Variation in an animal’s behavioral state is linked to fluctuations in brain activity and cognitive ability. In the neocortex, state-dependent circuit dynamics may reflect neuromodulatory influences such as that of acetylcholine (ACh). Although early literature suggested that ACh exerts broad, homogeneous control over cortical function, recent evidence indicates potential anatomical and functional segregation of cholinergic signaling. In addition, it is unclear whether states as defined by different behavioral markers reflect heterogeneous cholinergic and cortical network activity. Here, we perform simultaneous, dual-color mesoscopic imaging of both ACh and calcium across the neocortex of awake mice to investigate their relationships with behavioral variables. We find that higher arousal, categorized by different motor behaviors, is associated with spatiotemporally dynamic patterns of cholinergic modulation and enhanced large-scale network correlations. Overall, our findings demonstrate that ACh provides a highly dynamic and spatially heterogeneous signal that links fluctuations in behavior to functional reorganization of cortical networks.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Nature Neuroscience Open Access 23 January 2023
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 full datasets generated and analyzed in this study are available from the corresponding authors on reasonable request. Source data for figures have been made available as Excel files. Source data are provided with this paper.
Custom-written MATLAB scripts used in this study are available at https://github.com/cardin-higley-lab/Lohani_Moberly_et_al_2022. Source data are provided with this paper.
McGinley, M. J. et al. Waking state: rapid variations modulate neural and behavioral responses. Neuron 87, 1143–1161 (2015).
Niell, C. M. & Stryker, M. P. Modulation of visual responses by behavioral state in mouse visual cortex. Neuron 65, 472–479 (2010).
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).
Tang, L. & Higley, M. J. Layer 5 circuits in V1 differentially control visuomotor behavior. Neuron 105, 346–354.e5 (2020).
Poulet, J. F. A. & Crochet, S. The cortical states of wakefulness. Front. Syst. Neurosci. 12, 64 (2018).
Chen, N., Sugihara, H. & Sur, M. An acetylcholine-activated microcircuit drives temporal dynamics of cortical activity. Nat. Neurosci. 18, 892–902 (2015).
Munoz, W. & Rudy, B. Spatiotemporal specificity in cholinergic control of neocortical function. Curr. Opin. Neurobiol. 26, 149–160 (2014).
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, 255 (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).
Barson, D. et al. Simultaneous mesoscopic and two-photon imaging of neuronal activity in cortical circuits. Nat. Methods 17, 107–113 (2020).
Clancy, K. B., Orsolic, I. & Mrsic-Flogel, T. D. Locomotion-dependent remapping of distributed cortical networks. Nat. Neurosci. 22, 778–786 (2019).
Lee, S. H. & Dan, Y. Neuromodulation of brain states. Neuron 76, 209–222 (2012).
Buzsaki, G. et al. Nucleus basalis and thalamic control of neocortical activity in the freely moving rat. J. Neurosci. 8, 4007–4026 (1988).
Chubykin, A. A., Roach, E. B., Bear, M. F. & Shuler, M. G. A cholinergic mechanism for reward timing within primary visual cortex. Neuron 77, 723–735 (2013).
Picciotto, M. R., Higley, M. J. & Mineur, Y. S. Acetylcholine as a neuromodulator: cholinergic signaling shapes nervous system function and behavior. Neuron 76, 116–129 (2012).
Goard, M. & Dan, Y. Basal forebrain activation enhances cortical coding of natural scenes. Nat. Neurosci. 12, 1444–1449 (2009).
Herrero, J. L. et al. Acetylcholine contributes through muscarinic receptors to attentional modulation in V1. Nature 454, 1110–1114 (2008).
Constantinople, C. M. & Bruno, R. M. Effects and mechanisms of wakefulness on local cortical networks. Neuron 69, 1061–1068 (2011).
Eggermann, E., Kremer, Y., Crochet, S. & Petersen, C. C. H. Cholinergic signals in mouse barrel cortex during active whisker sensing. Cell Rep. 9, 1654–1660 (2014).
Meir, I., Katz, Y. & Lampl, I. Membrane potential correlates of network decorrelation and improved SNR by cholinergic activation in the somatosensory cortex. J. Neurosci. 38, 10692–10708 (2018).
Metherate, R., Cox, C. L. & Ashe, J. H. Cellular bases of neocortical activation: modulation of neural oscillations by the nucleus basalis and endogenous acetylcholine. J. Neurosci. 12, 4701–4711 (1992).
Minces, V., Pinto, L., Dan, Y. & Chiba, A. A. Cholinergic shaping of neural correlations. Proc. Natl Acad. Sci. USA 114, 5725–5730 (2017).
Pinto, L. et al. Fast modulation of visual perception by basal forebrain cholinergic neurons. Nat. Neurosci. 16, 1857–1863 (2013).
Steriade, M., Contreras, D., Curro Dossi, R. & Nunez, A. The slow (< 1 Hz) oscillation in reticular thalamic and thalamocortical neurons: scenario of sleep rhythm generation in interacting thalamic and neocortical networks. J. Neurosci. 13, 3284–3299 (1993).
Steriade, M., Nunez, A. & Amzica, F. Intracellular analysis of relations between the slow (< 1 Hz) neocortical oscillation and other sleep rhythms of the electroencephalogram. J. Neurosci. 13, 3266–3283 (1993).
Gasselin, C., Hohl, B., Vernet, A., Crochet, S. & Petersen, C.C.H. Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing. Neuron 109, 778–787.e3 (2021).
Hangya, B., Ranade, S. P., Lorenc, M. & Kepecs, A. Central cholinergic neurons are rapidly recruited by reinforcement feedback. Cell 162, 1155–1168 (2015).
Sturgill, J. F. et al. Basal forebrain-derived acetylcholine encodes valence-free reinforcement. Preprint at bioRxiv https://doi.org/10.1101/2020.02.17.953141 (2020).
Guo, W., Robert, B. & Polley, D. B. The cholinergic basal forebrain links auditory stimuli with delayed reinforcement to support learning. Neuron 103, 1164–1177.e6 (2019).
Kim, J. H. et al. Selectivity of neuromodulatory projections from the basal forebrain and locus ceruleus to primary sensory cortices. J. Neurosci. 36, 5314–5327 (2016).
Laszlovszky, T. et al. Distinct synchronization, cortical coupling and behavioral function of two basal forebrain cholinergic neuron types. Nat. Neurosci. 23, 992–1003 (2020).
Li, X. et al. Generation of a whole-brain atlas for the cholinergic system and mesoscopic projectome analysis of basal forebrain cholinergic neurons. Proc. Natl Acad. Sci. USA 115, 415–420 (2018).
Zaborszky, L. et al. Neurons in the basal forebrain project to the cortex in a complex topographic organization that reflects corticocortical connectivity patterns: an experimental study based on retrograde tracing and 3D reconstruction. Cereb. Cortex 25, 118–137 (2015).
Disney, A. A. & Higley, M. J. Diverse spatiotemporal scales of cholinergic signaling in the neocortex. J. Neurosci. 40, 720–725 (2020).
Dana, H. et al. Sensitive red protein calcium indicators for imaging neural activity. eLife 5, e12727 (2016).
Jing, M. et al. An optimized acetylcholine sensor for monitoring in vivo cholinergic activity. Nat. Methods 17, 1139–1146 (2020).
Hamodi, A. S., Martinez Sabino, A., Fitzgerald, N. D., Moschou, D. & Crair, M. C. Transverse sinus injections drive robust whole-brain expression of transgenes. eLife 9, e53639 (2020).
Wang, Q. et al. The Allen Mouse Brain Common Coordinate Framework: a 3D reference atlas. Cell 181, 936–953 e920 (2020).
Ma, Y. et al. Wide-field optical mapping of neural activity and brain haemodynamics: considerations and novel approaches. Phil. Trans. R. Soc. Lond. B 371, 20150360 (2016).
Valley, M. T. et al. Separation of hemodynamic signals from GCaMP fluorescence measured with wide-field imaging. J. Neurophysiol. 123, 356–366 (2020).
Bennett, C., Arroyo, S. & Hestrin, S. Subthreshold mechanisms underlying state-dependent modulation of visual responses. Neuron 80, 350–357 (2013).
McGinley, M. J., David, S. V. & McCormick, D. A. Cortical membrane potential signature of optimal states for sensory signal detection. Neuron 87, 179–192 (2015).
Neske, G. T., Nestvogel, D., Steffan, P. J. & McCormick, D. A. Distinct waking states for strong evoked responses in primary visual cortex and optimal visual detection performance. J. Neurosci. 39, 10044–10059 (2019).
Perrenoud, Q. et al. Flexible perceptual encoding by discrete gamma events. Preprint at bioRxiv https://doi.org/10.1101/2022.1105.1113.491832 (2022).
Reimer, J. et al. Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex. Nat. Commun. 7, 13289 (2016).
Benisty, H. et al. Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior. Preprint at bioRxiv https://doi.org/10.1101/2021.1108.1115.456390 (2021).
Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069 (2010).
Stafford, J. M. et al. Large-scale topology and the default mode network in the mouse connectome. Proc. Natl Acad. Sci. USA 111, 18745–18750 (2014).
Parikh, V., Kozak, R., Martinez, V. & Sarter, M. Prefrontal acetylcholine release controls cue detection on multiple timescales. Neuron 56, 141–154 (2007).
Soma, S., Shimegi, S., Suematsu, N. & Sato, H. Cholinergic modulation of response gain in the rat primary visual cortex. Sci Rep. 3, 1138 (2013).
Hasselmo, M. E. & Sarter, M. Modes and models of forebrain cholinergic neuromodulation of cognition. Neuropsychopharmacology 36, 52–73 (2011).
Avendano, C., Umbriaco, D., Dykes, R. W. & Descarries, L. Acetylcholine innervation of sensory and motor neocortical areas in adult cat: a choline acetyltransferase immunohistochemical study. J. Chem. Neuroanat. 11, 113–130 (1996).
Bigl, V., Woolf, N. J. & Butcher, L. L. Cholinergic projections from the basal forebrain to frontal, parietal, temporal, occipital, and cingulate cortices: a combined fluorescent tracer and acetylcholinesterase analysis. Brain Res. Bull. 8, 727–749 (1982).
Chaves-Coira, I., Barros-Zulaica, N., Rodrigo-Angulo, M. & Nunez, A. Modulation of specific sensory cortical areas by segregated basal forebrain cholinergic neurons demonstrated by neuronal tracing and optogenetic stimulation in mice. Front. Neural Circuits 10, 28 (2016).
Gielow, M. R. & Zaborszky, L. The input-output relationship of the cholinergic basal forebrain. Cell Rep. 18, 1817–1830 (2017).
Granger, A. J. et al. Cortical ChAT+ neurons co-transmit acetylcholine and GABA in a target- and brain-region-specific manner. eLife 9, e57749 (2020).
Parker, P. R. L., Brown, M. A., Smear, M. C. & Niell, C. M. Movement-related signals in sensory areas: roles in natural behavior. Trends Neurosci. 43, 581–595 (2020).
Sudhof, T. C. Calcium control of neurotransmitter release. Cold Spring Harb. Perspect. Biol. 4, a011353 (2012).
Sarter, M. & Kim, Y. Interpreting chemical neurotransmission in vivo: techniques, time scales, and theories. ACS Chem. Neurosci. 6, 8–10 (2015).
Lee, S. J. et al. Cell-type-specific asynchronous modulation of PKA by dopamine in learning. Nature 590, 451–456 (2020).
Polack, P. O., Friedman, J. & Golshani, P. Cellular mechanisms of brain state-dependent gain modulation in visual cortex. Nat. Neurosci. 16, 1331–1339 (2013).
Thiele, A. & Bellgrove, M. A. Neuromodulation of attention. Neuron 97, 769–785 (2018).
Crochet, S., Lee, S. H. & Petersen, C. C. H. Neural circuits for goal-directed sensorimotor transformations. Trends Neurosci. 42, 66–77 (2019).
Esmaeili, V. et al. Rapid suppression and sustained activation of distinct cortical regions for a delayed sensory-triggered motor response. Neuron 13, 2183–2201 (2021).
Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281 (2018).
Larsen, R.S. et al. Activation of neuromodulatory axon projections in primary visual cortex during periods of locomotion and pupil dilation. Preprint at bioRxiv https://doi.org/10.1101/502013 (2018).
Batista-Brito, R. et al. Developmental dysfunction of VIP interneurons impairs cortical circuits. Neuron 95, 884–895.e9 (2017).
We thank all members of the Higley and Cardin laboratories for helpful input throughout all stages of this study. We thank R. Pant for generation of AAV vectors. We thank D. Barson, G. Mishne and R. Coifman for helpful discussions regarding data analysis. We thank Q. Perrenoud for providing the locomotion change-point analysis code. We thank the GENIE Project for jRCaMP1b plasmids. This work was supported by funding from the NIH (R01MH099045, R21MH121841 and DP1EY033975 to M.J.H.; R01EY022951 to J.A.C.; R01MH113852 to M.J.H. and J.A.C.; EY031133 to A.H.M.; and EY026878 to the Yale Vision Core), an award from the Kavli Institute of Neuroscience (to J.A.C. and M.J.H.), a Simons Foundation SFARI Research Grant (to J.A.C. and M.J.H.), a Swebilius Foundation award (to J.A.C. and M.J.H.), a grant from the Aligning Science Across Parkinson’s Initiative (to M.J.H.), the Ludwig Foundation (to J.A.C.), a BBRF Young Investigator Grant (to S.L.) and an award from the Swartz Foundation (to H.B.).
The authors declare no competing interests.
Peer review information
Nature Neuroscience thanks the anonymous reviewers for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Viral expression of the cholinergic reporter ACh3.0 and the calcium indicator jRCaMP1b.
a, Schematic of the neonatal sinus injection approach. b, Example sagittal widefield fluorescent images from an adult mouse expressing jRCaMP1b (magenta) and ACh3.0 (yellow). Sections were immunostained for the neuronal marker NeuN (blue). Scale bar; 1 mm. c, Average intensity (mean ± SEM, n = 4 mice) from posterior to anterior neocortex normalized to NeuN intensity values for jRCaMP1b (left) and ACh3.0 (right). d, Example confocal images from an adult mouse co-expressing jRCaMP1b and ACh3.0. Sections were immunostained for GABA (cyan) to label inhibitory cells. Scale bar: 100 µm. e, Average cell counts (n = 4 mice) from fields of view from frontal (Fro), somatosensory (Som), or visual (Vis) cortex (example insets marked by boxes in (b). For each field of view, data indicate the number of positive cells per mm2 for jRCaMP1b (left, F(2,6) = 0.455, p = 0.655, repeated measures ANOVA), the proportion of jRCaMP1b-positive cells co-expressing ACh3.0 (middle, F(2,6) = 0.014, p = 0.986, repeated measures ANOVA), and the proportion of jRCaMP1b-positive cells co-expressing GABA (right, F(2,6) = 0.085, p = 0.920, repeated measures ANOVA).
a, Example brain slice from an ACh3.0-expressing mouse imaged with 470 nm (blue) and 395 (violet) excitation light on interleaved frames. Images show fluorescence at baseline (left) and 20 s after carbachol (20 μM) was puffed onto the slice (right). Scale bar; 1 mm. b, Peak carbachol-evoked ΔF/F responses for 395 nm versus 470 nm excitation, in control ACSF or in the presence of scopolamine (10 μM). c, Schematic showing bipolar stimulation electrodes implanted in the basal forebrain (left) and corresponding mesoscopic image showing baseline fluorescence (right). Scale bar; 1 mm. d, Example pixel-wise spatial maps of ΔF/F ACh3.0 signal averaged across trials before (left) and after (right) electrical stimulation of the basal forebrain. e, Schematic illustrating Allen CCFv3 parcels, corresponding abbreviations and color codes used in main figures. f, Mean ± SEM (n = 3 mice) ACh3.0 signal activity evoked by basal forebrain electrical stimulation for V1 (red) and M2 (purple) Allen CCFv3 parcels indicated in e. Corresponding mean ± SEM changes in locomotion, pupil, facial movement (FaceMap PC1) and an example ECoG trace from a single stimulation are illustrated below. g, Example images showing fluorescence under 470 nm excitation light (top) and 575 nm excitation light (bottom) in an ACh3.0-expressing mouse (left) and a jRCaMP1b-expressing mouse (right). Scale bar: 1 mm. Cumulative distribution plots of the pixel intensities from the example frames under both illumination conditions are shown on the far right.
a, GFP/mCherry fluorescence signals measured in V1 during presentation of drifting grating stimuli, illustrating different hemodynamic correction methods. Plots show averaged ΔF/F GFP (left) and mCherry (right) activity evoked by visual stimulation in one example session from a mouse co-expressing GFP and mCherry. Traces are for uncorrected fluorescence (blue) and images corrected using pixel-wise regression of 395 nm fluorescence data (purple) or 530 nm back-scatter data (light green), or spatial regression of 395 nm (orange) or 530 nm (dark green) data. b, ΔF/F GFP and mCherry activity across all parcels, evoked by visual stimulation. Population averaged (n = 3 mice) images are from uncorrected data and data corrected using different regression methods. c, Individual and population averaged (n = 3 mice) GFP and mCherry values in V1 for visually-evoked ΔF/F negativity (hemodynamic artifact), from uncorrected data. * indicates p < 0.05, two-tailed paired t-test (t(2) = −17.353, p = 0.003). d, Individual animal and population mean (n = 3 mice) values for visually-evoked ΔF/F GFP negativity for the different correction methods. * indicates p < 0.05, post hoc two-tailed paired t-tests following repeated measures ANOVA comparing uncorrected, pixelwise (395) and spatial (395) regression methods (F(2,4) =14.754, p = 0.014; uncorrected vs pixelwise: t(2) = −2.499, p = 0.130; uncorrected vs spatial: t(2) = −5.905,p = 0.028; pixelwise vs spatial: t(2) = −2.807, p = 0.107). e, as in d for mCherry data (F(2,4) = 6.270, p = 0.059). f, ACh3.0 signals measured in V1 during visual stimulus presentation (arrows), illustrating different hemodynamic correction methods. g, Average ΔF/F ACh3.0 activity evoked by visual stimulation in one example session with each correction method. h, Individual animal and population mean (n = 6 mice) values for visually-evoked ΔF/F negativity with each correction method. * indicates p < 0.05, post hoc two-tailed paired t-tests following repeated measures ANOVA (F(2,10) = 54.423, p < 0.001; uncorrected vs pixelwise: t(5) = −4.758, p = 0.005; uncorrected vs spatial: t(5) = -7.259, p = 0.001; pixelwise vs spatial: t(5) = −7.568, p = 0.001). i-k, as in (f-h) for jRCaMP1b data (n = 6 mice) in V1 (F(2,10) = 6.776, p = 0.014; uncorrected vs pixelwise: t(5) = −1.719, p = 0.146; uncorrected vs spatial: t(5) = −2.607, p = 0.048; pixelwise vs spatial: t(5) = −2.610, p = 0.048). l, Pixel-wise variance remaining from imaging of a GFP-expressing mouse following hemodynamic correction with pixel-wise (left) or spatial (right) regression of 395 nm fluorescence data. m, Average GFP fluorescence in V1 evoked by air-puff stimulus to the animal’s flank. Traces are for uncorrected fluorescence (blue) and images corrected using pixelwise (purple) or spatial (red) regression of 395 nm data. n, Individual animal and population mean (n = 3 mice) values for air-puff-evoked ΔF/F negativity for the different regression methods. * indicates p < 0.05, post hoc two-tailed paired t-tests following repeated measures ANOVA (F(2,4) = 36.002, p = 0.003; uncorrected vs pixelwise: t(2) = −2.583, p = 0.123; uncorrected vs spatial: t(2) = −6.821, p = 0.021; pixelwise vs spatial: t(2) = −8.203, p = 0.015).
a, Example behavioral and neural data time series from a period of sustained locomotion (left) and sustained high facial motion activity (right) in a mouse co-expressing ACh3.0 and jRCaMP1b. Neural data are V1 traces from ACh3.0 under 470 nm and 395 nm excitation light and jRCaMP1b under 575 nm excitation light before and after hemodynamic correction using spatial regression of the 395 nm fluorescence. Correction substantially reduces large negative deflections especially in the ACh3.0 signal. b, Same as in (b) for an example GFP-expressing mouse.
a, Experimental timeline and group schematic for animals used in the study. b, Behavioral raster plots indicating periods of sustained low and high locomotion and sustained low and high facial motion activity across all imaging session in the six dual ACh3.0/jRCaMP1b mice comprising Group 1. Below are histograms indicating the distribution of the sustained states over time. c, Behavioral raster plots indicating periods of sustained high face states during imaging sessions in mice comprising Group 6 (scopolamine and mecamylamine injected mice). Below are histograms indicating the distribution of the sustained state over time across the imaging sessions.
Extended Data Fig. 6 Relationship between FaceMap principal components and cholinergic and neural activity.
a, Cross validated R2 values (averaged across all parcels in single hemisphere) of ACh3.0 (left) and jRCaMP1b (right) based on regression model including FaceMap principal components 1–25 and each of these FaceMap PCs individually (n = 6 mice). b, Unique contribution to the full model (ΔR2) of FaceMap components 1–25 and each FaceMap component individually (n = 6 mice).
Extended Data Fig. 7 Spatial heterogeneity in cholinergic and calcium signaling during movement-defined transient and sustained behavioral states.
a, Traces show trial-averaged activity (mean ± SEM) for V1 (red) and M2 (purple) aligned to FaceMap PC1 (left) and locomotion (right) transition points for one session, with simultaneous locomotion, pupil area, facial movement (FaceMap PC1) and an example raw ECoG trace shown below. b, Average spatial maps (n = 6) showing parcel-wise differences in ACh3.0 and jRCaMP1b activity upon transition to high facial motion (top) and locomotion (bottom), showing peak ΔF/F values at 0 to 1 s post high facial motion or locomotion onset. c, Peak (at 0 to 1 s from state transition) ΔF/F difference values (post/pre-onset, mean ± SEM, n = 6 mice) from parcels in the left hemisphere are plotted against their anterior-to-posterior position based on center of mass. rs indicates Spearman’s rank-order correlation coefficient for correlation between mean value and anterior–posterior rank across parcels (FaceMap onset: Ach3.0 rs = −0.660, p < 0.001; jRCaMP1b rs = 0.010, p = 0.962; locomotion onset: Ach3.0 rs = −0.768, p < 0.001; jRCaMP1b rs = 0.029, p = 0.895). Line indicates linear fit for visualization. d, Same as in (c) for ΔF/F difference values (mean ± SEM, n = 6 mice) for sustained states (high-low facial motion PC1 onset, top and locomotion-high facial motion, bottom) parcels (FaceMap: Ach3.0 rs = −0.789, p < 0.001; jRCaMP1b rs = 0.321, p = 0.136; Locomotion: Ach3.0 rs = 0.509, p = 0.013; jRCaMP1b rs = 0.463, p = 0.026). e, Individual animal values and population means for low-frequency (1–10 Hz) ECoG power (n = 6 mice) for sustained low facial motion, high facial motion and locomotion states. *indicates p < 0.05 for post hoc two-tailed paired t-test comparisons (low vs high FM: t(5) = 3.291, p = 0.022; locomotion vs high FM: t(5) = 4.999, p = 0.004) following a significant main effect of behavioral state in repeated measures ANOVA (F(2,10) = 15.459, p = 0.001). f, Comparison of Ach 3.0 amplitude (n = 6 mice) in visual and motor areas compared to visual areas during low facial motion and locomotion. *indicates p < 0.05, two-tailed paired t-tests (low FM: t(5) = 3.674, p = 0.014; locomotion t(5) = −1.973, p = 0.106).
Extended Data Fig. 8 Cholinergic signal in ChAT-GCaMP mice during movement-defined behavioral states.
a, Example time series showing behavioral measures (locomotion and FaceMap PC1) and GCaMP6 signals from the M2 (purple) and V1 (red) parcels in a Chat-Cre+/0Ai162F/0 mouse. b, Representative image frames from a period of no locomotion and low facial motion activity (T1), no locomotion and high facial motion activity (T1) and locomotion and high facial motion activity (T3). c, Individual animal values and population mean whole cortex ΔF/F values during sustained low facial motion, high facial motion and locomotion states. Inset shows individual and population mean ΔF/F difference values (n = 3 mice) between high-low facial motion and locomotion-high facial motion. *indicates p < 0.05 for post hoc two-tailed paired t-test comparisons (low vs high FM: t(2) = −4.397, p = 0.048; locomotion vs high FM: t(2) = −0.835, p = 0.492) following repeated measures ANOVA (F(2,4) = 14.798, p = 0.014).
a, b, Individual correlation matrices for ACh 3.0 (a) and jrCaMP1b (b) during sustained behavioral states. Each row corresponds to one animal. c, Individual brain maps showing correlation between Ach 3.0 and jrCaMP1b during sustained behavioral states. Each row corresponds to one animal.
Statistical source data
Statistical source data
Statistical source data
Statistical source data
Statistical source data
Statistical source data
Statistical source data
Statistical source data
Statistical source data
Statistical source data
About this article
Cite this article
Lohani, S., Moberly, A.H., Benisty, H. et al. Spatiotemporally heterogeneous coordination of cholinergic and neocortical activity. Nat Neurosci 25, 1706–1713 (2022). https://doi.org/10.1038/s41593-022-01202-6
This article is cited by
Nature Neuroscience (2023)