Axial variation of deoxyhemoglobin density as a source of the low-frequency time lag structure in blood oxygenation level-dependent signals


Autoři: Toshihiko Aso aff001;  Shinnichi Urayama aff003;  Hidenao Fukuyama aff003;  Toshiya Murai aff001
Působiště autorů: Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan aff001;  Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan aff002;  Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan aff003;  Research and Educational Unit of Leaders for Integrated Medical System, Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, Kyoto, Japan aff004
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222787

Souhrn

Perfusion-related information is reportedly embedded in the low-frequency component of a blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal. The blood-propagation pattern through the cerebral vascular tree is detected as an interregional lag variation of spontaneous low-frequency oscillations (sLFOs). Mapping of this lag, or phase, has been implicitly treated as a projection of the vascular tree structure onto real space. While accumulating evidence supports the biological significance of this signal component, the physiological basis of the “perfusion lag structure,” a requirement for an integrative resting-state fMRI-signal model, is lacking. In this study, we conducted analyses furthering the hypothesis that the sLFO is not only largely of systemic origin, but also essentially intrinsic to blood, and hence behaves as a virtual tracer. By summing the small fluctuations of instantaneous phase differences between adjacent vascular regions, a velocity response to respiratory challenges was detected. Regarding the relationship to neurovascular coupling, the removal of the whole lag structure, which can be considered as an optimized global-signal regression, resulted in a reduction of inter-individual variance while preserving the fMRI response. Examination of the T2* and S0, or non-BOLD, components of the fMRI signal revealed that the lag structure is deoxyhemoglobin dependent, while paradoxically presenting a signal-magnitude reduction in the venous side of the cerebral vasculature. These findings provide insight into the origin of BOLD sLFOs, suggesting that they are highly intrinsic to the circulating blood.

Klíčová slova:

Analysis of variance – Blood – Blood flow – Blood pressure – Functional magnetic resonance imaging – Magnetic resonance imaging – Near-infrared spectroscopy – Oxygen


Zdroje

1. Buxton RB. The physics of functional magnetic resonance imaging (fMRI). Reports on Progress in Physics. 2013;76: 096601. doi: 10.1088/0034-4885/76/9/096601 24006360

2. Hillman EMC. Coupling Mechanism and Significance of the BOLD Signal: A Status Report. Annual Review of Neuroscience. 2014;37: 161–181. doi: 10.1146/annurev-neuro-071013-014111 25032494

3. Hoiland RL, Tymko MM, Bain AR, Wildfong KW, Monteleone B, Ainslie PN. Carbon dioxide‐mediated vasomotion of extra‐cranial cerebral arteries in humans: a role for prostaglandins? J Physiol. 2016;594: 3463–3481. doi: 10.1113/JP272012 26880615

4. Murphy K, Harris AD, Wise RG. Robustly measuring vascular reactivity differences with breath-hold: Normalising stimulus-evoked and resting state BOLD fMRI data. NeuroImage. 2011;54: 369–379. doi: 10.1016/j.neuroimage.2010.07.059 20682354

5. Willie CK, Tzeng Y-C, Fisher JA, Ainslie PN. Integrative regulation of human brain blood flow. The Journal of Physiology. 2014;592: 841–859. doi: 10.1113/jphysiol.2013.268953 24396059

6. Kim JH, Ress D. Arterial impulse model for the BOLD response to brief neural activation. NeuroImage. 2016;124: 394–408. doi: 10.1016/j.neuroimage.2015.08.068 26363350

7. Chang C, Cunningham JP, Glover GH. Influence of heart rate on the BOLD signal: The cardiac response function. NeuroImage. 2009;44: 857–869. doi: 10.1016/j.neuroimage.2008.09.029 18951982

8. Murphy K, Birn RM, Bandettini PA. Resting-state fMRI confounds and cleanup. NeuroImage. 2013;80: 349–59. doi: 10.1016/j.neuroimage.2013.04.001 23571418

9. Wise RG, Ide K, Poulin MJ, Tracey I. Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal. NeuroImage. 2004;21: 1652–64. doi: 10.1016/j.neuroimage.2003.11.025 15050588

10. Winder AT, Echagarruga C, Zhang Q, Drew PJ. Weak correlations between hemodynamic signals and ongoing neural activity during the resting state. Nature neuroscience. 2017;20: 1761–1769. doi: 10.1038/s41593-017-0007-y 29184204

11. Tong Y, Lindsey KP, Hocke LM, Vitaliano G, Mintzopoulos D, Frederick deB B. Perfusion information extracted from resting state functional magnetic resonance imaging. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism. 2017;37: 564–576. doi: 10.1177/0271678X16631755 26873885

12. Zhu DC, Tarumi T, Khan MA, Zhang R. Vascular coupling in resting-state fMRI: evidence from multiple modalities. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism. 2015;35: 1910–20. doi: 10.1038/jcbfm.2015.166 26174326

13. Birn RM, Murphy K, Bandettini PA. The effect of respiration variations on independent component analysis results of resting state functional connectivity. Human Brain Mapping. 2008;29: 740–750. doi: 10.1002/hbm.20577 18438886

14. Guyton AC, Harris JW. Pressoreceptor-autonomic oscillation; a probable cause of vasomotor waves. The American journal of physiology. 1951;165: 158–66. doi: 10.1152/ajplegacy.1951.165.1.158 14829585

15. Julien C. The enigma of Mayer waves: Facts and models. Cardiovascular research. 2006;70: 12–21. doi: 10.1016/j.cardiores.2005.11.008 16360130

16. Killip T. Oscillation of blood flow and vascular resistance during Mayer waves. Circulation research. 1962;11: 987–993. doi: 10.1161/01.res.11.6.987 14032613

17. Fagrell B, Fronek A, Intaglietta M. A microscope-television system for studying flow velocity in human skin capillaries. The American journal of physiology. 1977;233: H318–21. doi: 10.1152/ajpheart.1977.233.2.H318 888975

18. Giller CA, Hatab MR, Giller AM. Oscillations in cerebral blood flow detected with a transcranial Doppler index. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism. 1999;19: 452–9. doi: 10.1097/00004647-199904000-00011 10197515

19. Obrig H, Neufang M, Wenzel R, Kohl M, Steinbrink J, Einhäupl K, et al. Spontaneous Low Frequency Oscillations of Cerebral Hemodynamics and Metabolism in Human Adults. NeuroImage. 2000;12: 623–639. doi: 10.1006/nimg.2000.0657 11112395

20. Tgavalekos KT, Kainerstorfer JM, Sassaroli A, Fantini S. Blood-pressure-induced oscillations of deoxy- and oxyhemoglobin concentrations are in-phase in the healthy breast and out-of-phase in the healthy brain. Journal of Biomedical Optics. 2016;21: 101410. doi: 10.1117/1.JBO.21.10.101410 27020418

21. Rayshubskiy A, Wojtasiewicz TJ, Mikell CB, Bouchard MB, Timerman D, Youngerman BE, et al. Direct, intraoperative observation of ~0.1Hz hemodynamic oscillations in awake human cortex: Implications for fMRI. NeuroImage. 2014;87: 323–331. doi: 10.1016/j.neuroimage.2013.10.044 24185013

22. Nikulin VV, Fedele T, Mehnert J, Lipp A, Noack C, Steinbrink J, et al. Monochromatic Ultra-Slow (~0.1Hz) Oscillations in the human electroencephalogram and their relation to hemodynamics. NeuroImage. 2014;97: 71–80. doi: 10.1016/j.neuroimage.2014.04.008 24732648

23. Katura T, Tanaka N, Obata A, Sato H, Maki A. Quantitative evaluation of interrelations between spontaneous low-frequency oscillations in cerebral hemodynamics and systemic cardiovascular dynamics. NeuroImage. 2006;31: 1592–1600. doi: 10.1016/j.neuroimage.2006.02.010 16549367

24. Tian F, Niu H, Khan B, Alexandrakis G, Behbehani K, Liu H. Enhanced Functional Brain Imaging by Using Adaptive Filtering and a Depth Compensation Algorithm in Diffuse Optical Tomography. IEEE Transactions on Medical Imaging. 2011;30: 1239–1251. doi: 10.1109/TMI.2011.2111459 21296704

25. Sassaroli A, Pierro M, Bergethon PR, Fantini S. Low-frequency spontaneous oscillations of cerebral hemodynamics investigated with near-infrared spectroscopy: A review. IEEE Journal on Selected Topics in Quantum Electronics. 2012;18: 1478–1492. doi: 10.1109/JSTQE.2012.2183581

26. Tong Y, Hocke LM, Licata SC, Frederick deB B. Low-frequency oscillations measured in the periphery with near-infrared spectroscopy are strongly correlated with blood oxygen level-dependent functional magnetic resonance imaging signals. Journal of biomedical optics. 2012;17: 106004. doi: 10.1117/1.JBO.17.10.106004 23224003

27. Anderson JS, Druzgal TJ, Lopez-Larson M, Jeong E, Desai K, Yurgelun-Todd D. Network anticorrelations, global regression, and phase-shifted soft tissue correction. Human brain mapping. 2011;32: 919–34. doi: 10.1002/hbm.21079 20533557

28. Aso T, Jiang G, Urayama S, Fukuyama H. A Resilient, Non-neuronal Source of the Spatiotemporal Lag Structure Detected by BOLD Signal-Based Blood Flow Tracking. Frontiers in Neuroscience. 2017;11: 256. doi: 10.3389/fnins.2017.00256 28553198

29. Amemiya S, Kunimatsu A, Saito N, Ohtomo K. Cerebral Hemodynamic Impairment: Assessment with Resting-State Functional MR Imaging. Radiology. 2013;270: 1–8.

30. Christen T, Jahanian H, Ni WW, Qiu D, Moseley ME, Zaharchuk G. Noncontrast mapping of arterial delay and functional connectivity using resting-state functional MRI: A study in Moyamoya patients. Journal of Magnetic Resonance Imaging. 2015;41: 424–430. doi: 10.1002/jmri.24558 24419985

31. Khalil AA, Villringer K, Filleböck V, Hu J-Y, Rocco A, Fiebach JB, et al. Non-invasive monitoring of longitudinal changes in cerebral hemodynamics in acute ischemic stroke using BOLD signal delay. Journal of Cerebral Blood Flow & Metabolism. 2018; 0271678X1880395. doi: 10.1177/0271678X18803951 30334657

32. Lv Y, Margulies DS, Cameron Craddock R, Long X, Winter B, Gierhake D, et al. Identifying the perfusion deficit in acute stroke with resting-state functional magnetic resonance imaging. Annals of Neurology. 2013;73: 136–140. doi: 10.1002/ana.23763 23378326

33. Ni L, Li J, Li W, Zhou F, Wang F, Schwarz CG, et al. The value of resting-state functional MRI in subacute ischemic stroke: comparison with dynamic susceptibility contrast-enhanced perfusion MRI. Scientific Reports. 2017;7: 41586. doi: 10.1038/srep41586 28139701

34. Nishida S, Aso T, Takaya S, Takahashi Y, Kikuchi T, Funaki T, et al. Resting-state Functional Magnetic Resonance Imaging Identifies Cerebrovascular Reactivity Impairment in Patients With Arterial Occlusive Diseases: A Pilot Study. Neurosurgery. 2018; doi: 10.1093/neuros/nyy434 30247676

35. Chang C, Glover GH. Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI. NeuroImage. 2009;47: 1381–1393. doi: 10.1016/j.neuroimage.2009.04.048 19393322

36. Blockley NP, Driver ID, Francis ST, Fisher JA, Gowland PA. An improved method for acquiring cerebrovascular reactivity maps. Magnetic Resonance in Medicine. 2011;65: 1278–1286. doi: 10.1002/mrm.22719 21500256

37. Satow T, Aso T, Nishida S, Komuro T, Ueno T, Oishi N, et al. Alteration of Venous Drainage Route in Idiopathic Normal Pressure Hydrocephalus and Normal Aging. Frontiers in Aging Neuroscience. 2017;9. doi: 10.3389/fnagi.2017.00387 29218007

38. Tong Y, Yao JF, Chen JJ, Frederick deB B. The resting-state fMRI arterial signal predicts differential blood transit time through the brain. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism. 2018; 271678X17753329. doi: 10.1177/0271678X17753329 29333912

39. Power JD, Plitt M, Laumann TO, Martin A. Sources and implications of whole-brain fMRI signals in humans. NeuroImage. 2017;146: 609–625. doi: 10.1016/j.neuroimage.2016.09.038 27751941

40. Liu TT, Nalci A, Falahpour M. The global signal in fMRI: Nuisance or Information? NeuroImage. 2017;150: 213–229. doi: 10.1016/j.neuroimage.2017.02.036 28213118

41. Aguirre GK, Zarahn E, D’Esposito M. The inferential impact of global signal covariates in functional neuroimaging analyses. NeuroImage. 1998;8: 302–6. doi: 10.1006/nimg.1998.0367 9758743

42. Byrge L, Kennedy DP. Identifying and characterizing systematic temporally-lagged BOLD artifacts. NeuroImage. 2018;171: 376–392. doi: 10.1016/j.neuroimage.2017.12.082 29288128

43. Erdoğan SB, Tong Y, Hocke LM, Lindsey KP, Frederick deB B. Correcting for Blood Arrival Time in Global Mean Regression Enhances Functional Connectivity Analysis of Resting State fMRI-BOLD Signals. Frontiers in human neuroscience. 2016;10: 311. doi: 10.3389/fnhum.2016.00311 27445751

44. Amemiya S, Takao H, Hanaoka S, Ohtomo K. Global and structured waves of rs-fMRI signal identified as putative propagation of spontaneous neural activity. NeuroImage. 2016;133: 331–340. doi: 10.1016/j.neuroimage.2016.03.033 27012499

45. Taylor Webb J, Ferguson M a., Nielsen J a., Anderson JS. BOLD granger causality reflects vascular anatomy. PLoS ONE. 2013;8: 1–19. doi: 10.1371/journal.pone.0084279 24349569

46. Herman P, Sanganahalli BG, Hyder F. Multimodal Measurements of Blood Plasma and Red Blood Cell Volumes during Functional Brain Activation. Journal of Cerebral Blood Flow & Metabolism. 2009;29: 19–24. doi: 10.1038/jcbfm.2008.100 18766196

47. Hoge RD, Atkinson J, Gill B, Crelier GR, Marrett S, Pike GB. Investigation of BOLD signal dependence on cerebral blood flow and oxygen consumption: The deoxyhemoglobin dilution model. Magnetic Resonance in Medicine. 1999;42: 849–863. doi: 10.1002/(sici)1522-2594(199911)42:5<849::aid-mrm4>3.0.co;2-z 10542343

48. An H, Lin W. Cerebral oxygen extraction fraction and cerebral venous blood volume measurements using MRI: Effects of magnetic field variation. Magnetic Resonance in Medicine. 2002;47: 958–966. doi: 10.1002/mrm.10148 11979575

49. Yablonskiy DA, Haacke EM. Theory of NMR signal behavior in magnetically inhomogeneous tissues: the static dephasing regime. Magnetic resonance in medicine. 1994;32: 749–63. doi: 10.1002/mrm.1910320610 7869897

50. Cohen ER, Ugurbil K, Kim S-G. Effect of basal conditions on the magnitude and dynamics of the blood oxygenation level-dependent fMRI response. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism. 2002;22: 1042–1053. doi: 10.1097/00004647-200209000-00002 12218410

51. Tuunanen PI, Kauppinen RA. Effects of oxygen saturation on BOLD and arterial spin labelling perfusion fMRI signals studied in a motor activation task. NeuroImage. 2006;30: 102–9. doi: 10.1016/j.neuroimage.2005.09.021 16243545

52. Thomas DL, Lythgoe MF, Pell GS, Calamante F, Ordidge RJ. The measurement of diffusion and perfusion in biological systems using magnetic resonance imaging. Physics in medicine and biology. 2000;45: R97–138. doi: 10.1088/0031-9155/45/8/201 10958179

53. Collins J-A, Rudenski A, Gibson J, Howard L, O’Driscoll R. Relating oxygen partial pressure, saturation and content: the haemoglobin-oxygen dissociation curve. Breathe (Sheffield, England). 2015;11: 194–201. doi: 10.1183/20734735.001415 26632351

54. Ogawa S, Menon RS, Kim SG, Ugurbil K. On the characteristics of functional magnetic resonance imaging of the brain. AnnuRevBiophysBiomolStruct. 1998;27: 447–474. doi: 10.1146/annurev.biophys.27.1.447 9646874

55. Aso T, Urayama S, Fukuyama H. Temporal variation of cerebrovascular transit time measured by BOLD-based time lag mapping. Proceedings of the 25rd Annual Meeting of ISMRM. Honolulu; 2017.

56. Kundu P, Inati SJ, Evans JW, Luh W-M, Bandettini PA. Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI. NeuroImage. 2012;60: 1759–70. doi: 10.1016/j.neuroimage.2011.12.028 22209809

57. Posse S, Wiese S, Gembris D, Mathiak K, Kessler C, Grosse-Ruyken ML, et al. Enhancement of BOLD-contrast sensitivity by single-shot multi-echo functional MR imaging. Magnetic resonance in medicine. 1999;42: 87–97. doi: 10.1002/(sici)1522-2594(199907)42:1<87::aid-mrm13>3.0.co;2-o 10398954

58. Wu CW, Gu H, Zou Q, Lu H, Stein EA, Yang Y. TE-dependent spatial and spectral specificity of functional connectivity. NeuroImage. 2012;59: 3075–84. doi: 10.1016/j.neuroimage.2011.11.030 22119650

59. Yen CC-C, Papoti D, Silva AC. Investigating the spatiotemporal characteristics of the deoxyhemoglobin-related and deoxyhemoglobin-unrelated functional hemodynamic response across cortical layers in awake marmosets. NeuroImage. 2017;7: 83–8. doi: 10.1016/j.neuroimage.2017.03.005 28274833

60. Rostrup E, Knudsen GM, Law I, Holm S, Larsson HBW, Paulson OB. The relationship between cerebral blood flow and volume in humans. NeuroImage. 2005;24: 1–11. doi: 10.1016/j.neuroimage.2004.09.043 15588591

61. Feinberg D a, Moeller S, Smith SM, Auerbach E, Ramanna S, Gunther M, et al. Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PloS one. 2010;5: e15710. doi: 10.1371/journal.pone.0015710 21187930

62. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TEJ, Johansen-Berg H, et al. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage. 2004;23 Suppl 1: S208–19. doi: 10.1016/j.neuroimage.2004.07.051 15501092

63. Mazaika PK, Hoeft F, Glover GH, Reiss AL. Methods and Software for fMRI Analysis of Clinical Subjects. NeuroImage. 2009;47: S58. doi: 10.1016/S1053-8119(09)70238-1

64. Freire L, Mangin JF. Motion correction algorithms may create spurious brain activations in the absence of subject motion. NeuroImage. 2001;14: 709–722. doi: 10.1006/nimg.2001.0869 11506543

65. Satterthwaite TD, Elliott MA, Gerraty RT, Ruparel K, Loughead J, Calkins ME, et al. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. NeuroImage. 2013;64: 240–256. doi: 10.1016/j.neuroimage.2012.08.052 22926292

66. Friston KJ, Glaser DE, Henson RNA, Kiebel S, Phillips C, Ashburner J. Classical and Bayesian inference in neuroimaging: Applications. NeuroImage. 2002;16: 484–512. doi: 10.1006/nimg.2002.1091 12030833

67. Poline JB, Worsley KJ, Evans AC, Friston KJ. Combining spatial extent and peak intensity to test for activations in functional imaging. NeuroImage. 1997;5: 83–96. doi: 10.1006/nimg.1996.0248 9345540

68. van Dijk KRA, Sabuncu MR, Buckner RL. The influence of head motion on intrinsic functional connectivity MRI. NeuroImage. 2012;59: 431–438. doi: 10.1016/j.neuroimage.2011.07.044 21810475

69. Kumar V, Croxson PL, Simonyan K. Structural Organization of the Laryngeal Motor Cortical Network and Its Implication for Evolution of Speech Production. Journal of Neuroscience. 2016;36: 4170–4181. doi: 10.1523/JNEUROSCI.3914-15.2016 27076417

70. Poulin MJ, Liang PJ, Robbins PA. Dynamics of the cerebral blood flow response to step changes in end-tidal PCO2 and PO2 in humans. Journal of Applied Physiology. 1996;81: 1084–1095. doi: 10.1152/jappl.1996.81.3.1084 8889738

71. Malatino LS, Bellofiore S, Costa MP, Lo Manto G, Finocchiaro F, Di Maria GU. Cerebral blood flow velocity after hyperventilation-induced vasoconstriction in hypertensive patients. Stroke. 1992;23: 1728–32. doi: 10.1161/01.str.23.12.1728 1448822

72. Krings T, Erberich SG, Roessler F, Reul J, Thron A. MR blood oxygenation level-dependent signal differences in parenchymal and large draining vessels: implications for functional MR imaging. AJNR American journal of neuroradiology. 1999;20: 1907–14. 10588117

73. Boas DA, Dale AM. Simulation study of magnetic resonance imaging–guided cortically constrained diffuse optical tomography of human brain function. Applied Optics. 2005;44: 1957. doi: 10.1364/ao.44.001957 15813532

74. Kim S-G, Ogawa S. Biophysical and physiological origins of blood oxygenation level-dependent fMRI signals. Journal of Cerebral Blood Flow & Metabolism. 2012;32: 1188–1206. doi: 10.1038/jcbfm.2012.23 22395207

75. Kennerley AJ, Berwick J, Martindale J, Johnston D, Papadakis N, Mayhew JE. Concurrent fMRI and optical measures for the investigation of the hemodynamic response function. Magnetic Resonance in Medicine. 2005;54: 354–365. doi: 10.1002/mrm.20511 16032695

76. Fantini S. Dynamic model for the tissue concentration and oxygen saturation of hemoglobin in relation to blood volume, flow velocity, and oxygen consumption: Implications for functional neuroimaging and coherent hemodynamics spectroscopy (CHS). NeuroImage. 2014;85: 202–221. doi: 10.1016/j.neuroimage.2013.03.065 23583744

77. Intaglietta M. Vasomotion and flowmotion: physiological mechanisms and clinical evidence. Vascular Medicine Review. 1990;1: 101–112.

78. Hudetz a G, Wood JD, Biswal BB, Krolo I, Kampine JP. Effect of hemodilution on RBC velocity, supply rate, and hematocrit in the cerebral capillary network. Journal of applied physiology (Bethesda, Md : 1985). 1999;87: 505–9.

79. Fagrell B, Intaglietta M, Ostergren J. Relative hematocrit in human skin capillaries and its relation to capillary blood flow velocity. Microvascular research. 1980;20: 327–35. doi: 10.1016/0026-2862(80)90033-3 7207225

80. Intaglietta M, Johnson PC, Winslow RM. Microvascular and tissue oxygen distribution. Cardiovascular research. 1996;32: 632–43. 8915182

81. Birn RM, Diamond JB, Smith M a., Bandettini P a. Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. NeuroImage. 2006;31: 1536–1548. doi: 10.1016/j.neuroimage.2006.02.048 16632379

82. Morita Y, Hardebo JE, Bouskela E. Influence of cerebrovascular sympathetic, parasympathetic, and sensory nerves on autoregulation and spontaneous vasomotion. Acta Physiol Scand. 1995;154: 121–130. doi: 10.1111/j.1748-1716.1995.tb09894.x 7572208

83. Siegel AM, Culver JP, Mandeville JB, Boas DA. Temporal comparison of functional brain imaging with diffuse optical tomography and fMRI during rat forepaw stimulation. Physics in medicine and biology. 2003;48: 1391–403. doi: 10.1088/0031-9155/48/10/311 12812454

84. Kleinschmidt A, Obrig H, Requardt M, Merboldt K-D, Dirnagl U, Villringer A, et al. Simultaneous Recording of Cerebral Blood Oxygenation Changes during Human Brain Activation by Magnetic Resonance Imaging and Near-Infrared Spectroscopy. Journal of Cerebral Blood Flow & Metabolism. 1996;16: 817–826. doi: 10.1097/00004647-199609000-00006 8784226

85. Chen JJ, Pike GB. BOLD-specific cerebral blood volume and blood flow changes during neuronal activation in humans. NMR in biomedicine. 2009;22: 1054–62. doi: 10.1002/nbm.1411 19598180

86. Mayhew JEW, Askew S, Zheng Y, Porrill J, Westby GWMW, Redgrave P, et al. Cerebral Vasomotion: A 0.1-Hz Oscillation in Reflected Light Imaging of Neural Activity. NeuroImage. 1996;4: 183–93. doi: 10.1006/nimg.1996.0069 9345508

87. Pattinson KTS, Governo RJ, MacIntosh BJ, Russell EC, Corfield DR, Tracey I, et al. Opioids Depress Cortical Centers Responsible for the Volitional Control of Respiration. Journal of Neuroscience. 2009;29: 8177–8186. doi: 10.1523/JNEUROSCI.1375-09.2009 19553457

88. Dresel C, Castrop F, Haslinger B, Wohlschlaeger AM, Hennenlotter A, Ceballos-Baumann AO. The functional neuroanatomy of coordinated orofacial movements: Sparse sampling fMRI of whistling. NeuroImage. 2005;28: 588–597. doi: 10.1016/j.neuroimage.2005.06.021 16084116

89. Simonyan K, Jürgens U. Efferent subcortical projections of the laryngeal motorcortex in the rhesus monkey. Brain Research. 2003;974: 43–59. doi: 10.1016/s0006-8993(03)02548-4 12742623

90. Radna RJ, MacLean PD. Vagal elicitation of respiratory-type and other unit responses in striopallidum of squirrel monkeys. Brain Research. 1981;213: 29–44. doi: 10.1016/0006-8993(81)91246-4 7237149

91. McKay LC, Adams L, Frackowiak RSJ, Corfield DR. A bilateral cortico-bulbar network associated with breath holding in humans, determined by functional magnetic resonance imaging. NeuroImage. 2008;40: 1824–1832. doi: 10.1016/j.neuroimage.2008.01.058 18343687

92. Krings T, Reinges MHT, Erberich S, Kemeny S, Rohde V, Spetzger U, et al. Functional MRI for presurgical planning: problems, artefacts, and solution strategies. J Neurol Neurosurg Psychiatry. 2001;70: 749. doi: 10.1136/jnnp.70.6.749 11385009

93. Caballero-Gaudes C, Reynolds RC. Methods for cleaning the BOLD fMRI signal. NeuroImage. 2017;154: 128–149. doi: 10.1016/j.neuroimage.2016.12.018 27956209

94. Chang C, Glover GH. Effects of model-based physiological noise correction on default mode network anti-correlations and correlations. NeuroImage. 2009;47: 1448–59. doi: 10.1016/j.neuroimage.2009.05.012 19446646

95. Smith SM, Beckmann CF, Andersson J, Auerbach EJ, Bijsterbosch J, Douaud G, et al. Resting-state fMRI in the Human Connectome Project. NeuroImage. 2013;80: 144–168. doi: 10.1016/j.neuroimage.2013.05.039 23702415

96. Salimi-Khorshidi G, Douaud G, Beckmann CF, Glasser MF, Griffanti L, Smith SM. Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers. NeuroImage. 2014;90: 449–468. doi: 10.1016/j.neuroimage.2013.11.046 24389422

97. Tong Y, Frederick deB B. Tracking cerebral blood flow in BOLD fMRI using recursively generated regressors. Human brain mapping. 2014;35: 5471–85. doi: 10.1002/hbm.22564 24954380

98. Power JD, Barnes K a., Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage. 2012;59: 2142–54. doi: 10.1016/j.neuroimage.2011.10.018 22019881

99. Glasser MF, Coalson TS, Bijsterbosch JD, Harrison SJ, Harms MP, Anticevic A, et al. Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data. NeuroImage. 2018;181: 692–717. doi: 10.1016/j.neuroimage.2018.04.076 29753843


Článek vyšel v časopise

PLOS One


2019 Číslo 9
Nejčtenější tento týden