Reducing false discoveries in resting-state functional connectivity using short channel correction: an fNIRS study

Area of Science:

• Neuroimaging
• Neuroscience
• Biomedical Engineering

Background:

• Functional near-infrared spectroscopy (fNIRS) measures brain activity but is susceptible to non-neuronal signal interference.
• These artifacts can lead to inaccurate resting-state functional connectivity (rsFC) findings and misinterpretation of brain networks.
• Accurate rsFC is crucial for understanding brain function and dysfunction.

Purpose of the Study:

• To evaluate the impact of short channel correction on rsFC analysis using fNIRS data.
• To determine if removing non-neuronal signals improves the ability to distinguish between brain regions with known differences in connectivity.
• To test the hypothesis that short channel correction reduces false connectivity discoveries.

Main Methods:

• Applied a principal component analysis-based short channel correction technique to resting-state fNIRS data from 10 healthy adults.
• Analyzed connectivity using magnitude-squared coherence between channel pairs.
• Compared connectivity in homologous brain regions (expected high connectivity) versus control regions (expected lower connectivity).

Main Results:

• Short channel correction significantly reduced coherence in oxy-hemoglobin concentration changes within frequency bands overlapping Mayer waves.
• The correction effectively reduced spurious correlations in connectivity measures.
• Improved discriminability between homologous and control brain region connectivity groups was observed.

Conclusions:

• Short channel correction is a valuable technique for refining fNIRS rsFC analysis.
• This method enhances the reliability of fNIRS by mitigating non-neuronal signal artifacts.
• fNIRS analysis with short channel correction demonstrates superior performance in differentiating functional brain networks.