Functional Near-Infrared Spectroscopy and Its Clinical Application in the Field of Neuroscience: Advances and Future Directions

Wei-Liang Chen, Julie Wagner, Nicholas Heugel, Jeffrey Sugar, Yu-Wen Lee, Lisa Conant, Marsha Malloy, Joseph Heffernan, Brendan Quirk, Anthony Zinos, Scott A Beardsley, Robert Prost, Harry T Whelan, Wei-Liang Chen, Julie Wagner, Nicholas Heugel, Jeffrey Sugar, Yu-Wen Lee, Lisa Conant, Marsha Malloy, Joseph Heffernan, Brendan Quirk, Anthony Zinos, Scott A Beardsley, Robert Prost, Harry T Whelan

Abstract

Similar to functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS) detects the changes of hemoglobin species inside the brain, but via differences in optical absorption. Within the near-infrared spectrum, light can penetrate biological tissues and be absorbed by chromophores, such as oxyhemoglobin and deoxyhemoglobin. What makes fNIRS more advantageous is its portability and potential for long-term monitoring. This paper reviews the basic mechanisms of fNIRS and its current clinical applications, the limitations toward more widespread clinical usage of fNIRS, and current efforts to improve the temporal and spatial resolution of fNIRS toward robust clinical usage within subjects. Oligochannel fNIRS is adequate for estimating global cerebral function and it has become an important tool in the critical care setting for evaluating cerebral oxygenation and autoregulation in patients with stroke and traumatic brain injury. When it comes to a more sophisticated utilization, spatial and temporal resolution becomes critical. Multichannel NIRS has improved the spatial resolution of fNIRS for brain mapping in certain task modalities, such as language mapping. However, averaging and group analysis are currently required, limiting its clinical use for monitoring and real-time event detection in individual subjects. Advances in signal processing have moved fNIRS toward individual clinical use for detecting certain types of seizures, assessing autonomic function and cortical spreading depression. However, its lack of accuracy and precision has been the major obstacle toward more sophisticated clinical use of fNIRS. The use of high-density whole head optode arrays, precise sensor locations relative to the head, anatomical co-registration, short-distance channels, and multi-dimensional signal processing can be combined to improve the sensitivity of fNIRS and increase its use as a wide-spread clinical tool for the robust assessment of brain function.

Keywords: autonomic dysfunction; cerebral autoregulation; cytochrome c oxidase; epilepsy; functional MRI; functional NIRS; migraine; near-infrared spectroscopy.

Copyright © 2020 Chen, Wagner, Heugel, Sugar, Lee, Conant, Malloy, Heffernan, Quirk, Zinos, Beardsley, Prost and Whelan.

Figures

FIGURE 1
FIGURE 1
Monte Carlo photon propagation simulation using a point LED source incident at an angle normal to the scalp. Anatomy modeled on the harbor porpoise.
FIGURE 2
FIGURE 2
Emitter and detector arrangement on an adult human subject. (A) A two channel emitter-detector pair placement on the scalp. Arrows from the emitter to the detectors indicate the measured light path of each channel. The inset highlights emitter-detector distances and the incorporation of short-distance channels to measure scalp blood flow. (B) Full head imaging cap example on an adult human subject. Blue lines represent measurement channels (n = 102 channels) between emitter (n = 32) and detector (n = 32) pairs.
FIGURE 3
FIGURE 3
Electron transfer chain allows electrons to be transferred from the TCA cycle to oxygen via cytochrome c oxidase (CCO), resulting in changes in the redox states of CCO.
FIGURE 4
FIGURE 4
Idealized GLM output for a right-hand finger tapping task at the group (left), current single subject fNIRS analysis capabilities (middle), and where single subject fNIRS analysis needs to be in order to be clinically relevant (right). The example of oxyhemoglobin is used in this depiction.

References

    1. Abdelnour A. F., Huppert T. (2009). Real-time imaging of human brain function by near-infrared spectroscopy using an adaptive general linear model. Neuroimage 46 133–143. 10.1016/j.neuroimage.2009.01.033
    1. Adelson P. D., Nemoto E., Scheuer M., Painter M., Morgan J., Yonas H. (1999). Noninvasive continuous monitoring of cerebral oxygenation periictally using near-infrared spectroscopy: a preliminary report. Epilepsia 40 1484–1489.
    1. Akin A., Bilensoy D. (2006). Cerebrovascular reactivity to hypercapnia in migraine patients measured with near-infrared spectroscopy. Brain Res. 1107 206–214. 10.1016/j.brainres.2006.06.002
    1. Andersen A. V., Simonsen S. A., Schytz H. W., Iversen H. K. (2018). Assessing low-frequency oscillations in cerebrovascular diseases and related conditions with near-infrared spectroscopy: a plausible method for evaluating cerebral autoregulation? Neurophotonics 5:030901 10.1117/1.NPh.5.3.030901
    1. Arifler D., Zhu T., Madaan S., Tachtsidis I. (2015). Optimal wavelength combinations for near-infrared spectroscopic monitoring of changes in brain tissue hemoglobin and cytochrome c oxidase concentrations. Biomed. Opt. Express 6 933–947. 10.1364/BOE.6.000933
    1. Ayers M. D., Lawrence D. K. (2015). Near-infrared spectroscopy to assess cerebral perfusion during head-up tilt-table test in patients with syncope. Congenit. Heart Dis. 10 333–339. 10.1111/chd.12236
    1. Balakrishnan B., Dasgupta M., Gajewski K., Hoffmann R. G., Simpson P. M., Havens P. L., et al. (2018). Low near infrared spectroscopic somatic oxygen saturation at admission is associated with need for lifesaving interventions among unplanned admissions to the pediatric intensive care unit. J. Clin. Monit. Comput. 32 89–96. 10.1007/s10877-017-0007-1
    1. Bale G., Elwell C. E., Tachtsidis I. (2016). From Jobsis to the present day: a review of clinical near-infrared spectroscopy measurements of cerebral cytochrome-c-oxidase. J. Biomed. Opt. 21:091307 10.1117/1.JBO.21.9.091307
    1. Bale G., Rajaram A., Kewin M., Morrison L., Bainbridge A., Diop M., et al. (2018). Broadband NIRS cerebral cytochrome-C-oxidase response to anoxia before and after hypoxic-ischaemic injury in piglets. Adv. Exp. Med. Biol. 1072 151–156. 10.1007/978-3-319-91287-5_24
    1. Barker J. W., Arabi A., Huppert T. J. (2013). Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS. Biomed. Opt. Express 4 1366–1379.
    1. Binder J. R. (2011). Functional MRI is a valid noninvasive alternative to Wada testing. Epilepsy Behav. 20 214–222. 10.1016/j.yebeh.2010.08.004
    1. Bindra J., Pham P., Aneman A., Chuan A., Jaeger M. (2016). Non-invasive monitoring of dynamic cerebrovascular autoregulation using near infrared spectroscopy and the finometer photoplethysmograph. Neurocrit. Care 24 442–447. 10.1007/s12028-015-0200-3
    1. Blanco B., Molnar M., Caballero-Gaudes C. (2018). Effect of prewhitening in resting-state functional near-infrared spectroscopy data. Neurophotonics 5:040401 10.1117/1.NPh.5.4.040401
    1. Buchheim K., Obrig H., v Pannwitz W., Muller A., Heekeren H., Villringer A., et al. (2004). Decrease in haemoglobin oxygenation during absence seizures in adult humans. Neurosci. Lett. 354 119–122.
    1. Budohoski K. P., Czosnyka M., Smielewski P., Kasprowicz M., Helmy A., Bulters D., et al. (2012). Impairment of cerebral autoregulation predicts delayed cerebral ischemia after subarachnoid hemorrhage: a prospective observational study. Stroke 43 3230–3237. 10.1161/STROKEAHA.112.669788
    1. Coelli S., Nobili L., Boly M., Riedner B., Bianchi A. M. (2019). Optimization of the Cortical Traveling Wave Analysis framework for feasibility in Stereo-Electroencephalography. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2019 3854–3857. 10.1109/EMBC.2019.8857664
    1. Cooper C. E., Cope M., Quaresima V., Ferrari M., Nemoto E., Springett R., et al. (1997). Measurement of cytochrome oxidase redox state by near infrared spectroscopy. Adv. Exp. Med. Biol. 413 63–73. 10.1007/978-1-4899-0056-2_7
    1. Cordes D., Haughton V. M., Arfanakis K., Carew J. D., Turski P. A., Moritz C. H., et al. (2001). Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. AJNR Am. J. Neuroradiol. 22 1326–1333.
    1. Cui X., Bray S., Bryant D. M., Glover G. H., Reiss A. L. (2011). A quantitative comparison of NIRS and fMRI across multiple cognitive tasks. Neuroimage 54 2808–2821. 10.1016/j.neuroimage.2010.10.069
    1. Czosnyka M., Brady K., Reinhard M., Smielewski P., Steiner L. A. (2009). Monitoring of cerebrovascular autoregulation: facts, myths, and missing links. Neurocrit. Care 10 373–386. 10.1007/s12028-008-9175-7
    1. Dias C., Silva M. J., Pereira E., Monteiro E., Maia I., Barbosa S., et al. (2015). Optimal cerebral perfusion pressure management at bedside: a single-center pilot study. Neurocrit. Care 23 92–102. 10.1007/s12028-014-0103-8
    1. Fox P. T., Raichle M. E. (1984). Stimulus rate dependence of regional cerebral blood flow in human striate cortex, demonstrated by positron emission tomography. J. Neurophysiol. 51 1109–1120. 10.1152/jn.1984.51.5.1109
    1. Funane T., Sato H., Yahata N., Takizawa R., Nishimura Y., Kinoshita A., et al. (2015). Concurrent fNIRS-fMRI measurement to validate a method for separating deep and shallow fNIRS signals by using multidistance optodes. Neurophotonics 2:015003 10.1117/1.NPh.2.1.015003
    1. Gagnon L., Yucel M. A., Boas D. A., Cooper R. J. (2014). Further improvement in reducing superficial contamination in NIRS using double short separation measurements. Neuroimage 85(Pt 1), 127–135. 10.1016/j.neuroimage.2013.01.073
    1. Gallagher A., Theriault M., Maclin E., Low K., Gratton G., Fabiani M., et al. (2007). Near-infrared spectroscopy as an alternative to the Wada test for language mapping in children, adults and special populations. Epileptic Disord. 9 241–255. 10.1684/epd.2007.0118
    1. Giacalone G., Zanoletti M., Re R., Germinario B., Contini D., Spinelli L., et al. (2019). Time-domain near-infrared spectroscopy in acute ischemic stroke patients. Neurophotonics 6:015003 10.1117/1.NPh.6.1.015003
    1. Goadsby P. J., Holland P. R., Martins-Oliveira M., Hoffmann J., Schankin C., Akerman S. (2017). Pathophysiology of migraine: a disorder of sensory processing. Physiol. Rev. 97 553–622. 10.1152/physrev.00034.2015
    1. Heinzel S., Haeussinger F. B., Hahn T., Ehlis A. C., Plichta M. M., Fallgatter A. J. (2013). Variability of (functional) hemodynamics as measured with simultaneous fNIRS and fMRI during intertemporal choice. Neuroimage 71 125–134. 10.1016/j.neuroimage.2012.12.074
    1. Hiraoka M., Firbank M., Essenpreis M., Cope M., Arridge S. R., van der Zee P., et al. (1993). A Monte Carlo investigation of optical pathlength in inhomogeneous tissue and its application to near-infrared spectroscopy. Phys. Med. Biol. 38 1859–1876. 10.1088/0031-9155/38/12/011
    1. Hoffman G. M., Ghanayem N. S., Scott J. P., Tweddell J. S., Mitchell M. E., Mussatto K. A. (2017). Postoperative cerebral and somatic near-infrared spectroscopy saturations and outcome in hypoplastic left heart syndrome. Ann. Thorac. Surg. 103 1527–1535. 10.1016/j.athoracsur.2016.09.100
    1. Holper L., Mann J. J. (2018). Test-retest reliability of brain mitochondrial cytochrome-c-oxidase assessed by functional near-infrared spectroscopy. J. Biomed. Opt. 23 1–9. 10.1117/1.JBO.23.5.056006
    1. Hong K. S., Khan M. J. (2017). Hybrid brain-computer interface techniques for improved classification accuracy and increased number of commands: a review. Front. Neurorobot. 11:35. 10.3389/fnbot.2017.00035
    1. Hong K. S., Khan M. J., Hong M. J. (2018). Feature extraction and classification methods for hybrid fNIRS-EEG brain-computer interfaces. Front Hum Neurosci 12:246. 10.3389/fnhum.2018.00246
    1. Hong K. S., Naseer N. (2016). Reduction of delay in detecting initial dips from functional near-infrared spectroscopy signals using vector-based phase analysis. Int. J. Neural Syst. 26:1650012. 10.1142/S012906571650012X
    1. Hong K. S., Yaqub M. A. (2019). Application of functional near-infrared spectroscopy in the healthcare industry: a review. J. Innov. Opt. Health Sci. 12:1930012.
    1. Hoshi Y., Kobayashi N., Tamura M. (2001). Interpretation of near-infrared spectroscopy signals: a study with a newly developed perfused rat brain model. J. Appl. Physiol. 90 1657–1662. 10.1152/jappl.2001.90.5.1657
    1. Huppert T. J. (2016). Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy. Neurophotonics 3:010401 10.1117/1.NPh.3.1.010401
    1. Huppert T. J., Hoge R. D., Diamond S. G., Franceschini M. A., Boas D. A. (2006). A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans. Neuroimage 29 368–382. 10.1016/j.neuroimage.2005.08.065
    1. Hyvarinen A., Oja E. (2000). Independent component analysis: algorithms and applications. Neural Netw. 13 411–430. 10.1016/s0893-6080(00)00026-5
    1. Izzetoglu M., Chitrapu P., Bunce S., Onaral B. (2010). Motion artifact cancellation in NIR spectroscopy using discrete Kalman filtering. Biomed. Eng. Online 9:16. 10.1186/1475-925X-9-16
    1. Jahani S., Setarehdan S. K., Boas D. A., Yucel M. A. (2018). Motion artifact detection and correction in functional near-infrared spectroscopy: a new hybrid method based on spline interpolation method and Savitzky-Golay filtering. Neurophotonics 5:015003 10.1117/1.NPh.5.1.015003
    1. Janecek J. K., Swanson S. J., Sabsevitz D. S., Hammeke T. A., Raghavan M., Mueller W., et al. (2013a). Naming outcome prediction in patients with discordant Wada and fMRI language lateralization. Epilepsy Behav. 27 399–403. 10.1016/j.yebeh.2013.02.030
    1. Janecek J. K., Winstanley F. S., Sabsevitz D. S., Raghavan M., Mueller W., Binder J. R., et al. (2013b). Naming outcome after left or right temporal lobectomy in patients with bilateral language representation by Wada testing. Epilepsy Behav. 28 95–98. 10.1016/j.yebeh.2013.04.006
    1. Jeppesen J., Beniczky S., Johansen P., Sidenius P., Fuglsang-Frederiksen A. (2015). Exploring the capability of wireless near infrared spectroscopy as a portable seizure detection device for epilepsy patients. Seizure 26 43–48. 10.1016/j.seizure.2015.01.015
    1. Jessen F., Wiese B., Bachmann C., Eifflaender-Gorfer S., Haller F., Kolsch H., et al. (2010). Prediction of dementia by subjective memory impairment: effects of severity and temporal association with cognitive impairment. Arch. Gen. Psychiatry 67 414–422. 10.1001/archgenpsychiatry.2010.30
    1. Jobsis F. F. (1977). Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters. Science 198 1264–1267.
    1. Kadamati J., Sugar M. R., Sannagowdara K., Malloy M., Chen W., Quirk B., et al. (2018). “Cerebral oxygen saturation and cytochrome oxidase redox state in children with epilepsy: a pilot study -MULTICHANNEL NIRS for epilepsy seizure detection,” in Proceedings of the International Congress of Clinical Neurophysiology, Washington, DC.
    1. Kadamati P., Sugar J. J., Quirk B. J., Mehrvar S., Chelimsky G. G., Whelan H. T., et al. (2018). Near-infrared spectroscopy muscle oximetry of patients with postural orthostatic tachycardia syndrome. J. Innov. Opt. Health Sci. 11:1850026. 10.1142/S1793545818500268
    1. Khan M. J., Ghafoor U., Hong K. S. (2018). Early detection of hemodynamic responses using EEG: a hybrid EEG-fNIRS study. Front. Hum. Neurosci. 12:479. 10.3389/fnhum.2018.00479
    1. Khan M. J., Hong K. S. (2017). Hybrid EEG-fNIRS-based eight-command decoding for BCI: application to Quadcopter control. Front. Neurorobot. 11:6. 10.3389/fnbot.2017.00006
    1. Kohno S., Miyai I., Seiyama A., Oda I., Ishikawa A., Tsuneishi S., et al. (2007). Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging datathrough independent component analysis. J. Biomed. Opt. 12 0621111–0621119.
    1. Lange F., Dunne L., Hale L., Tachtsidis I. (2019). MAESTROS: a multiwavelength time-domain NIRS system to monitor changes in oxygenation and oxidation state of cytochrome-C-oxidase. IEEE J. Sel. Top. Quantum Electron. 25:7100312. 10.1109/JSTQE.2018.2833205
    1. Lankford J., Numan M., Hashmi S. S., Gourishankar A., Butler I. J. (2015). Cerebral blood flow during HUTT in young patients with orthostatic intolerance. Clin. Auton. Res. 25 277–284. 10.1007/s10286-015-0295-9
    1. Lauritzen M. (1994). Pathophysiology of the migraine aura. The spreading depression theory. Brain 117(Pt 1), 199–210. 10.1093/brain/117.1.199
    1. Li Z., Wang Y., Li Y., Wang Y., Li J., Zhang L. (2010). Wavelet analysis of cerebral oxygenation signal measured by near infrared spectroscopy in subjects with cerebral infarction. Microvasc. Res. 80 142–147. 10.1016/j.mvr.2010.02.004
    1. Liu Y., Ayaz H., Shewokis P. A. (2017). Multisubject “Learning” for mental workload classification using concurrent EEG, fNIRS, and physiological measures. Front. Hum. Neurosci. 11:389. 10.3389/fnhum.2017.00389
    1. Maggioni E., Molteni E., Zucca C., Reni G., Cerutti S., Triulzi F. M., et al. (2015). Investigation of negative BOLD responses in human brain through NIRS technique. A visual stimulation study. Neuroimage 108 410–422. 10.1016/j.neuroimage.2014.12.074
    1. Masataka N., Perlovsky L., Hiraki K. (2015). Near-infrared spectroscopy (NIRS) in functional research of prefrontal cortex. Front. Hum. Neurosci. 9:274. 10.3389/fnhum.2015.00274
    1. Meilan J. J. G., Martinez-Sanchez F., Martinez-Nicolas I., Llorente T. E., Carro J. (2020). Changes in the rhythm of speech difference between people with nondegenerative mild cognitive impairment and with preclinical dementia. Behav. Neurol. 2020:4683573. 10.1155/2020/4683573
    1. Molavi B., Dumont G. A. (2012). Wavelet-based motion artifact removal for functional near-infrared spectroscopy. Physiol. Meas. 33 259–270. 10.1088/0967-3334/33/2/259
    1. Monrad P., Sannagowdara K., Bozarth X., Bhosrekar S., Hecox K., Nwosu M., et al. (2015). Haemodynamic response associated with both ictal and interictal epileptiform activity using simultaneous video electroencephalography/near infrared spectroscopy in a within-subject study. J. Near Infrared Spectrosc. 23 209–218. 10.1255/jnirs.1170
    1. Moriguchi Y., Noda T., Nakayashiki K., Takata Y., Setoyama S., Kawasaki S., et al. (2017). Validation of brain-derived signals in near-infrared spectroscopy through multivoxel analysis of concurrent functional magnetic resonance imaging. Hum. Brain Mapp. 38 5274–5291. 10.1002/hbm.23734
    1. Mueller K. D., Hermann B., Mecollari J., Turkstra L. S. (2018). Connected speech and language in mild cognitive impairment and Alzheimer’s disease: a review of picture description tasks. J. Clin. Exp. Neuropsychol. 40 917–939. 10.1080/13803395.2018.1446513
    1. Nguyen D. K., Tremblay J., Pouliot P., Vannasing P., Florea O., Carmant L., et al. (2012). Non-invasive continuous EEG-fNIRS recording of temporal lobe seizures. Epilepsy Res. 99 112–126. 10.1016/j.eplepsyres.2011.10.035
    1. Nguyen H. D., Yoo S. H., Bhutta M. R., Hong K. S. (2018). Adaptive filtering of physiological noises in fNIRS data. Biomed. Eng. Online 17:180. 10.1186/s12938-018-0613-2
    1. Nguyen T., Kim M., Gwak J., Lee J. J., Choi K. Y., Lee K. H., et al. (2019). Investigation of brain functional connectivity in patients with mild cognitive impairment: a functional near-infrared spectroscopy (fNIRS) study. J. Biophotonics 12:e201800298. 10.1002/jbio.201800298
    1. Obrig H. (2014). NIRS in clinical neurology - a ‘promising’ tool? Neuroimage 85(Pt 1), 535–546. 10.1016/j.neuroimage.2013.03.045
    1. Okamoto M., Dan H., Sakamoto K., Takeo K., Shimizu K., Kohno S., et al. (2004). Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10-20 system oriented for transcranial functional brain mapping. Neuroimage 21 99–111. 10.1016/j.neuroimage.2003.08.026
    1. Osharina V., Ponchel E., Aarabi A., Grebe R., Wallois F. (2010). Local haemodynamic changes preceding interictal spikes: a simultaneous electrocorticography (ECoG) and near-infrared spectroscopy (NIRS) analysis in rats. Neuroimage 50 600–607. 10.1016/j.neuroimage.2010.01.009
    1. Phillip D., Schytz H. W., Iversen H. K., Selb J., Boas D. A., Ashina M. (2014). Spontaneous low frequency oscillations in acute ischemic stroke – a near infrared spectroscopy (NIRS) study. J. Neurol. Neurophysiol. 5:241 10.4172/2155-9562.1000241
    1. Pourshoghi A., Danesh A., Tabby D. S., Grothusen J., Pourrezaei K. (2015). Cerebral reactivity in migraine patients measured with functional near-infrared spectroscopy. Eur. J. Med. Res. 20:96. 10.1186/s40001-015-0190-9
    1. Rizki E. E., Uga M., Dan I., Dan H., Tsuzuki D., Yokota H., et al. (2015). Determination of epileptic focus side in mesial temporal lobe epilepsy using long-term noninvasive fNIRS/EEG monitoring for presurgical evaluation. Neurophotonics 2:025003 10.1117/1.NPh.2.2.025003
    1. Robertson F. C., Douglas T. S., Meintjes E. M. (2010). Motion artifact removal for functional near infrared spectroscopy: a comparison of methods. IEEE Trans. Biomed. Eng. 57 1377–1387. 10.1109/TBME.2009.2038667
    1. Rolinski R., Austermuehle A., Wiggs E., Agrawal S., Sepeta L. N., Gaillard W. D., et al. (2019). Functional MRI and direct cortical stimulation: prediction of postoperative language decline. Epilepsia 60 560–570. 10.1111/epi.14666
    1. Sato Y., Fukuda M., Oishi M., Shirasawa A., Fujii Y. (2013). Ictal near-infrared spectroscopy and electrocorticography study of supplementary motor area seizures. J. Biomed. Opt. 18:76022 10.1117/1.JBO.18.7.076022
    1. Scholkmann F., Spichtig S., Muehlemann T., Wolf M. (2010). How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation. Physiol. Meas. 31 649–662. 10.1088/0967-3334/31/5/004
    1. Shinoura N., Yamada R. (2005). Decreased vasoreactivity to right cerebral hemisphere pressure in migraine without aura: a near-infrared spectroscopy study. Clin. Neurophysiol. 116 1280–1285. 10.1016/j.clinph.2005.01.016
    1. Sokol D. K., Markand O. N., Daly E. C., Luerssen T. G., Malkoff M. D. (2000). Near infrared spectroscopy (NIRS) distinguishes seizure types. Seizure 9 323–327. 10.1053/seiz.2000.0406
    1. Springett R., Newman J., Cope M., Delpy D. T. (2000). Oxygen dependency and precision of cytochrome oxidase signal from full spectral NIRS of the piglet brain. Am. J. Physiol. Heart Circ. Physiol. 279 H2202–H2209. 10.1152/ajpheart.2000.279.5.H2202
    1. Steinbrink J., Villringer A., Kempf F., Haux D., Boden S., Obrig H. (2006). Illuminating the BOLD signal: combined fMRI-fNIRS studies. Magn. Reson. Imaging 24 495–505. 10.1016/j.mri.2005.12.034
    1. Su H., Huo C., Wang B., Li W., Xu G., Liu Q., et al. (2018). Alterations in the coupling functions between cerebral oxyhaemoglobin and arterial blood pressure signals in post-stroke subjects. PLoS One 13:e0195936. 10.1371/journal.pone.0195936
    1. Tanaka H., Matsushima R., Tamai H., Kajimoto Y. (2002). Impaired postural cerebral hemodynamics in young patients with chronic fatigue with and without orthostatic intolerance. J. Pediatr. 140 412–417. 10.1067/mpd.2002.122725
    1. Uemura K., Shimada H., Doi T., Makizako H., Tsutsumimoto K., Park H., et al. (2016). Reduced prefrontal oxygenation in mild cognitive impairment during memory retrieval. Int. J. Geriatr. Psychiatry 31 583–591. 10.1002/gps.4363
    1. Verdecchia K., Diop M., Lee T. Y., St Lawrence K. (2013). Quantifying the cerebral metabolic rate of oxygen by combining diffuse correlation spectroscopy and time-resolved near-infrared spectroscopy. J. Biomed. Opt. 18:27007 10.1117/1.JBO.18.2.027007
    1. Vermeij A., Kessels R. P. C., Heskamp L., Simons E. M. F., Dautzenberg P. L. J., Claassen J. (2017). Prefrontal activation may predict working-memory training gain in normal aging and mild cognitive impairment. Brain Imaging Behav. 11 141–154. 10.1007/s11682-016-9508-7
    1. Vernieri F., Rosato N., Pauri F., Tibuzzi F., Passarelli F., Rossini P. M. (1999). Near infrared spectroscopy and transcranial Doppler in monohemispheric stroke. Eur. Neurol. 41 159–162. 10.1159/000008041
    1. Vespa S., Baroumand A. G., Ferrao Santos S., Vrielynck P., de Tourtchaninoff M., Feys O., et al. (2020). Ictal EEG source imaging and connectivity to localize the seizure onset zone in extratemporal lobe epilepsy. Seizure 78 18–30. 10.1016/j.seizure.2020.03.001
    1. Villringer A., Planck J., Stodieck S., Botzel K., Schleinkofer L., Dirnagl U. (1994). Noninvasive assessment of cerebral hemodynamics and tissue oxygenation during activation of brain cell function in human adults using near infrared spectroscopy. Adv. Exp. Med. Biol. 345 559–565.
    1. Viola S., Viola P., Litterio P., Buongarzone M. P., Fiorelli L. (2010). Pathophysiology of migraine attack with prolonged aura revealed by transcranial Doppler and near infrared spectroscopy. Neurol. Sci. 31(Suppl. 1), S165–S166. 10.1007/s10072-010-0318-1
    1. Watanabe E., Maki A., Kawaguchi F., Takashiro K., Yamashita Y., Koizumi H., et al. (1998). Non-invasive assessment of language dominance with near-infrared spectroscopic mapping. Neurosci. Lett. 256 49–52.
    1. Wijeakumar S., Huppert T. J., Magnotta V. A., Buss A. T., Spencer J. P. (2017). Validating an image-based fNIRS approach with fMRI and a working memory task. Neuroimage 147 204–218. 10.1016/j.neuroimage.2016.12.007
    1. Wroblewski G. J., Matsuo K., Hirata K., Matsubara T., Harada K., Watanabe Y., et al. (2017). Effects of task language and second-language proficiency on the neural correlates of phonemic fluency in native Japanese speakers: a functional near-infrared spectroscopy study. Neuroreport 28 884–889. 10.1097/WNR.0000000000000852
    1. Yang D., Hong K. S., Yoo S. H., Kim C. S. (2019). Evaluation of neural degeneration biomarkers in the prefrontal cortex for early identification of patients with mild cognitive impairment: an fNIRS study. Front. Hum. Neurosci. 13:317. 10.3389/fnhum.2019.00317
    1. Yeung M. K., Chan A. S. (2020). Functional near-infrared spectroscopy reveals decreased resting oxygenation levels and task-related oxygenation changes in mild cognitive impairment and dementia: a systematic review. J. Psychiatr. Res. 124 58–76. 10.1016/j.jpsychires.2020.02.017
    1. Yeung M. K., Sze S. L., Woo J., Kwok T., Shum D. H., Yu R., et al. (2016). Reduced frontal activations at high working memory load in mild cognitive impairment: near-infrared spectroscopy. Dement. Geriatr. Cogn. Disord. 42 278–296. 10.1159/000450993
    1. Yoo S. H., Hong K. S. (2019). Hemodynamics analysis of patients with mild cognitive impairment during working memory tasks. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2019 4470–4473. 10.1109/EMBC.2019.8856956
    1. Yu J. W., Lim S. H., Kim B., Kim E., Kim K., Kyu Park S., et al. (2020). Prefrontal functional connectivity analysis of cognitive decline for early diagnosis of mild cognitive impairment: a functional near-infrared spectroscopy study. Biomed. Opt. Express 11 1725–1741. 10.1364/BOE.382197
    1. Yucel M. A., Selb J., Boas D. A., Cash S. S., Cooper R. J. (2014). Reducing motion artifacts for long-term clinical NIRS monitoring using collodion-fixed prism-based optical fibers. Neuroimage 85(Pt 1), 192–201. 10.1016/j.neuroimage.2013.06.054
    1. Zafar A., Hong K. S. (2017). Detection and classification of three-class initial dips from prefrontal cortex. Biomed. Opt. Express 8 367–383. 10.1364/BOE.8.000367
    1. Zafar A., Hong K. S. (2018). Neuronal activation detection using vector phase analysis with dual threshold circles: a functional near-infrared spectroscopy study. Int. J. Neural Syst. 28:1850031. 10.1142/S0129065718500314
    1. Zeiler F. A., Donnelly J., Calviello L., Smielewski P., Menon D. K., Czosnyka M. (2017). Pressure autoregulation measurement techniques in adult traumatic brain injury, Part II: a scoping review of continuous methods. J. Neurotrauma 34 3224–3237. 10.1089/neu.2017.5086
    1. Zhang T., Zhou J., Jiang R., Yang H., Carney P. R., Jiang H. (2014). Pre-seizure state identified by diffuse optical tomography. Sci. Rep. 4:3798. 10.1038/srep03798
    1. Zhang X., Noah J. A., Hirsch J. (2016). Separation of the global and local components in functional near-infrared spectroscopy signals using principal component spatial filtering. Neurophotonics 3:015004 10.1117/1.NPh.3.1.015004

Source: PubMed

3
Abonnieren