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Parallel factor analysis for multidimensional decomposition of functional near-infrared spectroscopy data.
Alejandra Hüsser1,2, Laura Caron-Desrochers1,2, Julie Tremblay1
1Research Center of the Sainte-Justine University Hospital, Neurodevelopmental Optical Imaging Laboratory (LIONlab), Montreal, Quebec, Canada.
Neurophotonics
|November 21, 2022
Summary
Parallel factor analysis (PARAFAC) offers improved artifact correction for functional near-infrared spectroscopy (fNIRS) data. This multidimensional technique enhances data quality by integrating wavelength information, outperforming traditional methods.
Area of Science:
- Neuroimaging
- Biomedical Engineering
- Signal Processing
Background:
- Current functional near-infrared spectroscopy (fNIRS) data analysis methods, including artifact correction, often overlook the integrated information from multiple wavelengths.
- These techniques typically focus only on the temporal and spatial aspects of the fNIRS signal structure.
- Parallel factor analysis (PARAFAC) is a validated multidimensional decomposition technique used in other neuroimaging fields.
Purpose of the Study:
- To introduce and validate the application of Parallel factor analysis (PARAFAC) for analyzing functional near-infrared spectroscopy (fNIRS) data.
- To leverage PARAFAC's ability to handle inherently multidimensional fNIRS data, encompassing time, space, and wavelength.
- To assess PARAFAC's efficacy in artifact correction for fNIRS signals.
Main Methods:
- Acquired fNIRS data from 17 healthy adults during a verbal fluency task.
- Compared the performance of PARAFAC for motion artifact correction against traditional 2D decomposition techniques: target principal component analysis (tPCA) and independent component analysis (ICA).
- Evaluated correction performance using simulated artifacts and hemodynamic response functions under controlled conditions.
Main Results:
- PARAFAC demonstrated significantly superior data quality improvement compared to tPCA and ICA.
- PARAFAC's effectiveness was further validated through correction of several simulated signals, highlighting its robustness.
- The method proved to be independent of specific artifact characteristics.
Conclusions:
- This study presents the first implementation and validation of PARAFAC for artifact correction in functional near-infrared spectroscopy (fNIRS).
- PARAFAC emerges as a promising, data-driven alternative for multidimensional data analysis in fNIRS.
- The findings pave the way for broader applications of PARAFAC in fNIRS research and data processing.
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