Dynamic functional connectivity revealed by resting-state functional near-infrared spectroscopy

Abstract

The brain is a complex network with time-varying functional connectivity (FC) and network organization. However, it remains largely unknown whether resting-state fNIRS measurements can be used to characterize dynamic characteristics of intrinsic brain organization. In this study, for the first time, we used the whole-cortical fNIRS time series and a sliding-window correlation approach to demonstrate that fNIRS measurement can be ultimately used to quantify the dynamic characteristics of resting-state brain connectivity. Our results reveal that the fNIRS-derived FC is time-varying, and the variability strength (Q) is correlated negatively with the time-averaged, static FC. Furthermore, the Q values also show significant differences in connectivity between different spatial locations (e.g., intrahemispheric and homotopic connections). The findings are reproducible across both sliding-window lengths and different brain scanning sessions, suggesting that the dynamic characteristics in fNIRS-derived cerebral functional correlation results from true cerebral fluctuation.

Keywords: (170.2655) Functional monitoring and imaging; (170.3880) Medical and biological imaging; (170.5380) Physiology.

Fig. 1

Illustration of fNIRS-based FC analysis. (A)Photograph of whole-head fNIRS data acquisition on a participant. (B) The schematic of whole-head imaging pad (12 sources, red, and 24 detectors, blue). The sources and detectors were symmetrically placed on the left and right hemispheres and constituted 46 measurement channels, which allowed for the whole brain (i.e., frontal, temporal, parietal, and occipital lobes to be measured. (C) Static FC analysis. The static FC was calculated from time series of entire scanning between any two channels. (D) Dynamic FC analysis. The dynamic FC was calculated using sliding-window correlation approach. In this approach, a time window of fixed length was selected, and data points within that window were used to calculate the FC. The window was then shifted in time by a fixed number of data points. This process results in quantification of the time-varying FC over the duration of the scan.

Fig. 2

FC dynamics derived from HbO (left) and HbR (right) signals. (A) An example of dynamic FC maps displayed at an interval of 4 s on an arbitrary participant (Subject 14). (B) The Pearson “between-map” correlation coefficients, r_m, calculated between the successive dynamic FC maps and the static FC map from Subject 14. The dotted straight line represents the mean and standard derivation of r_m across the 10-min time window. (C) Similar calculation to (B) on all 18 subjects. The sliding-window length used to evaluate dynamic FC was 60 s.

Fig. 3

Pattern comparison between FC variability strength, Q, and static FC strength. (A)The group-averaged Q values, (B) static FC strength and (C) the linear relationship between them derived from HbO and HbR, respectively. The values of index Q were quantified as the area under the curve of the power amplitude of the FC time series across the low-frequency band (< 0.1Hz).

Fig. 4

Q difference and the static FC-strength difference in spatially different connectivity groups. (A) Configurations of selected connectivity groups (homotopic, long and short intrahemispheric and heterotopic connections) (B) Group-averaged power spectra of the dynamic FC time series in the four connectivity groups. The subplot showed the average of the power spectra across connections in each group. The lines with blue, red, yellow and green color represent the homotopic, long and short intrahemispheric and heterotopic connections, respectively. (C) Group differences in values of index Q between connectivity groups. (D) Group differences in static FC strength between connectivity groups. In (C and D), one and two asterisks represent significant group differences with permutation test at p <0.05 and 0.01 (Bonferroni-corrected), respectively. Error bars indicate standard deviations.

Fig. 5

Reproducibility of the primary findings derived from HbO and HbR signals over two various sliding-window lengths (30 s and 90 s). (A, B) The negative correlation relationship between index Q and the static connectivity strength. (C, D) The group differences in values of index Q between connectivity groups. (E, F). The group-averaged power spectra of dynamic FC time series in the four connectivity groups. The lines with blue, red, yellow and green color represent the homotopic, long and short intrahemispheric and heterotopic connections, respectively. All these results show good reproducibility over varying sliding-window lengths.

Fig. 6

Reproducibility of our primary findings derived from HbO and HbR signals across different fNIRS test data sets (Session 2). (A) The negative correlation relationship between index Q and static connectivity strength. (B, C) The group differences in values of index Q among four connectivity groups as well as the static connectivity strength among groups, respectively. The SWC approach with 60 s lengths was used to derive the dynamic FC.

Fig. 7

Power spectral analyses of dynamic FC time series for all 18 subjects and two types of hemoglobin concentration signals (HbO and HbR). The sliding-window length was 60 s. In each panel, there are 46 × 45/2 power spectra curves that are overlapped and become a gray-shaded area.