frontolateral but not parietal nodes resulted in a sharp decrease in tongue extension which recovered on average after about 0.5 s. d Distribution of durations to pick pellet after trial start during control versus inhibition of frontal (n = 76), parietal (n = 88), and frontolateral (n = 89) nodes. Inhibition of frontal and frontolateral but not parietal nodes resulted in a significant delay and disruption in retrieving pellet to mouth. e Probability of hand lift events during inhibition of frontal (n = 67), parietal (n = 87) frontolateral (n = 100) nodes compared to control. During the hand-lift phase after PIM, inhibition of frontal and frontolateral but not parietal nodes prior to hand lift onset led to substantial deficit in the ability to lift hands towards mouth, resulting in a sharp decrease in the number of hand lifts. f Left: 5 example hand lift trajectories from side view (top) during control (black) and inhibition of parietal node and corresponding absolute velocities (bottom). Right: Mean vertical hand trajectory from side view (top) and absolute velocity (bottom, shading around trace ±2 s.e.m) during control (black) and inhibition of frontal (n = 41), parietal (n = 83) and frontolateral (n = 59) nodes. Insets: zoomed mean signals. Note increase in velocity fluctuation during parietal inhibition. g Distribution of absolute velocity integral for 1 sec post hand lift during control and inhibition of frontal (n = 41), parietal (n = 83) and frontolateral (n = 59) nodes. While there was no decrease in handlift probability on parietal inhibition, it resulted in substantial deficits in hand lift trajectory, characterized by erratic and jerky movements. This was reflected in the significant modulation of absolute velocity during lift (see Methods). h Mean normalized vertical trajectory of left finger from front view during food handling from control (black) and inhibition of frontal (n = 58), parietal (n = 89) and frontolateral (n = 101) nodes (shaded region indicates ±2 s.e.m). Inhibiting the frontal and frontolateral nodes severely impeded mice’s ability to bring pellet to mouth during food handling, which recovered immediately after the release of inhibition. Inhibiting the parietal node resulted in only a slight disruption. i, j. Distribution of mean normalized finger to mouth distance (i) and duration of hand held close to mouth (j) during control versus inhibition of frontal (n = 58), parietal (n = 89) and frontolateral (n = 101 trials) nodes during food handling. Frontolateral node consisted of data pooled from FLA and FLP since no major difference was observed. All data is pooled from 3 mice across 7 sessions. *p < 0.05, **p < 0.005, ***p < 0.0005. For box plots, central mark indicates median, bottom and top edges indicate 25th and 75th percentiles and the whiskers extend to extreme points excluding outliers. All statistics in Supplementary table 1.

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Extended Data Fig. 7 Comparison of inhibition effects between ITPlxnD1 and PTFezf2.

a Distribution of the difference in mean tongue length between control and inhibition trials of frontal ITPlxnD1 (n = 173), PTFezf2 (n = 140) and FLA ITPlxnD1 (n = 165), PTFezf2 (n = 98) nodes. b Distribution of the difference in mean normalized hand to mouth distance for 5 s between control and inhibition trials of frontal ITPlxnD1 (n = 353), PTFezf2 (n = 167) and FLA ITPlxnD1 (n = 455) and PTFezf2 (n = 202) nodes. c Distribution of the difference in mean absolute hand velocity for 5 s between control and inhibition trials of frontal ITPlxnD1 (n = 353), PTFezf2 (n = 167) and FLA ITPlxnD1 (n = 455) and PTFezf2 (n = 202) nodes. All data pooled from 4 mice for ITPlxnD1 and 3 for PTFezf2. *p < 0.05, **p < 0.005, ***p < 0.0005. For box plots, central mark indicates median, bottom and top edges indicate 25th and 75th percentiles and the whiskers extend to extreme points excluding outliers. All statistics in Supplementary table 1.

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Extended Data Fig. 8 Axonal projection of ITPlxnD1 and PTFezf2 in subcortical structures.

a Three dimensional rendering of axonal projections of ITPlxnD1 from FLA and FLP and PTFezf2 from frontal and parietal node. Yellow circle indicates injection site. b Spatial distribution of axonal projections of ITPlxnD1 from FLA and FLP (top) and PTFezf2 parietal and frontal nodes (bottom) within the striatum projected onto the coronal and sagittal plane. c Spatial distribution of axonal projections of PTFezf2 from parietal and frontal nodes within the primary and association thalamus projected onto the coronal and sagittal plane. d Spatial distribution of axonal projections of PTFezf2 from parietal and frontal nodes within the motor Superior colliculus (SCm, magenta), sensory superior colliculus (SCs, yellow) and inferior colliculus (IC, brown) projected onto the coronal and sagittal plane. e Spatial distribution of axonal projections of PTFezf2 from parietal and frontal nodes (bottom) within the hindbrain projected onto the coronal and sagittal plane. f Brain-wide volume and peak normalized projection intensity maps of ITPlxnD1 from FLA and FLP and PTFezf2 from frontal and parietal nodes from two mice. Black font indicates injection site; larger gray font indicates regions with significant projections; smaller gray font indicates regions analyzed.

Extended Data Fig. 9 ITPlxnD1 and PTFezf2 show distinct spatiotemporal dynamics and spectral properties under ketamine anesthesia.

a Example single trial traces of ITPlxnD1 (blue) and PTFezf2 (green) activities from 6 different cortical areas during ketamine/xylazine anesthesia. Colors represent cortical areas as indicated (VC – primary visual Cortex, RSp – medial retrosplenial cortex, HL – primary hindlimb sensory cortex, MOs – secondary motor Cortex, MOp – primary motor cortex, BC – barrel cortex). b Example spectrogram of ITPlxnD1 and PTFezf2 activity from MOp and RSp of one mouse. c Mean relative power spectral density of ITPlxnD1 (blue) and PTFezf2 (green) activity from MOp and RSp (18 sessions across 6 mice each, shaded region indicates ±2 s.e.m). While ITPlxnD1 exhibited oscillations at approximately 1–1.4 Hz, PTFezf2 fluctuated predominantly at 0.6–0.9 Hz. d Distribution of average relative power within MOp and RSp of ITPlxnD1 and PTFezf2 between 0.6–0.9 Hz and 1–1.4 Hz (n = 18 sessions across 6 mice each). e Spatial map of the average relative power between 0.6–0.9 Hz and 1–1.4 Hz from ITPlxnD1 (top) and PTFezf2 at each pixel (bottom, 18 sessions across 6 mice each). While ITPlxnD1 was strongly active within the frontolateral at both frequency bands, PTFezf2 was predominantly active in the retrosplenial regions at 0.6–0.9 Hz. f Example space-time plots of the neural activity across a slice of the dorsal cortex (red dashed line) from ITPlxnD1 (top) and PTFezf2 (bottom). Middle, zoomed-in activity from indicated top and bottom panels visualizing the distinct spatial dynamics across dorsal cortex. g Example activation sequence of the most dominant pattern (1st dimension) identified by seqNMF from ITPlxnD1 (top) and PTFezf2 (bottom, Supplementary Methods). The top dimension accounted for more than 80 % of the variance in ITPlxnD1 and over 90% in PTFezf2. *p < 0.05, **p < 0.005, ***p < 0.0005. For box plots, central mark indicates median, bottom and top edges indicate 25th and 75th percentiles and the whiskers extend to extreme points excluding outliers. All statistics in Supplementary table 1.

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Extended Data Fig. 10 Spatiotemporal dynamics of ITPlxnD1 and PTFezf2 under ketamine anesthesia across mice.

a Mean spectrogram of ITPlxnD1 and PTFezf2 activity from MOp and RSp (18 sessions across 6 mice for each cell type). b Difference between ITPlxnD1 and PTFezf2 average relative power maps for each frequency bands (18 sessions from 6 mice for each cell type). Only significantly different pixels are displayed (two-sided Wilcoxon rank sum test with p-value adjusted by FDR = 0.05). Blue pixels indicate values significantly larger in ITPlxnD1 compared to PTFezf2 and vice versa for green pixels. c Distribution of ITPlxnD1 (blue) and PTFezf2 (green) spatial power maps for each frequency band projected to the subspace spanned by the top two principal components (n = 18 maps in each group). ITPlxnD1 and PTFezf2 both clustered independently with further segregation between ITPlxnD1 0.6–0.9 Hz and 1–1.4 Hz frequency bands, substantiating the distinct activation patterns between the two populations. d Activation sequence of the most dominant pattern (1st dimension) identified by seqNMF from ITPlxnD1 (top) and PTFezf2 (bottom) activity combined across mice and sessions.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–4, Supplementary Table 1 and Supplementary Methods.

Reporting Summary

Supplementary Video 1

Segment of single-trial calcium activity across dorsal cortex during awake resting state from one example ITPlxnD1 mouse (with overlaid dorsal Allen map (CCF v3)) with temporally matched behavior video frames. Each frame is indicated as quiescent or active based on behavior variance crossing a predefined threshold.

Supplementary Video 2

Segment of single-trial calcium activity across dorsal cortex during awake resting state from one example PTFezf2 mouse (with overlaid dorsal Allen map (CCF v3)) with temporally matched behavior video frames. Each frame is indicated as quiescent or active based on behavior variance crossing a predefined threshold.

Supplementary Video 3

Example video of a single-trial head-fixed feeding behavior with body parts tracked using DeepLabCut displaying both front and side view with simultaneous traces of the tracked body parts and behavior features.

Supplementary Video 4

Single-trial calcium dynamics across dorsal cortex during head-fixed feeding paradigm from one example ITPlxnD1 mouse with temporally matched behavior video frames from front and side view.

Supplementary Video 5

Single-trial calcium dynamics across dorsal cortex during head-fixed feeding paradigm from one example PTFezf2 mouse with temporally matched behavior video frames from front and side view.

Supplementary Video 6

Video demonstrating the effect on licking (tongue movement) after bilateral inhibition of frontal node in a PTFezf2 mouse expressing GtACR1 (right). Video on left shows behavior from the same mouse on the previous trial during the same period with no inhibition (control trial).

Supplementary Video 7

Video demonstrating the effect on licking (tongue movement) after bilateral inhibition of frontal node in an ITPlxnD1 mouse expressing GtACR1 (right). Video on left shows behavior from the same mouse on the previous trial during the same period with no inhibition (control trial).

Supplementary Video 8

Video demonstrating the obstruction of hand lift onset on bilateral inhibition of frontal node in a PTFezf2 mouse expressing GtACR1 (right). Video on left shows behavior from the same mouse on the previous trial during the same period with no inhibition (control trial).

Supplementary Video 9

Video demonstrating the impairment in proper handling and manipulation of food pellet on bilateral inhibition of FLA node in a PTFezf2 mouse expressing GtACR1 (right). Video on left shows behavior from the same mouse on the previous trial during the same period with no inhibition (control trial).

Supplementary Video 10

Video demonstrating the impairment in proper handling and manipulation of food pellet on bilateral inhibition of FLA node in a ITPlxnD1 mouse expressing GtACR1 (right). Video on left shows behavior from the same mouse on the previous trial during the same period with no inhibition (control trial).

Supplementary Video 11

Video of full-brain STP-imaged serial coronal sections with registered Allen map (CCF v3) overlaid from an ITPlxnD1 mouse injected with anterograde tracer in right FLA node with its whole-brain axonal projections.

Supplementary Video 12

Video of full-brain STP-imaged serial coronal sections with registered Allen map (CCF v3) overlaid from an ITPlxnD1 mouse injected with anterograde tracer in right FLP node with its whole-brain axonal projections.

Supplementary Video 13

Video of full-brain STP-imaged serial coronal sections with registered Allen map (CCF v3) overlaid from a PTFezf2 mouse injected with anterograde tracer in right frontal node with its whole-brain axonal projections.

Supplementary Video 14

Video of full-brain STP-imaged serial coronal sections with registered Allen map (CCF v3) overlaid from a PTFezf2 mouse injected with anterograde tracer in right parietal node with its whole-brain axonal projections.

Supplementary Video 15

Segment of single-trial calcium activity across dorsal cortex during ketamine–xylazine-anesthetized state from one example ITPlxnD1 mouse with overlaid dorsal Allen map (CCF v3).

Supplementary Video 16

Segment of single-trial calcium activity across dorsal cortex during ketamine–xylazine-anesthetized state from one example PTFezf2 mouse with overlaid dorsal Allen map (CCF v3).

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Mohan, H., An, X., Xu, X.H. et al. Cortical glutamatergic projection neuron types contribute to distinct functional subnetworks. Nat Neurosci (2023). https://doi.org/10.1038/s41593-022-01244-w

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