Temporal Derivative Distribution Repair (TDDR): A motion correction method for fNIRS.

Area of Science:

• Neuroimaging
• Biomedical Engineering
• Optical Imaging

Background:

• Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical neuroimaging technique used to measure cortical activity.
• Motion artifacts, though less severe than in fMRI, can still impact fNIRS data quality.
• Accurate motion correction is crucial for reliable fNIRS analysis, particularly in mobile or pediatric populations.

Purpose of the Study:

• To introduce a novel, parameter-free motion correction method for fNIRS data.
• To evaluate the performance of this new method against existing techniques through simulations and empirical validation.
• To enhance the utility of fNIRS for neuroimaging research, especially in populations difficult to scan with fMRI.

Main Methods:

• Development of a motion correction algorithm based on robust regression.
• Simulation studies comparing the proposed method with five other motion correction techniques.
• Empirical validation using fNIRS data from a working memory task in children aged 7-15 years.

Main Results:

• The robust regression method effectively removes baseline shift and spike artifacts without requiring user-defined parameters.
• Simulations demonstrated superior activation detection performance compared to five other current motion correction methods.
• Empirical validation showed stronger and more extensive activation detection in children using the novel method.

Conclusions:

• The proposed robust regression-based motion correction significantly improves fNIRS data analysis.
• This method enhances the reliability and sensitivity of fNIRS for studying brain activity.
• The technique makes fNIRS a more viable neuroimaging tool for populations unsuitable for fMRI, such as children.