iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/S10916-015-0205-7
EEG-NIRS Based Assessment of Neurovascular Coupling During Anodal Transcranial Direct Current Stimulation - a Stroke Case Series | Journal of Medical Systems Skip to main content
Springer Nature Link
Log in

EEG-NIRS Based Assessment of Neurovascular Coupling During Anodal Transcranial Direct Current Stimulation - a Stroke Case Series

  • Non-invasive Diagnostic Systems
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

A method for electroencephalography (EEG) - near-infrared spectroscopy (NIRS) based assessment of neurovascular coupling (NVC) during anodal transcranial direct current stimulation (tDCS). Anodal tDCS modulates cortical neural activity leading to a hemodynamic response, which was used to identify impaired NVC functionality. In this study, the hemodynamic response was estimated with NIRS. NIRS recorded changes in oxy-hemoglobin (HbO2) and deoxy-hemoglobin (Hb) concentrations during anodal tDCS-induced activation of the cortical region located under the electrode and in-between the light sources and detectors. Anodal tDCS-induced alterations in the underlying neuronal current generators were also captured with EEG. Then, a method for the assessment of NVC underlying the site of anodal tDCS was proposed that leverages the Hilbert-Huang Transform. The case series including four chronic (>6 months) ischemic stroke survivors (3 males, 1 female from age 31 to 76) showed non-stationary effects of anodal tDCS on EEG that correlated with the HbO2 response. Here, the initial dip in HbO2 at the beginning of anodal tDCS corresponded with an increase in the log-transformed mean-power of EEG within 0.5Hz-11.25Hz frequency band. The cross-correlation coefficient changed signs but was comparable across subjects during and after anodal tDCS. The log-transformed mean-power of EEG lagged HbO2 response during tDCS but then led post-tDCS. This case series demonstrated changes in the degree of neurovascular coupling to a 0.526 A/m2 square-pulse (0–30 s) of anodal tDCS. The initial dip in HbO2 needs to be carefully investigated in a larger cohort, for example in patients with small vessel disease.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Nitsche, M. A., and Paulus, W., Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. J. Physiol. 527(Pt 3):633–9, 2000.

    Article  Google Scholar 

  2. Nunez, P. L., and Srinivasan, R., Electric fields of the brain: the neurophysics of EEG. Oxford University Press, London, p. 629, 2006.

    Book  Google Scholar 

  3. Girouard, H., and Iadecola, C., Neurovascular coupling in the normal brain and in hypertension, stroke, and Alzheimer disease. J. Appl. Physiol. 100(1):328–35, 2006.

    Article  Google Scholar 

  4. Siesler, H. W., Ozaki, Y., Kawata, S., and Heise, H. M., Near-infrared spectroscopy: principles, instruments, applications. Wiley, New York, p. 365, 2008.

    Google Scholar 

  5. Dutta, A., and Nitsche, M. A., A neural mass model for simulating modulation of cortical activity with transcranial direct current stimulation. In proceeding of: 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

  6. David, O., and Friston, K. J., A neural mass model for MEG/EEG: coupling and neuronal dynamics. NeuroImage 20(3):1743–55, 2003.

    Article  Google Scholar 

  7. Rahman, A., Reato, D., Arlotti, M., Gasca, F., Datta, A., Parra, L. C., et al., Cellular effects of acute direct current stimulation: somatic and synaptic terminal effects. J. Physiol. 591(Pt 10):2563–78, 2013.

    Article  Google Scholar 

  8. Radman, T., Ramos, R. L., Brumberg, J. C., and Bikson, M., Role of cortical cell type and morphology in subthreshold and suprathreshold uniform electric field stimulation in vitro. Brain Stimulat. 2(4):215–228, 2009. 228.e1–3.

    Article  Google Scholar 

  9. Holthoff, K., and Witte, O. W., Directed spatial potassium redistribution in rat neocortex. Glia 29(3):288–92, 2000.

    Article  Google Scholar 

  10. Fricke, K., Seeber, A. A., Thirugnanasambandam, N., Paulus, W., Nitsche, M. A., and Rothwell, J. C., Time course of the induction of homeostatic plasticity generated by repeated transcranial direct current stimulation of the human motor cortex. J. Neurophysiol. 105:1141–1149, 2011. doi:10.1152/jn.00608.2009.

    Article  Google Scholar 

  11. Islam, N., Aftabuddin, M., Moriwaki, A., Hattori, Y., and Hori, Y., Increase in the calcium level following anodal polarization in the rat brain. Brain Res. 684:206–208, 1995.

    Article  Google Scholar 

  12. Nitsche, M. A., Fricke, K., Henschke, U., Schlitterlau, A., Liebetanz, D., Lang, N., Henning, S., Tergau, F., and Paulus, W., Pharmacological modulation of cortical excitability shifts induced by transcranial direct current stimulation in humans. J. Physiol. 553:293–301, 2003. doi:10.1113/jphysiol.2003.049916.

    Article  Google Scholar 

  13. Halnes G, Ostby I, Pettersen K. H., Omholt S. W., Einevoll G. T., Electrodiffusive Model for Astrocytic and Neuronal Ion Concentration Dynamics. PLoS Comput Biol [Internet]. 2013 Dec [cited 2014 May 11];9(12).

  14. Eckman, D. M., and Nelson, M. T., Potassium ions as vasodilators: role of inward rectifier potassium channels. Circ. Res. 88(2):132–3, 2001.

    Article  Google Scholar 

  15. Stagg, C. J., and Nitsche, M. A., Physiological basis of transcranial direct current stimulation. Neurosci. Rev. J. Bringing Neurobiol. Neurol. Psychiatry 17(1):37–53, 2011.

    Google Scholar 

  16. Pellerin, L., and Magistretti, P. J., Glutamate uptake into astrocytes stimulates aerobic glycolysis: a mechanism coupling neuronal activity to glucose utilization. Proc. Natl. Acad. Sci. U. S. A. 91(22):10625–9, 1994.

    Article  Google Scholar 

  17. DeFazio, R. A., Keros, S., Quick, M. W., and Hablitz, J. J., Potassium-coupled chloride cotransport controls intracellular chloride in rat neocortical pyramidal neurons. J. Neurosci. Off. J. Soc. Neurosci. 20(21):8069–76, 2000.

    Google Scholar 

  18. Hübel, N., Schöll, E., and Dahlem, M. A., Bistable dynamics underlying excitability of ion homeostasis in neuron models. PLoS Comput. Biol. 10(5):e1003551, 2014.

    Article  Google Scholar 

  19. Dutta, A., Roy Chowdhury, S., Das, A., A novel method for capturing cerebrovascular reactivity using near-infrared spectroscopy during transcranial direct current stimulation: a stroke case series, 30th International Congress of Clinical Neurophysiology 2014.

  20. Bozzo, L., Puyal, J., and Chatton, J.-Y., Lactate modulates the activity of primary cortical neurons through a receptor-mediated pathway. PLoS ONE 8(8):e71721, 2013.

    Article  Google Scholar 

  21. Nikulin, V. V., 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 Apr 13.

  22. Dutta, A., EEG-NIRS based low-cost screening and monitoring of cerebral microvessels functionality. International Stroke Conference, San Diego, 2014.

    Google Scholar 

  23. Barbour, R. L., Graber, H. L., Xu, Y., Pei, Y., Schmitz, C. H., Pfeil, D. S., et al., A programmable laboratory testbed in support of evaluation of functional brain activation and connectivity. IEEE Trans. Neural. Syst. Rehabil. Eng. Publ. IEEE. Eng. Med. Biol. Soc. 20(2):170–83, 2012.

    Article  Google Scholar 

  24. Choi, J., Wolf, M., Toronov, V., Wolf, U., Polzonetti, C., Hueber, D., et al., Noninvasive determination of the optical properties of adult brain: near-infrared spectroscopy approach. J. Biomed. Opt. 9(1):221–9, 2004.

    Article  Google Scholar 

  25. Huppert, T. J., Diamond, S. G., Franceschini, M. A., and Boas, D. A., HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. Appl. Opt. 48(10):D280–298, 2009.

    Article  Google Scholar 

  26. Delorme, A., and Makeig, S., EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1):9–21, 2004.

    Article  Google Scholar 

  27. Molaee-Ardekani, B., Márquez-Ruiz, J., Merlet, I., Leal-Campanario, R., Gruart, A., Sánchez-Campusano, R., et al., Effects of transcranial direct current stimulation (tDCS) on cortical activity: a computational modeling study. Brain Stimulat. 6(1):25–39, 2013.

    Article  Google Scholar 

  28. Buzsáki, G., Anastassiou, C. A., and Koch, C., The origin of extracellular fields and currents–EEG, ECoG, LFP and spikes. Nat. Rev. Neurosci. 13(6):407–20, 2012.

    Article  Google Scholar 

  29. Carvalhaes, C. G., and Suppes, P., A spline framework for estimating the EEG surface laplacian using the Euclidean metric. Neural Comput. 23(11):2974–3000, 2011.

    Article  MATH  Google Scholar 

  30. Perrin, F., Pernier, J., Bertrand, O., and Echallier, J. F., Spherical splines for scalp potential and current density mapping. Electroencephalogr. Clin. Neurophysiol. 72(2):184–7, 1989.

    Article  Google Scholar 

  31. Viswanathan, A., and Freeman, R. D., Neurometabolic coupling in cerebral cortex reflects synaptic more than spiking activity. Nat. Neurosci. 10(10):1308–12, 2007.

    Article  Google Scholar 

  32. Villringer, A., and Chance, B., Non-invasive optical spectroscopy and imaging of human brain function. Trends Neurosci. 20(10):435–42, 1997.

    Article  Google Scholar 

  33. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. Ser. Math. Phys. Eng. Sci. 454(1971):903–95, 1998.

    Article  MATH  MathSciNet  Google Scholar 

  34. Zhou, D., Thompson, W. K., and Siegle, G., MATLAB toolbox for functional connectivity. NeuroImage 47(4):1590–607, 2009.

    Article  Google Scholar 

  35. Dhamala, M., Rangarajan, G., and Ding, M., Analyzing information flow in brain networks with nonparametric Granger causality. NeuroImage 41(2):354–62, 2008.

    Article  Google Scholar 

  36. Dietzel, I., and Heinemann, U., Dynamic variations of the brain cell microenvironment in relation to neuronal hyperactivity. Ann. N. Y. Acad. Sci. 481:72–86, 1986.

    Article  Google Scholar 

  37. Meeks, J. P., and Mennerick, S., Selective effects of potassium elevations on glutamate signaling and action potential conduction in hippocampus. J. Neurosci. Off. J. Soc. Neurosci. 24(1):197–206, 2004.

    Article  Google Scholar 

  38. Gruetter, R., Novotny, E. J., Boulware, S. D., Rothman, D. L., and Shulman, R. G., 1H NMR studies of glucose transport in the human brain. J. Cereb. Blood Flow Metab. 16(3):427–38, 1996.

    Article  Google Scholar 

  39. Fray, A. E., Forsyth, R. J., Boutelle, M. G., and Fillenz, M., The mechanisms controlling physiologically stimulated changes in rat brain glucose and lactate: a microdialysis study. J. Physiol. 496(Pt 1):49–57, 1996.

    Article  Google Scholar 

  40. Brown, A. M., and Ransom, B. R., Astrocyte glycogen and brain energy metabolism. Glia 55:1263–1271, 2007. doi:10.1002/glia.20557.

    Article  Google Scholar 

  41. Soraghan, C., Matthews, F., Markham, C., Pearlmutter, B. A., O’Neill, R., Ward, T. E., A 12-channel, real-time near-infrared spectroscopy instrument for brain-computer interface applications. Conf Proc Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Conf 2008:5648–5651. doi:10.1109/IEMBS.2008.4650495, 2008.

  42. Anirban Dutta, M. M., Development of an EEG-fNIRS based online monitoring tool towards delivery of non-invasive brain stimulation, 2014.

  43. Safaie, J., Grebe, R., Abrishami Moghaddam, H., and Wallois, F., Toward a fully integrated wireless wearable EEG-NIRS bimodal acquisition system. J. Neural Eng. 10:056001, 2013. doi:10.1088/1741-2560/10/5/056001.

    Article  Google Scholar 

  44. Lareau, E., Simard, G., Lesage, F., Nguyen, D., Sawan, M., Near infrared spectrometer combined with multichannel EEG for functional brain imaging. 2011 5th Int. Symp. Med. Inf. Commun. Technol. ISMICT. pp 122–126, 2011.

  45. Scholkmann, F., Kleiser, S., Metz, A. J., Zimmermann, R., Mata Pavia, J., Wolf, U., and Wolf, M., A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. NeuroImage 85(Pt 1):6–27, 2014. doi:10.1016/j.neuroimage.2013.05.004.

    Article  Google Scholar 

  46. Akin, M., and Kiymik, M. K., Application of periodogram and AR spectral analysis to EEG signals. J. Med. Syst. 24:247–256, 2000.

    Article  Google Scholar 

  47. Kiymik, M. K., Subasi, A., and Ozcalik, H. R., Neural networks with periodogram and autoregressive spectral analysis methods in detection of epileptic seizure. J. Med. Syst. 28:511–522, 2004.

    Article  Google Scholar 

  48. Ubeyli, E. D., Cvetkovic, D., and Cosic, I., AR spectral analysis technique for human PPG, ECG and EEG signals. J. Med. Syst. 32:201–206, 2008.

    Article  Google Scholar 

  49. Moreno, L., Sánchez, J. L., Mañas, S., Piñeiro, J. D., Merino, J. J., Sigut, J., Aguilar, R. M., Estévez, J. I., and Marichal, R., Tools for acquisition, processing and knowledge-based diagnostic of the electroencephalogram and visual evoked potentials. J. Med. Syst. 25:177–194, 2001.

    Article  Google Scholar 

  50. Das, S., Roy Chowdhury, S., and Saha, H., Accuracy enhancement in a fuzzy expert decision making system through appropriate determination of membership functions and its application in a medical diagnostic decision making system. J. Med. Syst. 36:1607–1620, 2012. doi:10.1007/s10916-010-9623-8.

    Article  Google Scholar 

  51. Roy Chowdhury, S., Roy, A., and Saha, H., ASIC design of a digital fuzzy system on chip for medical diagnostic applications. J. Med. Syst. 35:221–235, 2011. doi:10.1007/s10916-009-9359-5.

    Article  Google Scholar 

Download references

Acknowledgments

Research conducted within the context of the Franco-German PHC-PROCOPE 2014 funding. A. Jacob would like to thank German Academic Exchange Service (DAAD) for supporting her internship in Germany. The help received from the Neuro Rehab Services LLP, India in conducting the clinical study is gratefully acknowledged. This project was further supported by the German Ministry for Education and Research (BMBF, Project EYE-TSS 03IPT605E).

Author information

Authors and Affiliations

  1. Charité - Universitätsmedizin Berlin, Berlin, Germany

    Anirban Dutta

  2. DEMAR (INRIA Sophia Antipolis), INRIA – CNRS : UMR5506 – Université Montpellier II - Sciences et techniques – Université Montpellier I, Montpellier, France

    Anirban Dutta

  3. Indian Institute of Technology, Chennai, India

    Athira Jacob

  4. International Institute of Information Technology, Hyderabad, India

    Shubhajit Roy Chowdhury

  5. Institute of Neuroscience, Kolkata, India

    Abhijit Das

  6. University of Medicine Goettingen, Goettingen, Germany

    Michael A. Nitsche

Authors
  1. Anirban Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Athira Jacob
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shubhajit Roy Chowdhury
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abhijit Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael A. Nitsche
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anirban Dutta.

Additional information

This article is part of the Topical Collection on Patient Facing Systems

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dutta, A., Jacob, A., Chowdhury, S.R. et al. EEG-NIRS Based Assessment of Neurovascular Coupling During Anodal Transcranial Direct Current Stimulation - a Stroke Case Series. J Med Syst 39, 36 (2015). https://doi.org/10.1007/s10916-015-0205-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-015-0205-7

Keywords

Search

Navigation