Reporting fNIRS results
Understanding and communicating your results in fNIRS research can be a tricky task. Here you can find some practical solution on interpreting fNIRS output, analysis and best practices for reporting in papers.
Understanding signals and data
The fNIRS signals reflect concentration changes in oxygenated and deoxygenated haemoglobin, which you can get familiar in the Getting started page. By that we can indirectly measure the BOLD response, similar to fMRI signals, thus, we can interpret oxygenated haemoglobin increase as an indicator of activated regions. Meanwhile, we also expect deoxygenated haemoglobin to decrease. We usually look at this signals evoked by specific stimulus (temporal patterns) or based on which channel showed changes (spatial patterns).
These signals are recorded as raw data with you device. Always avoid interpreting raw data directly. When interpreting changes, it is important to report in which phase of the processing steps you are looking at your data (e.g., with or without filtering). The best way is to report each step of the preprocessing pipeline, with specific values mentioned especially for filtering and smoothing.
Statistical analysis
Always report clearly the statistical method used for evaluation of your data. This can include for instance GLM or block averaging models. Keep in mind to report whether you carried out the analysis on the individual or the group level, and how you corrected for multiple comparisons and inter-subject variability (e.g., introducing random intercept).
Visualization
Understanding and reporting fNIRS activity data can be easier when using different visualization methods. For example time course plots are often used to evaluate trial-averaged responses and variability across trials or conditions.
You can also use activation maps, or topomaps to show spatial distribution, but always keep in mind that fNIRS has limited spatial resolution.
Reporting checklist
- Preprocessing steps (e.g. filtering, motion correction)
- Data quality (e.g., number of rejected channels/epochs, exclusion, SCI)
- Analysis method (e.g., GLM, model regressors)
- Extra statistical corrections used (e.g., Bonferroni correction)
- Table of statistical analysis results (e.g., per channel or ROI)
- Anatomical referencing of channels (e.g., MNI coordinates or coregistration)
- Visualization of the data (e.g., topoplots)
- Use appropriate terminology and always define abbreviations
- Do not oversimplify without reasoning (e.g., only one significant channel)
- Always account for physiological and systemic noise (e.g., breathing)
- When possible share your ananonymized data for reproducibility
Learn more about reporting:
Best practices for fNIRS publications
Systematic review of fNIRS studies reveals inconsistent chromophore data reporting practices
Using preregistration as a tool for transparent fNIRS study design
The fNIRS glossary project