Functional near-infrared spectroscopy

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fNIRS with a Gowerlabs NTS system Blonde fNIRS lady.jpg
fNIRS with a Gowerlabs NTS system

Functional near-infrared spectroscopy (fNIRS), sometimes referred to as NIRS or Optical Topography (OT), is an optical brain monitoring technique which uses near-infrared spectroscopy for the purpose of functional neuroimaging. [1] [2] Using fNIRS, brain activity is measured by using near-infrared light to estimate cortical hemodynamic activity that occurs in response to neural activity. The use of fNIRS has led to advances in different fields such as cognitive neuroscience, [3] [4] clinical applications, [5] [6] [7] developmental science [8] [9] and sport and exercise science. [10] [11] The signal is often compared with the BOLD signal measured by fMRI and is capable of measuring changes both in oxy- and deoxyhemoglobin concentration, [12] but can only measure from regions near the cortical surface.

Contents

How it Works

Basic functional near infrared spectroscopy (fNIRS) abbreviations
BFi = blood flow index

CBF = cerebral blood flow

CBV = cerebral blood volume

CMRO2= metabolic rate of oxygen

CW= continuous wave

DCS = diffuse correlation spectroscopy

FD = frequency-domain

Hb, HbR= deoxygenated hemoglobin

HbO, HbO2= oxygenated hemoglobin

HbT= total hemoglobin concentration

HGB = blood hemoglobin

SaO2= arterial saturation

SO2= hemoglobin saturation

SvO2= venous saturation

TD=time-domain

fNIRS estimates the concentration of hemoglobin from changes in absorption of near infrared light. As light moves or propagates through the head, it is alternately scattered or absorbed by the tissue through which it travels. Because hemoglobin is a significant absorber of near-infrared light, changes in absorbed light can be used to reliably measure changes in hemoglobin concentration. Different fNIRS techniques can also use the way in which light propagates to estimate blood volume and oxygenation. The technique is safe, non-invasive, and can be used with other imaging modalities [citation needed].

Oxygenated and deoxygenated hemoglobin Oxygenated vs deoxygenated RBC.jpg
Oxygenated and deoxygenated hemoglobin

fNIRS is a non-invasive imaging method involving the quantification of chromophore concentration resolved from the measurement of near infrared (NIR) light attenuation or temporal or phasic changes. The technique takes advantage of the optical window in which (a) skin, tissue, and bone are mostly transparent to NIR light (700–900 nm spectral interval) and (b) hemoglobin (Hb) and deoxygenated-hemoglobin (deoxy-Hb) are strong absorbers of light [citation needed].

Absorption spectra for oxy-Hb and deoxy-Hb for near-infrared wavelengths Oxy and Deoxy Hemoglobin Near-Infrared absorption spectra.svg
Absorption spectra for oxy-Hb and deoxy-Hb for near-infrared wavelengths

There are different ways for infrared light to interact with the brain tissue [13] . fNIRS focuses primarily on absorption: differences in the absorption spectra of deoxy-Hb and oxy-Hb allow the measurement of relative changes in hemoglobin concentration through the use of light attenuation at multiple wavelengths. Two or more wavelengths are selected, with one wavelength above and one below the isosbestic point of 810 nm—at which deoxy-Hb and oxy-Hb have identical absorption coefficients. Using the modified Beer-Lambert law (mBLL), relative changes in concentration can be calculated as a function of total photon path length. [14]

Typically, the light emitter and detector are placed ipsilaterally (each emitter/detector pair on the same side) on the subject's skull so recorded measurements are due to back-scattered (reflected) light following elliptical pathways. [15] fNIRS is most sensitive to hemodynamic changes which occur nearest to the scalp [16] and these superficial artifacts are often addressed using additional light detectors located closer to the light source (short-separation detectors). [17]

Modified Beer–Lambert law

Changes in light intensity can be related to changes in relative concentrations of hemoglobin through the modified Beer–Lambert law (mBLL). The Beer Lambert-law has to deal with concentration of hemoglobin. This technique also measures relative changes in light attenuation as well as using mBLL to quantify hemoglobin concentration changes. [18]

Equipment and Software

fNIRS cap

10-20 system International 10-20 system for EEG-MCN.svg
10-20 system

fNIRS electrode locations can be defined using a variety of layouts, including names and locations that are specified by the International 10–20 system as well as other layouts that are specifically optimized to maintain a consistent 30mm distance between each location. In addition to the standard positions of electrodes, short separation channels can be added. Short separation channels allow the measurement of scalp signals. Since the short separation channels measure the signal coming from the scalp, they allow the removal of the signal of superficial layers. This leaves behind the actual brain response. Short separation channel detectors are usually placed 8mm away from a source. They do not need to be in a specific direction or in the same direction as a detector. [19]

Software

HOMER3

HOMER3 allows users to obtain estimates and maps of brain activation. It is a set of matlab scripts used for analyzing fNIRS data. This set of scripts has evolved since the early 1990s first as the Photon Migration Imaging toolbox, then HOMER1 and HOMER2, and now HOMER3. [20]

NIRS toolbox

This toolbox is a set of Matlab-based tools for the analysis of functional near-infrared spectroscopy (fNIRS). This toolbox defines the +nirs namespace and includes a series of tools for signal processing, display, and statistics of fNIRS data. This toolbox is built around an object-oriented framework of Matlab classes and namespaces. [21]

AtlasViewer

AtlasViewer allows fNIRS data to be visualized on a model of the brain. In addition, it also allows the user to design probes which can eventually be placed onto a subject. [22]

History

In 1977, Jöbsis [23] reported that brain tissue transparency to NIR light allowed a non-invasive and continuous method of tissue oxygen saturation using transillumination. Transillumination (forward-scattering) was of limited utility in adults because of light attenuation and was quickly replaced by reflectance-mode based techniques - resulting in development of NIRS systems proceeding rapidly. In the mid-80's, Japanese researchers at the central research laboratory of Hitachi Ltd set out to build a NIRS-based brain monitoring system using a pulse of 70-picosecond rays. This effort came into light when the team, held an open symposium to announce the principle of "Optical Topography" in January 1995 [citation needed]. The term "Optical Topography" derives from the concept of using light on "2-Dimensional mapping combined with 1-Dimensional information", or topography. The idea was implemented in launching their first fNIRS (or Optical Topography) device based on Frequency Domain in 2001: Hitachi ETG-100. Harumi Oishi's (大石 晴美), doctoral dissertation in 2003 with the subject of "language learners' cortical activation patterns measured by ETG-100" presented a new use of fNIRS [citation needed].

Hitachi ETG-4000 FNIRS head Hitachi ETG4000 2.jpg
Hitachi ETG-4000

By 1985, the first studies on cerebral oxygenation were conducted by M. Ferrari. Later, in 1989, following work with David Delpy at University College London, Hamamatsu developed the first commercial NIRS system: NIR-1000 cerebral oxygenation monitor. NIRS methods were initially used for cerebral oximetry in the 1990s. In 1993, four publications demonstrated the feasibility of fNIRS in adult humans. [24] [25] [26] [27]

Diffuse Optical Spectroscopy/Imaging (DOI/DOS)

Spectroscopic techniques

Continuous wave

Continuous wave (CW) system uses light sources with constant frequency and amplitude. In fact, to measure absolute changes in HbO concentration with the mBLL, we need to know photon path-length. However, CW-fNIRS does not provide any knowledge of photon path-length, so changes in HbO concentration are relative to an unknown path-length. Many CW-fNIRS commercial systems use estimations of photon path-length derived from computerized Monte-Carlo simulations and physical models, to approximate absolute quantification of hemoglobin concentrations [citation needed].

Where is the optical density or attenuation, is emitted light intensity, is measured light intensity, is the attenuation coefficient, is the chromophore concentration, is the distance between source and detector and is the differential path length factor, and is a geometric factor associated with scattering [citation needed].

When the attenuation coefficients are known, constant scattering loss is assumed, and the measurements are treated differentially in time, the equation reduces to:

Where is the total corrected photon path-length.

Using a dual wavelength system, measurements for HbO2 and Hb can be solved from the matrix equation: [28]

Due to their simplicity and cost-effectiveness, CW-fNIRS is by far the most common form of functional NIRS since it is the cheapest to make, applicable with more channels, and ensures a high temporal resolution. However, it does not distinguish between absorption and scattering changes, and cannot measure absolute absorption values: which means that it is only sensitive to relative change in HbO concentration [citation needed].

Still, the simplicity and cost-effectiveness of CW-based devices prove themselves to be the most favorable for a number of clinical applications: neonatal care, patient monitoring systems, diffuse optical tomography, and so forth. Moreover, thanks to its portability, wireless CW systems have been developed—allowing individuals to be monitored in ambulatory, clinical and sports environments. [29] [30] [31]

Frequency domain [citation needed]

Frequency domain (FD) system comprises NIR laser sources which provide an amplitude-modulated sinusoid at frequencies near 100 MHz. FD-fNIRS measures attenuation, phase shift and the average path length of light through the tissue.

Changes in the back-scattered signal's amplitude and phase provide a direct measurement of absorption and scattering coefficients of the tissue, thus obviating the need for information about photon path-length; and from the coefficients we determine the changes in the concentration of hemodynamic parameters.

Because of the need for modulated lasers as well as phasic measurements, FD system-based devices are more technically complex (therefore more expensive and much less portable) than CW-based ones. However, the system is capable of providing absolute concentrations of HbO and HbR.

Time domain [citation needed]

Time domain (TD) system introduces a short NIR pulse with a pulse length usually in the order of picoseconds—around 70 ps. Through time-of-flight measurements, photon path-length may be directly observed by dividing resolved time by the speed of light. Information about hemodynamic changes can be found in the attenuation, decay, and time profile of the back-scattered signal. For this photon-counting technology is introduced, which counts 1 photon for every 100 pulses to maintain linearity. TD-fNIRS does have a slow sampling rate as well as a limited number of wavelengths. Because of the need for a photon-counting device, high-speed detection, and high-speed emitters, time-resolved methods are the most expensive and technically complicated.

TD-based devices have the highest depth sensitivity and are capable of presenting most accurate values of baseline hemoglobin concentration and oxygenation.

Diffuse correlation spectroscopy

Diffuse correlation spectroscopy (DCS) is a non-invasive optical imaging technique that utilizes coherent near-infrared light to measure local microvascular cerebral blood flow by quantifying the temporal light intensity fluctuations generated by dynamic scattering of moving red blood cells. This dynamic scattering from moving cells causes the detected intensity to temporally fluctuate. These fluctuations can be quantified by the temporal intensity autocorrelation curve of a single speckle. The decay of the autocorrelation curve is fitted with the solution of the correlation diffusion equation to obtain an index of cerebral blood flow. [32] [33] [34] [35]

Measurement of brain oxyhemoglobin and deoxyhemoglobin concentration changes at high alltitude induced hypoxia with a portable fNIRS device (PortaLite, Artinis Medical Systems) Picture of NIRS measurement at high alltitude.jpg
Measurement of brain oxyhemoglobin and deoxyhemoglobin concentration changes at high alltitude induced hypoxia with a portable fNIRS device (PortaLite, Artinis Medical Systems)

Application

fNIRS has been successfully implemented as a control signal for brain–computer interface systems. [36] [37] [38] [39] [40] Modern fNIRS systems are combined with virtual or augmented reality in studies on brain-computer interfaces, [41] neurorehabilitation [42] or social perception. [43] fNIRS can be used to monitor musicians' brain activity while playing musical instruments. [44] [45] [46] [47] fNIRS is compatible with some other neuroimaging modalities, including: MRI, EEG, and MEG. [48]

Hypoxia & altitude studies

With our constant need for oxygen, our body has developed multiple mechanisms that detect oxygen levels, which in turn can activate appropriate responses to counter hypoxia and generate a higher oxygen supply. Moreover, understanding the physiological mechanism underlying the bodily response to oxygen deprivation is of major importance and NIRS devices have shown to be a great tool in this field of research. [49]

Mobile and wireless fNIRS and EEG systems synchronized with all-in-one head mounted display (PhotonCap, Cortivision) FNIRS EEG HMD.jpg
Mobile and wireless fNIRS and EEG systems synchronized with all-in-one head mounted display (PhotonCap, Cortivision)

Brain mapping

Functional connectivity

fNIRS measurements can be used to calculate a limited degree of functional connectivity. Multi-channel fNIRS measurements create a topographical map of neural activation, whereby temporal correlation between spatially separated events can be analyzed. Functional connectivity is typically assessed in terms correlations between the hemodynamic responses of spatially distinct regions of interest (ROIs). In brain studies, functional connectivity measurements are commonly taken for resting state patient data, as well as data recorded over stimulus paradigms. A study led by Alessandro Crimi team highlighted that the functional connectivity measures obtained with fNIRS measurements are quite different from those obtained via EEG caps. [50]

Cerebral oximetry

NIRS monitoring is helpful in a number of ways. Preterm infants can be monitored reducing cerebral hypoxia and hyperoxia with different patterns of activities. [51] It is an effective aid in Cardiopulmonary bypass, is strongly considered to improve patient outcomes and reduce costs and extended stays.

There are inconclusive results for use of NIRS with patients with traumatic brain injury, so it has been concluded that it should remain a research tool [citation needed].

Diffuse optical tomography

Diffuse optical tomography is the 3D version of Diffuse optical imaging. Diffuse optical images are obtained using NIRS or fluorescence-based methods. These images can be used to develop a 3D volumetric model which is known as the Diffuse Optical Tomography. [52]

Functional neuroimaging

The use of fNIRS as a functional neuroimaging method relies on the principle of neuro-vascular coupling also known as the haemodynamic response or blood-oxygen-level dependent (BOLD) response. This principle also forms the core of fMRI techniques. Through neuro-vascular coupling, neuronal activity is linked to related changes in localized cerebral blood flow. fNIRS and fMRI are sensitive to similar physiologic changes and are often comparative methods. Studies relating fMRI and fNIRS show highly correlated results in cognitive tasks. fNIRS has several advantages in cost and portability over fMRI, but cannot be used to measure cortical activity more than 4 cm deep due to limitations in light emitter power and has more limited spatial resolution. fNIRS includes the use of diffuse optical tomography (DOT/NIRDOT) for functional purposes. Multiplexing fNIRS channels can allow 2D topographic functional maps of brain activity (e.g. with Hitachi ETG-4000, Artinis Oxymon, NIRx NIRScout, etc.) while using multiple emitter spacings may be used to build 3D tomographic maps [citation needed].

fNIRS hyperscanning with two violinists

Hyperscanning

Hyperscanning involves two or more brains monitored simultaneously to investigate interpersonal (across-brains) neural correlates in various social situations, which proves fNIRS to be a suitable modality for investigating live brain-to-brain social interactions. [53]

Advantages and Limitations

Advantages

The advantages of fNIRS are, among other things: noninvasiveness, low-cost modalities, perfect safety, high temporal resolution, compatibility with other imaging modalities, and multiple hemodynamic biomarkers. [12] People who use medical implants in their brains such as cochlear implant, or metal brain plates are able to wear fNIRs with no risk of device displacement or heating, which can occur in MRI. [54]

Limitations

fNIRs have low brain sensitivity due to it only being able to detect changes on the cortical surface and low spatial resolution, about 1-3 centimeters deep. [48] The signal is sensitive to hair and skin pigment differences, making it difficult to do between-subject designs. Dense or extremely curly hair may prohibit placement of optodes close to the scalp, limiting the ability to use the technique with all individuals. [12] Although this device can be used with brain implant users, the signal over these areas will be disrupted meaning that spatial oversampling must occur to retain spatial resolution of the target area. Alternative strategies must be implemented such as more pressure on optodes, or specific montages in order to compensate for these limitations. [55]

See also

References

  1. Ferrari, Marco; Quaresima, Valentina (November 2012). "A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application". NeuroImage. 63 (2): 921–935. doi:10.1016/j.neuroimage.2012.03.049. PMID   22510258. S2CID   18367840.
  2. Stute, Katharina; Gossé, Louisa K.; Montero-Hernandez, Samuel; Perkins, Guy A.; Yücel, Meryem A.; Cutini, Simone; Durduran, Turgut; Ehlis, Ann-Christine; Ferrari, Marco; Gervain, Judit; Mesquita, Rickson C.; Orihuela-Espina, Felipe; Quaresima, Valentina; Scholkmann, Felix; Tachtsidis, Ilias (2025-04-18). "The fNIRS glossary project: a consensus-based resource for functional near-infrared spectroscopy terminology". Neurophotonics. 12 (2) 027801. doi:10.1117/1.NPh.12.2.027801. ISSN   2329-423X. PMC   12007957 . PMID   40256456.
  3. Pinti, Paola; Tachtsidis, Ilias; Hamilton, Antonia; Hirsch, Joy; Aichelburg, Clarisse; Gilbert, Sam; Burgess, Paul W. (2020). "The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience". Annals of the New York Academy of Sciences. 1464 (1): 5–29. Bibcode:2020NYASA1464....5P. doi:10.1111/nyas.13948. ISSN   1749-6632. PMC   6367070 . PMID   30085354.
  4. Cutini, Simone; Moro, Sara Basso; Bisconti, Silvia (2012-02-01). "Functional near Infrared Optical Imaging in Cognitive Neuroscience: An Introductory Review". Journal of Near Infrared Spectroscopy. 20 (1): 75–92. doi:10.1255/jnirs.969. ISSN   0967-0335.
  5. Obrig, Hellmuth (2014-01-15). "NIRS in clinical neurology — a 'promising' tool?" . NeuroImage. Celebrating 20 Years of Functional Near Infrared Spectroscopy (fNIRS). 85: 535–546. doi:10.1016/j.neuroimage.2013.03.045. ISSN   1053-8119. PMID   23558099.
  6. Ehlis, Ann-Christine; Schneider, Sabrina; Dresler, Thomas; Fallgatter, Andreas J. (2014-01-15). "Application of functional near-infrared spectroscopy in psychiatry" . NeuroImage. Celebrating 20 Years of Functional Near Infrared Spectroscopy (fNIRS). 85: 478–488. doi:10.1016/j.neuroimage.2013.03.067. ISSN   1053-8119. PMID   23578578.
  7. Lange, Frédéric; Tachtsidis, Ilias (2019-04-18). "Clinical Brain Monitoring with Time Domain NIRS: A Review and Future Perspectives". Applied Sciences. 9 (8): 1612. doi: 10.3390/app9081612 . ISSN   2076-3417.
  8. Wilcox, Teresa; Biondi, Marisa (2015). "fNIRS in the developmental sciences". WIREs Cognitive Science. 6 (3): 263–283. doi:10.1002/wcs.1343. ISSN   1939-5086. PMC   4979552 . PMID   26263229.
  9. Zhan, Zehui; Yang, Qinchen; Luo, Lixia; Zhang, Xia (2024-03-01). "Applying functional near-infrared spectroscopy (fNIRS) in educational research: a systematic review". Current Psychology. 43 (11): 9676–9691. doi:10.1007/s12144-023-05094-y. ISSN   1936-4733.
  10. Herold, Fabian; Wiegel, Patrick; Scholkmann, Felix; Müller, Notger (2018-11-22). "Applications of Functional Near-Infrared Spectroscopy (fNIRS) Neuroimaging in Exercise–Cognition Science: A Systematic, Methodology-Focused Review". Journal of Clinical Medicine. 7 (12): 466. doi: 10.3390/jcm7120466 . ISSN   2077-0383. PMC   6306799 . PMID   30469482.
  11. Herold, Fabian; Wiegel, Patrick; Scholkmann, Felix; Thiers, Angelina; Hamacher, Dennis; Schega, Lutz (2017-08-01). "Functional near-infrared spectroscopy in movement science: a systematic review on cortical activity in postural and walking tasks". Neurophotonics. 4 (4) 041403. doi:10.1117/1.NPh.4.4.041403. ISSN   2329-423X. PMC   5538329 . PMID   28924563.
  12. 1 2 3 Cui, Xu; Bray, Signe; Bryant, Daniel M.; Glover, Gary H.; Reiss, Allan L. (February 2011). "A quantitative comparison of NIRS and fMRI across multiple cognitive tasks". NeuroImage. 54 (4): 2808–2821. doi:10.1016/j.neuroimage.2010.10.069. PMC   3021967 . PMID   21047559.
  13. Song, Lequan; Wang, Hui; Peng, Ruiyun (2024-01-11). "Advances in the Regulation of Neural Function by Infrared Light". International Journal of Molecular Sciences. 25 (2): 928. doi: 10.3390/ijms25020928 . ISSN   1422-0067. PMC   10815576 . PMID   38256001.
  14. Villringer, A.; Chance, B. (1997). "Non-invasive optical spectroscopy and imaging of human brain function". Trends in Neurosciences. 20 (10): 435–442. doi: 10.1016/S0166-2236(97)01132-6 . PMID   9347608. S2CID   18077839.
  15. Li, Ting; Gong, Hui; Luo, Qingming (1 April 2011). "Visualization of light propagation in visible Chinese human head for functional near-infrared spectroscopy". Journal of Biomedical Optics. 16 (4): 045001. Bibcode:2011JBO....16d5001L. doi: 10.1117/1.3567085 . PMID   21529068.
  16. Kohno, Satoru; Miyai, Ichiro; Seiyama, Akitoshi; Oda, Ichiro; Ishikawa, Akihiro; Tsuneishi, Shoichi; Amita, Takashi; Shimizu, Koji (2007). "Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis". Journal of Biomedical Optics. 12 (6): 062111. Bibcode:2007JBO....12f2111K. doi: 10.1117/1.2814249 . PMID   18163814.
  17. Brigadoi, Sabrina; Cooper, Robert J. (26 May 2015). "How short is short? Optimum source–detector distance for short-separation channels in functional near-infrared spectroscopy". Neurophotonics. 2 (2) 025005. doi:10.1117/1.NPh.2.2.025005. PMC   4478880 . PMID   26158009.
  18. Modified Beer Lambert Law, 22 January 2019, archived from the original on 2021-12-21, retrieved 2020-03-26
  19. 1 2 Yücel, Meryem A.; Selb, Juliette; Aasted, Christopher M.; Petkov, Mike P.; Becerra, Lino; Borsook, David; Boas, David A. (11 September 2015). "Short separation regression improves statistical significance and better localizes the hemodynamic response obtained by near-infrared spectroscopy for tasks with differing autonomic responses". Neurophotonics. 2 (3) 035005. doi:10.1117/1.NPh.2.3.035005. PMC   4717232 . PMID   26835480.
  20. "HOMER2". HOMER2. Retrieved 2019-11-26.
  21. Santosa, H., Zhai, X., Fishburn, F., & Huppert, T. (2018). The NIRS Brain AnalyzIR Toolbox. Algorithms, 11(5), 73.
  22. Aasted, Christopher M.; Yücel, Meryem A.; Cooper, Robert J.; Dubb, Jay; Tsuzuki, Daisuke; Becerra, Lino; Petkov, Mike P.; Borsook, David; Dan, Ippeita; Boas, David A. (5 May 2015). "Anatomical guidance for functional near-infrared spectroscopy: AtlasViewer tutorial". Neurophotonics. 2 (2) 020801. doi:10.1117/1.NPh.2.2.020801. PMC   4478785 . PMID   26157991.
  23. Jöbsis (1997). "Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters". Science. 198 (4323): 1264–1267. doi:10.1126/science.929199. PMID   929199.
  24. Hoshi, Yoko; Hazeki, Osamu; Kakihana, Yasuyuki; Tamura, Mamoru (1997-12-01). "Redox behavior of cytochrome oxidase in the rat brain measured by near-infrared spectroscopy" . Journal of Applied Physiology. 83 (6): 1842–1848. doi:10.1152/jappl.1997.83.6.1842. ISSN   8750-7587. PMID   9390953.
  25. Kato, Toshinori; Kamei, Atsushi; Takashima, Sachio; Ozaki, Takeo (May 1993). "Human Visual Cortical Function during Photic Stimulation Monitoring by Means of near-Infrared Spectroscopy" . Journal of Cerebral Blood Flow & Metabolism. 13 (3): 516–520. doi:10.1038/jcbfm.1993.66. ISSN   0271-678X. PMID   8478409.
  26. Villringer, A.; Planck, J.; Hock, C.; Schleinkofer, L.; Dirnagl, U. (May 1993). "Near infrared spectroscopy (NIRS): A new tool to study hemodynamic changes during activation of brain function in human adults" . Neuroscience Letters. 154 (1–2): 101–104. Bibcode:1993NeuL..154..101V. doi:10.1016/0304-3940(93)90181-j. ISSN   0304-3940. PMID   8361619.
  27. Chance, B; Zhuang, Z; UnAh, C; Alter, C; Lipton, L (1993-04-15). "Cognition-activated low-frequency modulation of light absorption in human brain". Proceedings of the National Academy of Sciences. 90 (8): 3770–3774. Bibcode:1993PNAS...90.3770C. doi: 10.1073/pnas.90.8.3770 . ISSN   0027-8424. PMC   46383 . PMID   8475128.
  28. Ayaz, Hasan; Shewokis, Patricia A.; Curtin, Adrian; Izzetoglu, Meltem; Izzetoglu, Kurtulus; Onaral, Banu (8 October 2011). "Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation". Journal of Visualized Experiments (56): 3443. doi: 10.3791/3443 . PMC   3227178 . PMID   22005455.
  29. Piper, Sophie K.; Krueger, Arne; Koch, Stefan P.; Mehnert, Jan; Habermehl, Christina; Steinbrink, Jens; Obrig, Hellmuth; Schmitz, Christoph H. (January 2014). "A wearable multi-channel fNIRS system for brain imaging in freely moving subjects". NeuroImage. 85 (1): 64–71. doi:10.1016/j.neuroimage.2013.06.062. PMC   3859838 . PMID   23810973.
  30. Curtin, Adrian; Ayaz, Hasan (October 2018). "The Age of Neuroergonomics: Towards Ubiquitous and Continuous Measurement of Brain Function with fNIRS: The age of neuroergonomics and fNIRS". Japanese Psychological Research. 60 (4): 374–386. doi: 10.1111/jpr.12227 .
  31. Quaresima, Valentina; Ferrari, Marco (January 2019). "Functional Near-Infrared Spectroscopy (fNIRS) for Assessing Cerebral Cortex Function During Human Behavior in Natural/Social Situations: A Concise Review". Organizational Research Methods. 22 (1): 46–68. doi:10.1177/1094428116658959. S2CID   148042299.
  32. Durduran, T.; Yodh, A. G. (2013). "Diffuse correlation spectroscopy for non-invasive, micro-vascular cerebral blood flow measurement". NeuroImage. 85 (1): 51–63. doi:10.1016/j.neuroimage.2013.06.017. PMC   3991554 . PMID   23770408.
  33. Sutin, Jason; Zimmerman, Bernhard; Tyulmankov, Danil; Tamborini, Davide; Wu, Kuan Cheng; Selb, Juliette; Gulinatti, Angelo; Rech, Ivan; Tosi, Alberto; Boas, David A.; Franceschini, Maria Angela (20 September 2016). "Time-domain diffuse correlation spectroscopy". Optica. 3 (9): 1006–1013. Bibcode:2016Optic...3.1006S. doi:10.1364/OPTICA.3.001006. PMC   5166986 . PMID   28008417.
  34. Carp, S. A.; Tamborini, D.; Mazumder, D.; Wu, K. C.; Robinson, M. R.; Stephens, K. A.; Shatrovoy, O.; Lue, N.; Ozana, N.; Blackwell, M. H.; Franceschini, M. A. (2020). "Diffuse correlation spectroscopy measurements of blood flow using 1064 nm light". Journal of Biomedical Optics. 25 (9) 097003. Bibcode:2020JBO....25i7003C. doi:10.1117/1.JBO.25.9.097003. PMC   7522668 . PMID   32996299.
  35. Buckley, Erin M.; Parthasarathy, Ashwin B.; Grant, P. Ellen; Yodh, Arjun G.; Franceschini, Maria Angela (2014). "Diffuse correlation spectroscopy for measurement of cerebral blood flow: Future prospects". Neurophotonics. 1 (1) 011009. doi:10.1117/1.NPh.1.1.011009. PMC   4292799 . PMID   25593978. S2CID   13208535.
  36. Ayaz, H.; Shewokis, P. A.; Bunce, S.; Onaral, B. (2011). "An optical brain computer interface for environmental control". Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. Vol. 2011. pp. 6327–6330. doi:10.1109/IEMBS.2011.6091561. ISBN   978-1-4577-1589-1. PMID   22255785. S2CID   4951918.
  37. Coyle, Shirley M; Ward, Tomás E; Markham, Charles M (September 2007). "Brain–computer interface using a simplified functional near-infrared spectroscopy system" (PDF). Journal of Neural Engineering. 4 (3): 219–226. Bibcode:2007JNEng...4..219C. doi:10.1088/1741-2560/4/3/007. PMID   17873424. S2CID   18723855.
  38. Sitaram, Ranganatha; Zhang, Haihong; Guan, Cuntai; Thulasidas, Manoj; Hoshi, Yoko; Ishikawa, Akihiro; Shimizu, Koji; Birbaumer, Niels (February 2007). "Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain–computer interface". NeuroImage. 34 (4): 1416–1427. doi:10.1016/j.neuroimage.2006.11.005. PMID   17196832. S2CID   15471179.
  39. Naseer, Noman; Hong, Melissa Jiyoun; Hong, Keum-Shik (February 2014). "Online binary decision decoding using functional near-infrared spectroscopy for the development of brain–computer interface". Experimental Brain Research. 232 (2): 555–564. doi:10.1007/s00221-013-3764-1. PMID   24258529. S2CID   15250694.
  40. Naseer, Noman; Hong, Keum-Shik (October 2013). "Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain–computer interface". Neuroscience Letters. 553: 84–89. doi:10.1016/j.neulet.2013.08.021. PMID   23973334. S2CID   220773.
  41. Piper, Sophie K.; Krueger, Arne; Koch, Stefan P.; Mehnert, Jan; Habermehl, Christina; Steinbrink, Jens; Obrig, Hellmuth; Schmitz, Christoph H. (15 January 2014). "A wearable multi-channel fNIRS system for brain imaging in freely moving subjects". NeuroImage. 85 (1): 64–71. doi:10.1016/j.neuroimage.2013.06.062. PMC   3859838 . PMID   23810973.
  42. Holper, Lisa; Muehlemann, Thomas; Scholkmann, Felix; Eng, Kynan; Kiper, Daniel; Wolf, Martin (December 2010). "Testing the potential of a virtual reality neurorehabilitation system during performance of observation, imagery and imitation of motor actions recorded by wireless functional near-infrared spectroscopy (fNIRS)". Journal of NeuroEngineering and Rehabilitation. 7 (1): 57. doi: 10.1186/1743-0003-7-57 . PMC   3014953 . PMID   21122154.
  43. Kim, Gyoung; Buntain, Noah; Hirshfield, Leanne; Costa, Mark R.; Chock, T. Makana (2019). "Processing Racial Stereotypes in Virtual Reality: An Exploratory Study Using Functional Near-Infrared Spectroscopy (FNIRS)". Augmented Cognition. Lecture Notes in Computer Science. Vol. 11580. pp. 407–417. doi:10.1007/978-3-030-22419-6_29. ISBN   978-3-030-22418-9. S2CID   195891659.
  44. "YouTube". www.youtube.com. 15 December 2015. Archived from the original on 2021-12-21. Retrieved 2020-03-26.
  45. fNIRS of playing piano, 9 August 2016, archived from the original on 2021-12-21, retrieved 2020-03-26
  46. fNIRS of Observation, 10 August 2016, archived from the original on 2021-12-21, retrieved 2020-03-26
  47. fNIRS of Imagery, 9 August 2016, archived from the original on 2021-12-21, retrieved 2020-03-26
  48. 1 2 Yang, Lirui; Wang, Zehua (2025-03-18). "Applications and advances of combined fMRI-fNIRs techniques in brain functional research". Frontiers in Neurology. 16 1542075. doi: 10.3389/fneur.2025.1542075 . ISSN   1664-2295. PMC   11958174 . PMID   40170894.
  49. Shaw, Keely; Singh, Jyotpal; Sirant, Luke; Neary, J. Patrick; Chilibeck, Philip D. (November 2020). "Effect of Dark Chocolate Supplementation on Tissue Oxygenation, Metabolism, and Performance in Trained Cyclists at Altitude". International Journal of Sport Nutrition and Exercise Metabolism. 30 (6): 420–426. doi:10.1123/ijsnem.2020-0051. PMID   32916656. S2CID   221635672.
  50. Blanco, R; Koba, C; Crimi, A (2024). "Investigating the interaction between EEG and fNIRS: a multimodal network analysis of brain connectivity". Journal of Computational Science. 82 102416. doi: 10.1016/j.jocs.2024.102416 .
  51. Rahimpour, Ali; Noubari, Hosein Ahmadi; Kazemian, Mohammad (2018). "A case-study of NIRS application for infant cerebral hemodynamic monitoring: A report of data analysis for feature extraction and infant classification into healthy and unhealthy". Informatics in Medicine Unlocked. 11: 44–50. doi: 10.1016/j.imu.2018.04.001 .
  52. Durduran, T.; Choe, R.; Baker, W. B.; Yodh, A. G. (July 2010). "Diffuse Optics for Tissue Monitoring and Tomography". Reports on Progress in Physics. 73 (7) 076701. Bibcode:2010RPPh...73g6701D. doi:10.1088/0034-4885/73/7/076701. PMC   4482362 . PMID   26120204.
  53. mari (2018-02-04). "fNIRS Hyperscanning: A door to real-world social neuroscience research". The Society for functional Near Infrared Spectroscopy. Retrieved 2020-03-26.
  54. Alemi, Razieh; Wolfe, Jace; Neumann, Sara; Manning, Jacy; Towler, Will; Koirala, Nabin; Gracco, Vincent L.; Deroche, Mickael (December 2023). "Audiovisual integration in children with cochlear implants revealed through EEG and fNIRS". Brain Research Bulletin. 205 110817. doi: 10.1016/j.brainresbull.2023.110817 . ISSN   0361-9230. PMID   37989460.
  55. Saliba, Joe; Bortfeld, Heather; Levitin, Daniel J.; Oghalai, John S. (August 2016). "Functional near-infrared spectroscopy for neuroimaging in cochlear implant recipients". Hearing Research. 338: 64–75. doi:10.1016/j.heares.2016.02.005. ISSN   1878-5891. PMC   4967399 . PMID   26883143.
  56. "NIRx | fNIRS Systems | NIRS Devices". NIRx Medical Technologies. Retrieved 2019-11-26.