Global Representations of Goal-Directed Behavior in Distinct Cell Types of Mouse Neocortex

Neuron

Volumn 94, Issue 4, 2017, Pages 891-907.e6

  Allen, William E ^a,b   Kauvar, Isaac V ^a,b   Chen, Michael Z ^b   Richman, Ethan B ^a,b   Yang, Samuel J ^a,b   Chan, Ken ^c   Gradinaru, Viviana ^c   Deverman, Benjamin E ^c   Luo, Liqun ^a   Deisseroth, Karl ^a,b  

^a STANFORD UNIVERSITY   (United States)

^b Stanford University   (United States)

^c California Institute of Technology HHMI   (United States)

Author keywords

calcium imaging; cell type; cortex; goal directed behavior; optogenetics; widefield

Indexed keywords

CALCIUM ION; CALCIUM;

ANIMAL BEHAVIOR; ANIMAL CELL; ANIMAL EXPERIMENT; ANIMAL TISSUE; ARTICLE; BRAIN NERVE CELL; CALCIUM IMAGING; CONTROLLED STUDY; DECISION MAKING; FEMALE; FRONTAL CORTEX; GOAL DIRECTED BEHAVIOR; MALE; MOUSE; NEOCORTEX; NEUROIMAGING; NEUROPIL; NONHUMAN; OPTOGENETICS; PREMOTOR CORTEX; PRIORITY JOURNAL; TASK PERFORMANCE; TRANSGENIC MOUSE; ANIMAL; CYTOLOGY; FLUORESCENCE IMAGING; FRONTAL LOBE; METABOLISM; MOTIVATION; NERVE CELL; PHYSIOLOGY;

ANIMALS; BEHAVIOR, ANIMAL; CALCIUM; DECISION MAKING; FRONTAL LOBE; GOALS; MICE; NEOCORTEX; NEURONS; OPTICAL IMAGING; OPTOGENETICS;

EID: 85020069938     PISSN: 08966273     EISSN: 10974199     Source Type: Journal    
DOI: 10.1016/j.neuron.2017.04.017     Document Type: Article

References (75)

1. Ahrens, M.B., Orger, M.B., Robson, D.N., Li, J.M., Keller, P.J., Whole-brain functional imaging at cellular resolution using light-sheet microscopy. Nat. Methods 10 (2013), 413–420.
2. Akemann, W., Mutoh, H., Perron, A., Park, Y.K., Iwamoto, Y., Knöpfel, T., Imaging neural circuit dynamics with a voltage-sensitive fluorescent protein. J. Neurophysiol. 108 (2012), 2323–2337.
3. Aravanis, A.M., Wang, L.P., Zhang, F., Meltzer, L.A., Mogri, M.Z., Schneider, M.B., Deisseroth, K., An optical neural interface: in vivo control of rodent motor cortex with integrated fiberoptic and optogenetic technology. J. Neural Eng. 4 (2007), S143–S156.
4. Bressler, S.L., Coppola, R., Nakamura, R., Episodic multiregional cortical coherence at multiple frequencies during visual task performance. Nature 366 (1993), 153–156.
5. Buschman, T.J., Miller, E.K., Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science 315 (2007), 1860–1862.
6. Cauller, L., Layer I of primary sensory neocortex: where top-down converges upon bottom-up. Behav. Brain Res. 71 (1995), 163–170.
7. Chen, J.L., Carta, S., Soldado-Magraner, J., Schneider, B.L., Helmchen, F., Behaviour-dependent recruitment of long-range projection neurons in somatosensory cortex. Nature 499 (2013), 336–340.
8. Chen, T.-W., Wardill, T.J., Sun, Y., Pulver, S.R., Renninger, S.L., Baohan, A., Schreiter, E.R., Kerr, R.A., Orger, M.B., Jayaraman, V., et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499 (2013), 295–300.
9. Chen, J.L., Voigt, F.F., Javadzadeh, M., Krueppel, R., Helmchen, F., Long-range population dynamics of anatomically defined neocortical networks. eLife, 5, 2016, e14679.
10. Cisek, P., Kalaska, J.F., Neural mechanisms for interacting with a world full of action choices. Annu. Rev. Neurosci. 33 (2010), 269–298.
11. Cook-Snyder, D.R., Jones, A., Reijmers, L.G., A retrograde adeno-associated virus for collecting ribosome-bound mRNA from anatomically defined projection neurons. Front. Mol. Neurosci., 8, 2015, 56.
12. Dana, H., Chen, T.W., Hu, A., Shields, B.C., Guo, C., Looger, L.L., Kim, D.S., Svoboda, K., Thy1-GCaMP6 transgenic mice for neuronal population imaging in vivo. PLoS ONE, 9, 2014, e108697.
13. Deverman, B.E., Pravdo, P.L., Simpson, B.P., Kumar, S.R., Chan, K.Y., Banerjee, A., Wu, W.-L., Yang, B., Huber, N., Pasca, S.P., Gradinaru, V., Cre-dependent selection yields AAV variants for widespread gene transfer to the adult brain. Nat. Biotechnol. 34 (2016), 204–209.
14. Douglas, R.J., Martin, K.A., Recurrent neuronal circuits in the neocortex. Curr. Biol. 17 (2007), R496–R500.
15. Engel, A.K., Fries, P., Singer, W., Dynamic predictions: oscillations and synchrony in top-down processing. Nat. Rev. Neurosci. 2 (2001), 704–716.
16. Ferezou, I., Haiss, F., Gentet, L.J., Aronoff, R., Weber, B., Petersen, C.C.H., Spatiotemporal dynamics of cortical sensorimotor integration in behaving mice. Neuron 56 (2007), 907–923.
17. Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., Raichle, M.E., The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. USA 102 (2005), 9673–9678.
18. Frostig, R.D., Lieke, E.E., Ts'o, D.Y., Grinvald, A., Cortical functional architecture and local coupling between neuronal activity and the microcirculation revealed by in vivo high-resolution optical imaging of intrinsic signals. Proc. Natl. Acad. Sci. USA 87 (1990), 6082–6086.
19. Gilbert, C.D., Li, W., Top-down influences on visual processing. Nat. Rev. Neurosci. 14 (2013), 350–363.
20. Goard, M.J., Pho, G.N., Woodson, J., Sur, M., Distinct roles of visual, parietal, and frontal motor cortices in memory-guided sensorimotor decisions. eLife 5 (2016), 1–30.
21. Guo, Z.V., Li, N., Huber, D., Ophir, E., Gutnisky, D., Ting, J.T., Feng, G., Svoboda, K., Flow of cortical activity underlying a tactile decision in mice. Neuron 81 (2014), 179–194.
22. Harris, K.D., Mrsic-Flogel, T.D., Cortical connectivity and sensory coding. Nature 503 (2013), 51–58.
23. Hernández, A., Nácher, V., Luna, R., Zainos, A., Lemus, L., Alvarez, M., Vázquez, Y., Camarillo, L., Romo, R., Decoding a perceptual decision process across cortex. Neuron 66 (2010), 300–314.
24. Huang, Z.J., Zeng, H., Genetic approaches to neural circuits in the mouse. Annu. Rev. Neurosci. 36 (2013), 183–215.
25. Husson, T.R., Mallik, A.K., Zhang, J.X., Issa, N.P., Functional imaging of primary visual cortex using flavoprotein autofluorescence. J. Neurosci. 27 (2007), 8665–8675.
26. Kato, S., Kaplan, H.S., Schrödel, T., Skora, S., Lindsay, T.H., Yemini, E., Lockery, S., Zimmer, M., Global brain dynamics embed the motor command sequence of Caenorhabditis elegans. Cell 163 (2015), 656–669.
27. Katz, L.N., Yates, J.L., Pillow, J.W., Huk, A.C., Dissociated functional significance of decision-related activity in the primate dorsal stream. Nature 535 (2016), 285–288.
28. Kepecs, A., Fishell, G., Interneuron cell types are fit to function. Nature 505 (2014), 318–326.
29. Kim, C.K., Yang, S.J., Pichamoorthy, N., Young, N.P., Kauvar, I., Jennings, J.H., Lerner, T.N., Berndt, A., Lee, S.Y., Ramakrishnan, C., et al. Simultaneous fast measurement of circuit dynamics at multiple sites across the mammalian brain. Nat. Methods 13 (2016), 325–328.
30. Klapoetke, N.C., Murata, Y., Kim, S.S., Pulver, S.R., Birdsey-Benson, A., Cho, Y.K., Morimoto, T.K., Chuong, A.S., Carpenter, E.J., Tian, Z., et al. Independent optical excitation of distinct neural populations. Nat. Methods 11 (2014), 338–346.
31. Kobak, D., Brendel, W., Constantinidis, C., Feierstein, C.E., Kepecs, A., Mainen, Z.F., Qi, X.L., Romo, R., Uchida, N., Machens, C.K., Demixed principal component analysis of neural population data. eLife 5 (2016), 1–37.
32. Komiyama, T., Sato, T.R., O'Connor, D.H., Zhang, Y.-X., Huber, D., Hooks, B.M., Gabitto, M., Svoboda, K., Learning-related fine-scale specificity imaged in motor cortex circuits of behaving mice. Nature 464 (2010), 1182–1186.
33. Kramer, R.S., Pearlstein, R.D., Cerebral cortical microfluorometry at isosbestic wavelengths for correction of vascular artifact. Science 205 (1979), 693–696.
34. Kvitsiani, D., Ranade, S., Hangya, B., Taniguchi, H., Huang, J.Z., Kepecs, A., Distinct behavioural and network correlates of two interneuron types in prefrontal cortex. Nature 498 (2013), 363–366.
35. Ledberg, A., Bressler, S.L., Ding, M., Coppola, R., Nakamura, R., Large-scale visuomotor integration in the cerebral cortex. Cereb. Cortex 17 (2007), 44–62.
36. Lerner, T.N., Shilyansky, C., Davidson, T.J., Evans, K.E., Beier, K.T., Zalocusky, K.A., Crow, A.K., Malenka, R.C., Luo, L., Tomer, R., Deisseroth, K., Intact-brain analyses reveal distinct information carried by SNc dopamine subcircuits. Cell 162 (2015), 635–647.
37. Li, N., Chen, T.W., Guo, Z.V., Gerfen, C.R., Svoboda, K., A motor cortex circuit for motor planning and movement. Nature 519 (2015), 51–56.
38. Ma, Y., Shaik, M.A., Kozberg, M.G., Kim, S.H., Portes, J.P., Timerman, D., Hillman, E.M., Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons. Proc. Natl. Acad. Sci. USA 113 (2016), E8463–E8471.
39. Madisen, L., Garner, A.R., Shimaoka, D., Chuong, A.S., Klapoetke, N.C., Li, L., van der Bourg, A., Niino, Y., Egolf, L., Monetti, C., et al. Transgenic mice for intersectional targeting of neural sensors and effectors with high specificity and performance. Neuron 85 (2015), 942–958.
40. Makino, H., Komiyama, T., Learning enhances the relative impact of top-down processing in the visual cortex. Nat. Neurosci. 18 (2015), 1116–1122.
41. Miller, E.K., Buschman, T.J., Cortical circuits for the control of attention. Curr. Opin. Neurobiol. 23 (2013), 216–222.
42. Miller, E.K., Cohen, J.D., An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24 (2001), 167–202.
43. Mohajerani, M.H., Chan, A.W., Mohsenvand, M., LeDue, J., Liu, R., McVea, D.A., Boyd, J.D., Wang, Y.T., Reimers, M., Murphy, T.H., Spontaneous cortical activity alternates between motifs defined by regional axonal projections. Nat. Neurosci. 16 (2013), 1426–1435.
44. Oh, S.W., Harris, J.A., Ng, L., Winslow, B., Cain, N., Mihalas, S., Wang, Q., Lau, C., Kuan, L., Henry, A.M., et al. A mesoscale connectome of the mouse brain. Nature 508 (2014), 207–214.
45. 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 (2005), 597–603.
46. Peron, S.P., Freeman, J., Iyer, V., Guo, C., Svoboda, K., A cellular resolution map of barrel cortex activity during tactile behavior. Neuron 86 (2015), 783–799.
47. Peters, A.J., Chen, S.X., Komiyama, T., Emergence of reproducible spatiotemporal activity during motor learning. Nature 510 (2014), 263–267.
48. Petreanu, L., Gutnisky, D.A., Huber, D., Xu, N.L., O'Connor, D.H., Tian, L., Looger, L., Svoboda, K., Activity in motor-sensory projections reveals distributed coding in somatosensation. Nature 489 (2012), 299–303.
49. Pfeffer, C.K., Xue, M., He, M., Huang, Z.J., Scanziani, M., Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons. Nat. Neurosci. 16 (2013), 1068–1076.
50. Pinto, L., Dan, Y., Cell-type-specific activity in prefrontal cortex during goal-directed behavior. Neuron 87 (2015), 437–450.
51. Pisauro, M.A., Dhruv, N.T., Carandini, M., Benucci, A., Fast hemodynamic responses in the visual cortex of the awake mouse. J. Neurosci. 33 (2013), 18343–18351.
52. Pnevmatikakis, E.A., Soudry, D., Gao, Y., Machado, T.A., Merel, J., Pfau, D., Reardon, T., Mu, Y., Lacefield, C., Yang, W., et al. Simultaneous denoising, deconvolution, and demixing of calcium imaging data. Neuron 89 (2016), 285–299.
53. Poort, J., Khan, A.G., Pachitariu, M., Nemri, A., Orsolic, I., Krupic, J., Bauza, M., Sahani, M., Keller, G.B., Mrsic-Flogel, T.D., Hofer, S.B., Learning enhances sensory and multiple non-sensory representations in primary visual cortex. Neuron 86 (2015), 1478–1490.
54. Rajasethupathy, P., Sankaran, S., Marshel, J.H., Kim, C.K., Ferenczi, E., Lee, S.Y., Berndt, A., Ramakrishnan, C., Jaffe, A., Lo, M., et al. Projections from neocortex mediate top-down control of memory retrieval. Nature 526 (2015), 653–659.
55. Rajasethupathy, P., Ferenczi, E., Deisseroth, K., Targeting neural circuits. Cell 165 (2016), 524–534.
56. Ratzlaff, E.H., Grinvald, A., A tandem-lens epifluorescence macroscope: hundred-fold brightness advantage for wide-field imaging. J. Neurosci. Methods 36 (1991), 127–137.
57. Robles, F.E., Chowdhury, S., Wax, A., Assessing hemoglobin concentration using spectroscopic optical coherence tomography for feasibility of tissue diagnostics. Biomed. Opt. Express 1 (2010), 310–317.
58. Roelfsema, P.R., Engel, A.K., König, P., Singer, W., Visuomotor integration is associated with zero time-lag synchronization among cortical areas. Nature 385 (1997), 157–161.
59. Rudy, B., Fishell, G., Lee, S., Hjerling-Leffler, J., Three groups of interneurons account for nearly 100% of neocortical GABAergic neurons. Dev. Neurobiol. 71 (2011), 45–61.
60. Shibata, T., Shimoyama, I., Ito, T., Abla, D., Iwasa, H., Koseki, K., Yamanouchi, N., Sato, T., Nakajima, Y., The time course of interhemispheric EEG coherence during a GO/NO-GO task in humans. Neurosci. Lett. 233 (1997), 117–120.
61. Siegel, M., Buschman, T.J., Miller, E.K., Cortical information flow during flexible sensorimotor decisions. Science 348 (2015), 1352–1355.
62. Sofroniew, N.J., Cohen, J.D., Lee, A.K., Svoboda, K., Natural whisker-guided behavior by head-fixed mice in tactile virtual reality. J. Neurosci. 34 (2014), 9537–9550.
63. Spellman, T.J., Gordon, J.A., Synchrony in schizophrenia: a window into circuit-level pathophysiology. Curr. Opin. Neurobiol. 30 (2015), 17–23.
64. Spors, H., Grinvald, A., Spatio-temporal dynamics of odor representations in the mammalian olfactory bulb. Neuron 34 (2002), 301–315.
65. Stirman, J.N., Smith, I.T., Kudenov, M.W., Smith, S.L., Wide field-of-view, multi-region, two-photon imaging of neuronal activity in the mammalian brain. Nat. Biotechnol. 34 (2016), 857–862.
66. Stuart, G.J., Spruston, N., Dendritic integration: 60 years of progress. Nat. Neurosci. 18 (2015), 1713–1721.
67. Swanson, L.W., Cerebral hemisphere regulation of motivated behavior. Brain Res. 886 (2000), 113–164.
68. Thévenaz, P., Ruttimann, U.E., Unser, M., A pyramid approach to subpixel registration based on intensity. IEEE Trans. Image Process. 7 (1998), 27–41.
69. 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 (2014), 15931–15946.
70. 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 (2016), 2852–2866.
71. Xiao, D., Vanni, M.P., Mitelut, C.C., Chan, A.W., LeDue, J.M., Xie, Y., Chen, A.C., Swindale, N.V., Murphy, T.H., Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons. eLife, 6, 2017, e19976.
72. Yuste, R., From the neuron doctrine to neural networks. Nat. Rev. Neurosci. 16 (2015), 487–497.
73. Zalocusky, K.A., Ramakrishnan, C., Lerner, T.N., Davidson, T.J., Knutson, B., Deisseroth, K., Nucleus accumbens D2R cells signal prior outcomes and control risky decision-making. Nature 531 (2016), 642–646.
74. Zhang, S., Xu, M., Kamigaki, T., Hoang Do, J.P., Chang, W.-C., Jenvay, S., Miyamichi, K., Luo, L., Dan, Y., Selective attention. Long-range and local circuits for top-down modulation of visual cortex processing. Science 345 (2014), 660–665.
75. Zingg, B., Hintiryan, H., Gou, L., Song, M.Y., Bay, M., Bienkowski, M.S., Foster, N.N., Yamashita, S., Bowman, I., Toga, A.W., Dong, H.W., Neural networks of the mouse neocortex. Cell 156 (2014), 1096–1111.