UW Center for Human Neuroscience

fNIRS System

NIRx NIRSport 2 fNIRS system

Thanks to a Student Technology Fee grant to Carly Gray (supervised by Professor Peter Kahn), a NIRx NIRSport 2 wearable fNIRS system, located in a quiet room in CHN (Kincaid Hall), is available to interested researchers across UW.

Priority is given to undergraduate and graduate student researchers interested in using the system.

The system includes 8 sources and 16 detectors, 16 short separation channels, a mobility pack, the NIRx WINGS system for measuring peripheral physiological signals (e.g., heart rate, body temperature, SpO2, etc.), and data acquisition software (Aurora fNIRS recording software). Nine cap sizes are available between sizes 40-60cm.

It can record at sampling between 70-240 Hz. Data and event trigger signals may be transmitted via WiFi (for ambulatory recordings) or USB. Data may also be recorded directly to the system.

Investigators are responsible for obtaining IRB approval. 

To gain access to this resource please email chnadmin@uw.edu and ionefine@uw.edu.

Once approved for using the system you can book time on the calendar here.

USEFUL RESOURCES (Thanks to Carly Gray)

Articles

General reviews (especially focused on use of fNIRS in real-world, ecologically valid settings):

Pinti, P., Tachtsidis, I., Burgess, P. W., & Hamilton, A. F. D. C. (2023). Non-invasive optical imaging of brain function with fNIRS: Current status and way forward. In Reference Module in Neuroscience and Biobehavioral Psychology (p. B9780128204801000280). Elsevier. https://doi.org/10.1016/B978-0-12-820480-1.00028-0

Pinti, P., Aichelburg, C., Lind, F., Power, S., Swingler, E., Merla, A., Hamilton, A., Gilbert, S., Burgess, P., & Tachtsidis, I. (2015). Using fiberless, wearable fNIRS to monitor brain activity in real-world cognitive tasks. JoVE (Journal of Visualized Experiments), 106, e53336. https://doi.org/10.3791/53336

Yücel, M. A., Lühmann, A. v., Scholkmann, F., Gervain, J., Dan, I., Ayaz, H., Boas, D., Cooper, R. J., Culver, J., Elwell, C. E., Eggebrecht, A., Franceschini, M. A., Grova, C., Homae, F., Lesage, F., Obrig, H., Tachtsidis, I., Tak, S., Tong, Y., … Wolf, M. (2021). Best practices for fNIRS publications. Neurophotonics, 8(1), 012101. https://doi.org/10.1117/1.NPh.8.1.012101

Analytic approaches and issues (especially focused on motion artifacts):

Barker, J. W., Aarabi, A., & Huppert, T. J. (2013). Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS. Biomedical Optics Express, 4(8), 1366–1379. https://doi.org/10.1364/BOE.4.001366

Brigadoi, S., Ceccherini, L., Cutini, S., Scarpa, F., Scatturin, P., Selb, J., Gagnon, L., Boas, D. A., & Cooper, R. J. (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

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(1), 010401. https://doi.org/10.1117/1.NPh.3.1.010401

Pinti, P., Scholkmann, F., Hamilton, A., Burgess, P., & Tachtsidis, I. (2019). Current status and issues regarding pre-processing of fNIRS neuroimaging data: An investigation of diverse signal filtering methods within a general linear model framework. Frontiers in Human Neuroscience, 12. https://www.frontiersin.org/articles/10.3389/fnhum.2018.00505

Tachtsidis, I., & Scholkmann, F. (2016). False positives and false negatives in functional near-infrared spectroscopy: Issues, challenges, and the way forward. Neurophotonics, 3(3), 031405. https://doi.org/10.1117/1.NPh.3.3.031405

Tak, S., & Ye, J. C. (2014). Statistical analysis of fNIRS data: A comprehensive review. NeuroImage, 85, 72–91. https://doi.org/10.1016/j.neuroimage.2013.06.016

von Lühmann, A., Li, X., Müller, K.-R., Boas, D. A., & Yücel, M. A. (2020). Improved physiological noise regression in fNIRS: A multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis. NeuroImage, 208, 116472. https://doi.org/10.1016/j.neuroimage.2019.116472

von Lühmann, A., Ortega-Martinez, A., Boas, D. A., & Yücel, M. A. (2020). Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective. Frontiers in Human Neuroscience, 14. https://www.frontiersin.org/articles/10.3389/fnhum.2020.00030

Inclusivity of darker skin tones and hair types in fNIRS research:

Kwasa, J., Peterson, H. M., Karrobi, K., Jones, L., Parker, T., Nickerson, N., & Wood, S. (2023). Demographic reporting and phenotypic exclusion in fNIRS. Frontiers in Neuroscience, 17. https://www.frontiersin.org/articles/10.3389/fnins.2023.1086208

Videos I found useful:

NIRx’s YouTube channel is really useful in general!

Software:

  • fOLD: this is the main montage creation software. You can select your preferred brain atlas and regions of interest and it will give you a suggested montage, including details about the specificity of each optode. devFOLD is the version for montages for young kids!
  • Aurora: this is the proprietary data acquisition software from NIRx. I believe you can also do some analyses directly in Aurora, but most people export their data to analyze in Homer or NIRSToolbox.
    • User manual and install link are below

 UMA_Aurora_2023.9.6.pdf

  • Homer3: this is the software with more of a point-and-click GUI that runs ontop of Matlab. You can visualize your data, do some preprocessing, and do some types of analyses (I would have done visual inspection and some very preliminary preprocessing here and then switched to NIRSToolbox)
  • NIRSToolbox: this is the software where you’re doing more traditional Matlab programming, though it has tons of prewritten functions.
  • There are other analysis softwares out there, but Homer3 and NIRSToolbox seem to be the most popular and were created by two leaders/pioneers of the field.

Helpful websites in general:

  • Society for functional near-infrared spectroscopy (fnirs.org): includes some educational tutorials and webinars on their website, hosts a conference every two years, newsletter, etc.
  • Open fNIRS (openfnirs.org): primary an open data repository specific to fNIRS but also includes some training resources. I signed up for a trial of Homer Premium to access some extra webinars and trainings which I found to be really helpful in the relatively short time I used them.
  • NIRx: Everyone using the system at UW can have their own account to access support from NIRx. Simply register at http://support.nirx.de using the serial number for the system (NSP2_2338_0723_A, also listed in attached documents).

UW Trainings with NIRx

 NIRx acquisition training – Day1.mp4

  • Day 1 was more theory of fNIRS, basic experimental design, and orientation to what we ordered

 NIRx Acquisition Training – Day2.mp4

  • Day 2 was the finger tapping motor task as an initial example of how to record fNIRS data
  • Required files for running the experiment are attached (Fingertapping_LSL.psyexp, Psychopy_TrialTypes.xlsx, and all .wav and .bmp files) 

 NIRx acquisition training slides.pdf