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Weak correlations between hemodynamic signals and ongoing neural activity during the resting state

Nature Neuroscience volume 20, pages 1761–1769 (2017)Cite this article

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An Author Correction to this article was published on 30 May 2022

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Abstract

Spontaneous fluctuations in hemodynamic signals in the absence of a task or overt stimulation are used to infer neural activity. We tested this coupling by simultaneously measuring neural activity and changes in cerebral blood volume (CBV) in the somatosensory cortex of awake, head-fixed mice during periods of true rest and during whisker stimulation and volitional whisking. We found that neurovascular coupling was similar across states and that large, spontaneous CBV changes in the absence of sensory input were driven by volitional whisker and body movements. Hemodynamic signals during periods of rest were weakly correlated with neural activity. Spontaneous fluctuations in CBV and vessel diameter persisted when local neural spiking and glutamatergic input were blocked, as well as during blockade of noradrenergic receptors, suggesting a non-neuronal origin for spontaneous CBV fluctuations. Spontaneous hemodynamic signals reflect a combination of behavior, local neural activity, and putatively non-neural processes.

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Fig. 1: Behavior drives CBV fluctuations.
Fig. 2: Neurovascular coupling was consistent across behavioral states.
Fig. 3: Predicting CBV changes from neural activity using HRFs.
Fig. 4: Variance of residual signal is similar across behavioral states.
Fig. 5: Spontaneous CBV fluctuations continue in the absence of neural activity and noradrenergic input.

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References

  1. Fox, M. D. & Raichle, M. E. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711 (2007).

    Article  CAS  PubMed  Google Scholar 

  2. Smith, S. M. et al. Functional connectomics from resting-state fMRI. Trends Cogn. Sci. 17, 666–682 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Maier, A. et al. Divergence of fMRI and neural signals in V1 during perceptual suppression in the awake monkey. Nat. Neurosci. 11, 1193–1200 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Sirotin, Y. B. & Das, A. Anticipatory haemodynamic signals in sensory cortex not predicted by local neuronal activity. Nature 457, 475–479 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Huo, B.-X., Smith, J. B. & Drew, P. J. Neurovascular coupling and decoupling in the cortex during voluntary locomotion. J. Neurosci. 34, 10975–10981 (2014).

    Article  CAS  PubMed  Google Scholar 

  6. Vazquez, A. L., Fukuda, M., Crowley, J. C. & Kim, S.-G. Neural and hemodynamic responses elicited by forelimb- and photo-stimulation in channelrhodopsin-2 mice: insights into the hemodynamic point spread function. Cereb. Cortex 24, 2908–2919 (2014).

    Article  PubMed  Google Scholar 

  7. Drew, P. J., Shih, A. Y. & Kleinfeld, D. Fluctuating and sensory-induced vasodynamics in rodent cortex extend arteriole capacity. Proc. Natl. Acad. Sci. USA 108, 8473–8478 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Gao, Y. R. et al. Time to wake up: Studying neurovascular coupling and brain-wide circuit function in the un-anesthetized animal. Neuroimage 153, 382–398 (2017).

    Article  PubMed  Google Scholar 

  9. Huo, B.-X., Gao, Y.-R. & Drew, P. J. Quantitative separation of arterial and venous cerebral blood volume increases during voluntary locomotion. Neuroimage 105, 369–379 (2015).

    Article  PubMed  Google Scholar 

  10. Hall, C. N. et al. Capillary pericytes regulate cerebral blood flow in health and disease. Nature 508, 55–60 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Kim, S.-G. & Ogawa, S. Biophysical and physiological origins of blood oxygenation level-dependent fMRI signals. J. Cereb. Blood Flow Metab. 32, 1188–1206 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Logothetis, N. K. What we can do and what we cannot do with fMRI. Nature 453, 869–878 (2008).

    Article  CAS  PubMed  Google Scholar 

  13. Hirano, Y., Stefanovic, B. & Silva, A. C. Spatiotemporal evolution of the functional magnetic resonance imaging response to ultrashort stimuli. J. Neurosci. 31, 1440–1447 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Fukuda, M. et al. Localization of activity-dependent changes in blood volume to submillimeter-scale functional domains in cat visual cortex. Cereb. Cortex 15, 823–833 (2005).

    Article  PubMed  Google Scholar 

  15. Guipponi, O., Odouard, S., Pinède, S., Wardak, C. & Ben Hamed, S. fMRI Cortical correlates of spontaneous eye blinks in the nonhuman primate. Cereb. Cortex 25, 2333–2345 (2015).

    Article  PubMed  Google Scholar 

  16. Galton, F. The measure of fidget. Nature 32, 174–175 (1885).

    Google Scholar 

  17. O’Connor, D. H. et al. Neural coding during active somatosensation revealed using illusory touch. Nat. Neurosci. 16, 958–965 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Liu, X. & Duyn, J. H. Time-varying functional network information extracted from brief instances of spontaneous brain activity. Proc. Natl. Acad. Sci. USA 110, 4392–4397 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Laumann, T. O. et al. On the stability of BOLD fMRI correlations. Cereb. Cortex 27, 4719–4732 (2017).

    PubMed  Google Scholar 

  20. Schölvinck, M. L., Maier, A., Ye, F. Q., Duyn, J. H. & Leopold, D. A. Neural basis of global resting-state fMRI activity. Proc. Natl. Acad. Sci. USA 107, 10238–10243 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Theis, L. et al. Benchmarking spike rate inference in population calcium imaging. Neuron 90, 471–482 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Goense, J. B. M. & Logothetis, N. K. Neurophysiology of the BOLD fMRI signal in awake monkeys. Curr. Biol. 18, 631–640 (2008).

    Article  CAS  PubMed  Google Scholar 

  23. de Kock, C. P. J. & Sakmann, B. Spiking in primary somatosensory cortex during natural whisking in awake head-restrained rats is cell-type specific. Proc. Natl. Acad. Sci. USA 106, 16446–16450 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Chapin, J. K. & Lin, C. S. Mapping the body representation in the SI cortex of anesthetized and awake rats. J. Comp. Neurol. 229, 199–213 (1984).

    Article  CAS  PubMed  Google Scholar 

  25. Sachidhanandam, S., Sreenivasan, V., Kyriakatos, A., Kremer, Y. & Petersen, C. C. H. Membrane potential correlates of sensory perception in mouse barrel cortex. Nat. Neurosci. 16, 1671–1677 (2013).

    Article  CAS  PubMed  Google Scholar 

  26. Boynton, G. M., Engel, S. A., Glover, G. H. & Heeger, D. J. Linear systems analysis of functional magnetic resonance imaging in human V1. J. Neurosci. 16, 4207–4221 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kay, K. N., Naselaris, T., Prenger, R. J. & Gallant, J. L. Identifying natural images from human brain activity. Nature 452, 352–355 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. O’Connor, D. H. et al. Vibrissa-based object localization in head-fixed mice. J. Neurosci. 30, 1947–1967 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Sreenivasan, V. et al. Movement initiation signals in mouse whisker motor cortex. Neuron 92, 1368–1382 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Howarth, C., Gleeson, P. & Attwell, D. Updated energy budgets for neural computation in the neocortex and cerebellum. J. Cereb. Blood Flow Metab. 32, 1222–1232 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).

    Article  CAS  PubMed  Google Scholar 

  32. Cardoso, M. M. B., Sirotin, Y. B., Lima, B., Glushenkova, E. & Das, A. The neuroimaging signal is a linear sum of neurally distinct stimulus- and task-related components. Nat. Neurosci. 15, 1298–1306 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Murphy, K., Birn, R. M. & Bandettini, P. A. Resting-state fMRI confounds and cleanup. Neuroimage 80, 349–359 (2013).

    Article  PubMed  Google Scholar 

  34. Attwell, D. et al. Glial and neuronal control of brain blood flow. Nature 468, 232–243 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Mayhew, J. E. W. et al. Cerebral vasomotion: a 0.1-Hz oscillation in reflected light imaging of neural activity. Neuroimage 4, 183–193 (1996).

    Article  CAS  PubMed  Google Scholar 

  36. Thrane, A. S. et al. General anesthesia selectively disrupts astrocyte calcium signaling in the awake mouse cortex. Proc. Natl. Acad. Sci. USA 109, 18974–18979 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Osol, G. & Halpern, W. Spontaneous vasomotion in pressurized cerebral arteries from genetically hypertensive rats. Am. J. Physiol. 254, H28–H33 (1988).

    CAS  PubMed  Google Scholar 

  38. Wölfle, S. E. et al. Non-linear relationship between hyperpolarisation and relaxation enables long distance propagation of vasodilatation. J. Physiol. (Lond.) 589, 2607–2623 (2011).

    Article  CAS  Google Scholar 

  39. Gao, Y. R., Greene, S. E. & Drew, P. J. Mechanical restriction of intracortical vessel dilation by brain tissue sculpts the hemodynamic response. Neuroimage 115, 162–176 (2015).

    Article  PubMed  Google Scholar 

  40. Mishra, A. et al. Astrocytes mediate neurovascular signaling to capillary pericytes but not to arterioles. Nat. Neurosci. 19, 1619–1627 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Chaigneau, E. et al. The relationship between blood flow and neuronal activity in the rodent olfactory bulb. J. Neurosci. 27, 6452–6460 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Drew, P. J., Duyn, J. H., Golanov, E. & Kleinfeld, D. Finding coherence in spontaneous oscillations. Nat. Neurosci. 11, 991–993 (2008).

    Article  CAS  PubMed  Google Scholar 

  43. van den Brink, R. L. et al. Catecholaminergic neuromodulation shapes intrinsic MRI functional connectivity in the human brain. J. Neurosci. 36, 7865–7876 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  44. O’Herron, P. et al. Neural correlates of single-vessel haemodynamic responses in vivo. Nature 534, 378–382 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Ma, Y. et al. Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons. Proc. Natl. Acad. Sci. USA 113, E8463–E8471 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Nimmerjahn, A., Mukamel, E. A. & Schnitzer, M. J. Motor behavior activates Bergmann glial networks. Neuron 62, 400–412 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Murphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B. & Bandettini, P. A. The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage 44, 893–905 (2009).

    Article  PubMed  Google Scholar 

  48. Cauli, B. et al. Cortical GABA interneurons in neurovascular coupling: relays for subcortical vasoactive pathways. J. Neurosci. 24, 8940–8949 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Iliff, J. J. et al. Cerebral arterial pulsation drives paravascular CSF-interstitial fluid exchange in the murine brain. J. Neurosci. 33, 18190–18199 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Mohajerani, M. H. et al. Spontaneous cortical activity alternates between motifs defined by regional axonal projections. Nat. Neurosci. 16, 1426–1435 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Drew, P. J. et al. Chronic optical access through a polished and reinforced thinned skull. Nat. Methods 7, 981–984 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Drew, P. J. & Feldman, D. E. Intrinsic signal imaging of deprivation-induced contraction of whisker representations in rat somatosensory cortex. Cereb. Cortex 19, 331–348 (2009).

    Article  PubMed  Google Scholar 

  53. Shirey, M. J. et al. Brief anesthesia, but not voluntary locomotion, significantly alters cortical temperature. J. Neurophysiol. 114, 309–322 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Gehrmann, J. et al. Phenotypic screening for heart rate variability in the mouse. Am. J. Physiol. Heart Circ. Physiol 279, H733–H740 (2000).

    Article  CAS  PubMed  Google Scholar 

  55. Huo, B.-X., Greene, S. E. & Drew, P. J. Venous cerebral blood volume increase during voluntary locomotion reflects cardiovascular changes. Neuroimage 118, 301–312 (2015).

    Article  PubMed  Google Scholar 

  56. O’Connor, S. M., Berg, R. W. & Kleinfeld, D. Coherent electrical activity between vibrissa sensory areas of cerebellum and neocortex is enhanced during free whisking. J. Neurophysiol. 87, 2137–2148 (2002).

    Article  PubMed  Google Scholar 

  57. Ganguly, K. & Kleinfeld, D. Goal-directed whisking increases phase-locking between vibrissa movement and electrical activity in primary sensory cortex in rat. Proc. Natl. Acad. Sci. USA 101, 12348–12353 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Henze, D. A. et al. Intracellular features predicted by extracellular recordings in the hippocampus in vivo. J. Neurophysiol. 84, 390–400 (2000).

    Article  CAS  PubMed  Google Scholar 

  59. Drew, P. J., Blinder, P., Cauwenberghs, G., Shih, A. Y. & Kleinfeld, D. Rapid determination of particle velocity from space-time images using the Radon transform. J. Comput. Neurosci. 29, 5–11 (2010).

    Article  PubMed  Google Scholar 

  60. Gao, Y.-R. & Drew, P. J. Effects of voluntary locomotion and calcitonin gene-related peptide on the dynamics of single dural vessels in awake mice. J. Neurosci. 36, 2503–2516 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Zhao, X., Chen, H., Liu, X. & Cang, J. Orientation-selective responses in the mouse lateral geniculate nucleus. J. Neurosci. 33, 12751–12763 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Constantinople, C. M. & Bruno, R. M. Effects and mechanisms of wakefulness on local cortical networks. Neuron 69, 1061–1068 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Napoleone, P., Erdö, S. & Amenta, F. Autoradiographic localization of the GABAA receptor agonist [3H]muscimol in rat cerebral vessels. Brain Res. 423, 109–115 (1987).

    Article  CAS  PubMed  Google Scholar 

  64. de Blas, A. L., Vitorica, J. & Friedrich, P. Localization of the GABAA receptor in the rat brain with a monoclonal antibody to the 57,000 Mr peptide of the GABAA receptor/benzodiazepine receptor/Cl- channel complex. J. Neurosci. 8, 602–614 (1988).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).

    Article  CAS  PubMed  Google Scholar 

  66. Anenberg, E., Chan, A. W., Xie, Y., LeDue, J. M. & Murphy, T. H. Optogenetic stimulation of GABA neurons can decrease local neuronal activity while increasing cortical blood flow. J. Cereb. Blood Flow Metab. 35, 1579–1586 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Gao, Y.-R. & Drew, P. J. Determination of vessel cross-sectional area by thresholding in Radon space. J. Cereb. Blood Flow Metab. 34, 1180–1187 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Mitra, P. & Bokil, H. Observed Brain Dynamics (Oxford University Press, 2008).

  69. Hutchison, R. M. et al. Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80, 360–378 (2013).

    Article  PubMed  Google Scholar 

  70. Glover, G. H. Deconvolution of impulse response in event-related BOLD fMRI. Neuroimage 9, 416–429 (1999).

    Article  CAS  PubMed  Google Scholar 

  71. de Zwart, J. A. et al. Temporal dynamics of the BOLD fMRI impulse response. Neuroimage 24, 667–677 (2005).

    Article  PubMed  Google Scholar 

  72. Pisauro, M. A., Benucci, A. & Carandini, M. Local and global contributions to hemodynamic activity in mouse cortex. J. Neurophysiol. 115, 2931–2936 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Aarts, E., Verhage, M., Veenvliet, J. V., Dolan, C. V. & van der Sluis, S. A solution to dependency: using multilevel analysis to accommodate nested data. Nat. Neurosci. 17, 491–496 (2014).

    Article  CAS  PubMed  Google Scholar 

  74. Mateo, C. I., Knutsen, P. M., Tsai, P. S., Shih, A. Y. & Kleinfeld, D. Neuron https://dx.doi.org/10.1016/j.neuron.2017.10.012 (2017).

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Acknowledgements

We thank L. Abbott, M. Adams, P. Blinder, D. Feldman, and N. Zhang for comments on the manuscript, and J. Berwick, D. Kleinfeld, and C. Mateo for discussions. This work was supported a Scholar Award from the McKnight Endowment Fund for Neuroscience, and grants R01NS078168, R01EB021703, and R01NS079737 from the NIH to P.J.D.

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Authors and Affiliations

  1. Center for Neural Engineering, Pennsylvania State University, University Park, PA, USA

    Aaron T. Winder, Christina Echagarruga, Qingguang Zhang & Patrick J. Drew

  2. Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA

    Aaron T. Winder, Qingguang Zhang & Patrick J. Drew

  3. Graduate Program in Bioengineering, Pennsylvania State University, University Park, PA, USA

    Christina Echagarruga & Patrick J. Drew

  4. Department of Neurosurgery and Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA

    Patrick J. Drew

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Contributions

A.T.W. and P.J.D. designed the experiments, A.T.W., C.E., and Q.Z. performed experiments and analyzed the data, A.T.W. and P.J.D. wrote the paper.

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Correspondence to Patrick J. Drew.

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Integrated Supplementary Information

Supplementary Figure 1 Details of the experimental model.

a: Photo of the experimental setup showing an awake, head-fixed mouse. The stereotrode and cannula are visible on the head. b: An example of the automated detection of whisker angle. Images of the whiskers at various times are indicated by colored boxes and arrows. The light blue box shows the whiskers at rest. The purple and orange boxes show the whiskers at various positions during volitional movement. c: An example of neural data during sensory stimulation (vertical gray bar) and the subsequent whisking (orange tick marks) showing wideband neural activity (0–10 kHz), multi-unit activity (300–3000 Hz), and local field potentials (0–150 Hz). Individual spikes (black tick marks, >5 standard deviations threshold shown with horizontal dashed lines) were detected in all animals. d: An example of the resting CBV signal (blue) compared to the reflectance obtained from a piece of clay placed over the cranial window during an experimental session (black). This shows the instrumentation noise is minuscule compared to the CBV signal. e: A comparison of the resting variance to the variance measured from the occluded cranial window which served as an illumination control surface. The variance of the instrumentation noise was 0.16% of the mean resting variance across all animals (n = 12). f: A comparison of the mean CBV amplitude changes following contralateral whisker stimulation between animals with only implanted electrodes (n = 12) or cannula and electrodes (n = 9) and animals with no implants (n = 3). The circles show individual animals. Bars show the mean of each group. The sensory-evoked CBV response was not affected by implantation of stereotrodes or cannulas (One-way ANOVA, n = {3,12,9}, p = 0.40, F(2,23) = 0.95). g: A reconstruction of the position and depth of implanted stereotrodes (colored circles) and the position of cannulas (black squares) for all animals. Stereotrode depths are designated by color. Scale bar = 1 mm. h: Whisking and CBV response to auditory stimuli for a representative animal. Auditory stimuli elicited volitional whisking behaviors, which then drove a hemodynamic response. Top: 28 examples of the detected whisker movements following auditory stimulation. Bottom: the average measured reflectance following auditory stimuli and compared to the response following volitional whisking without any stimulus. See Figure 1e, for population comparison of behavioral CBV variance. i: Averaged reflectance changes relative to baseline (top left) for a representative animal in response to a contralateral whisker stimulation (middle row) and during volitional whisking (bottom row). Brightness shows the mean normalized reflectance change from baseline. The light blue circular regions show the positions of individual macro-vibrissae barrel reconstructed from layer IV cytochrome oxidase staining. The locations of the implanted stereotrode (blue filled circle) and sensory regions are shown (top right). UJ: upper jaw, LJ: lower jaw, N: nose, FP: forepaw, FL: forelimb, HP: hindpaw, HL: hindlimb, TR: trunk, DZ: dysgranular zone. Scale bars = 1 mm.

Supplementary Figure 2 Heart-rate fluctuations are poorly correlated with sensory-evoked and spontaneous resting CBV changes.

a: Example of heart-rate fluctuations from a ~35-second-long period of rest. b: A scatterplot showing the root mean squared amplitude of heart rate fluctuations at rest for all animals (n = 12). c: The stimulus-triggered average heart rate among animals shows a small heart rate increase following contralateral (n = 9, mean±std:2.2±1.3%, blue, shaded region indicates standard deviation) and auditory (mean±std:0.4±1.6%, gray) stimulation. The peak heart rate for each animal (circles) is plotted to the right. Black circles indicate the mean of all animals. The heart rate increase following contralateral stimulation was not significantly larger than increases following an auditory stimulus (paired t-test, p = 0.92, t(8) = 0.11, n = 9). d: A comparison of the average stimulus-triggered reflectance changes driven by contralateral and auditory stimuli (solid blue and gray traces, respectively, shaded region shows the standard deviation). The contralateral puff elicited a larger CBV response than the auditory stimulus (blue, gray circles, paired t-test, p = 1×10−3, t(8) = −5.06). e: There was no significant linear relationship between CBV and heart rate increases following whisker and auditory stimuli in the data shown in c and d (n = 9, iteratively reweighted least squares, bisquare weighting, MATLAB function: robustfit, slope 0.002±0.002(mean±standard error), p = 0.35, t(7) = 0.99). Each animal is represented by a circle, fitted line shown as solid gray line, standard error is shown by shaded region and includes the zero slope. f: The power spectral density of the heart rate during periods of rest, averaged over all animals (n = 12). g: The mean cross-correlation between heart rate and CBV. Shaded region shows the standard deviation across animals (n = 12). The dashed red lines represent the 95% confidence interval based on reshuffled data (bootstrap, MATLAB function: bootci, 1000 resamples). h: A pooled scatter plot of the mean heart rate and root mean squared (R.M.S.) CBV during individual periods of rest (colored points) for all animals. Black line shows the linear fit using linear mixed-effects regression. There was a significant relationship between heart rate and the amplitude of the resting CBV variance (p = 0.004, t(532) = −2.9) but this relationship only explained 12.5% of differences in resting CBV variance (R 2 = 0.125).

Supplementary Figure 3 Neurovascular coupling in facial-nerve-transected animals.

a: A comparison of the mean stimulus triggered average CBV between animals with facial nerve transections (red, n = 3) and intact animals (black, n = 12) (left). Statistical comparison of the responses shows no significant difference between the response amplitudes (middle left, unpaired t-test, p = 0.52, t(13) = 0.66), full-width at half-max (middle right, unpaired t-test, Bonferroni corrected, p = 0.12, t(13) = 2.33), and the time to peak response (right, unpaired t-test, p = 0.24, t(13) = 1.24). Circles show results from individual animals. b: The mean cross-correlation between gamma-band neural power and ΔR/R during non-sensory evoked behavior for animals with facial nerve transections (red, n = 3) and intact animals (black, n = 12) (left). There were no significant difference in the maximum correlation coefficients between gamma-band power and normalized reflectance (middle left, unpaired t-test, p = 0.41, t(13) = −0.86), width of the cross correlation (middle right, unpaired t-test, p = 0.08, t(13) = −1.92), or the lag at maximum correlation (right, unpaired t-test, p = 0.81, t(13) = 0.24) compared between mice with intact facial nerves (black circles) and mice with transected facial nerves (red circles). c: same as b, but for the correlation between MUA and ∆R/R. (Unpaired t-test, Max correlation coefficient: p = 0.88, t(13) = −0.16; FWHM: p = 0.07, t(13) = −1.95; Time to peak: p = 0.31, t(13) = 1.05).

Supplementary Figure 4 Spontaneous cross-correlation between the LFP and reflectance in the barrel cortex in the absence of sensory stimulation.

Top: cross-correlation between MUA power and ∆R/R during all periods without whisker stimulation. The shaded region shows the population standard deviation (n = 12). Bottom: cross-correlation between ΔR/R and various frequency bands of the LFP. Color bar shows the magnitude of the correlations.

Supplementary Figure 5 Attributes of gamma-band power and MUA HRFs.

a: The amplitude, time to peak (TTP) and full-width at half-max (FWHM) of the gamma-band derived HRFs. Each circle represents the attribute from a single animal (n = 12) normalized by the sensory-evoked HRF attribute. Horizontal bars and shaded boxes show the median and interquartile range, respectively (amplitude comparison: Wilcoxon signed rank, Bonferroni corrected, stimulation vs. whisking: p = 0.64, z = −0.47, stimulation vs. rest: p = 0.06, z = −1.88. Time to peak comparison: paired t-test, stimulation vs. whisking: p = 0.23, t(11) = −1.28; stimulation vs. rest: p = 0.06, t(11) = 2.09. Full-width-half-maximum comparison: paired t-test, Bonferroni corrected, stimulation vs whisking: p = 0.11, t(11) = −1.74, Stimulation vs rest: p = 0.95, t(11) = −0.07). b: Same as a but for HRFs calculated from MUA power. (amplitude comparison: Wilcoxon signed rank, Bonferroni corrected, stimulation vs. whisking: p = 0.05, z = −2.27, stimulation vs. rest: p = 0.07, z = −2.12. Time to peak: Paired t-test, stimulation vs. whisking: p = 0.41, t(11) = −0.86; stimulation vs. rest: p = 0.80, t(11) = −0.26. Full-width-half-maximum comparison: paired t-test, stimulation vs. whisking: p = 0.97, t(11) = 0.04; stimulation vs. rest: p = 0.47, t(11) = 0.75). c: The attributes of the gamma-band derived HRF categorized according to stereotrode depth. Colored circles denote the depth of the implanted stereotrode obtained from histological reconstructions of each animal (Supragranular: n = 6, Granular: n = 3, Infragranular: n = 3). There was no detectable dependency of HRF attributes on the cortical depth of the recording site(HRF amplitude: Mann-Whitney U Test, Supragranular vs. granular: p = 0.07, z = 1.83, supragranular vs. infragranular: p = 0.74, z = 0.33, granular vs. infragranular: p = 0.30, z = −1.04; HRF time to peak: unpaired t-test, supragranular vs. granular: p = 0.81, t(16) = −0.38, supragranular vs. infragranular: p = 0.53, t(16) = −0.64, granular vs. infragranular: p = 0.51, t(10) = 0.69; HRF FWHM: Mann-Whitney U-test, supragranular vs. granular: p = 0.21, z = −1.26, supragranular vs. infragranular: p = 0.15, z = 1.45; granular vs. infragranular: p = 0.13, z = −1.52). d: Same as c, but for MUA derived HRF attributes (HRF amplitude: Mann-Whitney U-test, supragranular vs. granular: p = 0.81, z = 0.23, supragranular vs. infragranular: p = 0.21, z = 1.26, granular vs. infragranular: p = 0.30, z = −1.04; HRF TTP: unpaired t-test, supragranular vs. granular: p = 0.39, t(16) = −0.87, supragranular vs. infragranular: p = 0.39, t(16) = −0.88, granular vs. infragranular: p = 0.10, t(10) = −1.83; HRF FWHM: Mann-Whitney U-test, supragranular vs. granular: p = 0.07, z = 1.83; supragranular vs. infragranular: p = 0.07, z = 1.83, granular vs. infragranular: p = 0.30, z = 1.04).

Supplementary Figure 6 HRFs calculated from lower frequencies of the LFP.

a: Mean stimulus (top) and whisking (bottom) triggered neural power in the LFP at frequencies below 5 Hz (n = 12). b: A comparison of the mean HRFs derived from neural power in the 10–30 Hz band. Each HRF was calculated from a single behavioral state: stimulation (dark blue), volitional whisking (orange), and rest (light blue) and averaged across animals (n = 12). c: Same as b, but calculated from neural power in the 0.1–8 Hz band. d: Population summary of the goodness of fit (R2) of the CBV predictions based on neural power in the 10–30 Hz band. Circles represent median R2 for each animal. R2 calculated on predictions of the average behavior-triggered CBV are outlined in black, filled black circles show the population medians (Rest: R2 = 0.015). Circles without black outlines show medians of predictions from individual trials. e: Same as d, but for HRFs calculated from neural power in the 0.1–8 Hz. The median R2 of predictions using the 0.1–8 Hz band of the LFP of resting CBV was 0.018.

Supplementary Figure 7 Additional analyses of the CBV signal.

a: Population summary of the correlation between gamma-band power derived CBV predictions and measured CBV, separated according to behavior. Circles represent median correlation coefficient (R) for each animal (n = 12). R values calculated using predictions of the average behavior-triggered CBV are outlined in black (Friedman test, p = 1×10−4, χ2(2,22)  =  18.5. Post-hoc: Wilcoxon signed rank with Bonferroni correction, sensory evoked vs. extended movement: p = 0.18, z = 1.88; sensory evoked vs volitional whisking: p = 6×10−3, z = 3.06; extended movement vs. volitional whisking: p = 0.08, z = 2.20). The other colored circles indicate the median R values calculated for predictions of CBV during individual periods of behavior, (Two-way ANOVA, p = 9×10−12, F(3,11) = 45.01. Post-hoc: paired t-test with Bonferroni correction. sensory evoked vs. extended movement: p = 0.01, t(11) = 3.81; sensory evoked vs. volitional whisking: p = 6×10−5, t(11) = 7.15, sensory evoked vs rest: p = 6×10−7, t(11) = 11.40, extended movement vs. volitional whisking: p = 1×10−3, t(11) = 5.11, volitional whisking vs. rest: p = 0.13, t(11) = 2.29, extended movement vs rest: p = 1×10−4, t(11) = 6.46). The filled black circles indicate the population medians. b: Same as in a, but corresponding to MUA-derived HRFs (Averaged CBV: Friedman Test, p = 0.04, χ2(2,22) = 6.5, Post-hoc: Wilcoxon signed-rank with Bonferroni correction, sensory-evoked vs. extended movement: p = 0.08, z = 2.19, sensory-evoked vs. volitional whisk: p = 0.12, z = 2.04, volitional whisk vs. rest: p = 0.4, t(11) = −0.86; Individual CBV: Friedman test, p = 4×10−3, χ2(3,33) = 13.2, Post-hoc: Wilcoxon signed rank with Bonferroni correction, sensory evoked vs. extended movement: p = 0.05, z = 2.67, sensory evoked vs. volitional whisk: p = 0.05, z = 2.59, sensory evoked vs. rest: p = 0.09, z = 2.43, extended movement vs. whisk: p = 0.39, z = −0.86, extended movement vs. rest: p = 0.81, z = −0.23, volitional whisk vs. rest: p = 0.53, z = 0.63). Inset waveforms show examples of average sensory-evoked predictions (gray) with high and low correlations to the measured CBV (black). c: Example power spectra of the measured (black) and predicted (blue) CBV over all trials for a single animal. d: The mean power of the resting CBV prediction residuals (using the gamma-band HRF) across all animals (n = 12, black line). For comparison, the mean resting power spectrum following infusion of muscimol was also plotted (n = 9, blue line) was also plotted (see right axis for scale). Shaded regions indicate the population standard deviations. e: Subtraction of the global CBV signal did not substantially improve the gamma-band (paired t-test, p = 0.39, t(11) = 0.90) or MUA derived (Paired t-test, p = 0.03, t(11) = 2.87) prediction of CBV at rest. Colored circles denote single animals (n = 12). Black-filled circles indicate the mean. f: A 2-dimensional histogram of correlation coefficients between the gamma-band power derived predictions and the measured CBV during periods of spontaneous behavior, pooled across all animals, and the normalized CBV variance from the same period. Black line shows the best fit line (Linear mixed-effects regression, p = 1.44×10−8, t(3420) = 5.68) g: same as f, but for MUA derived CBV predictions (Linear mixed-effects regression, p = 0.17, t(3420) = 1.35). h: Slopes from f and g plotted for each animal. Slopes which were significantly different from zero (p<0.05) are black, all others are gray. The average MUA response to whisking was compared between animals with significant and not significant slopes showing differences in the whisking triggered MUA between the groups (inset). i: The changes in sensory-evoked summed MUA power and CBV (circles). Circles and bars outside the axes show the mean and standard deviation of each measure. We observed an 86±3% reduction in MUA standard deviation compared to aCSF controls and an 87±2% reduction following muscimol+CNQX+AP5 infusions, sensory evoked CBV were reduced 23±55% and 44±9% respectively. The reduction in CBV response to sensory stimulation was not significantly different between muscimol and muscimol+CNQX+AP5 (Welch’s t-test, p = 0.15, t(4.5) = 1.13). j: Same as for i, except for measured of summed gamma-band power following stimulation. Sensory-evoked gamma-band power was reduced 69±19% following infusion of muscimol and 82±8% following infusion of muscimol+CNQX+AP5 (unpaired t-test, one-sided, p = 0.12, t(11) = 1.24).

Supplementary Figure 8 Examples of the effects of pharmacological infusions on sensory-evoked and resting responses in individual animals.

a: Top left: image of a thinned-skull window showing the locations of the CBV ROI (yellow shaded region), implanted cannula, and stereotrode scale bar  =  1 mm. Top center: spatial map of the difference between the average aCSF and muscimol CBV responses to a contralateral whisker stimulus 0.5 to 2 seconds after stimulation. Top right: the spatial map of the difference between the standard deviation of CBV at rest following aCSF and muscimol infusions. Bottom left: the average sensory evoked MUA power for the example animal compared between aCSF (black) and muscimol (blue) infusions (bottom left). Bottom right: two examples of the CBV measured during single periods of rest following aCSF (black) and muscimol (blue) infusions (bottom right). b,c: Same as a, but for an animal infused with muscimol+CNQX+AP5 and the muscimol+adrenergic blockers, respectively.

Supplementary Figure 9 Changes in sensory-evoked neural activity following infusion.

a: Example data from two representative trials following aCSF (left panel) and muscimol (right panel) infusions. Movement of the whiskers (orange tick marks) were detected from the whisker angle (top), movement of the body (gray tick marks) and stimulation times (blue triangles) were also tracked. Normalized reflectance changes were averaged over a region of interest within the thinned skull window (second from top). Gamma-band power (third from top) and MUA (bottom) following infusion. b,c: Same as a, but from different animals following muscimol+CNQX+AP5 and muscimol+adrenergic blocker infusions, respectively.

Supplementary Figure 10 CBV fluctuations after muscimol infusions do not propagate from nearby regions.

a: Mean (solid line) and standard deviation (shaded region) of cross-correlograms between the reflectance within the region of interest and the reflectance from the rest of the window (n = 6, three animals omitted which had windows that were spanned by the ROI). b: Comparison of the correlation coefficients between aCSF and muscimol infusions. The correlations were not significantly different (paired t-test, p = 0.12, t(5) = −1.9). c: Comparison of the lags at maximum correlation between aCSF and muscimol infusions. The lags were not significantly different (paired t-test, p = 0.3, t(5) = −1.17).

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Supplementary Figures 1–10

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Supplementary Video 1

Example CBV and neural fluctuations in the context of animal behavior. The normalized reflectance (ΔR/R), measured from a thinned skull window, is shown during rest and behavior. Note that decreased reflectance corresponds to increased CBV. Images of the whiskers are provided for behavioral context. An image of the thinned skull window with the location of the stereotrode (dark blue circle) and whisker barrels (light blue shading) are also given. A time-frequency spectrogram of the measured local field potential, MUA and the detected whisker angle are plotted. The sliding black bar indicates the trial time for each CBV and whisker image.

Supplementary Video 2

Example CBV and neural fluctuations during rest following infusion of aCSF. The normalized reflectance (ΔR/R), measured from a thinned skull window, is shown during rest. Note that decreased reflectance corresponds to increased CBV. Images of the whiskers are provided for behavioral context. An image of the thinned skull window with the location of the stereotrode, cannula and the region of muscimol effect (shaded yellow, compare to Movie 3, see Methods) are also given. A time-frequency spectrogram of the measured local field potential, MUA and the detected whisker angle are plotted. The sliding black bar indicates the time of each CBV and whisker image.

Supplementary Video 3

Example CBV and neural fluctuations during rest following infusion of muscimol. An example of the normalized reflectance (ΔR/R) measured from a thinned skull window during rest. Note that decreased reflectance corresponds to increased CBV. Images of the whiskers are provided for behavioral context. An image of the thinned skull window with the location of the stereotrode, cannula and the region of muscimol effect (shaded yellow, see Methods) are also given. A time-frequency spectrogram of the measured local field potential, MUA and the detected angle are plotted. The sliding black bar indicates the time of each CBV and whisker image.

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Winder, A.T., Echagarruga, C., Zhang, Q. et al. Weak correlations between hemodynamic signals and ongoing neural activity during the resting state. Nat Neurosci 20, 1761–1769 (2017). https://doi.org/10.1038/s41593-017-0007-y

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