Single-trial neural dynamics are dominated by richly varied movements.
Journal
Nature neuroscience
ISSN: 1546-1726
Titre abrégé: Nat Neurosci
Pays: United States
ID NLM: 9809671
Informations de publication
Date de publication:
10 2019
10 2019
Historique:
received:
17
04
2019
accepted:
20
08
2019
entrez:
26
9
2019
pubmed:
26
9
2019
medline:
7
11
2019
Statut:
ppublish
Résumé
When experts are immersed in a task, do their brains prioritize task-related activity? Most efforts to understand neural activity during well-learned tasks focus on cognitive computations and task-related movements. We wondered whether task-performing animals explore a broader movement landscape and how this impacts neural activity. We characterized movements using video and other sensors and measured neural activity using widefield and two-photon imaging. Cortex-wide activity was dominated by movements, especially uninstructed movements not required for the task. Some uninstructed movements were aligned to trial events. Accounting for them revealed that neurons with similar trial-averaged activity often reflected utterly different combinations of cognitive and movement variables. Other movements occurred idiosyncratically, accounting for trial-by-trial fluctuations that are often considered 'noise'. This held true throughout task-learning and for extracellular Neuropixels recordings that included subcortical areas. Our observations argue that animals execute expert decisions while performing richly varied, uninstructed movements that profoundly shape neural activity.
Identifiants
pubmed: 31551604
doi: 10.1038/s41593-019-0502-4
pii: 10.1038/s41593-019-0502-4
pmc: PMC6768091
mid: NIHMS1537931
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1677-1686Subventions
Organisme : NEI NIH HHS
ID : R01 EY022979
Pays : United States
Références
Shadlen, M. N. & Newsome, W. T. Motion perception: seeing and deciding. Proc. Natl Acad. Sci. USA 93, 628–633 (1996).
doi: 10.1073/pnas.93.2.628
Maimon, G. & Assad, J. A. A cognitive signal for the proactive timing of action in macaque LIP. Nat. Neurosci. 9, 948–955 (2006).
doi: 10.1038/nn1716
Horwitz, G. D., Batista, A. P. & Newsome, W. T. Representation of an abstract perceptual decision in macaque superior colliculus. J. Neurophysiol. 91, 2281–2296 (2004).
doi: 10.1152/jn.00872.2003
Gold, J. I. & Shadlen, M. N. The influence of behavioral context on the representation of a perceptual decision in developing oculomotor commands. J. Neurosci. 23, 632–651 (2003).
doi: 10.1523/JNEUROSCI.23-02-00632.2003
Roitman, J. D. & Shadlen, M. N. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci. 22, 9475–9489 (2002).
doi: 10.1523/JNEUROSCI.22-21-09475.2002
Churchland, A. K., Kiani, R. & Shadlen, M. N. Decision-making with multiple alternatives. Nat. Neurosci. 11, 693–702 (2008).
doi: 10.1038/nn.2123
Erlich, J. C., Bialek, M. & Brody, C. D. A cortical substrate for memory-guided orienting in the rat. Neuron 72, 330–343 (2011).
doi: 10.1016/j.neuron.2011.07.010
Niell, C. M. & Stryker, M. P. Modulation of visual responses by behavioral state in mouse visual cortex. Neuron 65, 472–479 (2010).
doi: 10.1016/j.neuron.2010.01.033
Saleem, A. B., Ayaz, A., Jeffery, K. J., Harris, K. D. & Carandini, M. Integration of visual motion and locomotion in mouse visual cortex. Nat. Neurosci. 16, 1864–1869 (2013).
doi: 10.1038/nn.3567
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).
doi: 10.1152/jn.01056.2015
Guo, Z. V. et al. Flow of cortical activity underlying a tactile decision in mice. Neuron 81, 179–194 (2014).
doi: 10.1016/j.neuron.2013.10.020
Allen, W. E. et al. Global representations of goal-directed behavior in distinct cell types of mouse neocortex. Neuron 94, 891–907.e6 (2017).
doi: 10.1016/j.neuron.2017.04.017
Runyan, C. A., Piasini, E., Panzeri, S. & Harvey, C. D. Distinct timescales of population coding across cortex. Nature 548, 92–96 (2017).
doi: 10.1038/nature23020
Scott, B. B. et al. Fronto-parietal cortical circuits encode accumulated evidence with a diversity of timescales. Neuron 95, 385–398.e5 (2017).
doi: 10.1016/j.neuron.2017.06.013
Caballero-Gaudes, C. & Reynolds, R. C. Methods for cleaning the BOLD fMRI signal. NeuroImage 154, 128–149 (2017).
doi: 10.1016/j.neuroimage.2016.12.018
Stringer, C. et al. Spontaneous behaviors drive multidimensional, brainwide activity. Science 364, eaav7893 (2019).
doi: 10.1126/science.aav7893
Shimaoka, D., Harris, K. D. & Carandini, M. Effects of arousal on mouse sensory cortex depend on modality. Cell Rep. 22, 3160–3167 (2018).
doi: 10.1016/j.celrep.2018.02.092
Hanks, T. D. et al. Distinct relationships of parietal and prefrontal cortices to evidence accumulation. Nature 520, 220–223 (2015).
doi: 10.1038/nature14066
Erlich, J. C., Brunton, B. W., Duan, C. A., Hanks, T. D. & Brody, C. D. Distinct effects of prefrontal and parietal cortex inactivations on an accumulation of evidence task in the rat. eLife Sci. 4, e05457 (2015).
doi: 10.7554/eLife.05457
Steinmetz, N. A. et al. Aberrant cortical activity in multiple GCaMP6-expressing transgenic mouse lines. eNeuro 4, ENEURO.0207-17.2017 (2017).
doi: 10.1523/ENEURO.0207-17.2017
Zhuang, J. et al. An extended retinotopic map of mouse cortex. eLife 6, e18372 (2017).
doi: 10.7554/eLife.18372
Chen, T.-W., Li, N., Daie, K. & Svoboda, K. A map of anticipatory activity in mouse motor cortex. Neuron 94, 866–879.e4 (2017).
doi: 10.1016/j.neuron.2017.05.005
Garrett, M. E., Nauhaus, I., Marshel, J. H. & Callaway, E. M. Topography and areal organization of mouse visual cortex. J. Neurosci. 34, 12587–12600 (2014).
doi: 10.1523/JNEUROSCI.1124-14.2014
Guo, Z. V. et al. Maintenance of persistent activity in a frontal thalamocortical loop. Nature 545, 181–186 (2017).
doi: 10.1038/nature22324
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).
doi: 10.1038/nature14178
Salkoff, D. B., Zagha, E., McCarthy, E., McCormick, D.A. Movement and performance predict widespread cortical activity in a visual detection task. Cereb. Cortex (in the press).
Raposo, D., Kaufman, M. T. & Churchland, A. K. A category-free neural population supports evolving demands during decision-making. Nat. Neurosci. 17, 1784–1792 (2014).
doi: 10.1038/nn.3865
Horwitz, G. D. & Newsome, W. T. Separate signals for target selection and movement specification in the superior colliculus. Science 284, 1158–1161 (1999).
doi: 10.1126/science.284.5417.1158
Jun, J. J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature 551, 232–236 (2017).
doi: 10.1038/nature24636
Vinck, M., Batista-Brito, R., Knoblich, U. & Cardin, J. A. Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding. Neuron 86, 740–754 (2015).
doi: 10.1016/j.neuron.2015.03.028
Polack, P.-O., Friedman, J. & Golshani, P. Cellular mechanisms of brain-state-dependent gain modulation in visual cortex. Nat. Neurosci. 16, 1331–1339 (2013).
doi: 10.1038/nn.3464
Reimer, J. et al. Pupil fluctuations track fast switching of cortical states during quiet wakefulness. Neuron 84, 355–362 (2014).
doi: 10.1016/j.neuron.2014.09.033
Pereira, T. D. et al. Fast animal pose estimation using deep neural networks. Nat. Methods 16, 117 (2019).
doi: 10.1038/s41592-018-0234-5
Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281 (2018).
doi: 10.1038/s41593-018-0209-y
Le Merre, P. et al. Reward-based learning drives rapid sensory signals in medial prefrontal cortex and dorsal hippocampus necessary for goal-directed behavior. Neuron 97, 83–91.e5 (2018).
doi: 10.1016/j.neuron.2017.11.031
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.e7 (2018).
doi: 10.1016/j.neuron.2018.07.029
Euston, D. R. & McNaughton, B. L. Apparent encoding of sequential context in rat medial prefrontal cortex is accounted for by behavioral variability. J. Neurosci. 26, 13143–13155 (2006).
doi: 10.1523/JNEUROSCI.3803-06.2006
Kawai, R. et al. Motor cortex is required for learning but not for executing a motor skill. Neuron 86, 800–812 (2015).
doi: 10.1016/j.neuron.2015.03.024
Coddington, L. T. & Dudman, J. T. The timing of action determines reward prediction signals in identified midbrain dopamine neurons. Nat. Neurosci. 21, 1563 (2018).
doi: 10.1038/s41593-018-0245-7
Ayaz, A., Saleem, A. B., Schölvinck, M. L. & Carandini, M. Locomotion controls spatial integration in mouse visual cortex. Curr. Biol. 23, 890–894 (2013).
doi: 10.1016/j.cub.2013.04.012
Sommer, M. A. & Wurtz, R. H. Brain circuits for the internal monitoring of movements. Annu. Rev. Neurosci. 31, 317 (2008).
doi: 10.1146/annurev.neuro.31.060407.125627
Schmitt, L. I. et al. Thalamic amplification of cortical connectivity sustains attentional control. Nature 545, 219–223 (2017).
doi: 10.1038/nature22073
Wang, L., Rangarajan, K. V., Gerfen, C. R. & Krauzlis, R. J. Activation of striatal neurons causes a perceptual decision bias during visual change detection in mice. Neuron 97, 1369–1381.e5 (2018).
doi: 10.1016/j.neuron.2018.01.049
Engel, T. A., Chaisangmongkon, W., Freedman, D. J. & Wang, X.-J. Choice-correlated activity fluctuations underlie learning of neuronal category representation. Nat. Commun. 6, 6454 (2015).
doi: 10.1038/ncomms7454
Keller, G. B., Bonhoeffer, T. & Hübener, M. Sensorimotor mismatch signals in primary visual cortex of the behaving mouse. Neuron 74, 809–815 (2012).
doi: 10.1016/j.neuron.2012.03.040
Wolpert, D. M., Ghahramani, Z. & Jordan, M. I. An internal model for sensorimotor integration. Science 269, 1880–1882 (1995).
doi: 10.1126/science.7569931
Wolpert, D. M. & Miall, R. C. Forward models for physiological motor control. Neural Netw. 9, 1265–1279 (1996).
doi: 10.1016/S0893-6080(96)00035-4
Wolpert, D. M. & Kawato, M. Multiple paired forward and inverse models for motor control. Neural Netw. 11, 1317–1329 (1998).
doi: 10.1016/S0893-6080(98)00066-5
Schultz, W. Dopamine neurons and their role in reward mechanisms. Curr. Opin. Neurobiol. 7, 191–197 (1997).
doi: 10.1016/S0959-4388(97)80007-4
Wolpert, D. M., Miall, R. C. & Kawato, M. Internal models in the cerebellum. Trends Cogn. Sci. (Regul. Ed.) 2, 338–347 (1998).
doi: 10.1016/S1364-6613(98)01221-2
Juavinett, A. L., Bekheet, G. & Churchland, A. K. Chronically implanted Neuropixels probes enable high-yield recordings in freely moving mice. eLife 8, e47188 (2018).
Ratzlaff, E. H. & Grinvald, A. A tandem-lens epifluorescence macroscope: hundred-fold brightness advantage for wide-field imaging. J. Neurosci. Methods 36, 127–137 (1991).
doi: 10.1016/0165-0270(91)90038-2
Lerner, T. N. et al. Intact-brain analyses reveal distinct information carried by SNc dopamine subcircuits. Cell 162, 635–647 (2015).
doi: 10.1016/j.cell.2015.07.014
Pachitariu, M. et al. Suite2p: beyond 10,000 neurons with standard two-photon microscopy. Preprint at bioRxiv https://doi.org/10.1101/061507 (2016).
Jia, H., Rochefort, N. L., Chen, X. & Konnerth, A. In vivo two-photon imaging of sensory-evoked dendritic calcium signals in cortical neurons. Nat. Protoc. 6, 28–35 (2011).
doi: 10.1038/nprot.2010.169
Pachitariu, M., Steinmetz, N., Kadir, S., Carandini, M. & Harris, K. D. Fast and accurate spike sorting of high-channel count probes with KiloSort. Adv. Neural Inf. Proc. Sys. 29, 6326 (2016).
Powell, K., Mathy, A., Duguid, I. & Häusser, M. Synaptic representation of locomotion in single cerebellar granule cells. eLife Sci. 4, e07290 (2015).
doi: 10.7554/eLife.07290
Mumford, J. A., Poline, J.-B. & Poldrack, R. A. Orthogonalization of regressors in fMRI models. PLoS One 10, e0126255 (2015).
doi: 10.1371/journal.pone.0126255
Karabatsos, G. Marginal maximum likelihood estimation methods for the tuning parameters of ridge, power ridge, and generalized ridge regression. Commun. Stat. Simulat. (2017).