Signal processing of functional NIRS data acquired during overt speaking

Area of Science:

• Neuroscience
• Cognitive Science
• Biomedical Engineering

Background:

• Functional near-infrared spectroscopy (fNIRS) allows greater participant movement than fMRI.
• Neuroimaging speech is challenging due to systemic artifacts in critical brain regions.
• Existing fNIRS methods struggle to isolate neural signals during active speech.

Purpose of the Study:

• To develop and validate a method for investigating cortical activity during spoken language tasks using fNIRS.
• To refine deoxyhemoglobin (deoxyHb) signals by removing global artifacts using principal component analysis (PCA) spatial filtering.
• To enhance the utility of fNIRS for studying natural, ecologically valid communication.

Main Methods:

• Overt picture naming tasks were performed by participants.
• Deoxyhemoglobin (deoxyHb) and oxyhemoglobin (oxyHb) signals were analyzed with and without PCA spatial filtering.
• General linear model approaches were used to compare signal changes.
• fNIRS data was spatially compared with functional magnetic resonance imaging (fMRI) and meta-analysis data.

Main Results:

• PCA spatial filtering applied to the deoxyHb signal revealed activity in Broca's region and supplementary motor cortex.
• This observed activity was spatially comparable to fMRI and meta-analysis findings.
• Oxyhemoglobin (oxyHb) signals did not show expected activity in Broca's region, even after filtering.

Conclusions:

• Principal component analysis (PCA) spatial filtering effectively refines deoxyhemoglobin (deoxyHb) signals for fNIRS.
• This method enhances fNIRS's capability to detect neural activity related to spoken language.
• The refined fNIRS approach expands its application to naturalistic speech studies.