Analyzing the resting state functional connectivity in the human language system using near infrared spectroscopy

Area of Science:

• Neuroscience
• Developmental Neuroscience
• Cognitive Neuroscience

Background:

• Resting state functional connectivity (RSFC) studies are crucial for understanding brain development.
• Investigating the language system in infants presents unique methodological challenges.
• Functional near-infrared spectroscopy (fNIRS) offers a non-invasive approach for infant brain imaging.

Purpose of the Study:

• To evaluate phase synchronization as a method for identifying the language system in infants using fNIRS.
• To assess the feasibility and reliability of fNIRS-based phase synchronization for mapping functional connectivity.
• To compare fNIRS findings with established neuroimaging techniques like fMRI.

Main Methods:

• Utilized phase synchronization analysis on fNIRS data from infants.
• Employed joint probability distribution of phase with a seed channel in the language area.
• Estimated phase relations to delineate the language network.

Main Results:

• Demonstrated the feasibility of phase synchronization for identifying the infant language system.
• Generated connectivity maps consistent with known anatomical cortical connections.
• Achieved results comparable to functional magnetic resonance imaging (fMRI) studies.
• Observed significant left hemisphere lateralization of the language network.

Conclusions:

• Phase synchronization is a viable technique for mapping infant language networks using fNIRS.
• fNIRS-based phase synchronization provides reliable and comparable data to fMRI.
• The findings support the left-lateralized nature of the language system in infants.