The development of functional network organization in early childhood and early adolescence: A resting-state fNIRS study

Abstract

Early childhood (7-8 years old) and early adolescence (11-12 years old) constitute two landmark developmental stages that comprise considerable changes in neural cognition. However, very limited information from functional neuroimaging studies exists on the functional topological configuration of the human brain during specific developmental periods. In the present study, we utilized continuous resting-state functional near-infrared spectroscopy (rs-fNIRS) imaging data to examine topological changes in network organization during development from early childhood and early adolescence to adulthood. Our results showed that the properties of small-worldness and modularity were not significantly different across development, demonstrating the developmental maturity of important functional brain organization in early childhood. Intriguingly, young children had a significantly lower global efficiency than early adolescents and adults, which revealed that the integration of the distributed networks strengthens across the developmental stages underlying cognitive development. Moreover, local efficiency of young children and adolescents was significantly lower than that of adults, while there was no difference between these two younger groups. This finding demonstrated that functional segregation remained relatively steady from early childhood to early adolescence, and the brain in these developmental periods possesses no optimal network configuration. Furthermore, we found heterogeneous developmental patterns in the regional nodal properties in various brain regions, such as linear increased nodal properties in the frontal cortex, indicating increasing cognitive capacity over development. Collectively, our results demonstrated that significant topological changes in functional network organization occurred during these two critical developmental stages, and provided a novel insight into elucidating subtle changes in brain functional networks across development.

Keywords: Brain development; Brain networks; Connectome; Resting state; fNIRS.

Fig. 1

fNIRS data collection and MRI Neuroanatomical Co-Registration. (A) The arrangement of the 46 measurement channels across the whole head. The green and purple dots represent the sources and detectors, respectively. The digits represent the positions of the measurement channels. (B) MRI co-registration was conducted by asking a participant to wear probe arrays with vitamin E capsules in MRI. (C) The anatomical position corresponding to each measurement channel. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

The topological properties of networks. We illustrate the topological properties of networks by a network composed of 10 nodes and 13 edges. (A) The characteristic path length between nodes a and b is the shortest path length, as indicated by the three black arrow lines. (B) The clustering coefficient of node c is the number of existing connections (i.e., 1–2) among the node’s neighbors divided by all of their possible connections (i.e., 1–2, 1–3, 2–3), which is 1/3 (the dashed lines indicate the absence of a connection between the neighbors of node c). (C) Shows a network with a highly connected hub node d, which plays a central position in the overall network. (D) Shows the presence of a clustered module, as indicated by the three nodes (encircled in pink) being mutually strongly interconnected, but sparsely connected to the rest of the network.

Fig. 3

The distribution of r values within the raw r value correlation matrices of three age groups (A). The averaged population-level correlation matrices of three age groups (B) (digits in matrices represent measurement channels).

Fig. 4

The global network metrics in a range of sparsity thresholds (5%–25%). (A) The clustering coefficient and (B) the characteristic path length are shown as a function of the sparsity thresholds compared with the matched random networks. (C) The small-worldness is shown as a function of the sparsity thresholds. (D) Global efficiency, (E) local efficiency, and (F) modularity are presented as a function of the sparsity thresholds compared with the matched random networks. Error bars (A, B, D, E, F) correspond to the standard errors of the mean for 1000 comparable random null networks. Error bars in (C) indicate the standard errors in all subjects.

Fig. 5

Group differences in the global network metrics among the three age groups. *p < 0.05, **p < 0.01. The error bars indicate bootstrapped 95% confidence intervals. Overlapping confidence intervals suggest a lack of difference.

Fig. 6

Development changes in the regional nodal properties and the distributions of hub regions in each age group. (A) The developmental trajectories for significant nodes in nodal degree. (B) The developmental trajectories for significant nodes in nodal efficiency. Regions with significantly linear positive and linear negative correlations are indicated by red and blue spheres, respectively. The significances were set at p < 0.05 (corrected by FDR correction). The hubs in each age group are defined as the brain regions with higher values (Mean + SD) in any of (C) node degree and (D) node efficiency. The hubs are shown in red with node sizes that indicate the values in regional nodal properties. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 7

The effects of different network sparsity thresholds (15% and 25%) on the main findings. Binary networks were used to evaluate the effects.

Fig. 8

The effects of weighted network analysis strategy on the main findings. Sparsity threshold of 20% was used o evaluate the effects.