The past decade has witnessed remarkable advances in the simultaneous measurement of neuronal activity across many brain regions, enabling fundamentally new explorations of the brain-spanning cellular dynamics that underlie sensation, cognition and action. These recently developed multiregion recording techniques have provided many experimental opportunities, but thoughtful consideration of methodological trade-offs is necessary, especially regarding field of view, temporal acquisition rate and ability to guarantee cellular resolution. When applied in concert with modern optogenetic and computational tools, multiregion recording has already made possible fundamental biological discoveries — in part via the unprecedented ability to perform unbiased neural activity screens for principles of brain function, spanning dozens of brain areas and from local to global scales.
This is a preview of subscription content, access via your institution
Subscribe to Nature+
Get immediate online access to Nature and 55 other Nature journal
Subscribe to Journal
Get full journal access for 1 year
only $6.58 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
The Event Horizon Telescope Collaboration. et al. First M87 event horizon telescope results. I. The shadow of the supermassive black hole. Astrophys. J. Lett. 875, L1 (2019).
Galilei, G. Sidereus Nuncius (Univ. Chicago Press, 1610).
Adrian, E. D. The Basis of Sensation (WW Norton & Co, 1928).
Brock, L. G., Coombs, J. S. & Eccles, J. C. The recording of potentials from motoneurones with an intracellular electrode. J. Physiol. 117, 431–460 (1952).
Woodbury, J. W. & Patton, H. D. in Cold Spring Harbor Symposia on Quantitative Biology vol. 17, 185–188 (Cold Spring Harbor Laboratory Press, 1952).
Ren, C. & Komiyama, T. Characterizing cortex-wide dynamics with wide-field calcium imaging. J. Neurosci. 41, 4160–4168 (2021).
Kim, T. H. & Schnitzer, M. J. Fluorescence imaging of large-scale neural ensemble dynamics. Cell 185, 9–41 (2022).
Urai, A. E., Doiron, B., Leifer, A. M. & Churchland, A. K. Large-scale neural recordings call for new insights to link brain and behavior. Nat. Neurosci. 25, 11–19 (2022).
Siegel, M., Buschman, T. J. & Miller, E. K. Cortical information flow during flexible sensorimotor decisions. Science 348, 1352–1355 (2015).
Hernández, A. et al. Procedure for recording the simultaneous activity of single neurons distributed across cortical areas during sensory discrimination. Proc. Natl Acad. Sci. USA 105, 16785–16790 (2008).
Hernández, A. et al. Decoding a perceptual decision process across cortex. Neuron 66, 300–314 (2010).
Paulk, A. C. et al. Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex. Nat. Neurosci. 25, 252–263 (2022).
Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014). This study uses viral anterograde tracing in mice to systematically map mesoscale connectivity between brain regions and produce a foundational brain atlas.
Han, Y. et al. The logic of single-cell projections from visual cortex. Nature 556, 51–56 (2018).
Brown, C. E., Aminoltejari, K., Erb, H., Winship, I. R. & Murphy, T. H. In vivo voltage-sensitive dye imaging in adult mice reveals that somatosensory maps lost to stroke are replaced over weeks by new structural and functional circuits with prolonged modes of activation within both the peri-infarct zone and distant sites. J. Neurosci. 29, 1719–1734 (2009).
Ferezou, I. et al. Spatiotemporal dynamics of cortical sensorimotor integration in behaving mice. Neuron 56, 907–923 (2007).
Santos, L., Opris, I., Fuqua, J., Hampson, R. E. & Deadwyler, S. A. A novel tetrode microdrive for simultaneous multi-neuron recording from different regions of primate brain. J. Neurosci. Methods 205, 368–374 (2012).
Allen, W. E. et al. Thirst regulates motivated behavior through modulation of brainwide neural population dynamics. Science 364, eaav3932 (2019).
Engel, T. A. & Steinmetz, N. A. New perspectives on dimensionality and variability from large-scale cortical dynamics. Curr. Opin. Neurobiol. 58, 181–190 (2019).
Schneider, D. M. Reflections of action in sensory cortex. Curr. Opin. Neurobiol. 64, 53–59 (2020).
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).
Stringer, C. et al. Spontaneous behaviors drive multidimensional, brainwide activity. Science 364, eaav7893 (2019). This study uses eight simultaneously deployed Neuropixels probes to discover brainwide activity driven by spontaneous facial movements, including in the primary visual cortex.
Kauvar, I. V. et al. Cortical observation by synchronous multifocal optical sampling reveals widespread population encoding of actions. Neuron 107, 351–367.e19 (2020). This article introduces and applies COSMOS, a method for recording near cellular resolution activity at video rates from thousands of neuronal sources spanning the mouse dorsal cortex.
Schneider, D. M., Nelson, A. & Mooney, R. A synaptic and circuit basis for corollary discharge in the auditory cortex. Nature 513, 189–194 (2014).
Saxena, S. & Cunningham, J. P. Towards the neural population doctrine. Curr. Opin. Neurobiol. 55, 103–111 (2019).
Dong, H. W. The Allen Reference Atlas: A Digital Color Brain Atlas of the C57Bl/6J Male Mouse (Wiley, 2008).
Paxinos, G. & Franklin, K. B. J. Paxinos and Franklin’s the Mouse Brain in Stereotaxic Coordinates (Academic, 2019).
Swanson, L. Brain Maps: Structure of the Rat Brain (Gulf Professional Publishing, 2004).
Zingg, B. et al. Neural networks of the mouse neocortex. Cell https://doi.org/10.1016/j.cell.2014.02.023 (2014).
Jones, A. R., Overly, C. C. & Sunkin, S. M. The Allen Brain Atlas: 5 years and beyond. Nat. Rev. Neurosci. 10, 821–828 (2009).
Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168 (2007).
Wang, Q. et al. The Allen mouse brain common coordinate framework: a 3D reference atlas. Cell 181, 936–953.e20 (2020).
Siegle, J. H. et al. Survey of spiking in the mouse visual system reveals functional hierarchy. Nature 592, 86–92 (2021). This study uses six simultaneously deployed Neuropixels probes to establish the hierarchical nature of functional connectivity in the mouse visual system.
Steinmetz, N. A., Zatka-Haas, P., Carandini, M. & Harris, K. D. Distributed coding of choice, action and engagement across the mouse brain. Nature 576, 266–273 (2019).
Hafting, T., Fyhn, M., Molden, S., Moser, M.-B. & Moser, E. I. Microstructure of a spatial map in the entorhinal cortex. Nature 436, 801–806 (2005).
da Silva, J. A., Tecuapetla, F., Paixão, V. & Costa, R. M. Dopamine neuron activity before action initiation gates and invigorates future movements. Nature 554, 244–248 (2018).
Carus-Cadavieco, M. et al. Gamma oscillations organize top-down signalling to hypothalamus and enable food seeking. Nature 542, 232–236 (2017).
Senzai, Y., Fernandez-Ruiz, A. & Buzsáki, G. Layer-specific physiological features and interlaminar interactions in the primary visual cortex of the mouse. Neuron 101, 500–513.e5 (2019).
Couto, J. et al. Chronic, cortex-wide imaging of specific cell populations during behavior. Nat. Protoc. 16, 3241–3263 (2021).
Dockès, J. et al. NeuroQuery, comprehensive meta-analysis of human brain mapping. eLife 9, e53385 (2020).
Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C. & Wager, T. D. Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8, 665–670 (2011).
Kim, C. K., Adhikari, A. & Deisseroth, K. Integration of optogenetics with complementary methodologies in systems neuroscience. Nat. Rev. Neurosci. 18, 222–235 (2017).
Tervo, D. G. R. et al. A designer AAV variant permits efficient retrograde access to projection neurons. Neuron 92, 372–382 (2016).
Fenno, L. E. et al. Targeting cells with single vectors using multiple-feature Boolean logic. Nat. Methods 11, 763–772 (2014).
Andalman, A. S. et al. Neuronal dynamics regulating brain and behavioral state transitions. Cell 177, 970–985.e20 (2019).
Lovett-Barron, M. et al. Ancestral circuits for the coordinated modulation of brain state. Cell 171, 1411–1423.e17 (2017).
Xu, S. et al. Behavioral state coding by molecularly defined paraventricular hypothalamic cell type ensembles. Science 370, eabb2494 (2020).
Allen, W. E. et al. Global representations of goal-directed behavior in distinct cell types of mouse neocortex. Neuron 94, 891–907.e6 (2017).
Chan, K. Y. et al. Engineered AAVs for efficient noninvasive gene delivery to the central and peripheral nervous systems. Nat. Neurosci. 20, 1172–1179 (2017).
Lima, S. Q., Hromádka, T., Znamenskiy, P. & Zador, A. M. PINP: a new method of tagging neuronal populations for identification during in vivo electrophysiological recording. PLoS ONE 4, e6099 (2009).
Wolff, S. B. E. et al. Amygdala interneuron subtypes control fear learning through disinhibition. Nature 509, 453–458 (2014).
Cohen, J. Y., Haesler, S., Vong, L., Lowell, B. B. & Uchida, N. Neuron-type-specific signals for reward and punishment in the ventral tegmental area. Nature 482, 85–88 (2012).
Herrera, C. G. et al. Hypothalamic feedforward inhibition of thalamocortical network controls arousal and consciousness. Nat. Neurosci. 19, 290–298 (2016).
Juavinett, A. L., Bekheet, G. & Churchland, A. K. Chronically implanted Neuropixels probes enable high-yield recordings in freely moving mice. eLife 8, e47188 (2019).
Schoonover, C. E., Ohashi, S. N., Axel, R. & Fink, A. J. P. Representational drift in primary olfactory cortex. Nature 594, 541–546 (2021).
Steinmetz, N. A. et al. Neuropixels 2.0: a miniaturized high-density probe for stable, long-term brain recordings. Science 372, eabf4588 (2021).
Chung, J. E. et al. High-density, long-lasting, and multi-region electrophysiological recordings using polymer electrode arrays. Neuron 101, 21–31.e5 (2019).
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).
Grinvald, A. & Hildesheim, R. VSDI: a new era in functional imaging of cortical dynamics. Nat. Rev. Neurosci. 5, 874–885 (2004).
Mohajerani, M. H. et al. Spontaneous cortical activity alternates between motifs defined by regional axonal projections. Nat. Neurosci. 16, 1426–1435 (2013).
Dana, H. et al. Thy1-GCaMP6 transgenic mice for neuronal population imaging in vivo. PLoS ONE 9, e108697 (2014).
Madisen, L. et al. Transgenic mice for intersectional targeting of neural sensors and effectors with high specificity and performance. Neuron 85, 942–958 (2015).
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).
Deverman, B. E. et al. Cre-dependent selection yields AAV variants for widespread gene transfer to the adult brain. Nat. Biotechnol. 34, 204–209 (2016).
Guo, Z. V. et al. Flow of cortical activity underlying a tactile decision in mice. Neuron 81, 179–194 (2014).
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).
Kim, C. K. et al. Simultaneous fast measurement of circuit dynamics at multiple sites across the mammalian brain. Nat. Methods 13, 325–328 (2016). This article introduces a technique for simultaneously recording cell type-specific neural activity from seven regions throughout the brain.
Valley, M. T. et al. Separation of hemodynamic signals from GCaMP fluorescence measured with wide-field imaging. J. Neurophysiol. 123, 356–366 (2020).
Daigle, T. L. et al. A suite of transgenic driver and reporter mouse lines with enhanced brain-cell-type targeting and functionality. Cell 174, 465–480.e22 (2018).
Gerfen, C. R., Paletzki, R. & Heintz, N. GENSAT BAC Cre-recombinase driver lines to study the functional organization of cerebral cortical and basal ganglia circuits. Neuron 80, 1368–1383 (2013).
Matho, K. S. et al. Genetic dissection of the glutamatergic neuron system in cerebral cortex. Nature 598, 182–187 (2021).
Callaway, E. M. et al. A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 598, 86–102 (2021).
Fenno, L. E. et al. Comprehensive dual- and triple-feature intersectional single-vector delivery of diverse functional payloads to cells of behaving mammals. Neuron 107, 836–853.e11 (2020).
Harris, J. et al. Anatomical characterization of Cre driver mice for neural circuit mapping and manipulation. Front. Neural Circuits https://doi.org/10.3389/fncir.2014.00076 (2014).
Waters, J. Sources of widefield fluorescence from the brain. eLife 9, e59841 (2020).
Lohani, S. et al. Dual color mesoscopic imaging reveals spatiotemporally heterogeneous coordination of cholinergic and neocortical activity. Preprint at bioRxiv https://doi.org/10.1101/2020.12.09.418632 (2020).
Sabatini, B. L. & Tian, L. Imaging neurotransmitter and neuromodulator dynamics in vivo with genetically encoded indicators. Neuron 108, 17–32 (2020).
Shen, Y., Nasu, Y., Shkolnikov, I., Kim, A. & Campbell, R. E. Engineering genetically encoded fluorescent indicators for imaging of neuronal activity: progress and prospects. Neurosci. Res. 152, 3–14 (2020).
Steinmetz, N. A. et al. Aberrant cortical activity in multiple GCaMP6-expressing transgenic mouse lines. eNeuro 4, ENEURO.0207-17.2017 (2017).
Heffley, W. et al. Coordinated cerebellar climbing fiber activity signals learned sensorimotor predictions. Nat. Neurosci. 21, 1431–1441 (2018).
Ackman, J. B., Burbridge, T. J. & Crair, M. C. Retinal waves coordinate patterned activity throughout the developing visual system. Nature 490, 219–225 (2012).
Li, Y., Turan, Z. & Meister, M. Functional architecture of motion direction in the mouse superior colliculus. Curr. Biol. 30, 3304–3315.e4 (2020).
Scott, B. B. et al. Imaging cortical dynamics in GCaMP transgenic rats with a head-mounted widefield macroscope. Neuron 100, 1045–1058.e5 (2018).
Rynes, M. L. et al. Miniaturized head-mounted microscope for whole-cortex mesoscale imaging in freely behaving mice. Nat. Methods 18, 417–425 (2021).
Lake, E. M. R. et al. Simultaneous cortex-wide fluorescence Ca2+ imaging and whole-brain fMRI. Nat. Methods 17, 1262–1271 (2020).
Murphy, T. H. et al. High-throughput automated home-cage mesoscopic functional imaging of mouse cortex. Nat. Commun. 7, 11611 (2016).
Murphy, T. H. et al. Automated task training and longitudinal monitoring of mouse mesoscale cortical circuits using home cages. eLife 9, e55964 (2020).
Kim, T. H. et al. Long-term optical access to an estimated one million neurons in the live mouse cortex. Cell Rep. 17, 3385–3394 (2016).
Ghanbari, L. et al. Cortex-wide neural interfacing via transparent polymer skulls. Nat. Commun. 10, 1500 (2019).
Freeman, J. et al. Mapping brain activity at scale with cluster computing. Nat. Methods 11, 941–950 (2014).
Kim, C. K. et al. Prolonged, brain-wide expression of nuclear-localized GCaMP3 for functional circuit mapping. Front. Neural Circuits 8, 138 (2014).
Vladimirov, N. et al. Light-sheet functional imaging in fictively behaving zebrafish. Nat. Methods 11, 883–884 (2014).
Chen, Y. et al. Soma-targeted imaging of neural circuits by ribosome tethering. Neuron 107, 454–469.e6 (2020).
Shemesh, O. A. et al. Precision calcium imaging of dense neural populations via a cell-body-targeted calcium indicator. Neuron 107, 470–486.e11 (2020).
Lim, S. T., Antonucci, D. E., Scannevin, R. H. & Trimmer, J. S. A novel targeting signal for proximal clustering of the Kv2.1 K+ channel in hippocampal neurons. Neuron 25, 385–397 (2000).
Cramer, S. W. et al. Through the looking glass: a review of cranial window technology for optical access to the brain. J. Neurosci. Methods 354, 109100 (2021).
Fan, J. et al. Video-rate imaging of biological dynamics at centimetre scale and micrometre resolution. Nat. Photonics 13, 809–816 (2019).
Broxton, M. et al. Wave optics theory and 3-D deconvolution for the light field microscope. Opt. Express 21, 25418–25439 (2013).
Nöbauer, T. et al. Video rate volumetric Ca2+ imaging across cortex using seeded iterative demixing (SID) microscopy. Nat. Methods 14, 811–818 (2017).
Voleti, V. et al. Real-time volumetric microscopy of in vivo dynamics and large-scale samples with SCAPE 2.0. Nat. Methods 16, 1054–1062 (2019).
Chang, C.-P. (Jonathan) & Holy, T. E. in Optical Techniques in Neurosurgery, Neurophotonics, and Optogenetics vol. 11629, 20–28 (SPIE, 2021).
Kumar, M., Kishore, S., Nasenbeny, J., McLean, D. L. & Kozorovitskiy, Y. Integrated one- and two-photon scanned oblique plane illumination (SOPi) microscopy for rapid volumetric imaging. Opt. Express 26, 13027–13041 (2018).
Xue, Y., Davison, I. G., Boas, D. A. & Tian, L. Single-shot 3D wide-field fluorescence imaging with a Computational Miniature Mesoscope. Sci. Adv. 6, eabb7508 (2020).
Ebrahimi, S. et al. Emergent reliability in sensory cortical coding and inter-area communication. Nature https://doi.org/10.1038/s41586-022-04724-y (2022).
Gunaydin, L. A. et al. Natural neural projection dynamics underlying social behavior. Cell 157, 1535–1551 (2014).
Cui, G. et al. Concurrent activation of striatal direct and indirect pathways during action initiation. Nature 494, 238–242 (2013).
Lütcke, H. et al. Optical recording of neuronal activity with a genetically-encoded calcium indicator in anesthetized and freely moving mice. Front. Neural Circuits 4, 9 (2010).
Schulz, K. et al. Simultaneous BOLD fMRI and fiber-optic calcium recording in rat neocortex. Nat. Methods 9, 597–602 (2012).
Stroh, A. et al. Making waves: initiation and propagation of corticothalamic Ca2+ waves in vivo. Neuron 77, 1136–1150 (2013).
Pisanello, M. et al. The three-dimensional signal collection field for fiber photometry in brain tissue. Front. Neurosci. 13, 82 (2019).
Marshall, J. D. et al. Cell-type-specific optical recording of membrane voltage dynamics in freely moving mice. Cell 167, 1650–1662.e15 (2016).
Pisano, F. et al. Depth-resolved fiber photometry with a single tapered optical fiber implant. Nat. Methods 16, 1185–1192 (2019).
Sych, Y., Chernysheva, M., Sumanovski, L. T. & Helmchen, F. High-density multi-fiber photometry for studying large-scale brain circuit dynamics. Nat. Methods 16, 553–560 (2019).
Pnevmatikakis, E. A. et al. Simultaneous denoising, deconvolution, and demixing of calcium imaging data. Neuron 89, 285–299 (2016).
Siegle, J. H. et al. Reconciling functional differences in populations of neurons recorded with two-photon imaging and electrophysiology. eLife 10, e69068 (2021).
Wei, Z. et al. A comparison of neuronal population dynamics measured with calcium imaging and electrophysiology. PLoS Comput. Biol. 16, e1008198 (2020).
Abdelfattah, A. S. et al. Bright and photostable chemigenetic indicators for extended in vivo voltage imaging. Science 365, 699–704 (2019).
Jin, L. et al. Single action potentials and subthreshold electrical events imaged in neurons with a fluorescent protein voltage probe. Neuron 75, 779–785 (2012).
Lin, M. Z. & Schnitzer, M. J. Genetically encoded indicators of neuronal activity. Nat. Neurosci. 19, 1142–1153 (2016).
Piatkevich, K. D. et al. Population imaging of neural activity in awake behaving mice. Nature 574, 413–417 (2019).
Xu, Y., Zou, P. & Cohen, A. E. Voltage imaging with genetically encoded indicators. Curr. Opin. Chem. Biol. 39, 1–10 (2017).
Villette, V. et al. Ultrafast two-photon imaging of a high-gain voltage indicator in awake behaving mice. Cell 179, 1590–1608.e23 (2019).
Adam, Y. et al. Voltage imaging and optogenetics reveal behaviour-dependent changes in hippocampal dynamics. Nature 569, 413–417 (2019).
Fan, L. Z. et al. All-optical synaptic electrophysiology probes mechanism of ketamine-induced disinhibition. Nat. Methods 15, 823–831 (2018).
Fan, L. Z. et al. All-optical electrophysiology reveals the role of lateral inhibition in sensory processing in cortical layer 1. Cell 180, 521–535.e18 (2020).
Hochbaum, D. R. et al. All-optical electrophysiology in mammalian neurons using engineered microbial rhodopsins. Nat. Methods 11, 825–833 (2014).
Piatkevich, K. D. et al. A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters. Nat. Chem. Biol. 14, 352–360 (2018).
Marshel, J. H. et al. Cortical layer–specific critical dynamics triggering perception. Science 365, eaaw5202 (2019).
Wu, J. et al. Kilohertz two-photon fluorescence microscopy imaging of neural activity in vivo. Nat. Methods 17, 287–290 (2020).
Carandini, M. et al. Imaging the awake visual cortex with a genetically encoded voltage indicator. J. Neurosci. 35, 53–63 (2015).
Platisa, J. et al. Voltage imaging using transgenic mouse lines expressing the GEVI ArcLight in two olfactory cell types. Preprint at bioRxiv https://doi.org/10.1101/2020.08.26.268904 (2020).
Platisa, J. et al. High-speed low-light in vivo two-photon voltage imaging of large neuronal populations. Preprint at bioRxiv https://doi.org/10.1101/2021.12.07.471668 (2021).
Gottschalk, S. et al. Rapid volumetric optoacoustic imaging of neural dynamics across the mouse brain. Nat. Biomed. Eng. 3, 392–401 (2019).
Rabut, C. et al. Ultrasound technologies for imaging and modulating neural activity. Neuron 108, 93–110 (2020).
Helmchen, F. & Denk, W. Deep tissue two-photon microscopy. Nat. Methods 2, 932–940 (2005).
Lecoq, J. et al. Visualizing mammalian brain area interactions by dual-axis two-photon calcium imaging. Nat. Neurosci. 17, 1825–1829 (2014).
Wagner, M. J. et al. Shared cortex-cerebellum dynamics in the execution and learning of a motor task. Cell 177, 669–682.e24 (2019).
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).
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, 857–862 (2016).
Chen, J. L., Voigt, F. F., Javadzadeh, M., Krueppel, R. & Helmchen, F. Long-range population dynamics of anatomically defined neocortical networks. eLife 5, e14679 (2016).
Condylis, C. et al. Context-dependent sensory processing across primary and secondary somatosensory cortex. Neuron 106, 515–525.e5 (2020).
Yu, C.-H., Stirman, J. N., Yu, Y., Hira, R. & Smith, S. L. Diesel2p mesoscope with dual independent scan engines for flexible capture of dynamics in distributed neural circuitry. Nat. Commun. 12, 6639 (2021).
Clough, M. et al. Flexible simultaneous mesoscale two-photon imaging of neural activity at high speeds. Nat. Commun. 12, 6638 (2021).
Lu, R. et al. Rapid mesoscale volumetric imaging of neural activity with synaptic resolution. Nat. Methods 17, 291–294 (2020).
Demas, J. et al. High-speed, cortex-wide volumetric recording of neuroactivity at cellular resolution using light beads microscopy. Nat. Methods 18, 1103–1111 (2021). This article introduces light beads microscopy, a two-photon method that enables cellular resolution imaging from hundreds of thousands of neurons at rates of a few hertz.
Rumyantsev, O. I. et al. Fundamental bounds on the fidelity of sensory cortical coding. Nature 580, 100–105 (2020).
Zhang, T. et al. Kilohertz two-photon brain imaging in awake mice. Nat. Methods 16, 1119–1122 (2019).
Yang, S. J. et al. Extended field-of-view and increased-signal 3D holographic illumination with time-division multiplexing. Opt. Express 23, 32573 (2015).
Barson, D. et al. Simultaneous mesoscopic and two-photon imaging of neuronal activity in cortical circuits. Nat. Methods 17, 107–113 (2020).
Zong, W. et al. Large-scale two-photon calcium imaging in freely moving mice. Cell 185, 1240–1256.e30 (2021).
Jun, J. J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature 551, 232–236 (2017).
Voigts, J., Newman, J. P., Wilson, M. A. & Harnett, M. T. An easy-to-assemble, robust, and lightweight drive implant for chronic tetrode recordings in freely moving animals. J. Neural Eng. 17, 026044 (2020).
Varol, E. et al. Decentralized motion inference and registration of neuropixel data. In ICASSP 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 1085–1089 (IEEE, 2021).
Luo, T. Z. et al. An approach for long-term, multi-probe Neuropixels recordings in unrestrained rats. eLife 9, e59716 (2020).
Jensen, K. H. R. & Berg, R. W. CLARITY-compatible lipophilic dyes for electrode marking and neuronal tracing. Sci. Rep. 6, 32674 (2016).
Vázquez-Guardado, A., Yang, Y., Bandodkar, A. J. & Rogers, J. A. Recent advances in neurotechnologies with broad potential for neuroscience research. Nat. Neurosci. 23, 1522–1536 (2020).
Wang, X. et al. A parylene neural probe array for multi-region deep brain recordings. J. Microelectromech. Syst. 29, 499–513 (2020).
Liu, X. et al. Multimodal neural recordings with Neuro-FITM uncover diverse patterns of cortical–hippocampal interactions. Nat. Neurosci. 24, 886–896 (2021).
Clancy, K. B., Orsolic, I. & Mrsic-Flogel, T. D. Locomotion-dependent remapping of distributed cortical networks. Nat. Neurosci. 22, 778–786 (2019). In this study, the authors simultaneously use single-neuron recordings and OEG to describe how locomotion influences the relationship between single-neuron firing and cortex-wide activity patterns.
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).
Kleinfeld, D. et al. Can one concurrently record electrical spikes from every neuron in a mammalian brain? Neuron 103, 1005–1015 (2019).
Trautmann, E. et al. Accurate estimation of neural population dynamics without spike sorting. Neuron 103, 292–308.e4 (2019). This article illustrates how many popular population-level analyses of neural activity can successfully be applied to multi-unit electrophysiology data that lack single-cell resolution.
Ecker, A. S. et al. Decorrelated neuronal firing in cortical microcircuits. Science 327, 584–587 (2010).
Renart, A. et al. The asynchronous state in cortical circuits. Science 327, 587–590 (2010).
Dacre, J. et al. A cerebellar-thalamocortical pathway drives behavioral context-dependent movement initiation. Neuron 109, 2326–2338.e8 (2021).
Wang, W. et al. Coordination of escape and spatial navigation circuits orchestrates versatile flight from threats. Neuron 109, 1848–1860.e8 (2021).
Adrian, E. D. The impulses produced by sensory nerve endings. J. Physiol. 61, 49–72 (1926).
Sherrington, C. The Integrative Action of the Nervous System (Charles Scribner’s Sons, 1906).
Hubel, D. H. & Wiesel, T. N. Receptive fields of single neurones in the cat’s striate cortex. J. Physiol. 148, 574–591 (1959).
Chichilnisky, E. J. A simple white noise analysis of neuronal light responses. Netw. Comput. Neural Syst. 12, 199–213 (2001).
Schwartz, O., Pillow, J. W., Rust, N. C. & Simoncelli, E. P. Spike-triggered neural characterization. J. Vis. 6, 13–13 (2006).
Pillow, J. W. et al. Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature 454, 995–999 (2008).
Truccolo, W., Eden, U. T., Fellows, M. R., Donoghue, J. P. & Brown, E. N. A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. J. Neurophysiol. 93, 1074–1089 (2005).
Kang, B. & Druckmann, S. Approaches to inferring multi-regional interactions from simultaneous population recordings. Curr. Opin. Neurobiol. 65, 108–119 (2020).
Keeley, S. L., Zoltowski, D. M., Aoi, M. C. & Pillow, J. W. Modeling statistical dependencies in multi-region spike train data. Curr. Opin. Neurobiol. 65, 194–202 (2020).
Yates, J. L., Park, I. M., Katz, L. N., Pillow, J. W. & Huk, A. C. Functional dissection of signal and noise in MT and LIP during decision-making. Nat. Neurosci. 20, 1285–1292 (2017).
Benjamin, A. S. et al. Modern machine learning as a benchmark for fitting neural responses. Front. Comput. Neurosci. 12, 56 (2018).
Linderman, S., Adams, R. P. & Pillow, J. W. Bayesian latent structure discovery from multi-neuron recordings. in Advances in Neural Information Processing Systems Vol. 29 (NIPS, 2016).
Pedregosa, F. et al. Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Vesuna, S. et al. Deep posteromedial cortical rhythm in dissociation. Nature 586, 87–94 (2020). Using multiregion recording techniques, the authors reveal a unique oscillatory firing pattern in the retrosplenial cortex that relates to a dissociation-like behavioural state.
Harris, K. D. Nonsense correlations in neuroscience. Preprint at bioRxiv https://doi.org/10.1101/2020.11.29.402719 (2021).
Meijer, G. Neurons in the mouse brain correlate with cryptocurrency price: a cautionary tale. Preprint at PsyArXiv https://doi.org/10.31234/osf.io/fa4wz (2021).
Zagha, E. et al. The importance of accounting for movement when relating neuronal activity to sensory and cognitive processes. J. Neurosci. 42, 1375–1382 (2022).
Cunningham, J. P. & Yu, B. M. Dimensionality reduction for large-scale neural recordings. Nat. Neurosci. 17, 1500–1509 (2014).
Churchland, M. M. et al. Neural population dynamics during reaching. Nature 487, 51–56 (2012).
Gao, P. et al. A theory of multineuronal dimensionality, dynamics and measurement. Preprint at bioRxiv https://doi.org/10.1101/214262 (2017).
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).
Yu, B. M. et al. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. J. Neurophysiol. 102, 614–635 (2009).
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).
Linderman, S. et al. Bayesian learning and inference in recurrent switching linear dynamical systems. in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics 914–922 (PMLR, 2017).
Linderman, S., Nichols, A., Blei, D., Zimmer, M. & Paninski, L. Hierarchical recurrent state space models reveal discrete and continuous dynamics of neural activity in C. elegans. Preprint at bioRxiv https://doi.org/10.1101/621540 (2019).
Humphries, M. D. Strong and weak principles of neural dimension reduction. Preprint at https://doi.org/10.48550/arXiv.2011.08088 (2021).
Gollisch, T. & Meister, M. Rapid neural coding in the retina with relative spike latencies. Science 319, 1108–1111 (2008).
Maimon, G. & Assad, J. A. Beyond Poisson: increased spike-time regularity across primate parietal cortex. Neuron 62, 426–440 (2009).
Vyas, S., Golub, M. D., Sussillo, D. & Shenoy, K. V. Computation through neural population dynamics. Annu. Rev. Neurosci. 43, 249–275 (2020).
Ames, K. C., Ryu, S. I. & Shenoy, K. V. Neural dynamics of reaching following incorrect or absent motor preparation. Neuron 81, 438–451 (2014).
Elsayed, G. F. & Cunningham, J. P. Structure in neural population recordings: an expected byproduct of simpler phenomena? Nat. Neurosci. 20, 1310–1318 (2017). In this study, the authors develop a statistical framework for testing whether population-level structure in neuronal firing patterns is explainable by simpler features of single-neuron responses.
Li, N., Daie, K., Svoboda, K. & Druckmann, S. Robust neuronal dynamics in premotor cortex during motor planning. Nature 532, 459–464 (2016).
Geladi, P. & Kowalski, B. R. Partial least-squares regression: a tutorial. Anal. Chim. Acta 185, 1–17 (1986).
Remedios, R. et al. Social behaviour shapes hypothalamic neural ensemble representations of conspecific sex. Nature 550, 388–392 (2017).
Kobak, D. et al. Demixed principal component analysis of neural population data. eLife 5, 614–635 (2016).
Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).
Williams, A. H. et al. Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor component analysis. Neuron 98, 1099–1115.e8 (2018).
Sani, O. G., Abbaspourazad, H., Wong, Y. T., Pesaran, B. & Shanechi, M. M. Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification. Nat. Neurosci. 24, 140–149 (2021).
Sani, O. G., Pesaran, B. & Shanechi, M. M. Where is all the nonlinearity: flexible nonlinear modeling of behaviorally relevant neural dynamics using recurrent neural networks. Preprint at bioRxiv https://doi.org/10.1101/2021.09.03.458628 (2021).
Scangos, K. W., Makhoul, G. S., Sugrue, L. P., Chang, E. F. & Krystal, A. D. State-dependent responses to intracranial brain stimulation in a patient with depression. Nat. Med. 27, 229–231 (2021).
Semedo, J. D., Gokcen, E., Machens, C. K., Kohn, A. & Yu, B. M. Statistical methods for dissecting interactions between brain areas. Curr. Opin. Neurobiol. 65, 59–69 (2020).
Kohn, A. et al. Principles of corticocortical communication: proposed schemes and design considerations. Trends Neurosci. 43, 725–737 (2020).
Hahn, G., Ponce-Alvarez, A., Deco, G., Aertsen, A. & Kumar, A. Portraits of communication in neuronal networks. Nat. Rev. Neurosci. 20, 117–127 (2019).
Zandvakili, A. & Kohn, A. Coordinated neuronal activity enhances corticocortical communication. Neuron 87, 827–839 (2015).
Buzsáki, G. & Wang, X.-J. Mechanisms of gamma oscillations. Annu. Rev. Neurosci. 35, 203–225 (2012).
Sohal, V. S., Zhang, F., Yizhar, O. & Deisseroth, K. Parvalbumin neurons and gamma rhythms enhance cortical circuit performance. Nature 459, 698–702 (2009).
Kaufman, M. T., Churchland, M. M., Ryu, S. I. & Shenoy, K. V. Cortical activity in the null space: permitting preparation without movement. Nat. Neurosci. 17, 440–448 (2014).
Semedo, J. D., Zandvakili, A., Machens, C. K., Yu, B. M. & Kohn, A. Cortical areas interact through a communication subspace. Neuron 102, 249–259.e4 (2019).
Keller, E. L. Participation of medial pontine reticular formation in eye movement generation in monkey. J. Neurophysiol. 37, 316–332 (1974).
Kupfermann, I. & Weiss, K. R. The command neuron concept. Behav. Brain Sci. 1, 3–10 (1978).
Darlington, T. R. & Lisberger, S. G. Mechanisms that allow cortical preparatory activity without inappropriate movement. eLife 9, e50962 (2020).
Bassett, D. S. & Sporns, O. Network neuroscience. Nat. Neurosci. 20, 353–364 (2017).
Bassett, D. S., Zurn, P. & Gold, J. I. On the nature and use of models in network neuroscience. Nat. Rev. Neurosci. 19, 566–578 (2018).
Xie, M. E. et al. High-fidelity estimates of spikes and subthreshold waveforms from 1-photon voltage imaging in vivo. Cell Rep. 35, 108954 (2021).
Keshtkaran, M. R. et al. A large-scale neural network training framework for generalized estimation of single-trial population dynamics. Preprint at bioRxiv https://doi.org/10.1101/2021.01.13.426570 (2021).
Pandarinath, C. et al. Latent factors and dynamics in motor cortex and their application to brain–machine interfaces. J. Neurosci. 38, 9390–9401 (2018).
Pandarinath, C. et al. Inferring single-trial neural population dynamics using sequential auto-encoders. Nat. Methods 15, 805–815 (2018).
Sylwestrak, E. L. et al. Cell type-specific population-dynamics of diverse reward computations. Cell 185, 3568–3587.e27 (2022). In this study, the authors collect multiregion neural recording data during a reward-seeking behaviour, model the data with an RNN model called LFADS and then use the model to describe how a population of genetically defined neurons in the medial habenula integrated reward history over time.
Aitken, K. et al. The geometry of integration in text classification RNNs. Preprint at https://doi.org/10.48550/arXiv.2010.15114 (2020).
Maheswaranathan, N., Williams, A. H., Golub, M. D., Ganguli, S. & Sussillo, D. Universality and individuality in neural dynamics across large populations of recurrent networks. Preprint at https://doi.org/10.48550/arXiv.1907.08549 (2019).
McIntosh, L., Maheswaranathan, N., Nayebi, A., Ganguli, S. & Baccus, S. Deep learning models of the retinal response to natural scenes. Adv. Neural Inf. Process. Syst. 29, 1369–1377 (2016).
Sussillo, D. & Barak, O. Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks. Neural Comput. 25, 626–649 (2013).
Sauerbrei, B. A. et al. Cortical pattern generation during dexterous movement is input-driven. Nature 577, 386–391 (2019).
Perich, M. G. et al. Inferring brain-wide interactions using data-constrained recurrent neural network models. Preprint at bioRxiv https://doi.org/10.1101/2020.12.18.423348 (2021). This article describes current-based decomposition, an algorithm that uses RNN models to quantify interregional interactions in multiregion neural datasets.
Perich, M. G. & Rajan, K. Rethinking brain-wide interactions through multi-region ‘network of networks’ models. Curr. Opin. Neurobiol. 65, 146–151 (2020).
Lo, C.-C. & Wang, X.-J. Cortico–basal ganglia circuit mechanism for a decision threshold in reaction time tasks. Nat. Neurosci. 9, 956–963 (2006).
Pinto, L. et al. Task-dependent changes in the large-scale dynamics and necessity of cortical regions. Neuron 104, 810–824 (2019). This study uses OEG and optogenetics to relate cognitive task complexity to cortical engagement — and then analyses this effect in more detail by reproducing qualitative features of the dataset using a multiregion RNN model.
Hattori, R. & Komiyama, T. Context-dependent persistency as a coding mechanism for robust and widely distributed value coding. Neuron https://doi.org/10.1016/j.neuron.2021.11.001 (2021).
Javadzadeh, M. & Hofer, S. B. Dynamic causal communication channels between neocortical areas. Preprint at bioRxiv https://doi.org/10.1101/2021.06.28.449892 (2021).
Gokcen, E. et al. Disentangling the flow of signals between populations of neurons. Nat. Comput. Sci 2, 512–525 (2022).
Allen, W. E. et al. Thirst-associated preoptic neurons encode an aversive motivational drive. Science 357, 1149–1155 (2017).
Orsolic, I., Rio, M., Mrsic-Flogel, T. D. & Znamenskiy, P. Mesoscale cortical dynamics reflect the interaction of sensory evidence and temporal expectation during perceptual decision-making. Neuron 109, 1861–1875.e10 (2021).
Xiao, D. et al. Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons. eLife 6, e19976 (2017).
Carter, M. E. et al. Tuning arousal with optogenetic modulation of locus coeruleus neurons. Nat. Neurosci. https://doi.org/10.1038/nn.2682 (2010).
Deisseroth, K. From microbial membrane proteins to the mysteries of emotion. Cell 184, 5279–5285 (2021).
Cardin, J. A. Functional flexibility in cortical circuits. Curr. Opin. Neurobiol. 58, 175–180 (2019).
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).
Clancy, K. B. & Mrsic-Flogel, T. D. The sensory representation of causally controlled objects. Neuron 109, 677–689.e4 (2021).
Jacobs, E. A. K., Steinmetz, N. A., Peters, A. J., Carandini, M. & Harris, K. D. Cortical state fluctuations during sensory decision making. Curr. Biol. 30, 4944–4955.e7 (2020).
Colgin, L. L. Oscillations and hippocampal–prefrontal synchrony. Curr. Opin. Neurobiol. 21, 467–474 (2011).
Park, A. J. et al. Reset of hippocampal–prefrontal circuitry facilitates learning. Nature 591, 615–619 (2021).
Sigurdsson, T., Stark, K. L., Karayiorgou, M., Gogos, J. A. & Gordon, J. A. Impaired hippocampal–prefrontal synchrony in a genetic mouse model of schizophrenia. Nature 464, 763–767 (2010).
Ferenczi, E. A. et al. Prefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior. Science https://doi.org/10.1126/science.aac9698 (2016).
Mimica, B., Dunn, B. A., Tombaz, T., Bojja, V. P. T. N. C. S. & Whitlock, J. R. Efficient cortical coding of 3D posture in freely behaving rats. Science 362, 584–589 (2018).
Cramer, J. V. et al. In vivo widefield calcium imaging of the mouse cortex for analysis of network connectivity in health and brain disease. Neuroimage 199, 570–584 (2019).
Hultman, R. et al. Brain-wide electrical spatiotemporal dynamics encode depression vulnerability. Cell 173, 166–180.e14 (2018).
Wang, X. et al. Altered mGluR5-Homer scaffolds and corticostriatal connectivity in a Shank3 complete knockout model of autism. Nat. Commun. 7, 11459 (2016).
Buccino, A. P. et al. SpikeInterface, a unified framework for spike sorting. eLife 9, e61834 (2020).
Chung, J. E. et al. A fully automated approach to spike sorting. Neuron 95, 1381–1394.e6 (2017).
Lee, J. et al. YASS: yet another spike sorter. Preprint at bioRxiv https://doi.org/10.1101/151928 (2017).
Magland, J. et al. SpikeForest, reproducible web-facing ground-truth validation of automated neural spike sorters. eLife 9, e55167 (2020).
Yger, P. et al. A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo. eLife 7, e34518 (2018).
Hazan, L., Zugaro, M. & Buzsáki, G. Klusters, NeuroScope, NDManager: a free software suite for neurophysiological data processing and visualization. J. Neurosci. Methods 155, 207–216 (2006).
Giovannucci, A. et al. CaImAn an open source tool for scalable calcium imaging data analysis. eLife 8, e38173 (2019).
Mukamel, E. A., Nimmerjahn, A. & Schnitzer, M. J. Automated analysis of cellular signals from large-scale calcium imaging data. Neuron 63, 747–760 (2009).
Pachitariu, M. et al. Suite2p: beyond 10,000 neurons with standard two-photon microscopy. Preprint at bioRxiv https://doi.org/10.1101/061507 (2017).
Greenberg, D. S. Accurate action potential inference from a calcium sensor protein through biophysical modeling. Preprint at bioRxiv https://doi.org/10.1101/479055 (2018).
Pnevmatikakis, E. A., Merel, J., Pakman, A. & Paninski, L. Bayesian spike inference from calcium imaging data. in Signals, Systems and Computers, 2013 Asilomar Conference on 349–353 (IEEE, 2013).
Vogelstein, J. et al. Fast nonnegative deconvolution for spike train inference from population calcium imaging. J. Neurophysiol. 104, 3691–3704 (2010).
Zhou, P. et al. EASE: EM-assisted source extraction from calcium imaging data. Preprint at bioRxiv https://doi.org/10.1101/2020.03.25.007468 (2020).
Berens, P. et al. Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. PLoS Comput. Biol. 14, e1006157 (2018).
Rupprecht, P. et al. A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging. Nat. Neurosci. 24, 1324–1337 (2021).
Song, A., Gauthier, J. L., Pillow, J. W., Tank, D. W. & Charles, A. S. Neural anatomy and optical microscopy (NAOMi) simulation for evaluating calcium imaging methods. J. Neurosci. Methods 358, 109173 (2021).
Zhou, P. et al. Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data. eLife 7, e28728 (2018).
Lu, J. et al. MIN1PIPE: a miniscope 1-photon-based calcium imaging signal extraction pipeline. Cell Rep. 23, 3673–3684 (2018).
Friedrich, J., Giovannucci, A. & Pnevmatikakis, E. A. Online analysis of microendoscopic 1-photon calcium imaging data streams. PLoS Comput. Biol. 17, e1008565 (2021).
Cardin, J. A. et al. Targeted optogenetic stimulation and recording of neurons in vivo using cell-type-specific expression of Channelrhodopsin-2. Nat. Protoc. 5, 247–254 (2010).
Kim, K. et al. Artifact-free and high-temporal-resolution in vivo opto-electrophysiology with microLED optoelectrodes. Nat. Commun. 11, 2063 (2020).
Chen, R. et al. Deep brain optogenetics without intracranial surgery. Nat. Biotechnol. 39, 161–164 (2021).
Kishi, K. E. et al. Structural basis for channel conduction in the pump-like channelrhodopsin ChRmine. Cell 185, 672–689.e23 (2022).
Inoue, M. et al. Rational engineering of XCaMPs, a multicolor GECI suite for in vivo imaging of complex brain circuit dynamics. Cell 177, 1346–1360.e24 (2019).
T.A.M. is supported by an NIH NINDS Pathway to Independence Award (K99/NS116122), an A.P. Giannini Fellowship and a Stanford School of Medicine Dean’s Fellowship. I.V.K. is a Merck Awardee of the Life Science Research Foundation and a Wu Tsai Stanford Neurosciences Institute Interdisciplinary Scholar. K.D. is supported by NIMH, NIDA, the NIH BRAIN Initiative, the National Science Foundation NeuroNex programme, the NOMIS Foundation, the Else Kröner Fresenius Foundation, the Gatsby Foundation and the AE Foundation. The authors also thank W. Allen, S. Bradbury, M. Inoue, C. Kim, J. Kochalka, B. Midler, A. Mitra, S. Quirin, E. Richman, S. Vesuna and other current and former members of the Deisseroth laboratory for valuable discussions.
The authors declare no competing financial interests,
Peer review information
Nature Reviews Neuroscience thanks D. Peterka and the other, anonymous, reviewers for their contribution to the peer review of this work.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Machado, T.A., Kauvar, I.V. & Deisseroth, K. Multiregion neuronal activity: the forest and the trees. Nat Rev Neurosci 23, 683–704 (2022). https://doi.org/10.1038/s41583-022-00634-0