Poster No:
1980
Submission Type:
Abstract Submission
Authors:
Franziska Klein1, Katharina Stute2, David Mehler3, Beatrix Barth4, Christina Artemenko5, Xu Wu6, Mojtaba Soltanlou7, Hendrik Santosa8, Theodore Huppert8
Institutions:
1OFFIS e.V. - Institute for Computer Science, Oldenburg, Germany, 2Chemnitz University of Technology, Chemnitz, Germany, 3RWTH Aachen University, Aachen, Germany, 4Department of Psychiatry and Psychotherapy, University Hospital Tuebingen, Tuebingen, Germany, 5University of Tuebingen, Tuebingen, Germany, 6University of Oldenburg, Oldenburg, Germany, 7University College London, London, United Kingdom, 8University of Pittsburgh, Pittsburgh, United States
First Author:
Franziska Klein
OFFIS e.V. - Institute for Computer Science
Oldenburg, Germany
Co-Author(s):
Beatrix Barth
Department of Psychiatry and Psychotherapy, University Hospital Tuebingen
Tuebingen, Germany
Xu Wu
University of Oldenburg
Oldenburg, Germany
Introduction:
Functional near-infrared spectroscopy (fNIRS) is a noninvasive brain imaging technique that measures functional brain activity by quantifying changes in oxygenated and deoxygenated hemoglobin concentrations (Pinti et al., 2020). Its portability and ability to acquire hemodynamic-based data with minimal mobility constraints have made it an invaluable tool for studying brain function in naturalistic and dynamic settings (Klein et al., 2024). Over the past two decades, fNIRS has experienced exponential growth in research applications ranging from controlled cognitive performance tasks to investigations in everyday settings (Pinti et al., 2020; Yücel et al., 2021). However, this rapid expansion has revealed challenges for beginners, particularly the lack of standardized analysis pipelines, which makes preprocessing and analysis of fNIRS data a complex task.
To address this need, we present a beginner's guide to fNIRS data analysis, focusing on block-design paradigms, one of the most commonly used experimental frameworks. This guide provides a practical, step-by-step introduction to data standardization, preprocessing, and statistical analysis using the MATLAB-based NIRS Brain AnalyzIR (or simply nirs) toolbox (Santosa et al., 2018). While not intended to be a rigid standard, the guide aims to provide researchers with essential skills for conducting robust and reproducible analyses. The methods are demonstrated using open datasets and scripts, promoting hands-on learning and adaptability to different experimental designs.
Methods:
The pipeline described in this manual was implemented using the MATLAB-based nirs toolbox and is based on sample data from ten older adults performing a simple finger-tapping task which was recorded using a NIRx NIRSport2 device. Measurements were taken over motor areas using 16 regular-distance and 8 short-distance channels (SDCs). Data standardization included conversion to the standardized *.snirf format (Tucker et al., 2023) and organization in BIDS format (Luke et al., 2023 Preprint). Preprocessing steps included signal quality assessment, conversion of the raw light intensity data to changes in optical density and hemoglobin concentration changes using the modified Beer-Lambert law, correction of motion artifacts, temporal filtering, and correction of extracerebral systemic activity with and without SDCs. Data analysis included statistical methods based on block averaging and general linear model (GLM) analysis.
Results:
The proposed guide enables newcomers to learn a structured fNIRS data analysis pipeline to overcome the typical challenges in the field. By incorporating SDCs, the guide highlights state-of-the-art methods to mitigate systemic noise, thereby improving the interpretability and quality of fNIRS signals. Users will learn robust statistical approaches, including block averaging and GLM-based analyses, and gain insights into adapting these methods to different experimental designs. The accompanying openly available datasets and scripts provide a practical resource that enables researchers to analyze data independently, promote reproducibility, and approach fNIRS research with confidence.
Conclusions:
This beginner's guide provides a practical introduction to fNIRS data analysis, focusing on block design paradigms and equipping researchers with tools for data standardization, preprocessing, and statistical analysis. Using the nirs toolbox, it provides accessible resources, including open datasets and scripts, to promote reproducibility and hands-on learning.
Designed as a flexible resource rather than a definitive standard, the guide lowers the barriers for newcomers, encouraging broader participation in fNIRS research. By simplifying complex workflows, it enables users to adapt methods to their needs and realize the potential of this innovative technology.
Modeling and Analysis Methods:
Motion Correction and Preprocessing
Neuroinformatics and Data Sharing:
Workflows 2
Novel Imaging Acquisition Methods:
NIRS 1
Keywords:
Computing
Data analysis
Design and Analysis
Near Infra-Red Spectroscopy (NIRS)
Open Data
Open-Source Code
Statistical Methods
Workflows
1|2Indicates the priority used for review
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
I accept
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information.
Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
I do not want to participate in the reproducibility challenge.
Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Neurophysiology
Optical Imaging
Provide references using APA citation style.
Santosa, H., Zhai, X., Fishburn, F., & Huppert, T. (2018). The NIRS Brain AnalyzIR Toolbox. Algorithms, 11(5), 73.
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., Torricelli, A., … Wolf, M. (2021). Best practices for fNIRS publications. Neurophotonics, 8(1), 012101.
Pinti, P., Tachtsidis, I., Hamilton, A., Hirsch, J., Aichelburg, C., Gilbert, S., & Burgess, P. 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.
Klein, F., Kohl, S. H., Lührs, M., Mehler, D. M. A., & Sorger, B. (2024). From lab to life: challenges and perspectives of fNIRS for haemodynamic-based neurofeedback in real-world environments. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 379(1915), 20230087.
Luke, R., Oostenveld, R., Cockx, H., Niso, G., Shader, M., Orihuela-Espina, F., … Pollonini, L. (2023, Preprint). fNIRS-BIDS, the Brain Imaging Data Structure Extended to Functional Near-Infrared Spectroscopy.
Tucker, S., Dubb, J., Kura, S., von Lühmann, A., Franke, R., Horschig, J. M., Powell, S., Oostenveld, R., Lührs, M., Delaire, É., Aghajan, Z. M., Yun, H., Yücel, M. A., Fang, Q., Huppert, T. J., Frederick, B. B., Pollonini, L., Boas, D., & Luke, R. (2023). Introduction to the shared near infrared spectroscopy format. Neurophotonics, 10(1), 013507.
No