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

Functional connectivity has become one of the important approaches to understanding the functional organization of the human brain. Recently, functional near-infrared spectroscopy (fNIRS) was demonstrated as a feasible method to study resting-state functional connectivity (RSFC) in the sensory and motor systems. However, whether such fNIRS-based RSFC can be revealed in high-level and complex functional systems remains unknown. In the present study, the feasibility of such an approach is tested on the language system, of which the neural substrates have been well documented in the literature. After determination of a seed channel by a language localizer task, the correlation strength between the low frequency fluctuations of the fNIRS signal at the seed channel and those at all other channels is used to evaluate the language system RSFC. Our results show a significant RSFC between the left inferior frontal cortex and superior temporal cortex, components both associated with dominant language regions. Moreover, the RSFC map demonstrates left lateralization of the language system. In conclusion, the present study successfully utilized fNIRS-based RSFC to study a complex and high-level neural system, and provides further evidence for the validity of the fNIRS-based RSFC approach.