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Signal Processing in Functional Near-Infrared Spectroscopy (fNIRS): Methodological Differences Lead to Different
Mischa D Pfeifer1, Felix Scholkmann2, Rob Labruyère1,3
1Rehabilitation Center for Children and Adolescents, University Children's Hospital Zurich, Affoltern am Albis, Switzerland.
Frontiers in Human Neuroscience
|January 24, 2018
Summary
Signal processing in functional near-infrared spectroscopy (fNIRS) remains inconsistent. This study shows that accounting for physiological noise in fNIRS data processing leads to more realistic neuroimaging results, unlike standard methods.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Functional near-infrared spectroscopy (fNIRS) has been researched for over 20 years, yet lacks standardized signal processing methods.
- Novice researchers often overlook signal contamination from non-neural hemodynamic changes (e.g., scalp/systemic blood flow).
- This oversight may stem from using manufacturer-provided software without fully understanding the underlying signal processing steps.
Purpose of the Study:
- To investigate the impact of different signal processing approaches on fNIRS functional neuroimaging results.
- To compare a standard commercial signal processing method against three customized approaches, including those correcting for physiological noise.
- To evaluate the influence of signal processing choices on statistical outcomes using a clinical dataset of motor cortex activity.
Main Methods:
- Compared one standard commercial fNIRS signal processing method with three customized approaches.
- Applied two types of multi-channel corrections using multiple long-channels (no short-channels were used).
- Evaluated the statistical outcomes on a clinical dataset measuring task-evoked motor cortex activity.
Main Results:
- The choice of signal processing method significantly influenced the study's outcomes.
- Methods ignoring physiological noise contamination revealed numerous significant hemodynamic responses.
- These significant findings vanished when partial contamination was corrected using multi-channel regression.
Conclusions:
- Signal processing methods that correct for physiological confounding effects may provide more accurate results, especially when multi-distance measurements are not feasible.
- Standard signal processing methods from manufacturers should only be used by individuals with a deep understanding of each processing step.
- Standardized and validated fNIRS signal processing protocols are crucial for reliable neuroimaging research.
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