Separation of fNIRS signals into functional and systemic components based on differences in hemodynamic modalities

Area of Science:

• Neuroscience
• Biomedical Engineering
• Physiological Measurement

Background:

• Conventional functional near-infrared spectroscopy (fNIRS) signals are often contaminated by systemic physiological fluctuations from body motion and psychophysiological changes.
• These artifacts obscure the detection of true functional brain activity, limiting the reliability of fNIRS measurements.

Purpose of the Study:

• To develop and validate a novel method for separating functional brain signals from systemic noise in fNIRS data.
• To improve the accuracy and reliability of fNIRS measurements by isolating cortical activity.

Main Methods:

• Proposed a new method based on distinct hemodynamic differences between functional and systemic signals.
• Assumed differing linear relationships for oxy- and deoxyhemoglobin changes in functional versus systemic components.
• Validated the method by comparing its results to conventional fNIRS and multi-distance NIRS during various tasks.

Main Results:

• The proposed method effectively isolated functional components, showing greater distinctness in laterality during tasks compared to conventional fNIRS.
• Systemic components identified by the new method correlated with task-evoked, non-specific physiological changes.
• Functional components derived from the novel method showed high coincidence with signals from multi-distance NIRS, indicating cortical origin.

Conclusions:

• The proposed method successfully separates functional brain signals from systemic artifacts in fNIRS.
• This technique enhances the reliability of fNIRS measurements by accurately identifying cortical activity.
• The method is compatible with existing commercial fNIRS instruments, offering a practical improvement for neuroimaging research.