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The impact of physiological noise on hemodynamic-derived estimates of directed functional connectivity

  • F. Konrad Schumacher
  • Carmen Steinborn
  • Cornelius Weiller
  • Björn O. Schelter
  • Matthias Reinhard
  • Christoph P. KallerEmail author
Original Article
  • 80 Downloads

Abstract

Measuring the strength of directed functional interactions between brain regions is fundamental to understand neural networks. Functional near-infrared spectroscopy (fNIRS) is a suitable method to map directed interactions between brain regions but is based on the neurovascular coupling. It, thus, relies on vasomotor reactivity and is potentially biased by non-neural physiological noise. To investigate the impact of physiological noise on fNIRS-based estimates of directed functional connectivity within the rostro-caudal hierarchical organization of the prefrontal cortex (PFC), we systematically assessed the effects of pathological perturbations of vasomotor reactivity and of externally triggered arterial blood pressure (aBP) fluctuations. Fifteen patients with unilateral stenosis of the internal carotid artery (ICA) underwent multi-channel fNIRS during rest and during metronomic breathing, inducing aBP oscillations at 0.1 Hz. Comparisons between the healthy and pathological hemispheres served as quasi-experimental manipulation of the neurovascular system’s capability for vasomotor reactivity. Comparisons between rest and breathing served as experimental manipulation of two different levels of physiological noise that were expected to differ between healthy and pathological hemispheres. In the hemisphere affected by ICA stenosis, the rostro-caudal hierarchical organization of the PFC was compromised reflecting the pathological effect on the vascular and neural level. Breathing-induced aBP oscillations biased the magnitude of directed interactions in the PFC, but could be adjusted using either the aBP time series (intra-individual approach) or the aBP-induced fNIRS signal variance (inter-individual approach). Multi-channel fNIRS, hence, provides a sound basis for analyses of directed functional connectivity as potential bias due to physiological noise can be effectively controlled for.

Keywords

Prefrontal cortex Hierarchical organization Directed interactions Near-infrared spectroscopy Physiological noise Stenosis 

Abbreviations

aBP

Arterial blood pressure

fMRI

Functional magnetic resonance imaging

fNIRS

Functional near-infrared spectroscopy

PFC

Prefrontal cortex

ICA

Internal carotid artery

(P)DC

(partial) Directed coherence

PSD

Power spectral density

VAR

Vector autoregressive

Notes

Funding

This work was supported by a grant of the BrainLinks-BrainTools Cluster of Excellence funded by the German Research Foundation (DFG, Grant Number EXC 1086).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The study was approved by the local Ethics Committee.

Informed consent

All patients gave written informed consent prior to participation

Supplementary material

429_2019_1954_MOESM1_ESM.docx (555 kb)
Supplementary material 1 (DOCX 556 kb)

References

  1. Avirame K, Lesemann A, List J et al (2015) Cerebral autoregulation and brain networks in occlusive processes of the internal carotid artery. J Cereb Blood Flow Metab 35:240–247.  https://doi.org/10.1038/jcbfm.2014.190 CrossRefGoogle Scholar
  2. Badre D, D’Esposito M (2007) Functional magnetic resonance imaging evidence for a hierarchical organization of the prefrontal cortex. J Cogn Neurosci 19:2082–2099CrossRefGoogle Scholar
  3. Badre D, Nee DE (2018) Frontal cortex and the hierarchical control of behavior. Trends Cogn Sci 22:170–188.  https://doi.org/10.1016/j.tics.2017.11.005 CrossRefGoogle Scholar
  4. Barnett L, Seth AK (2011) Behaviour of Granger causality under filtering: theoretical invariance and practical application. J Neurosci Methods 201:404–419.  https://doi.org/10.1016/j.jneumeth.2011.08.010 CrossRefGoogle Scholar
  5. Barnett L, Seth AK (2017) Detectability of Granger causality for subsampled continuous-time neurophysiological processes. J Neurosci Methods 275:93–121.  https://doi.org/10.1016/j.jneumeth.2016.10.016 CrossRefGoogle Scholar
  6. Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48.  https://doi.org/10.18637/jss.v067.i01 CrossRefGoogle Scholar
  7. 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 34:537–541.  https://doi.org/10.1002/mrm.1910340409 CrossRefGoogle Scholar
  8. Blumenfeld RS, Nomura EM, Gratton C, D’Esposito M (2013) Lateral prefrontal cortex is organized into parallel dorsal and ventral streams along the rostro-caudal axis. Cereb Cortex 23:2457–2466.  https://doi.org/10.1093/cercor/bhs223 CrossRefGoogle Scholar
  9. Bokkers RPH, van Osch MJP, van der Worp HB et al (2010) Symptomatic carotid artery stenosis: impairment of cerebral autoregulation measured at the brain tissue level with arterial spin-labeling MR imaging. Radiology 256:201–208.  https://doi.org/10.1148/radiol.10091262 CrossRefGoogle Scholar
  10. Brigadoi S, Cooper RJ (2015) How short is short? Optimum source-detector distance for short-separation channels in functional near-infrared spectroscopy. Neurophotonics 2:025005.  https://doi.org/10.1117/1.NPh.2.2.025005 CrossRefGoogle Scholar
  11. Brigadoi S, Ceccherini L, Cutini S et al (2014) Motion artifacts in functional near-infrared spectroscopy: a comparison of motion correction techniques applied to real cognitive data. Neuroimage 85:181–191.  https://doi.org/10.1016/j.neuroimage.2013.04.082 CrossRefGoogle Scholar
  12. Christoff K, Gabrieli JDE (2000) The frontopolar cortex and human cognition: evidence for a rostrocaudal hierarchical organization within the human prefrontal cortex. Psychobiology 28:168–186Google Scholar
  13. Cooper RJ, Selb J, Gagnon L et al (2012) A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy. Front Neurosci 6:1–10.  https://doi.org/10.3389/fnins.2012.00147 CrossRefGoogle Scholar
  14. Cui X, Bray S, Reiss AL (2010) Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics. Neuroimage 49:3039–3046.  https://doi.org/10.1016/j.neuroimage.2009.11.050 CrossRefGoogle Scholar
  15. de Bray JM, Glatt B (1995) Quantification of atheromatous stenosis in the extracranial internal carotid artery. Cerebrovasc Dis 5:414–426.  https://doi.org/10.1159/000107895 CrossRefGoogle Scholar
  16. Delpy DT, Cope M, van der Zee P et al (1988) Estimation of optical pathlength through tissue from direct time of flight measurement. Phys Med Biol 33:1433–1442CrossRefGoogle Scholar
  17. Deshpande G, Hu X (2012) Investigating effective brain connectivity from fMRI data: past findings and current issues with reference to Granger causality analysis. Brain Connect 2:235–245.  https://doi.org/10.1089/brain.2012.0091 CrossRefGoogle Scholar
  18. Deshpande G, Sathian K, Hu X (2010) Effect of hemodynamic variability on Granger causality analysis of fMRI. Neuroimage 52:884–896.  https://doi.org/10.1016/j.neuroimage.2009.11.060 CrossRefGoogle Scholar
  19. Eggebrecht AT, Ferradal SL, Robichaux-Viehoever A et al (2014) Mapping distributed brain function and networks with diffuse optical tomography. Nat Photonics 8:448–454.  https://doi.org/10.1038/nphoton.2014.107 CrossRefGoogle Scholar
  20. Fairclough SH, Burns C, Kreplin U (2018) FNIRS activity in the prefrontal cortex and motivational intensity: impact of working memory load, financial reward, and correlation-based signal improvement. Neurophotonics 5:035001.  https://doi.org/10.1117/1.NPh.5.3.035001 CrossRefGoogle Scholar
  21. Fishburn FA, Ludlum RS, Vaidya CJ, Medvedev AV (2019) Temporal Derivative Distribution Repair (TDDR): a motion correction method for fNIRS. Neuroimage 184:171–179.  https://doi.org/10.1016/j.neuroimage.2018.09.025 CrossRefGoogle Scholar
  22. Florin E, Gross J, Pfeifer J et al (2010) The effect of filtering on Granger causality based multivariate causality measures. Neuroimage 50:577–588.  https://doi.org/10.1016/j.neuroimage.2009.12.050 CrossRefGoogle Scholar
  23. Frederick DB, Nickerson LD, Tong Y (2012) Physiological denoising of BOLD fMRI data using Regressor Interpolation at Progressive Time Delays (RIPTiDe) processing of concurrent fMRI and near-infrared spectroscopy (NIRS). Neuroimage 60:1913–1923.  https://doi.org/10.1016/j.neuroimage.2012.01.140 CrossRefGoogle Scholar
  24. Friston K, Moran R, Seth AK (2013) Analysing connectivity with Granger causality and dynamic causal modelling. Curr Opin Neurobiol 23:172–178.  https://doi.org/10.1016/j.conb.2012.11.010 CrossRefGoogle Scholar
  25. Friston KJ, Bastos AM, Oswal A et al (2014) Granger causality revisited. Neuroimage 101:796–808.  https://doi.org/10.1016/j.neuroimage.2014.06.062 CrossRefGoogle Scholar
  26. Fuster JM (2008) The prefrontal cortex, 4th edn. Academic Press/Elsevier, LondonGoogle Scholar
  27. Gagnon L, Yücel M, Boas DA, Cooper RJ (2014) Further improvement in reducing superficial contamination in NIRS using double short separation measurements. Neuroimage 85:127–135.  https://doi.org/10.1016/j.neuroimage.2013.01.073 CrossRefGoogle Scholar
  28. Germon TJ, Evans PD, Barnett NJ et al (1999) Cerebral near infrared spectroscopy: emitter-detector separation must be increased. Br J Anaesth 82:831–837CrossRefGoogle Scholar
  29. Granger CWJ (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37:424.  https://doi.org/10.2307/1912791 CrossRefGoogle Scholar
  30. Habermehl C, Holtze S, Steinbrink J et al (2012) Somatosensory activation of two fingers can be discriminated with ultrahigh-density diffuse optical tomography. Neuroimage 59:3201–3211.  https://doi.org/10.1016/j.neuroimage.2011.11.062 CrossRefGoogle Scholar
  31. Hartkamp NS, Hendrikse J, van der Worp HB et al (2012) Time course of vascular reactivity using repeated phase-contrast MR angiography in patients with carotid artery stenosis. Stroke 43:553–556.  https://doi.org/10.1161/STROKEAHA.111.637314 CrossRefGoogle Scholar
  32. Julien C (2006) The enigma of Mayer waves: facts and models. Cardiovasc Res 70:12–21.  https://doi.org/10.1016/j.cardiores.2005.11.008 CrossRefGoogle Scholar
  33. Kirilina E, Jelzow A, Heine A et al (2012) The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy. Neuroimage 61:70–81.  https://doi.org/10.1016/j.neuroimage.2012.02.074 CrossRefGoogle Scholar
  34. Koechlin E, Ody C, Kouneiher F (2003) The architecture of cognitive control in the human prefrontal cortex. Science 302:1181–1185.  https://doi.org/10.1126/science.1088545 CrossRefGoogle Scholar
  35. Kuznetsova A, Bruun Brockhoff P, Haubo Bojesen Christensen R (2016) lmerTest: tests in linear mixed effects models. R package version 2.0-33. https://CRAN.R-project.org/package=lmerTest
  36. Lawrence MA (2016) ez: easy analysis and visualization of factorial experiments. R package version 4.4-0. https://CRAN.R-project.org/package=ez
  37. Lenth RV (2016) Least-squares means: the R package lsmeans. J Stat Softw 69:1–33.  https://doi.org/10.18637/jss.v069.i01 CrossRefGoogle Scholar
  38. Mader W, Feess D, Lange R et al (2008) On the detection of direct directed information flow in fMRI. IEEE J Sel Top Signal Process 2:965–974.  https://doi.org/10.1109/JSTSP.2008.2008260 CrossRefGoogle Scholar
  39. Margulies DS, Ghosh SS, Goulas A et al (2016) Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc Natl Acad Sci USA 113:12574–12579.  https://doi.org/10.1073/pnas.1608282113 CrossRefGoogle Scholar
  40. Medvedev AV (2014) Does the resting state connectivity have hemispheric asymmetry? A near-infrared spectroscopy study. NeuroImage 85:400–407CrossRefGoogle Scholar
  41. Mukli P, Nagy Z, Racz FS, Eke HP (2018) Impact of healthy aging on multifractal hemodynamic fluctuations in the human prefrontal cortex. Front Physiol 9:1072.  https://doi.org/10.3389/fphys.2018.01072 CrossRefGoogle Scholar
  42. Nee DE, D’Esposito M (2016) The hierarchical organization of the lateral prefrontal cortex. Elife 5:1–26.  https://doi.org/10.7554/eLife.12112 CrossRefGoogle Scholar
  43. Noordmans HJ, van Blooijs D, Siero JCW et al (2018) Detailed view on slow sinusoidal, hemodynamic oscillations on the human brain cortex by Fourier transforming oxy/deoxy hyperspectral images. Hum Brain Mapp 39:3558–3573.  https://doi.org/10.1002/hbm.24194 CrossRefGoogle Scholar
  44. Novak V (2012) Cognition and hemodynamics. Curr Cardiovasc Risk Rep 6:380–396.  https://doi.org/10.1007/s12170-012-0260-2 CrossRefGoogle Scholar
  45. Novak V, Hajjar I (2010) The relationship between blood pressure and cognitive function. Nat Rev Cardiol 7:686–698.  https://doi.org/10.1038/nrcardio.2010.161 CrossRefGoogle Scholar
  46. Obrig H, Neufang M, Wenzel R et al (2000) Spontaneous low frequency oscillations of cerebral hemodynamics and metabolism in human adults. Neuroimage 12:623–639.  https://doi.org/10.1006/nimg.2000.0657 CrossRefGoogle Scholar
  47. Okada E, Firbank M, Schweiger M et al (1997) Theoretical and experimental investigation of near-infrared light propagation in a model of the adult head. Appl Opt 36:21–31CrossRefGoogle Scholar
  48. Pfurtscheller G, Schwerdtfeger A, Brunner C et al (2017) Distinction between neural and vascular BOLD oscillations and intertwined heart rate oscillations at 0.1 Hz in the resting state and during movement. PLoS One 12:0168097.  https://doi.org/10.1371/journal.pone.0168097 CrossRefGoogle Scholar
  49. Racz FS, Mukli P, Nagy Z, Eke A (2017) Increased prefrontal cortex connectivity during cognitive challenge assessed by fNIRS imaging. Biomed Opt Express 8:3842–3855.  https://doi.org/10.1364/BOE.8.003842 CrossRefGoogle Scholar
  50. Reinhard M, Müller T, Guschlbauer B et al (2003a) Dynamic cerebral autoregulation and collateral flow patterns in patients with severe carotid stenosis or occlusion. Ultrasound Med Biol 29:1105–1113.  https://doi.org/10.1016/S0301-5629(03)00954-2 CrossRefGoogle Scholar
  51. Reinhard M, Roth M, Müller T et al (2003b) Cerebral autoregulation in carotid artery occlusive disease assessed from spontaneous blood pressure fluctuations by the correlation coefficient index. Stroke 34:2138–2144.  https://doi.org/10.1161/01.STR.0000087788.65566.AC CrossRefGoogle Scholar
  52. Reinhard M, Schumacher FK, Rutsch S et al (2014) Spatial mapping of dynamic cerebral autoregulation by multichannel near-infrared spectroscopy in high-grade carotid artery disease. J Biomed Opt 19:097005.  https://doi.org/10.1117/1.JBO.19.9.097005 CrossRefGoogle Scholar
  53. Roebroeck A, Formisano E, Goebel R (2005) Mapping directed influence over the brain using Granger causality and fMRI. Neuroimage 25:230–242.  https://doi.org/10.1016/j.neuroimage.2004.11.017 CrossRefGoogle Scholar
  54. Rossini PM, Altamura C, Ferretti A et al (2004) Does cerebrovascular disease affect the coupling between neuronal activity and local haemodynamics? Brain 127:99–110.  https://doi.org/10.1093/brain/awh012 CrossRefGoogle Scholar
  55. Santosa H, Aarabi A, Perlman SB, Huppert TJ (2017) Characterization and correction of the false-discovery rates in resting state connectivity using functional near-infrared spectroscopy. J Biomed Opt 22:55002.  https://doi.org/10.1117/1.JBO.22.5.055002 CrossRefGoogle Scholar
  56. Sato T, Nambu I, Takeda K et al (2016) Reduction of global interference of scalp-hemodynamics in functional near-infrared spectroscopy using short distance probes. Neuroimage 141:120–132.  https://doi.org/10.1016/j.neuroimage.2016.06.054 CrossRefGoogle Scholar
  57. Satterthwaite TD, Wolf DH, Loughead J et al (2012) Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth. Neuroimage 60:623–632.  https://doi.org/10.1016/j.neuroimage.2011.12.063 CrossRefGoogle Scholar
  58. Schelter B, Winterhalder M, Eichler M et al (2006) Testing for directed influences among neural signals using partial directed coherence. J Neurosci Methods 152:210–219.  https://doi.org/10.1016/j.jneumeth.2005.09.001 CrossRefGoogle Scholar
  59. Schippers MB, Renken R, Keysers C (2011) The effect of intra- and inter-subject variability of hemodynamic responses on group level Granger causality analyses. Neuroimage 57:22–36.  https://doi.org/10.1016/j.neuroimage.2011.02.008 CrossRefGoogle Scholar
  60. Scholkmann F, Kleiser S, Metz AJ et al (2014) A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. Neuroimage 85(Pt 1):6–27.  https://doi.org/10.1016/j.neuroimage.2013.05.004 CrossRefGoogle Scholar
  61. Schumacher FK, Schumacher LV, Schelter BO, Kaller CP (2019) Functionally dissociating ventro-dorsal components within the rostro-caudal hierarchical organization of the human prefrontal cortex. Neuroimage 185:398–407.  https://doi.org/10.1016/j.neuroimage.2018.10.048 CrossRefGoogle Scholar
  62. Scouten A, Papademetris X, Constable RT (2006) Spatial resolution, signal-to-noise ratio, and smoothing in multi-subject functional MRI studies. Neuroimage 30:787–793.  https://doi.org/10.1016/j.neuroimage.2005.10.022 CrossRefGoogle Scholar
  63. Smith SM, Bandettini PA, Miller KL et al (2012) The danger of systematic bias in group-level FMRI-lag-based causality estimation. Neuroimage 59:1228–1229.  https://doi.org/10.1016/j.neuroimage.2011.08.015 CrossRefGoogle Scholar
  64. Stokes PA, Purdon PL (2017) A study of problems encountered in Granger causality analysis from a neuroscience perspective. Proc Natl Acad Sci 114:E7063–E7072.  https://doi.org/10.1073/pnas.1704663114 CrossRefGoogle Scholar
  65. Sutoko S, Chan YL, Obata A et al (2019) Denoising of neuronal signal from mixed systemic low-frequency oscillation using peripheral measurement as noise regressor in near-infrared imaging. Neurophotonics 6:015001.  https://doi.org/10.1117/1.NPh.6.1.015001 CrossRefGoogle Scholar
  66. Takahashi T, Takikawa Y, Kawagoe R et al (2011) Influence of skin blood flow on near-infrared spectroscopy signals measured on the forehead during a verbal fluency task. Neuroimage 57:991–1002.  https://doi.org/10.1016/j.neuroimage.2011.05.012 CrossRefGoogle Scholar
  67. Tong Y, Frederick BD (2010) Time lag dependent multimodal processing of concurrent fMRI and near-infrared spectroscopy (NIRS) data suggests a global circulatory origin for low-frequency oscillation signals in human brain. Neuroimage 53:553–564.  https://doi.org/10.1016/j.neuroimage.2010.06.049 CrossRefGoogle Scholar
  68. Tong Y, Hocke LM, Licata SC, Frederick DB (2012) Low-frequency oscillations measured in the periphery with near-infrared spectroscopy are strongly correlated with blood oxygen level-dependent functional magnetic resonance imaging signals. J Biomed Opt 17:106004.  https://doi.org/10.1117/1.JBO.17.10.106004 CrossRefGoogle Scholar
  69. Tong Y, Hocke LM, Nickerson LD et al (2013) Evaluating the effects of systemic low frequency oscillations measured in the periphery on the independent component analysis results of resting state networks. Neuroimage 76:202–215.  https://doi.org/10.1016/j.neuroimage.2013.03.019 CrossRefGoogle Scholar
  70. Webb JT, Ferguson MA, Nielsen JA, Anderson JS (2013) BOLD Granger causality reflects vascular anatomy. PLoS One 8:e84279.  https://doi.org/10.1371/journal.pone.0084279 CrossRefGoogle Scholar
  71. Winder AT, Echagarruga C, Zhang Q, Drew PJ (2017) Weak correlations between hemodynamic signals and ongoing neural activity during the resting state. Nat Neurosci 20:1761–1769.  https://doi.org/10.1038/s41593-017-0007-y CrossRefGoogle Scholar
  72. Zhang Q, Strangman GE, Ganis G (2009) Adaptive filtering to reduce global interference in non-invasive NIRS measures of brain activation: how well and when does it work? Neuroimage 45:788–794.  https://doi.org/10.1016/j.neuroimage.2008.12.048 CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Neurology, Medical CenterUniversity of FreiburgFreiburgGermany
  2. 2.Freiburg Brain Imaging CenterUniversity of FreiburgFreiburgGermany
  3. 3.Faculty of BiologyUniversity of FreiburgFreiburgGermany
  4. 4.Faculty of MedicineUniversity of FreiburgFreiburgGermany
  5. 5.BrainLinks-BrainTools Cluster of ExcellenceUniversity of FreiburgFreiburgGermany
  6. 6.Institute for Complex Systems and Mathematical BiologyUniversity of AberdeenAberdeenUK
  7. 7.Department of Neurology, Medical Center EsslingenTeaching Hospital of the University of TübingenEsslingenGermany
  8. 8.Department of Neuroradiology, Medical CenterUniversity of FreiburgFreiburgGermany

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