Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface.
Animals
Brain-Computer Interfaces
Calcium
/ metabolism
Calcium-Binding Proteins
/ metabolism
Dendrites
/ metabolism
Green Fluorescent Proteins
/ metabolism
Implants, Experimental
Intravital Microscopy
/ instrumentation
Macaca mulatta
Male
Models, Neurological
Motor Activity
/ physiology
Motor Cortex
/ diagnostic imaging
Multimodal Imaging
/ methods
Neurons
/ physiology
Photons
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
17 06 2021
17 06 2021
Historique:
received:
23
09
2019
accepted:
19
05
2021
entrez:
18
6
2021
pubmed:
19
6
2021
medline:
8
7
2021
Statut:
epublish
Résumé
Calcium imaging is a powerful tool for recording from large populations of neurons in vivo. Imaging in rhesus macaque motor cortex can enable the discovery of fundamental principles of motor cortical function and can inform the design of next generation brain-computer interfaces (BCIs). Surface two-photon imaging, however, cannot presently access somatic calcium signals of neurons from all layers of macaque motor cortex due to photon scattering. Here, we demonstrate an implant and imaging system capable of chronic, motion-stabilized two-photon imaging of neuronal calcium signals from macaques engaged in a motor task. By imaging apical dendrites, we achieved optical access to large populations of deep and superficial cortical neurons across dorsal premotor (PMd) and gyral primary motor (M1) cortices. Dendritic signals from individual neurons displayed tuning for different directions of arm movement. Combining several technical advances, we developed an optical BCI (oBCI) driven by these dendritic signalswhich successfully decoded movement direction online. By fusing two-photon functional imaging with CLARITY volumetric imaging, we verified that many imaged dendrites which contributed to oBCI decoding originated from layer 5 output neurons, including a putative Betz cell. This approach establishes new opportunities for studying motor control and designing BCIs via two photon imaging.
Identifiants
pubmed: 34140486
doi: 10.1038/s41467-021-23884-5
pii: 10.1038/s41467-021-23884-5
pmc: PMC8211867
doi:
Substances chimiques
Calcium-Binding Proteins
0
Green Fluorescent Proteins
147336-22-9
Calcium
SY7Q814VUP
Banques de données
Dryad
['10.5061/dryad.cnp5hqc4k']
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
3689Subventions
Organisme : NICHD NIH HHS
ID : DP1 HD075623
Pays : United States
Organisme : NINDS NIH HHS
ID : F31 NS089376
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH086373
Pays : United States
Organisme : Howard Hughes Medical Institute
Pays : United States
Commentaires et corrections
Type : CommentIn
Références
Collinger, J. L. et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381, 557–564 (2013).
pubmed: 23253623
pmcid: 3641862
doi: 10.1016/S0140-6736(12)61816-9
Pandarinath, C. et al. Neural population dynamics in human motor cortex during movements in people with ALS. eLife 4, e07436 (2015).
pubmed: 26099302
pmcid: 4475900
doi: 10.7554/eLife.07436
Shenoy, K. V. & Carmena, J. M. Combining decoder design and neural adaptation in brain-machine interfaces. Neuron 84, 665–680 (2014).
pubmed: 25459407
doi: 10.1016/j.neuron.2014.08.038
Taylor, D. M., Tillery, S. I. H. & Schwartz, A. B. Direct cortical control of 3D neuroprosthetic devices. Science 296, 1829–1832 (2002).
pubmed: 12052948
doi: 10.1126/science.1070291
Serruya, M. D., Hatsopoulos, N. G., Paninski, L., Fellows, M. R. & Donoghue, J. P. Instant neural control of a movement signal. Nature 416, 141–142 (2002).
pubmed: 11894084
doi: 10.1038/416141a
Carmena, J. M. et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 1, 193–208 (2003).
doi: 10.1371/journal.pbio.0000042
Musallam, S., Corneil, B. D., Greger, B., Scherberger, H. & Andersen, R. A. Cognitive control signals for neural prosthetics. Science 305, 258–262 (2004).
pubmed: 15247483
doi: 10.1126/science.1097938
Santhanam, G., Ryu, S. I., Yu, B. M., Afshar, A. & Shenoy, K. V. A high-performance brain–computer interface. Nature 442, 195–198 (2006).
pubmed: 16838020
doi: 10.1038/nature04968
Moritz, C. T., Perlmutter, S. I. & Fetz, E. E. Direct control of paralysed muscles by cortical neurons. Nature 456, 639–642 (2008).
pubmed: 18923392
pmcid: 3159518
doi: 10.1038/nature07418
Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S. & Schwartz, A. B. Cortical control of a prosthetic arm for self-feeding. Nature 453, 1098–1101 (2008).
pubmed: 18509337
doi: 10.1038/nature06996
Ethier, C., Oby, E. R., Bauman, M. J. & Miller, L. E. Restoration of Grasp Following Paralysis Through Brain-Controlled Stimulation of Muscles vol. 485 (Nature Publishing Group, 2012).
Gilja, V. et al. A high-performance neural prosthesis enabled by control algorithm design. Nat. Neurosci. 15, 1752–1757 (2012).
pubmed: 23160043
pmcid: 3638087
doi: 10.1038/nn.3265
Kao, J. C., Nuyujukian, P., Ryu, S. I., Churchland, M. M. & Cunningham, J. P. Single-trial dynamics of motor cortex and their applications to brain-machine interfaces. Nat. Commun. 6, 1–12 (2015).
doi: 10.1038/ncomms8759
Shenoy, K. V., Sahani, M. & Churchland, M. M. Cortical control of arm movements: a dynamical systems perspective. Annu. Rev. Neurosci. 36, 337–359 (2013).
pubmed: 23725001
doi: 10.1146/annurev-neuro-062111-150509
Capogrosso, M. et al. A brain-spine interface alleviating gait deficits after spinal cord injury in primates. Nature 539, 284–288 (2016).
pubmed: 27830790
pmcid: 5108412
doi: 10.1038/nature20118
Sofroniew, N. J., Flickinger, D., King, J. & Svoboda, K. A large field of view two-photon mesoscope with subcellular resolution for in vivo imaging. Elife 5, e14472 (2016).
Jun, J. J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature 551, 232–236 (2017).
pubmed: 29120427
pmcid: 5955206
doi: 10.1038/nature24636
Heider, B., Nathanson, J. L., Isacoff, E. Y., Callaway, E. M. & Siegel, R. M. Two-photon imaging of calcium in virally transfected striate cortical neurons of behaving monkey. PLoS ONE 5, 1–13 (2010).
doi: 10.1371/journal.pone.0013829
Ju, N., Jiang, R., Macknik, S. L., Martinez-Conde, S. & Tang, S. Long-term all-optical interrogation of cortical neurons in awake-behaving nonhuman primates. PLoS Biol. 16, e2005839 (2018).
pubmed: 30089111
pmcid: 6101413
doi: 10.1371/journal.pbio.2005839
Li, M., Liu, F., Jiang, H., Lee, T. S. & Tang, S. Long-term two-photon imaging in awake macaque monkey. Neuron 93, 1049–1057 (2017).
pubmed: 28215557
doi: 10.1016/j.neuron.2017.01.027
Seidemann, E. et al. Calcium imaging with genetically encoded indicators in behaving primates. Elife 5, e16178 (2016).
Garg, A. K., Li, P., Rashid, M. S. & Callaway, E. M. Color and orientation are jointly coded and spatially organized in primate primary visual cortex. Science 364, 1275–1279 (2019).
pubmed: 31249057
pmcid: 6689325
doi: 10.1126/science.aaw5868
Choi, J., Goncharov, V., Kleinbart, J., Orsborn, A. & Pesaran, B. Monkey-MIMMS: towards automated cellular resolution large-scale two-photon microscopy in the awake macaque monkey. Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2018, 3013–3016 (2018).
Tang, S. et al. Large-scale two-photon imaging revealed super-sparse population codes in the V1 superficial layer of awake monkeys. eLife 7, 1–12 (2018).
doi: 10.7554/eLife.33370
Sadakane, O. et al. Long-term two-photon calcium Imaging of neuronal populations with subcellular resolution in adult non-human primates. Cell Rep. 13, 1989–1999 (2015).
pubmed: 26655910
doi: 10.1016/j.celrep.2015.10.050
Yamada, Y., Matsumoto, Y., Okahara, N. & Mikoshiba, K. Chronic multiscale imaging of neuronal activity in the awake common marmoset. Sci. Rep. 6, 35722 (2016).
pubmed: 27786241
pmcid: 5082371
doi: 10.1038/srep35722
Ebina, T. et al. Two-photon imaging of neuronal activity in motor cortex of marmosets during upper-limb movement tasks. Nat. Commun. 9, 1–16 (2018).
doi: 10.1038/s41467-018-04286-6
Stringer, C., Pachitariu, M., Steinmetz, N., Carandini, M. & Harris, K. D. High-dimensional geometry of population responses in visual cortex. Nature 571, 361–365 (2019).
pubmed: 31243367
pmcid: 6642054
doi: 10.1038/s41586-019-1346-5
Trautmann, E. M. et al. Accurate estimation of neural population dynamics without spike sorting. Neuron 103, 292–308.e4 (2019).
pubmed: 31171448
pmcid: 7002296
doi: 10.1016/j.neuron.2019.05.003
Peters, A. J., Lee, J., Hedrick, N. G., O’Neil, K. & Komiyama, T. Reorganization of corticospinal output during motor learning. Nat. Neurosci. 20, 1133–1141 (2017).
pubmed: 28671694
pmcid: 5656286
doi: 10.1038/nn.4596
Jung, J. C., Mehta, A. D., Aksay, E., Stepnoski, R. & Schnitzer, M. J. In vivo mammalian brain imaging using one- and two-photon fluorescence microendoscopy. J. Neurophysiol. 92, 3121–3133 (2004).
pubmed: 15128753
doi: 10.1152/jn.00234.2004
Bollimunta, A. et al. Head-mounted microendoscopic calcium imaging in dorsal premotor cortex of behaving rhesus macaque. Cell Reports 35, 109239 https://doi.org/10.1016/j.celrep.2021.109239 (2021).
Trautmann, E. et al. Spatially heterogenous tuning in rhesus motor cortex revealed using neuropixels probes. Soc. Neurosci. (2019).
Beaulieu-Laroche, L. et al. Enhanced dendritic compartmentalization in human cortical neurons. Cell 175, 643–651 (2018).
pubmed: 30340039
pmcid: 6197488
doi: 10.1016/j.cell.2018.08.045
Ranganathan, G. N. et al. Active dendritic integration and mixed neocortical network representations during an adaptive sensing behavior. Nat. Neurosci. 21, 1583–1590 (2018).
pubmed: 30349100
pmcid: 6203624
doi: 10.1038/s41593-018-0254-6
Takahashi, N., Oertner, T. G., Hegemann, P. & Larkum, M. E. Active cortical dendrites modulate perception. Science 354, 1587–1590 (2016).
pubmed: 28008068
doi: 10.1126/science.aah6066
Xu, N. et al. Nonlinear dendritic integration of sensory and motor input during an active sensing task. Nature 492, 247–251 (2012).
pubmed: 23143335
doi: 10.1038/nature11601
Beaulieu-Laroche, L., Toloza, E. H. S., Brown, N. J. & Harnett, M. T. Widespread and highly correlated somato-dendritic activity in cortical layer 5 neurons. Neuron 103, 235–241 (2019).
pubmed: 31178115
pmcid: 6639136
doi: 10.1016/j.neuron.2019.05.014
Ju, N. et al. Spatiotemporal functional organization of excitatory synaptic inputs onto macaque V1 neurons. Nat. Commun. 11, 1–11 (2020).
Chung, K. & Deisseroth, K. CLARITY for mapping the nervous system. Nat. Methods 10, 508–513 (2013).
pubmed: 23722210
doi: 10.1038/nmeth.2481
Tomer, R., Ye, L., Hsueh, B. & Deisseroth, K. Advanced CLARITY for rapid and high-resolution imaging of intact tissues. Nat. Protoc. 9, 1682–1697 (2014).
pubmed: 24945384
pmcid: 4096681
doi: 10.1038/nprot.2014.123
O’Shea, D. J. et al. The need for calcium imaging in nonhuman primates: new motor neuroscience and brain-machine interfaces. Exp. Neurol. 287, 437–451 (2017).
pubmed: 27511294
doi: 10.1016/j.expneurol.2016.08.003
Sadtler, P. T. et al. Neural constraints on learning. Nature 512, 423–426 (2014).
pubmed: 25164754
pmcid: 4393644
doi: 10.1038/nature13665
Peixoto, D. et al. Population dynamics of choice representation in dorsal premotor and primary motor cortex. bioRxiv https://doi.org/10.1101/283960 . (2018).
Arieli, A., Grinvald, A. & Slovin, H. Dural substitute for long-term imaging of cortical activity in behaving monkeys and its clinical implications. J. Neurosci. Methods 114, 119–133 (2002).
pubmed: 11856563
doi: 10.1016/S0165-0270(01)00507-6
Chen, L. M. et al. A chamber and artificial dura method for long-term optical imaging in the monkey. J. Neurosci. Methods 113, 41–49 (2002).
pubmed: 11741720
doi: 10.1016/S0165-0270(01)00475-7
Shtoyerman, E., Arieli, A., Slovin, H., Vanzetta, I. & Grinvald, A. Long-term optical imaging and spectroscopy reveal mechanisms underlying the intrinsic signal and stability of cortical maps in V1 of behaving monkeys. J. Neurosci. 20, 8111–8121 (2000).
pubmed: 11050133
pmcid: 6772749
doi: 10.1523/JNEUROSCI.20-21-08111.2000
Tang, S. et al. Complex pattern selectivity in macaque primary visual cortex revealed by large-scale two-photon imaging. Curr. Biol. 28, 38–48 (2018).
pubmed: 29249660
doi: 10.1016/j.cub.2017.11.039
Davis, T. S., Torab, K., House, P. & Greger, B. A minimally invasive approach to long-term head fixation in behaving nonhuman primates. J. Neurosci. Methods 181, 106–110 (2009).
pubmed: 19394360
pmcid: 2696573
doi: 10.1016/j.jneumeth.2009.04.012
Azimi, K., Prescott, I. A., Marino, R. A., Winterborn, A. & Levy, R. Low profile halo head fixation in non-human primates. J. Neurosci. Methods 268, 23–30 (2016).
pubmed: 27132241
doi: 10.1016/j.jneumeth.2016.04.018
Isoda, M. et al. Design of a head fixation device for experiments in behaving monkeys. J. Neurosci. Methods 141, 277–282 (2005).
pubmed: 15661310
doi: 10.1016/j.jneumeth.2004.07.003
Mingozzi, F. & High, K. A. Immune responses to AAV vectors: overcoming barriers to successful gene therapy. Blood 122, 23–36 (2013).
pubmed: 23596044
pmcid: 3701904
doi: 10.1182/blood-2013-01-306647
Pachitariu, M. et al. Suite2p: beyond 10,000 neurons with standard two-photon microscopy. Preprint at bioRxiv (2016).
Kobak, D. et al. Demixed principal component analysis of neural population data. eLife 5, e10989 (2016).
pubmed: 27067378
pmcid: 4887222
doi: 10.7554/eLife.10989
Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).
pubmed: 24201281
pmcid: 4121670
doi: 10.1038/nature12742
Shenoy, K. V. et al. Neural prosthetic control signals from plan activity. Neuroreport 14, 591–596 (2003).
pubmed: 12657892
doi: 10.1097/00001756-200303240-00013
Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A. & Hudspeth, A. J. Principles of Neural Science, 5th edn (McGraw-Hill Education, 2012).
Kalaska, J. F. Emerging ideas and tools to study the emergent properties of the cortical neural circuits for voluntary motor control in non-human primates. F1000Res. 8, 749 (2019).
Dadarlat, M. C., O’Doherty, J. E. & Sabes, P. N. A learning-based approach to artificial sensory feedback leads to optimal integration. Nat. Neurosci. 18, 138–144 (2015).
pubmed: 25420067
doi: 10.1038/nn.3883
Flesher, S. N. et al. Intracortical microstimulation of human somatosensory cortex. Sci. Transl. Med. 8, 361ra141 (2016).
pubmed: 27738096
doi: 10.1126/scitranslmed.aaf8083
George, J. A. et al. Biomimetic sensory feedback through peripheral nerve stimulation improves dexterous use of a bionic hand. Sci. Robot. 4, eaax2352 (2019).
pubmed: 33137773
doi: 10.1126/scirobotics.aax2352
Histed, M. H., Bonin, V. & Reid, R. C. Direct activation of sparse, distributed populations of cortical neurons by electrical microstimulation. Neuron 63, 508–522 (2009).
pubmed: 19709632
pmcid: 2874753
doi: 10.1016/j.neuron.2009.07.016
O’Shea, D. J. & Shenoy, K. V. ERAASR: an algorithm for removing electrical stimulation artifacts from multielectrode array recordings. J. Neural Eng. 15, 026020 (2018).
pubmed: 29265009
pmcid: 5833982
doi: 10.1088/1741-2552/aaa365
Dana, H. et al. Sensitive red protein calcium indicators for imaging neural activity. eLife 5, e12727 (2016).
pubmed: 27011354
pmcid: 4846379
doi: 10.7554/eLife.12727
Dekleva, B. M., Kording, K. P. & Miller, L. E. Single reach plans in dorsal premotor cortex during a two-target task. Nat. Commun. https://doi.org/10.1038/s41467-018-05959-y . (2018).
Wei, Z. et al. A comparison of neuronal population dynamics measured with calcium imaging and electrophysiology. bioRxiv https://doi.org/10.1101/840686 . (2019).
Clancy, K. B., Koralek, A. C., Costa, R. M., Feldman, D. E. & Carmena, J. M. Volitional modulation of optically recorded calcium signals during neuroprosthetic learning. Nat. Neurosci. 17, 807–809 (2014).
pubmed: 24728268
pmcid: 4361947
doi: 10.1038/nn.3712
Vyas, S. et al. Neural population dynamics underlying motor learning transfer. Neuron 97, 1177–1186 (2018).
pubmed: 29456026
pmcid: 5843544
doi: 10.1016/j.neuron.2018.01.040
Lovett-Barron, M. et al. Ancestral circuits for the coordinated modulation of brain state. Cell 171, 1411–1423 (2017).
pubmed: 29103613
pmcid: 5725395
doi: 10.1016/j.cell.2017.10.021
Allen, W. E. et al. Thirst regulates motivated behavior through modulation of brainwide neural population dynamics. Science 364, 0–10 (2019).
doi: 10.1126/science.aav3932
Barretto, R. P. J., Messerschmidt, B. & Schnitzer, M. J. In vivo fluorescence imaging with high-resolution microlenses. Nat. Methods 6, 511–512 (2009).
pubmed: 19525959
pmcid: 2849805
doi: 10.1038/nmeth.1339
Abdelfattah, A. S. et al. Bright and photostable chemigenetic indicators for extended in vivo voltage imaging. Science https://doi.org/10.1126/science.aav6416 . (2019).
Anderson, H. E., Fontaine, A. K., Caldwell, J. H. & Weir, R. F. Imaging of electrical activity in small diameter fibers of the murine peripheral nerve with virally-delivered GCaMP6f. Sci. Rep. 8, 1–9 (2018).
doi: 10.1038/s41598-018-21528-1
Gunaydin, L. A. et al. Natural neural projection dynamics underlying social behavior. Cell 157, 1535–1551 (2014).
pubmed: 24949967
pmcid: 4123133
doi: 10.1016/j.cell.2014.05.017
Lewis, D. A. et al. Dopamine transporter immunoreactivity in monkey cerebral cortex: regional, laminar, and ultrastructural localization. J. Comp. Neurol. 432, 119–136 (2001).
pubmed: 11241381
doi: 10.1002/cne.1092
Brombas, A., Fletcher, L. N. & Williams, S. R. Activity-dependent modulation of layer 1 inhibitory neocortical circuits by acetylcholine. J. Neurosci. 34, 1932–1941 (2014).
pubmed: 24478372
pmcid: 6827591
doi: 10.1523/JNEUROSCI.4470-13.2014
Herrero, J. L., Gieselmann, M. A. & Thiele, A. Muscarinic and nicotinic contribution to contrast sensitivity of macaque area V1 neurons. Front. Neural Circuits 11, 106 (2017).
Soma, S., Shimegi, S., Osaki, H. & Sato, H. Cholinergic modulation of response gain in the primary visual cortex of the macaque. J. Neurophysiol. 107, 283–291 (2012).
pubmed: 21994270
doi: 10.1152/jn.00330.2011
Croxson, P. L., Kyriazis, D. A. & Baxter, M. G. Cholinergic modulation of a specific memory function of prefrontal cortex. Nat. Neurosci. 14, 1510–1512 (2011).
pubmed: 22057191
pmcid: 3432567
doi: 10.1038/nn.2971
Saunders, A. et al. A direct GABAergic output from the basal ganglia to frontal cortex. Nature 521, 85–89 (2015).
pubmed: 25739505
pmcid: 4425585
doi: 10.1038/nature14179
Strick, P. L. & Sterling, P. Synaptic termination of afferents from the ventrolateral nucleus of the thalamus in the cat motor cortex. A light and electron microscopy study. J. Comp. Neurol. 153, 77–106 (1974).
pubmed: 4817346
doi: 10.1002/cne.901530107
Roe, A. W., Chernov, M. M., Friedman, R. M. & Chen, G. In vivo mapping of cortical columnar networks in the monkey with focal electrical and optical stimulation. Front. Neuroanat. 9, 135 (2015).
pubmed: 26635539
pmcid: 4644798
doi: 10.3389/fnana.2015.00135
Grewe, B. F. & Helmchen, F. Optical probing of neuronal ensemble activity. Curr. Opin. Neurobiol. 19, 520–139 (2009).
pubmed: 19854041
doi: 10.1016/j.conb.2009.09.003
Peron, S., Chen, T.-W. & Svoboda, K. Comprehensive imaging of cortical networks. Curr. Opin. Neurobiol. 32, 115–123 (2015).
pubmed: 25880117
doi: 10.1016/j.conb.2015.03.016
Barthó, P. et al. Characterization of neocortical principal cells and interneurons by network interactions and extracellular features. J. Neurophysiol. 92, 600–608 (2004).
pubmed: 15056678
doi: 10.1152/jn.01170.2003
Kaufman, M. T. et al. Roles of monkey premotor neuron classes in movement preparation and execution. J. Neurophysiol. 104, 799–810 (2010).
pubmed: 20538784
pmcid: 2934936
doi: 10.1152/jn.00231.2009
Kaufman, M. T., Churchland, M. M. & Shenoy, K. V. The roles of monkey M1 neuron classes in movement preparation and execution. J. Neurophysiol. 110, 817–825 (2013).
pubmed: 23699057
pmcid: 3742981
doi: 10.1152/jn.00892.2011
Jia, X. et al. High-density extracellular probes reveal dendritic backpropagation and facilitate neuron classification. J. Neurophysiol. 121, 1831–1847 (2019).
pubmed: 30840526
doi: 10.1152/jn.00680.2018
Steinmetz, N. A., Koch, C., Harris, K. D. & Carandini, M. Challenges and opportunities for large-scale electrophysiology with Neuropixels probes. Curr. Opin. Neurobiol. 50, 92–100 (2018).
pubmed: 29444488
pmcid: 5999351
doi: 10.1016/j.conb.2018.01.009
Krienen, F. M. et al. Innovations in primate interneuron repertoire. bioRxiv https://doi.org/10.1101/709501 . (2019).
Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).
pubmed: 30382198
pmcid: 6456269
doi: 10.1038/s41586-018-0654-5
Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).
pubmed: 29930089
pmcid: 6339868
doi: 10.1126/science.aat5691
Hsueh, B. et al. Pathways to clinical CLARITY: volumetric analysis of irregular, soft, and heterogeneous tissues in development and disease. Sci. Rep. 7, 1–16 (2017).
doi: 10.1038/s41598-017-05614-4
Economo, M. N. et al. Distinct descending motor cortex pathways and their roles in movement. Nature 563, 79–84 (2018).
pubmed: 30382200
doi: 10.1038/s41586-018-0642-9
Li, N., Chen, T., Guo, Z. V., Gerfen, C. R. & Svoboda, K. A motor cortex circuit for motor planning and movement. Nature 519, 51–56 (2015).
pubmed: 25731172
doi: 10.1038/nature14178
Perich, M. G., Gallego, J. A. & Miller, L. E. A neural population mechanism for rapid learning. Neuron 100, 964–976 (2018).
pubmed: 30344047
pmcid: 6250582
doi: 10.1016/j.neuron.2018.09.030
Chestek, C. A. et al. Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex. J. Neural Eng. 8, 045005 (2011).
pubmed: 21775782
pmcid: 3644617
doi: 10.1088/1741-2560/8/4/045005
Gallego, J. A., Perich, M. G., Chowdhury, R. H., Solla, S. A. & Miller, L. E. Long-term stability of cortical population dynamics underlying consistent behavior. Nat. Neurosci. 23, 260–270 (2020).
Sussillo, D., Stavisky, S. D., Kao, J. C., Ryu, S. I. & Shenoy, K. V. Making brain-machine interfaces robust to future neural variability. Nat. Commun. 7, 13749 (2016).
pubmed: 27958268
pmcid: 5159828
doi: 10.1038/ncomms13749
Golub, M. D. et al. Learning by neural reassociation. Nat. Neurosci. 21, 607–616 (2018).
pubmed: 29531364
pmcid: 5876156
doi: 10.1038/s41593-018-0095-3
Oby, E. R. et al. New neural activity patterns emerge with long-term learning. Proc. Natl Acad. Sci. USA 116, 15210–15215 (2019).
pubmed: 31182595
doi: 10.1073/pnas.1820296116
pmcid: 6660765
Orsborn, A. L. et al. Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control. Neuron 82, 1380–1393 (2014).
pubmed: 24945777
doi: 10.1016/j.neuron.2014.04.048
Green, A. M. & Kalaska, J. F. Learning to move machines with the mind. Trends Neurosci. 34, 61–75 (2011).
pubmed: 21176975
doi: 10.1016/j.tins.2010.11.003
Ganguly, K. & Carmena, J. M. Emergence of a stable cortical map for neuroprosthetic control. PLoS Biol. 7, e1000153 (2009).
pubmed: 19621062
pmcid: 2702684
doi: 10.1371/journal.pbio.1000153
Santhanam, G. et al. HermesB: a continuous neural recording system for freely behaving primates. IEEE Trans. Biomed. Eng. 54, 2037–2050 (2007).
pubmed: 18018699
doi: 10.1109/TBME.2007.895753
Tolias, A. S. et al. Recording chronically from the same neurons in awake, behaving primates. J. Neurophysiol. 98, 3780–3790 (2007).
pubmed: 17942615
doi: 10.1152/jn.00260.2007
Stevenson, I. H. et al. Statistical assessment of the stability of neural movement representations. J. Neurophysiol. 106, 764–774 (2011).
pubmed: 21613593
pmcid: 3154833
doi: 10.1152/jn.00626.2010
Fraser, G. W. & Schwartz, A. B. Recording from the same neurons chronically in motor cortex. J. Neurophysiol. 107, 1970–1978 (2012).
pubmed: 22190623
doi: 10.1152/jn.01012.2010
Masamizu, Y. et al. Two distinct layer-specific dynamics of cortical ensembles during learning of a motor task. Nat. Neurosci. 17, 987–994 (2014).
pubmed: 24880217
doi: 10.1038/nn.3739
Pandarinath, C. et al. Inferring single-trial neural population dynamics using sequential auto-encoders. Nat. Methods 15, 805–815 (2018).
pubmed: 30224673
pmcid: 6380887
doi: 10.1038/s41592-018-0109-9
Chen, S. X., Kim, A. N., Peters, A. J. & Komiyama, T. Subtype-specific plasticity of inhibitory circuits in motor cortex during motor learning. Nat. Neurosci. 18, 1109–1115 (2015).
pubmed: 26098758
pmcid: 4519436
doi: 10.1038/nn.4049
Driscoll, L. N., Pettit, N. L., Minderer, M., Chettih, S. N. & Harvey, C. D. Dynamic reorganization of neuronal activity patterns in parietal cortex. Cell 170, 986–999 (2017).
pubmed: 28823559
pmcid: 5718200
doi: 10.1016/j.cell.2017.07.021
Huber, D., Gutnisky, D. & Peron, S. Multiple dynamic representations in the motor cortex during sensorimotor learning. Nature 484, 473–478 (2012).
pubmed: 22538608
pmcid: 4601999
doi: 10.1038/nature11039
Margolis, D. J. et al. Reorganization of cortical population activity imaged throughout long-term sensory deprivation. Nat. Neurosci. 15, 1539–1546 (2012).
pubmed: 23086335
doi: 10.1038/nn.3240
Peters, A. J., Chen, S. X. & Komiyama, T. Emergence of reproducible spatiotemporal activity during motor learning. Nature 510, 263–267 (2014).
pubmed: 24805237
doi: 10.1038/nature13235
Ziv, Y. et al. Long-term dynamics of CA1 hippocampal place codes. Nat. Neurosci. 16, 264–266 (2013).
pubmed: 23396101
pmcid: 3784308
doi: 10.1038/nn.3329
Sun, X., Kao, J. C., Marshel, J. H., Ryu, S. I. & Shenoy, K. V. Feasibility analysis of genetically-encoded calcium indicators as a neural signal source for all-optical brain-machine interfaces. In 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER) 174–180 (IEEE, 2017).
Helassa, N., Podor, B., Fine, A. & Török, K. Design and mechanistic insight into ultrafast calcium indicators for monitoring intracellular calcium dynamics. Sci Rep 6, 1–14 (2016).
Deisseroth, K. & Schnitzer, M. J. Engineering approaches to illuminating brain structure and dynamics. Neuron 80, 568–577 (2013).
pubmed: 24183010
pmcid: 5731466
doi: 10.1016/j.neuron.2013.10.032
Chen, T.-W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).
pubmed: 23868258
pmcid: 3777791
doi: 10.1038/nature12354
Emiliani, V., Cohen, A. E., Deisseroth, K. & Hausser, M. All-optical interrogation of neural circuits. J. Neurosci. 35, 13917–13926 (2015).
pubmed: 26468193
pmcid: 4604230
doi: 10.1523/JNEUROSCI.2916-15.2015
Galvan, A. et al. Nonhuman primate optogenetics: recent advances and future directions. J. Neurosci. 37, 10894–10903 (2017).
pubmed: 29118219
pmcid: 5678022
doi: 10.1523/JNEUROSCI.1839-17.2017
Watakabe, A. et al. Comparative analyses of adeno-associated viral vector serotypes 1, 2, 5, 8 and 9 in marmoset, mouse and macaque cerebral cortex. Neurosci. Res. 93, 144–157 (2015).
pubmed: 25240284
doi: 10.1016/j.neures.2014.09.002
Kotterman, M. A. et al. Antibody neutralization poses a barrier to intravitreal adeno-associated viral vector gene delivery to non-human primates. Gene Ther. 22, 116–126 (2014).
pubmed: 25503696
pmcid: 4393652
doi: 10.1038/gt.2014.115
Mendoza, S. D., El-Shamayleh, Y. & Horwitz, G. D. AAV-mediated delivery of optogenetic constructs to the macaque brain triggers humoral immune responses. J. Neurophysiol. 117, 2004–2013 (2017).
pubmed: 28202570
pmcid: 5411474
doi: 10.1152/jn.00780.2016
Churchland, M. M., Afshar, A. & Shenoy, K. V. A central source of movement variability. Neuron 52, 1085–1096 (2006).
pubmed: 17178410
pmcid: 1941679
doi: 10.1016/j.neuron.2006.10.034
Pedregosa, F. et al. Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Pnevmatikakis, E. A. & Giovannucci, A. NoRMCorre: an online algorithm for piecewise rigid motion correction of calcium imaging data. J. Neurosci. Methods 291, 83–94 (2017).
pubmed: 28782629
doi: 10.1016/j.jneumeth.2017.07.031
O’Shea, D., Trautmann, E., Sun, X., Deisseroth, K. & Shenoy, K. Code repository for dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface. github:djoshea/obci v1.0 https://doi.org/10.5281/zenodo.4702559. (2021).
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
pubmed: 32015543
pmcid: 7056644
doi: 10.1038/s41592-019-0686-2
Kovesi, P. D. MATLAB and Octave functions for computer vision and image processing. Cent. Explor. Target. Sch. Earth Environ. 147, 230 (2000).