Brain Structure and Function

, Volume 225, Issue 1, pp 227–240 | Cite as

Global brain signal in awake rats

  • Yuncong Ma
  • Zilu Ma
  • Zhifeng Liang
  • Thomas Neuberger
  • Nanyin ZhangEmail author
Original Article


Although often used as a nuisance in resting-state functional magnetic resonance imaging (rsfMRI), the global brain signal in humans and anesthetized animals has important neural basis. However, our knowledge of the global signal in awake rodents is sparse. To bridge this gap, we systematically analyzed rsfMRI data acquired with a conventional single-echo (SE) echo planar imaging (EPI) sequence in awake rats. The spatial pattern of rsfMRI frames during peaks of the global signal exhibited prominent co-activations in the thalamo-cortical and hippocampo-cortical networks, as well as in the basal forebrain, hinting that these neural networks might contribute to the global brain signal in awake rodents. To validate this concept, we acquired rsfMRI data using a multi-echo (ME) EPI sequence and removed non-neural components in the rsfMRI signal. Consistent co-activation patterns were obtained in extensively de-noised ME-rsfMRI data, corroborating the finding from SE-rsfMRI data. Furthermore, during rsfMRI experiments, we simultaneously recorded neural spiking activities in the hippocampus using GCaMP-based fiber photometry. The hippocampal calcium activity exhibited significant correspondence with the global rsfMRI signal. These data collectively suggest that the global rsfMRI signal contains significant neural components that involve coordinated activities in the thalamo-cortical and hippocampo-cortical networks. These results provide important insight into the neural substrate of the global brain signal in awake rodents.


Global Signal Resting-state fMRI Awake Rat 



The present study was supported by National Institute of Neurological Disorders and Stroke (R01NS085200, PI: Nanyin Zhang, PhD) and National Institute of Mental Health (R01MH098003 and RF1MH114224, PI: Nanyin Zhang, PhD).

Compliance with ethical standards

Conflict of interest

The author(s) declare that they have no conflict of interest.

Research involving human participants and/or animals

The research involved animals. All procedures were conducted in accordance with approved protocols from the Institutional Animal Care and Use Committee (IACUC) of the Pennsylvania State University.


  1. Aguirre GK, Zarahn E, D’Esposito M (1997) Empirical analyses of BOLD fMRI statistics. II. Spatially smoothed data collected under null-hypothesis and experimental conditions. Neuroimage 5(3):199–212CrossRefGoogle Scholar
  2. Bergmann E, Zur G, Bershadsky G, Kahn I (2016) The organization of mouse and human cortico-hippocampal networks estimated by intrinsic functional connectivity. Cereb Cortex 26(12):4497–4512. CrossRefPubMedPubMedCentralGoogle Scholar
  3. Birn RM, Smith MA, Jones TB, Bandettini PA (2008) The respiration response function: the temporal dynamics of fMRI signal fluctuations related to changes in respiration. Neuroimage 40(2):644–654. CrossRefPubMedGoogle Scholar
  4. Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34(4):537–541CrossRefGoogle Scholar
  5. Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM, Beckmann CF, Adelstein JS, Buckner RL, Colcombe S, Dogonowski AM, Ernst M, Fair D, Hampson M, Hoptman MJ, Hyde JS, Kiviniemi VJ, Kotter R, Li SJ, Lin CP, Lowe MJ, Mackay C, Madden DJ, Madsen KH, Margulies DS, Mayberg HS, McMahon K, Monk CS, Mostofsky SH, Nagel BJ, Pekar JJ, Peltier SJ, Petersen SE, Riedl V, Rombouts SA, Rypma B, Schlaggar BL, Schmidt S, Seidler RD, Siegle GJ, Sorg C, Teng GJ, Veijola J, Villringer A, Walter M, Wang L, Weng XC, Whitfield-Gabrieli S, Williamson P, Windischberger C, Zang YF, Zhang HY, Castellanos FX, Milham MP (2010) Toward discovery science of human brain function. Proc Natl Acad Sci USA 107(10):4734–4739. CrossRefPubMedGoogle Scholar
  6. Calhoun VD, Adali T, Pearlson GD, Pekar JJ (2001) A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 14(3):140–151CrossRefGoogle Scholar
  7. Chan RW, Leong ATL, Ho LC, Gao PP, Wong EC, Dong CM, Wang X, He J, Chan YS, Lim LW, Wu EX (2017) Low-frequency hippocampal-cortical activity drives brain-wide resting-state functional MRI connectivity. Proc Natl Acad Sci USA 114(33):E6972–E6981. CrossRefPubMedGoogle Scholar
  8. Chang C, Cunningham JP, Glover GH (2009) Influence of heart rate on the BOLD signal: the cardiac response function. Neuroimage 44(3):857–869. CrossRefPubMedGoogle Scholar
  9. Chang C, Leopold DA, Scholvinck ML, Mandelkow H, Picchioni D, Liu X, Ye FQ, Turchi JN, Duyn JH (2016a) Tracking brain arousal fluctuations with fMRI. Proc Natl Acad Sci USA 113(16):4518–4523. CrossRefPubMedGoogle Scholar
  10. Chang PC, Procissi D, Bao Q, Centeno MV, Baria A, Apkarian AV (2016b) Novel method for functional brain imaging in awake minimally restrained rats. J Neurophysiol 116(1):61–80. CrossRefPubMedPubMedCentralGoogle Scholar
  11. Ciric R, Rosen AFG, Erus G, Cieslak M, Adebimpe A, Cook PA, Bassett DS, Davatzikos C, Wolf DH, Satterthwaite TD (2018) Mitigating head motion artifact in functional connectivity MRI. Nat Protoc 13(12):2801–2826. CrossRefPubMedGoogle Scholar
  12. Colenbier N, Van de Steen F, Uddin LQ, Poldrack RA, Calhoun V, Marinazzo D (2019) Disambiguating the role of blood flow and global signal with Partial Information Decomposition. Front Neurosci. CrossRefGoogle Scholar
  13. Dopfel D, Zhang N (2018) Mapping stress networks using functional magnetic resonance imaging in awake animals. Neurobiol Stress 9:251–263. CrossRefPubMedPubMedCentralGoogle Scholar
  14. Dopfel D, Perez PD, Verbitsky A, Bravo-Rivera H, Ma Y, Quirk GJ, Zhang N (2019) Individual variability in behavior and functional networks predicts vulnerability using an animal model of PTSD. Nat Commun 10(1):2372. CrossRefPubMedPubMedCentralGoogle Scholar
  15. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME (2005) The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci USA 102(27):9673–9678CrossRefGoogle Scholar
  16. Gao YR, Ma Y, Zhang Q, Winder AT, Liang Z, Antinori L, Drew PJ, Zhang N (2016) Time to wake up: studying neurovascular coupling and brain-wide circuit function in the un-anesthetized animal. Neuroimage. CrossRefPubMedPubMedCentralGoogle Scholar
  17. Gutierrez-Barragan D, Basson MA, Panzeri S, Gozzi A (2019) Infraslow state fluctuations govern spontaneous fMRI network dynamics. Curr Biol. CrossRefPubMedPubMedCentralGoogle Scholar
  18. Hamilton C, Ma Y, Zhang N (2017) Global reduction of information exchange during anesthetic-induced unconsciousness. Brain Struct Funct 222(7):3205–3216. CrossRefPubMedPubMedCentralGoogle Scholar
  19. Kalthoff D, Seehafer JU, Po C, Wiedermann D, Hoehn M (2011) Functional connectivity in the rat at 11.7T: Impact of physiological noise in resting state fMRI. Neuroimage 54(4):2828–2839. CrossRefPubMedGoogle Scholar
  20. Kim CK, Yang SJ, Pichamoorthy N, Young NP, Kauvar I, Jennings JH, Lerner TN, Berndt A, Lee SY, Ramakrishnan C, Davidson TJ, Inoue M, Bito H, Deisseroth K (2016) Simultaneous fast measurement of circuit dynamics at multiple sites across the mammalian brain. Nat Methods 13(4):325–328. CrossRefPubMedPubMedCentralGoogle Scholar
  21. Kundu P, Inati SJ, Evans JW, Luh WM, Bandettini PA (2012) Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI. Neuroimage 60(3):1759–1770. CrossRefPubMedGoogle Scholar
  22. Kundu P, Brenowitz ND, Voon V, Worbe Y, Vertes PE, Inati SJ, Saad ZS, Bandettini PA, Bullmore ET (2013) Integrated strategy for improving functional connectivity mapping using multiecho fMRI. Proc Natl Acad Sci USA 110(40):16187–16192. CrossRefPubMedGoogle Scholar
  23. Kundu P, Santin MD, Bandettini PA, Bullmore ET, Petiet A (2014) Differentiating BOLD and non-BOLD signals in fMRI time series from anesthetized rats using multi-echo EPI at 11.7 T. Neuroimage 102(Pt 2):861–874. CrossRefGoogle Scholar
  24. Leong AT, Chan RW, Gao PP, Chan YS, Tsia KK, Yung WH, Wu EX (2016) Long-range projections coordinate distributed brain-wide neural activity with a specific spatiotemporal profile. Proc Natl Acad Sci USA 113(51):E8306–E8315. CrossRefPubMedGoogle Scholar
  25. Liang Z, King J, Zhang N (2011) Uncovering intrinsic connectional architecture of functional networks in awake rat brain. J Neurosci 31(10):3776–3783CrossRefGoogle Scholar
  26. Liang Z, King J, Zhang N (2012a) Anticorrelated resting-state functional connectivity in awake rat brain. Neuroimage 59(2):1190–1199. CrossRefGoogle Scholar
  27. Liang Z, King J, Zhang N (2012b) Intrinsic organization of the anesthetized brain. J Neurosci 32(30):10183–10191. CrossRefPubMedPubMedCentralGoogle Scholar
  28. Liang Z, Li T, King J, Zhang N (2013) Mapping thalamocortical networks in rat brain using resting-state functional connectivity. Neuroimage 83:237–244. CrossRefPubMedGoogle Scholar
  29. Liang Z, King J, Zhang N (2014) Neuroplasticity to a single-episode traumatic stress revealed by resting-state fMRI in awake rats. Neuroimage 103:485–491. CrossRefPubMedPubMedCentralGoogle Scholar
  30. Liang Z, Liu X, Zhang N (2015a) Dynamic resting state functional connectivity in awake and anesthetized rodents. Neuroimage 104:89–99. CrossRefPubMedGoogle Scholar
  31. Liang Z, Watson GD, Alloway KD, Lee G, Neuberger T, Zhang N (2015b) Mapping the functional network of medial prefrontal cortex by combining optogenetics and fMRI in awake rats. Neuroimage 117:114–123. CrossRefPubMedPubMedCentralGoogle Scholar
  32. Liang Z, Ma Y, Watson GDR, Zhang N (2017) Simultaneous GCaMP6-based fiber photometry and fMRI in rats. J Neurosci Methods 289:31–38. CrossRefPubMedPubMedCentralGoogle Scholar
  33. Liu TT (2016) Noise contributions to the fMRI signal: an overview. Neuroimage 143:141–151. CrossRefPubMedGoogle Scholar
  34. Liu Y, Zhang N (2019) Propagations of spontaneous brain activity in awake rats. Neuroimage 202:116176. CrossRefPubMedGoogle Scholar
  35. Liu TT, Nalci A, Falahpour M (2017) The global signal in fMRI: nuisance or information? Neuroimage 150:213–229. CrossRefPubMedPubMedCentralGoogle Scholar
  36. Liu X, de Zwart JA, Scholvinck ML, Chang C, Ye FQ, Leopold DA, Duyn JH (2018) Subcortical evidence for a contribution of arousal to fMRI studies of brain activity. Nat Commun 9(1):395. CrossRefPubMedPubMedCentralGoogle Scholar
  37. Logothetis NK, Eschenko O, Murayama Y, Augath M, Steudel T, Evrard HC, Besserve M, Oeltermann A (2012) Hippocampal-cortical interaction during periods of subcortical silence. Nature 491(7425):547–553. CrossRefPubMedGoogle Scholar
  38. Ma Z, Zhang N (2018) Temporal transitions of spontaneous brain activity. eLife 2018:7.
  39. Ma Y, Shaik MA, Kozberg MG, Kim SH, Portes JP, Timerman D, Hillman EM (2016) Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons. Proc Natl Acad Sci USA 113(52):E8463–E8471. CrossRefPubMedGoogle Scholar
  40. Ma Y, Hamilton C, Zhang N (2017) Dynamic connectivity patterns in conscious and unconscious brain. Brain connectivity 7(1):1–12. CrossRefPubMedPubMedCentralGoogle Scholar
  41. Ma Z, Perez P, Ma Z, Liu Y, Hamilton C, Liang Z, Zhang N (2018) Functional atlas of the awake rat brain: a neuroimaging study of rat brain specialization and integration. Neuroimage 170:95–112. CrossRefPubMedGoogle Scholar
  42. Matsui T, Murakami T, Ohki K (2016) Transient neuronal coactivations embedded in globally propagating waves underlie resting-state functional connectivity. Proc Natl Acad Sci USA 113(23):6556–6561. CrossRefPubMedGoogle Scholar
  43. Murphy K, Birn RM, Handwerker DA, Jones TB, Bandettini PA (2009) The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage 44(3):893–905CrossRefGoogle Scholar
  44. Nalci A, Rao BD, Liu TT (2017) Global signal regression acts as a temporal downweighting process in resting-state fMRI. Neuroimage 152:602–618. CrossRefPubMedGoogle Scholar
  45. Perez PD, Ma Z, Hamilton C, Sanchez C, Mork A, Pehrson AL, Bundgaard C, Zhang N (2018) Acute effects of vortioxetine and duloxetine on resting-state functional connectivity in the awake rat. Neuropharmacology 128:379–387. CrossRefPubMedGoogle Scholar
  46. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59(3):2142–2154. CrossRefPubMedGoogle Scholar
  47. Power JD, Schlaggar BL, Petersen SE (2015) Recent progress and outstanding issues in motion correction in resting state fMRI. Neuroimage 105:536–551. CrossRefPubMedGoogle Scholar
  48. Rack-Gomer AL, Liu TT (2012) Caffeine increases the temporal variability of resting-state BOLD connectivity in the motor cortex. Neuroimage 59(3):2994–3002. CrossRefPubMedGoogle Scholar
  49. Raichle ME (2006) Neuroscience. The brain’s dark energy. Science 314(5803):1249–1250. CrossRefPubMedGoogle Scholar
  50. Raichle ME (2010) The brain’s dark energy. Sci Am 302(3):44–49CrossRefGoogle Scholar
  51. Ramirez-Villegas JF, Logothetis NK, Besserve M (2015) Diversity of sharp-wave-ripple LFP signatures reveals differentiated brain-wide dynamical events. Proc Natl Acad Sci USA 112(46):E6379–E6387. CrossRefPubMedGoogle Scholar
  52. Rivera B, Miller S, Brown E, Price R (2005) A novel method for endotracheal intubation of mice and rats used in imaging studies. Contemp Top Lab Anim Sci 44(2):52–55PubMedGoogle Scholar
  53. Satterthwaite TD, Wolf DH, Loughead J, Ruparel K, Elliott MA, Hakonarson H, Gur RC, Gur RE (2012) Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth. Neuroimage 60(1):623–632. CrossRefPubMedPubMedCentralGoogle Scholar
  54. Scholvinck ML, Maier A, Ye FQ, Duyn JH, Leopold DA (2010) Neural basis of global resting-state fMRI activity. Proc Natl Acad Sci USA 107(22):10238–10243CrossRefGoogle Scholar
  55. Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM, Mackay CE, Filippini N, Watkins KE, Toro R, Laird AR, Beckmann CF (2009) Correspondence of the brain’s functional architecture during activation and rest. Proc Natl Acad Sci USA 106(31):13040–13045. CrossRefPubMedGoogle Scholar
  56. Smith JB, Liang Z, Watson GDR, Alloway KD, Zhang N (2017) Interhemispheric resting-state functional connectivity of the claustrum in the awake and anesthetized states. Brain Struct Funct 222(5):2041–2058. CrossRefPubMedGoogle Scholar
  57. Turchi J, Chang C, Ye FQ, Russ BE, Yu DK, Cortes CR, Monosov IE, Duyn JH, Leopold DA (2018) The basal forebrain regulates global resting-state fMRI fluctuations. Neuron 97(4):940–952 e944. CrossRefGoogle Scholar
  58. Van Dijk KR, Sabuncu MR, Buckner RL (2012) The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 59(1):431–438. CrossRefPubMedGoogle Scholar
  59. Wen H, Liu Z (2016) Broadband electrophysiological dynamics contribute to global resting-state fMRI signal. J Neurosci 36(22):6030–6040. CrossRefPubMedPubMedCentralGoogle Scholar
  60. Wong CW, Olafsson V, Tal O, Liu TT (2012) Anti-correlated networks, global signal regression, and the effects of caffeine in resting-state functional MRI. Neuroimage 63(1):356–364. CrossRefPubMedPubMedCentralGoogle Scholar
  61. Wong CW, Olafsson V, Tal O, Liu TT (2013) The amplitude of the resting-state fMRI global signal is related to EEG vigilance measures. Neuroimage 83:983–990. CrossRefPubMedGoogle Scholar
  62. Yang GJ, Murray JD, Repovs G, Cole MW, Savic A, Glasser MF, Pittenger C, Krystal JH, Wang XJ, Pearlson GD, Glahn DC, Anticevic A (2014) Altered global brain signal in schizophrenia. Proc Natl Acad Sci USA 111(20):7438–7443. CrossRefPubMedGoogle Scholar
  63. Yang GJ, Murray JD, Glasser M, Pearlson GD, Krystal JH, Schleifer C, Repovs G, Anticevic A (2017) Altered global signal topography in schizophrenia. Cereb Cortex 27(11):5156–5169. CrossRefPubMedGoogle Scholar
  64. Yoshida K, Mimura Y, Ishihara R, Nishida H, Komaki Y, Minakuchi T, Tsurugizawa T, Mimura M, Okano H, Tanaka KF, Takata N (2016) Physiological effects of a habituation procedure for functional MRI in awake mice using a cryogenic radiofrequency probe. J Neurosci Methods 274:38–48. CrossRefPubMedGoogle Scholar
  65. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128. CrossRefGoogle Scholar
  66. Zhang D, Raichle ME (2010) Disease and the brain’s dark energy. Nat Rev Neurol 6(1):15–28. CrossRefPubMedGoogle Scholar
  67. Zhang N, Rane P, Huang W, Liang Z, Kennedy D, Frazier JA, King J (2010) Mapping resting-state brain networks in conscious animals. J Neurosci Methods 189(2):186–196. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.The Huck Institutes of the Life SciencesThe Pennsylvania State UniversityUniversity ParkUSA
  3. 3.Institute of NeuroscienceChinese Academy of ScienceShanghaiChina

Personalised recommendations