Detecting resting-state functional connectivity in the language system using functional near-infrared spectroscopy

Area of Science:

• Neuroscience
• Cognitive Neuroscience
• Brain Imaging

Background:

• Functional connectivity analysis is crucial for understanding brain organization.
• Functional near-infrared spectroscopy (fNIRS) has shown promise for studying resting-state functional connectivity (RSFC) in sensory and motor systems.
• The applicability of fNIRS-based RSFC to high-level cognitive systems like language remains largely unexplored.

Purpose of the Study:

• To investigate the feasibility of using fNIRS to assess resting-state functional connectivity (RSFC) within the human language system.
• To determine if fNIRS can reveal known language network structures and lateralization patterns.

Main Methods:

• Utilized a language localizer task to identify a seed channel in the language network.
• Calculated resting-state functional connectivity (RSFC) by correlating low-frequency fluctuations from the seed channel with all other channels.
• Employed functional near-infrared spectroscopy (fNIRS) for non-invasive brain activity measurement.

Main Results:

• Identified significant RSFC between the left inferior frontal cortex and superior temporal cortex, key language processing regions.
• The generated RSFC map exhibited clear leftward lateralization, consistent with established language network organization.
• Demonstrated the successful application of fNIRS-based RSFC in a complex, high-level cognitive domain.

Conclusions:

• Functional near-infrared spectroscopy (fNIRS) is a viable tool for studying resting-state functional connectivity (RSFC) in complex neural systems, including language.
• The findings support the validity and potential of fNIRS-based RSFC for neuroscientific research on higher cognitive functions.
• This study provides evidence for the left lateralization of the language network using fNIRS-based RSFC.