fNIRS Analysis

fNIRS has many advantages over other neuroimaging modalities. It allows to measure blood oxygenation level changes related to neural activation in a direct and non-invasive manner, with quick set-up times and high temporal resolution. However, fNIRS signals are susceptible to measurement noise and physiology-based systemic interference. Therefore, successfully extracting neuronal activity-related signals from your fNIRS data requires careful statistical analyses.

Here, we offer an overview of fNIRS Analysis topics, starting with fundamentals and study design, moving into analysis software options, and discussing fNIRS analysis in real-time or multi-modal environments.

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Three aspects of the human body are fundamental to fNIRS neuroimaging. First, human tissue is relatively transparent to light in the near-infrared range, thus allowing photons to propagate. Second, hemoglobin has unique absorbing characteristics, allowing for oxygenation-dependent quantification of NIR light absorption. Finally, the brain’s demand for oxygen is altered by neuronal activation. NIRS allows to measure relative changes in cortical hemoglobin concentration, coupling this concentration to brain functioning is what designates fNIRS.  

A successful fNIRS study starts with a well-designed experiment. Before collecting data, the researcher should ensure that they have a clearly defined hypothesis, need for the equipment being used, and an idea of what kinds of analysis methods they will apply to their data. In addition to reviewing the literature, we recommend that our customers review the many resources on the NIRx help center, such as lectures from various fNIRS experts on experimental design. Customers may also consult with the NIRx support team at support@nirx.net to construct a paradigm suitable for their application of interest.

The fNIRS community has rapidly grown over the past decades, resulting into sophisticated and user-friendly analysis tools, each offering unique qualities that may suit your research best. NIRx data can be loaded seamlessly into many of these tools with its custom file format or the more commonly used *.nirs file format.

Selected Analysis Software platforms are: 

NIRS Toolbox is one of the newest and most sophisticated analysis software for fNIRS data. This toolbox consists of a set of MATLAB based tools, including functions for signal processing, visualization, and statistical analysis of fNIRS data. It allows for GLM, functional connectivity and multi-modal analysis, among many other features. The NIRS Toolbox was created an is maintained by Dr. Ted Huppert and his team at the University of Pittsburgh. While data from most any commercially available fNIRS system can be directly imported into the toolbox, NIRx data is the only kind that automatically imports 3-D probe and anatomical information. This functionality enables a greater range of analysis techniques within the toolbox.

Download NIRS Toolbox (requires MATLAB 2014a or later) from Github.

The Homer2 and Homer3 fNIRS analysis packages are some of the the most widely used for fNIRS analysis. Homer2 has evolved from the early 1990s, while Homer3 was just released in 2019. They both share a similar GUI, and support group analyses and re-configuration of the fNIRS analysis processing stream. Additionally, both software allows the user to integrate their own algorithms into the processing stream. All processing functions can also be accessed at the script level, for additional flexibility. All NIRx systems can save data in the *.nirs file format, which can be loaded seamlessly into both Homer2 and Homer3.

Download Homer2 from homer-fnirs.org | Download Homer3 from Github.

Download nirsLAB from the NITRC Site

fNIRS is a promising candidate for Brain-Computer Interface investigations. Its great performance in acquiring cortical signals in the presence of muscle movements and the possibility of setting up measurements in realistic environments, make this neuroimaging technique ideal for many applications. It is often paired with EEG in real time investigations, to improve upon the classification accuracy of both modalities. Real-time fNIRS analysis requires sophisticated algorithms, that (for example) dynamically adjust predicted activation patterns.

Many research institutes appreciate multi-modal applications with fNIRS. By gathering information from several modalities, measurements can be rendered more robust, resulting into  an advanced understanding of human brain functioning. Typical combinations are fNIRS and EEG, Eye-Tracking or fMRI, but tDCS and TMS have also been applied to concurrently modulate brain activity.

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About NIRS and NIRx

- fNIRS and NIRx Overview
- NIRx NIRS Publication Sample
- NIRx Newsletters
- NIRx Webinars
- fNIRS Analysis

Applications

- fMRI-concurrent fNIRS
- EEG-concurrent fNIRS
- TMS-concurrent fNIRS
- Customized Real-time Analysis in NIRS: BCI / Neurofeedback
- Combining eye-tracking with fNIRS
- Using NIRS to conduct child and infant measurements

Products

- NIRScout NIRS Imaging System for Multi-Modal Integration
- NIRSport Portable NIRS Imaging System for Multi-Modal Integration
- NIRS Caps & NIRS Optodes (Probes)
- NIRS software