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Connectivity underlying motor cortex activity during goal-directed behaviour

Nature volume 649, pages 416–422 (2026)Cite this article

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Abstract

Neural representations of information are shaped by long-range input and local network interactions. Previous studies linking neural coding and cortical connectivity have focused on input-driven activity in the sensory cortex1,2,3. Here we studied neural activity in the motor cortex while mice gathered rewards with multidirectional tongue reaching. This behaviour does not require training, allowing us to probe neural coding and connectivity before activity is shaped by extended learning. Motor cortex neurons were tuned to target location and reward outcome, and typically responded during and after movements. We studied the underlying network interactions in vivo by estimating causal neural connections using an all-optical method3,4,5,6. Mapping connectivity between more than 20,000,000 excitatory neuron pairs showed a multi-scale columnar architecture in layer 2/3 of the motor cortex. Neurons displayed local (less than 100 µm) like-to-like excitatory connectivity according to target-location tuning, and inhibition over longer spatial scales. Connectivity patterns comprised a continuum, with abundant sparsely connected neurons and rare densely connected neurons that function as network hubs. Hub neurons were weakly tuned to target location and reward outcome but influenced more neighbouring neurons. This network of neurons, encoding location and outcome of movements to different motor goals, may be a general substrate for rapid learning of complex, goal-directed behaviours.

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Fig. 1: Neural activity in ALM during multidirectional tongue reaching.
Fig. 2: Conjunctive tuning for target location and reward outcome.
Fig. 3: Optical mapping of causal connectivity and its anatomical organization.
Fig. 4: Causal connectivity and its relation to encoding of tongue-reaching movements.

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Data availability

Behavioural and neurophysiological data were stored and analysed in custom pipelines in the DataJoint framework71 and are publicly available to download in a NWB format at https://dandiarchive.org/dandiset/001612. Mouse illustration files are available at Zenodo (https://doi.org/10.5281/zenodo.3925900)72.

Code availability

Custom MATLAB code that was used for data analysis and modelling is available on GitHub https://github.com/arsenyf/Finkelstein_et_al_Nature_2025.

References

  1. Hubel, D. H. & Wiesel, T. N. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160, 106–154 (1962).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Cossell, L. et al. Functional organization of excitatory synaptic strength in primary visual cortex. Nature 518, 399–403 (2015).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  3. Chettih, S. N. & Harvey, C. D. Single-neuron perturbations reveal feature-specific competition in V1. Nature 567, 334–340 (2019).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  4. Rickgauer, J. P., Deisseroth, K. & Tank, D. W. Simultaneous cellular-resolution optical perturbation and imaging of place cell firing fields. Nat. Neurosci. 17, 1816–1824 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Daie, K., Svoboda, K. & Druckmann, S. Targeted photostimulation uncovers circuit motifs supporting short-term memory. Nat. Neurosci. 24, 259–265 (2021).

    Article  PubMed  CAS  Google Scholar 

  6. Randi, F., Sharma, A. K., Dvali, S. & Leifer, A. M. Neural signal propagation atlas of Caenorhabditis elegans. Nature 623, 406–414 (2023).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  7. Bruce, C. J. & Goldberg, M. E. Primate frontal eye fields. I. Single neurons discharging before saccades. J. Neurophysiol. 53, 603–635 (1985).

    Article  PubMed  CAS  Google Scholar 

  8. Georgopoulos, A. P., Kalaska, J. F., Caminiti, R. & Massey, J. T. On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. J. Neurosci. 2, 1527–1537 (1982).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  10. Peters, A. J., Chen, S. X. & Komiyama, T. Emergence of reproducible spatiotemporal activity during motor learning. Nature 510, 263–267 (2014).

    Article  ADS  PubMed  CAS  Google Scholar 

  11. Finkelstein, A. et al. Attractor dynamics gate cortical information flow during decision-making. Nat. Neurosci. 24, 843–850 (2021).

    Article  PubMed  CAS  Google Scholar 

  12. Xu, D. et al. Cortical processing of flexible and context-dependent sensorimotor sequences. Nature 603, 464–469 (2022).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  13. Stepanyants, A. et al. Local potential connectivity in cat primary visual cortex. Cereb. Cortex 18, 13–28 (2008).

    Article  PubMed  Google Scholar 

  14. Schneider-Mizell, C. M. et al. Inhibitory specificity from a connectomic census of mouse visual cortex. Nature 640, 448–458 (2025).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  15. DeNardo, L. A., Berns, D. S., DeLoach, K. & Luo, L. Connectivity of mouse somatosensory and prefrontal cortex examined with trans-synaptic tracing. Nat. Neurosci. 18, 1687–1697 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Rowland, J. M. et al. Propagation of activity through the cortical hierarchy and perception are determined by neural variability. Nat. Neurosci. 26, 1584–1594 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Scott, S. H. Inconvenient truths about neural processing in primary motor cortex. J. Physiol. 586, 1217–1224 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Tanji, J. & Evarts, E. V. Anticipatory activity of motor cortex neurons in relation to direction of an intended movement. J. Neurophysiol. 39, 1062–1068 (1976).

    Article  PubMed  CAS  Google Scholar 

  19. Komiyama, T. et al. Learning-related fine-scale specificity imaged in motor cortex circuits of behaving mice. Nature 464, 1182–1186 (2010).

    Article  ADS  PubMed  CAS  Google Scholar 

  20. Huber, D. et al. Multiple dynamic representations in the motor cortex during sensorimotor learning. Nature 484, 473–478 (2012).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  21. Esmaeili, V. et al. Rapid suppression and sustained activation of distinct cortical regions for a delayed sensory-triggered motor response. Neuron 109, 2183–2201 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Bollu, T. et al. Cortex-dependent corrections as the tongue reaches for and misses targets. Nature 594, 82–87 (2021).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  23. Inagaki, H. K. et al. Neural algorithms and circuits for motor planning. Annu. Rev. Neurosci. 45, 249–271 (2022).

    Article  PubMed  CAS  Google Scholar 

  24. Schultz, W. & Dayan, P. & Montague, P. R. A neural substrate of prediction and reward. Science 275, 1593–1599 (1997).

    Article  PubMed  CAS  Google Scholar 

  25. Stuphorn, V., Taylor, T. L. & Schall, J. D. Performance monitoring by the supplementary eye field. Nature 408, 857–860 (2000).

    Article  ADS  PubMed  CAS  Google Scholar 

  26. Hirokawa, J., Vaughan, A., Masset, P., Ott, T. & Kepecs, A. Frontal cortex neuron types categorically encode single decision variables. Nature 576, 446–451 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Levy, S. et al. Cell-type-specific outcome representation in the primary motor cortex. Neuron 107, 954–971 (2020).

    Article  PubMed  CAS  Google Scholar 

  28. Pereira-Obilinovic, U., Hou, H., Svoboda, K. & Wang, X.-J. Brain mechanism of foraging: Reward-dependent synaptic plasticity versus neural integration of values. Proc. Natl Acad. Sci. USA 121, e2318521121 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Franks, K. M. et al. Recurrent circuitry dynamically shapes the activation of piriform cortex. Neuron 72, 49–56 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Levy, R. B. & Reyes, A. D. Spatial profile of excitatory and inhibitory synaptic connectivity in mouse primary auditory cortex. J. Neurosci. 32, 5609–5619 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Oldenburg, I. A. et al. The logic of recurrent circuits in the primary visual cortex. Nat. Neurosci. 27, 137–147 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Emiliani, V., Cohen, A. E., Deisseroth, K. & Häusser, M. All-optical interrogation of neural circuits. J. Neurosci. 35, 13917–13926 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Packer, A. M., Russell, L. E., Dalgleish, H. W. P. & Häusser, M. Simultaneous all-optical manipulation and recording of neural circuit activity with cellular resolution in vivo. Nat. Methods 12, 140–146 (2015).

    Article  PubMed  CAS  Google Scholar 

  34. LaFosse, P. K. et al. Cellular-resolution optogenetics reveals attenuation-by-suppression in visual cortical neurons. Proc. Natl Acad. Sci. USA 121, e2318837121 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Hooks, B. M. et al. Laminar analysis of excitatory local circuits in vibrissal motor and sensory cortical areas. PLoS Biol. 9, e1000572 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Mateo, C. et al. In vivo optogenetic stimulation of neocortical excitatory neurons drives brain-state-dependent inhibition. Curr. Biol. 21, 1593–1602 (2011).

    Article  ADS  PubMed  CAS  Google Scholar 

  37. Sadeh, S. & Clopath, C. Theory of neuronal perturbome in cortical networks. Proc. Natl Acad. Sci. USA 117, 26966–26976 (2020).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  38. Perin, R., Berger, T. K. & Markram, H. A synaptic organizing principle for cortical neuronal groups. Proc. Natl Acad. Sci. USA 108, 5419–5424 (2011).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  39. Yu, Y.-C., Bultje, R. S., Wang, X. & Shi, S.-H. Specific synapses develop preferentially among sister excitatory neurons in the neocortex. Nature 458, 501–504 (2009).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  40. Khona, M. & Fiete, I. R. Attractor and integrator networks in the brain. Nat. Rev. Neurosci. 23, 744–766 (2022).

    Article  PubMed  CAS  Google Scholar 

  41. Rosenbaum, R., Smith, M. A., Kohn, A., Rubin, J. E. & Doiron, B. The spatial structure of correlated neuronal variability. Nat. Neurosci. 20, 107–114 (2017).

    Article  PubMed  CAS  Google Scholar 

  42. Darshan, R., van Vreeswijk, C. & Hansel, D. Strength of correlations in strongly recurrent neuronal networks. Phys. Rev. X 8, 031072 (2018).

    CAS  Google Scholar 

  43. Gal, E. et al. Rich cell-type-specific network topology in neocortical microcircuitry. Nat. Neurosci. 20, 1004–1013 (2017).

    Article  PubMed  CAS  Google Scholar 

  44. Ben-Yishai, R., Bar-Or, R. L. & Sompolinsky, H. Theory of orientation tuning in visual cortex. Proc. Natl Acad. Sci. USA 92, 3844–3848 (1995).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  45. Douglas, R. J., Koch, C., Mahowald, M., Martin, K. A. C. & Suarez, H. H. Recurrent excitation in neocortical circuits. Science 269, 981–985 (1995).

    Article  ADS  PubMed  CAS  Google Scholar 

  46. Platt, M. L. & Glimcher, P. W. Neural correlates of decision variables in parietal cortex. Nature 400, 233–238 (1999).

    Article  ADS  PubMed  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Hattori, R., Danskin, B., Babic, Z., Mlynaryk, N. & Komiyama, T. Area-specificity and plasticity of history-dependent value coding during learning. Cell 177, 1858–1872 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Lee, R. S., Sagiv, Y., Engelhard, B., Witten, I. B. & Daw, N. D. A feature-specific prediction error model explains dopaminergic heterogeneity. Nat. Neurosci. 27, 1574–1586 (2024).

    Article  PubMed  CAS  Google Scholar 

  50. Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J. & Hinton, G. Backpropagation and the brain. Nat. Rev. Neurosci. 21, 335–346 (2020).

    Article  PubMed  CAS  Google Scholar 

  51. Lee, W.-C. A. et al. Anatomy and function of an excitatory network in the visual cortex. Nature 532, 370–374 (2016).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  52. Kuan, A. T. et al. Synaptic wiring motifs in posterior parietal cortex support decision-making. Nature 627, 367–373 (2024).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  53. Ohki, K., Chung, S., Ch’ng, Y. H., Kara, P. & Reid, R. C. Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex. Nature 433, 597–603 (2005).

    Article  ADS  PubMed  CAS  Google Scholar 

  54. Georgopoulos, A. P., Merchant, H., Naselaris, T. & Amirikian, B. Mapping of the preferred direction in the motor cortex. Proc. Natl Acad. Sci. USA 104, 11068–11072 (2007).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  55. Dombeck, D. A., Graziano, M. S. & Tank, D. W. Functional clustering of neurons in motor cortex determined by cellular resolution imaging in awake behaving mice. J. Neurosci. 29, 13751–13760 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Song, S., Sjöström, P. J., Reigl, M., Nelson, S. & Chklovskii, D. B. Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biol. 3, e350 (2005).

    Article  PubMed Central  Google Scholar 

  57. Amsalem, O., Inagaki, H., Yu, J., Svoboda, K. & Darshan, R. Sub-threshold neuronal activity and the dynamical regime of cerebral cortex. Nat. Commun. 15, 7958 (2024).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  58. Towlson, E. K., Vértes, P. E., Ahnert, S. E., Schafer, W. R. & Bullmore, E. T. The rich club of the C. elegans neuronal connectome. J. Neurosci. 33, 6380–6387 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Bonifazi, P. et al. GABAergic hub neurons orchestrate synchrony in developing hippocampal networks. Science 326, 1419–1424 (2009).

    Article  ADS  PubMed  CAS  Google Scholar 

  60. Bollmann, Y. et al. Prominent in vivo influence of single interneurons in the developing barrel cortex. Nat. Neurosci. 26, 1555–1565 (2023).

    Article  PubMed  CAS  Google Scholar 

  61. Chen, T.-W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  62. Daie, K. et al. ALM window surgery. protocols.io https://dx.doi.org/10.17504/protocols.io.bqstmwen (2023).

  63. Klapoetke, N. C. et al. Independent optical excitation of distinct neural populations. Nat. Methods 11, 338–346 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  65. Pachitariu, M. et al. Suite2p: beyond 10,000 neurons with standard two-photon microscopy. Preprint at bioRxiv https://doi.org/10.1101/061507 (2017).

  66. Friedrich, J., Zhou, P. & Paninski, L. Fast online deconvolution of calcium imaging data. PLoS Comput. Biol. 13, e1005423 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  67. Svoboda, K., Denk, W., Kleinfeld, D. & Tank, D. W. In vivo dendritic calcium dynamics in neocortical pyramidal neurons. Nature 385, 161–165 (1997).

    Article  ADS  PubMed  CAS  Google Scholar 

  68. Friston, K. J. Functional and effective connectivity in neuroimaging: a synthesis. Human Brain Mapping 2, 56–78 (1994).

    Article  Google Scholar 

  69. Fornito, A., Zalesky, A., Bullmore, E. T. (eds) Fundamentals of Brain Network Analysis (Academic, 2016)

  70. Xie, X., Hahnloser, R. H. R. & Seung, H. S. Double-ring network model of the head-direction system. Phys. Rev. E 66, 041902 (2002).

    Article  ADS  Google Scholar 

  71. Yatsenko, D. et al. DataJoint: managing big scientific data using MATLAB or Python. Preprint at bioRxiv https://doi.org/10.1101/031658 (2015).

  72. Tyler, E. & Kravitz, L. Mouse. Zenodo https://doi.org/10.5281/zenodo.3925900 (2020).

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Acknowledgements

We thank J. Arnold and J. Talbot for mechanical engineering, D. Tsyboulski for optical engineering, S. Lindo for surgical support, L. Kinsey for behavioural experiments, D. Antes for graphics and N. Li for comments on the manuscript. This work was supported by Howard Hughes Medical Institute and the Paul G. Allen Family Foundation. A.F. was a Rothschild Foundation and EMBO Long-Term Fellow (ALTF 869–2015).

Author information

Authors and Affiliations

  1. Department of Neuroscience and Brain Disorders, Gray Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel

    Arseny Finkelstein & Ran Darshan

  2. Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel

    Arseny Finkelstein & Ran Darshan

  3. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA

    Arseny Finkelstein, Kayvon Daie, Márton Rózsa & Karel Svoboda

  4. Allen Institute for Neural Dynamics, Allen Institute for Science, Seattle, WA, USA

    Kayvon Daie, Márton Rózsa & Karel Svoboda

  5. School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel

    Ran Darshan

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  1. Arseny Finkelstein
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  2. Kayvon Daie
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  3. Márton Rózsa
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Contributions

A.F. and K.S. designed the experiments. A.F. collected the experimental data with help from K.D. and M.R. A.F. analysed the data with inputs from K.D., R.D. and K.S. R.D. performed model simulations with input from A.F. and K.S. A.F. and K.S. wrote the paper, with input from all authors.

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Correspondence to Arseny Finkelstein or Karel Svoboda.

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Extended data figures and tables

Extended Data Fig. 1 Multidirectional tongue-reaching behavior.

a, Videography of tongue trajectories in 4 behavioral sessions of one mouse in the multidirectional tongue reaching behavior. Vertical line (white) indicates the time of target appearance. In each trial (horizontal lines) the tongue location is color-coded according to its dorsal-ventral coordinate (normalized to the maximal dorsal-ventral tongue location detected in each session). Mice produced hundreds of trials already from the first behavioral session. b, Lick rate as a function of time from the target appearance. Red, the interval marking the target movement towards a reachable location (‘Target ON’); Blue, average target retraction time (‘Target OFF’). Target is retracted 1 s after reward delivery or maximal time-out time (Methods), therefore its retraction time (blue) is varied on each trial. Most licks occur within less than 2 s from the target appearance. c, Correlation between tongue horizontal location and target horizontal location during the trial. Data in b-c is displayed as mean ± s.e.m. across sessions. d-h, Lick statistics and tongue kinematics as a function of target location (top), session number (middle), or trial number in block of repeated target locations (bottom). Data in d-h is displayed as mean ± s.e.m. across mice (middle) or sessions (top, bottom).

Extended Data Fig. 2 Location-temporal tuning properties.

a-b, Distribution of temporal tuning modulation (a) and location tuning modulation (b) of all cells with significant tuning based on shuffling (Methods). c, Distribution of peak width of location tuning maps of location-tuned neurons. d, Traces showing trial-averaged responses in each target location (left) and location tuning maps (right) of 4 example cells imaged on the first day of behavior. A 4×4 location grid was used in this experiment. e, Percentage of significantly tuned cells across sessions. Data is displayed as mean ± s.e.m. across mice. f, Tuning modulation across sessions (left) and trial number in block (right). Data is displayed as mean ± s.e.m. across mice (left) or sessions (right). g, Location-temporal tuning of four example cells showing tuning stability. Left, traces showing trial-averaged responses in each target location. Right, location tuning map computed using two non-overlapping sets of trials (odd and even trials). A 5×5 location grid was used in this experiment. h-j, Distribution of stability of temporal tuning (h) and location-temporal tuning (i), and location tuning (j) of tuned cells computed as Pearson correlation (r) between tuning calculated based on odd versus even trials. k, Tuning stability (correlation between tuning maps from odd and even trials) computed based on tongue position versus based on target location. Each dot represents a cell. l, Preferred peak response time of neurons with locational tuning. m, Top, preferred response time of neurons. Bottom, neural activity of all neurons with significant temporal tuning; each row corresponds to the trial-averaged response of one neuron (of 82,148 neurons). Neural responses were aligned to first (left) or last (right) contact lick. n-o, Tuning similarity between pairs of neurons as a function of their anatomical distance for temporal tuning (n) and location tuning (o); displayed as mean ± s.e.m. across sessions.

Extended Data Fig. 3 Reward-outcome modulation is not explained by variations in lick-rate.

a, Distribution of lick-rates at different times during the trial, for standard reward, increased reward, or omitted reward trials. b, Temporal tuning of six example cells with reward-outcome modulation, averaged across trials with different reward conditions. c-d, Generalized linear model (GLM) predictions from Model 1 (c, time + licking) and Model 2 (d, time + licking + reward), for the same neurons shown in (b). Note that reward-related differences are captured in Model 2 but not in Model 1 for most neurons (Cells 1–5), while Cell 6 serves as a counterexample where licking does explain the apparent reward-outcome modulation. e, Histogram of P values of the reward-increase (top) and reward-omission (bottom) predictors, assessed using a likelihood ratio test (two-sided, chi-squared distribution) comparing GLM models with and without reward terms, for all reward-modulated neurons. In majority of neurons (e.g., Cells 1-5 in b), reward contributes significantly to explaining neural activity beyond licking and time. f, Histogram of P values for the licking predictor in Model 2, shown for all neurons (black), and for neurons modulated by reward-increase (orange) and reward-omission (green). Licking was a significant predictor in 70.8% of all neurons, including 74.1% and 76.5% of the reward-modulated neurons (omission and increase, respectively), indicating widespread, but not exclusive, lick-related modulation. Data in a is shown as mean ± s.e.m. across sessions. Data in b-d are shown as mean ± s.e.m. across trials. For e-f, significance was determined using two-sided statistical tests (likelihood ratio test in e, and licking-predictor coefficient P values from the GLM in f, Methods), without correction for multiple comparison with a threshold of p < 0.01 (indicated by vertical dashed lines).

Extended Data Fig. 4 Two-photon photostimulation and volumetric imaging protocol.

a, Left, Schematics of two-photon photostimulation of targeted neurons (magenta) and measurement of evoked responses in non-stimulated neurons (green) using volumetric calcium imaging. Right, example imaged volume of layer 2/3, motor cortex, with two photostimulation targets indicated (magenta). Both photostimulated target neurons and imaged neurons were excitatory cells. b, Schematics of photostimulation timing during volumetric imaging shown for two consecutive photostimulation trials. Target cells were photostimulated in the superficial plane (‘plane 1’). Imaging each plane lasted 33 ms, followed by a 15 ms gap without imaging. Four spiral photostimulations (each spiral lasting for 3 ms, see inset) occurred in the gap between each imaged plane (Methods).

Extended Data Fig. 5 Photostimulation evoked responses on single trials.

a-c, Comparison of the amplitude of photostimulation evoked response of the target neuron and causally connected neurons, shown for 3 example neuronal pairs. Trials were categorized according to the strength of the evoked response (Δ Activity) of the target neurons, into trials with ‘strong’ or ‘weak’ response of the target (Methods). Responses of both target and causally connected neurons are shown by trial-averaging the activity for strong (red), weak (blue), or all (black) trials, displayed as mean ± s.e.m. across trials. The number of trials in each category for example neuronal pairs: (a) 15 strong, 15 weak, 75 all; (b) 13 strong, 13 weak, 63 all; (c) 15 strong, 15 weak, 75 all. d, Left, Distribution of Pearson correlation (r) between Δ Activity of target neurons and causally connected neurons on a trial-by-trial basis, shown for all neuronal pairs with significant causal connections (n = 33,830 connection pairs; Methods). To control for shared network activity that could lead to spurious evoked response correlations, we recomputed correlations after projecting out the first principal component of neural activity (middle) or the first three principal components (right). While the magnitude of correlations decreased, positive correlations persisted (average correlations r = 0.23 for full activity, r = 0.18 after removing the first PC, and r = 0.16 after removing the first three PCs), suggesting that the covariation in activity reflects functional interactions rather than spontaneous network fluctuations.

Extended Data Fig. 6 Spatial localization and causal connectivity of control and target neurons.

a, Example field of view showing the top imaged plane with responding target neurons (magenta) and non-responding neurons referred to as ‘control targets’ (cyan). b, Distribution of lateral distances between each control target and the closest neuronal target for all control targets. c, Bottom, causal ‘connection probability’ as a function of anatomical distance (lateral and axial) between all pairs of targets and causally connected neurons, shown for target neurons (magenta), all control targets (cyan). We also analyzed connection probability only for control targets that were intermingled with target neurons (within less than 25 µm laterally from target neurons, blue). Top, display the marginal distribution of the three bottom panels as a function of lateral distance; displayed as mean ± s.e.m. across sessions.

Extended Data Fig. 7 Relationship between causal connectivity and tuning or noise correlations.

a, Comparison of causal connectivity strength with location tuning correlation measured during multidirectional reaching behavior (Methods). Top, Location-tuning correlations versus causal connectivity strength. Real data (‘Data’, red); distance-preserved shuffled control distribution (‘Distance shuffled’, gray) represents shuffling of the correlation for all neurons residing in the same distance from the target neuron (Methods). Bottom, Residual tuning correlations versus causal connectivity strength. Residual tuning correlation (‘Residual’, blue) is obtained by subtracting the ‘Distance shuffled’ from ‘Data’ (top; Methods), and represents the residual relationship that remains between connection strength and correlation in location tuning, beyond what is explained by mutual distance dependency. b-c, Comparison of causal connectivity strength measured during rest, with ‘noise correlations’ (sometimes referred to as ‘functional connectivity’) and ‘residual noise correlations’ (Methods) measured for the same neurons during rest (b) or multidirectional reaching behavior (c). Top, Noise correlations versus causal connectivity strength. Noise correlations are measured during rest (b) or multidirectional reaching behavior (c), for the same neurons. Causal connectivity is measured in an independent rest session epoch. Real data (‘Data’, red); distance-preserved shuffled control distribution (‘Distance shuffled’, gray) represents shuffling of the correlation for all neurons residing in the same distance from the target neuron (Methods). Bottom, Residual noise correlations versus causal connectivity strength. Residual noise correlation (‘Residual’, blue) is obtained by subtracting the ‘Distance shuffled’ from ‘Data’ (top; Methods), and represents the residual relationship that remains between connection strength and noise correlations, beyond what is explained by mutual distance dependency. Causal connectivity could accurately predict noise correlations across states. d-e Same in b-c, but for the opposite relationship, i.e., causal connectivity strength versus noise correlations. This indicates to what extent noise correlation can predict, on average, the strength of causal connectivity in the motor cortex. Data in all panels is displayed as mean ± s.e.m. across sessions.

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Finkelstein, A., Daie, K., Rózsa, M. et al. Connectivity underlying motor cortex activity during goal-directed behaviour. Nature 649, 416–422 (2026). https://doi.org/10.1038/s41586-025-09758-6

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