Graph theoretical approach to functional connectivity in prefrontal cortex via fNIRS

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Functional near-infrared spectroscopy (fNIRS) is a developing neuroimaging technique.
• Clinical adoption requires simplified data metrics, or biomarkers.
• Current fNIRS data analysis is complex for routine clinical use.

Purpose of the Study:

• To develop a straightforward signal processing algorithm for multichannel fNIRS data.
• To identify a reliable biomarker from fNIRS measurements during a cognitive task.
• To assess the feasibility of fNIRS as a clinical neuroimaging tool.

Main Methods:

• Wavelet-transform-based preprocessing of fNIRS data.
• Partial correlation analysis to compute functional connectivity matrices.
• Calculation of global efficiency values from connectivity data during a Stroop task.

Main Results:

• A significant Stroop interference effect was observed between incongruent and neutral conditions.
• Global efficiency values decreased progressively from neutral to congruent to incongruent conditions.
• The developed algorithm successfully generated distinct network metrics across task conditions.

Conclusions:

• The proposed algorithm effectively processes fNIRS data to yield a potential biomarker.
• Findings suggest fNIRS can provide clinically relevant metrics for cognitive function.
• This approach moves towards easier clinical integration of fNIRS technology.