Hi all,
I am attempting to create an analysis pipeline for some fNIRS data I have, and wanted some clarification on using the GLM method.
The experimental design for the data was 3 min rest, followed by 3 min of stimulus. This was repeated 4 times with 4 different stimulus.
My plan for preprocessing was band pass filtering, motion artifact detection and then correcting with wavelet techniques, converting to OD and then concentrations, then creation of the GLM.
My plan was to then create a glm for each rest/stimulus combination(4 in total), and then compare these.
Is this approach sound, or should I change anything in the pipeline?
Thank you for any help you can provide!
Best,
Mike
Your experimental design involves a rest period followed by a stimulus period repeated four times with four different stimuli. This is a well-designed block design experiment for fNIRS analysis. Make sure you have a clear understanding of the timing and order of stimulus presentations, as this will be important for setting up your GLM correctly. Your preprocessing steps are generally appropriate. However, the specific details of bandpass filtering, motion artifact detection, and wavelet correction should be carefully chosen based on the characteristics of your data and the literature in your field. Be sure to document and validate these steps thoroughly.
The conversion of raw fNIRS data to Optical Density (OD) and then to hemoglobin concentrations (HbO and HbR) is a standard approach. Ensure that you are using appropriate conversion algorithms and taking into account the differential pathlength factor (DPF) for your specific fNIRS system.
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