Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signals.

Wenyu Tu, Samuel R Cramer, Nanyin Zhang
Author Information
  1. Wenyu Tu: The Neuroscience Graduate Program, The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, USA.
  2. Samuel R Cramer: The Neuroscience Graduate Program, The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, USA.
  3. Nanyin Zhang: The Neuroscience Graduate Program, The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, USA. ORCID

Abstract

Resting-state brain networks (RSNs) have been widely applied in health and disease, but the interpretation of RSNs in terms of the underlying neural activity is unclear. To address this fundamental question, we conducted simultaneous recordings of whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions of rats. Our data reveal that for both recording sites, spatial maps derived from band-specific local field potential (LFP) power can account for up to 90% of the spatial variability in RSNs derived from rsfMRI signals. Surprisingly, the time series of LFP band power can only explain to a maximum of 35% of the temporal variance of the local rsfMRI time course from the same site. In addition, regressing out time series of LFP power from rsfMRI signals has minimal impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggests that electrophysiological activity alone does not fully explain the effects observed in the rsfMRI signal, implying the existence of an rsfMRI component contributed by "electrophysiology-invisible" signals. These findings offer a novel perspective on our understanding of RSN interpretation.

Keywords

References

  1. Proc Natl Acad Sci U S A. 2007 Nov 13;104(46):18265-9 [PMID: 17991778]
  2. Proc Natl Acad Sci U S A. 2012 Mar 6;109(10):3979-84 [PMID: 22355129]
  3. J Neurosci. 2014 Jan 8;34(2):356-62 [PMID: 24403137]
  4. Neuroimage. 2013 Dec;83:237-44 [PMID: 23777756]
  5. Cereb Cortex. 2016 Dec;26(12):4497-4512 [PMID: 27797832]
  6. Nat Commun. 2019 Dec 4;10(1):5515 [PMID: 31797933]
  7. Cereb Cortex. 2016 Feb;26(2):669-682 [PMID: 25316339]
  8. Elife. 2020 Oct 05;9: [PMID: 33016877]
  9. Proc Natl Acad Sci U S A. 2016 Dec 27;113(52):E8463-E8471 [PMID: 27974609]
  10. Neuroimage. 2014 Jul 15;95:232-47 [PMID: 24657355]
  11. Proc Natl Acad Sci U S A. 2009 Feb 10;106(6):2035-40 [PMID: 19188601]
  12. Neuron. 2010 Feb 25;65(4):550-62 [PMID: 20188659]
  13. J Neurosci. 2004 Apr 14;24(15):3850-61 [PMID: 15084666]
  14. Science. 2005 Aug 5;309(5736):951-4 [PMID: 16081741]
  15. Cereb Cortex. 2009 Jan;19(1):72-8 [PMID: 18403396]
  16. Nat Neurosci. 2008 Oct;11(10):1193-200 [PMID: 18711393]
  17. Elife. 2018 Mar 08;7: [PMID: 29517975]
  18. IEEE J Biomed Health Inform. 2018 Sep;22(5):1476-1485 [PMID: 29994175]
  19. Neuroimage. 2020 Feb 15;207:116390 [PMID: 31785420]
  20. J Neurosci. 2018 Apr 25;38(17):4230-4242 [PMID: 29626167]
  21. Curr Biol. 2008 May 6;18(9):631-40 [PMID: 18439825]
  22. Neuroimage. 2022 Apr 15;250:118960 [PMID: 35121182]
  23. Proc Natl Acad Sci U S A. 2023 May 2;120(18):e2220777120 [PMID: 37098063]
  24. Nat Neurosci. 2013 Oct;16(10):1426-35 [PMID: 23974708]
  25. J Cereb Blood Flow Metab. 2012 Dec;32(12):2135-45 [PMID: 22872230]
  26. Cereb Cortex. 2011 Feb;21(2):374-84 [PMID: 20530220]
  27. J Neurosci. 2009 Feb 11;29(6):1860-73 [PMID: 19211893]
  28. Alzheimer Dis Assoc Disord. 1995 Spring;9(1):28-38 [PMID: 7605619]
  29. Proc Natl Acad Sci U S A. 2017 May 16;114(20):5253-5258 [PMID: 28461461]
  30. Brain Connect. 2011;1(2):119-31 [PMID: 22433008]
  31. Proc Natl Acad Sci U S A. 2009 Aug 4;106(31):13040-5 [PMID: 19620724]
  32. Proc Natl Acad Sci U S A. 2006 Sep 12;103(37):13848-53 [PMID: 16945915]
  33. Nat Commun. 2024 Jan 3;15(1):229 [PMID: 38172111]
  34. Nat Biomed Eng. 2024 Jan;8(1):68-84 [PMID: 38082179]
  35. Magn Reson Med. 1995 Oct;34(4):537-41 [PMID: 8524021]
  36. Neuroimage. 2013 Jul 1;74:288-97 [PMID: 23481462]
  37. Neuroimage. 2022 Feb 15;247:118728 [PMID: 34923136]
  38. Elife. 2022 Oct 20;11: [PMID: 36263940]
  39. Nat Commun. 2023 Feb 6;14(1):375 [PMID: 36746938]
  40. Neuroimage. 2003 Aug;19(4):1463-76 [PMID: 12948703]
  41. Hum Brain Mapp. 2002 Apr;15(4):247-62 [PMID: 11835612]
  42. Neuron. 2017 Nov 15;96(4):936-948.e3 [PMID: 29107517]
  43. Neuron. 2015 Apr 22;86(2):578-90 [PMID: 25863718]
  44. Proc Natl Acad Sci U S A. 2008 Oct 14;105(41):16039-44 [PMID: 18843113]
  45. Neuroimage. 1998 Feb;7(2):119-32 [PMID: 9558644]
  46. Nat Commun. 2018 Jan 26;9(1):395 [PMID: 29374172]
  47. J Neurophysiol. 2016 Jul 1;116(1):61-80 [PMID: 27052584]
  48. Hum Brain Mapp. 2008 Jul;29(7):751-61 [PMID: 18465799]
  49. J Neurosci. 2011 Sep 7;31(36):12954-62 [PMID: 21900574]
  50. Nature. 2014 Apr 10;508(7495):207-14 [PMID: 24695228]
  51. Nature. 2001 Jul 12;412(6843):150-7 [PMID: 11449264]
  52. Neuroimage. 2019 Oct 15;200:405-413 [PMID: 31280011]
  53. Nat Neurosci. 2008 Sep;11(9):1100-8 [PMID: 19160509]
  54. Nat Neurosci. 2017 Dec;20(12):1761-1769 [PMID: 29184204]
  55. J Neurosci. 2016 May 11;36(19):5314-27 [PMID: 27170128]
  56. Proc Natl Acad Sci U S A. 2007 Aug 7;104(32):13170-5 [PMID: 17670949]
  57. J Neurosci. 2012 Jul 25;32(30):10183-91 [PMID: 22836253]
  58. J Neurosci. 2012 Jan 25;32(4):1395-407 [PMID: 22279224]
  59. Hum Brain Mapp. 2008 Jul;29(7):818-27 [PMID: 18438889]
  60. Nature. 2016 Aug 11;536(7615):171-178 [PMID: 27437579]
  61. Proc Natl Acad Sci U S A. 2010 Jun 1;107(22):10238-43 [PMID: 20439733]
  62. Neuron. 2015 Jan 21;85(2):390-401 [PMID: 25556836]
  63. Nat Neurosci. 2022 Aug;25(8):1093-1103 [PMID: 35902649]
  64. J Neurosci. 2013 Apr 10;33(15):6333-42 [PMID: 23575832]
  65. Neuroimage. 2017 Apr 1;149:446-457 [PMID: 28159686]
  66. Cereb Cortex. 2016 Feb;26(2):683-694 [PMID: 25331598]

Grants

  1. R01 NS085200/NINDS NIH HHS
  2. RF1 MH114224/NIMH NIH HHS

Word Cloud

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