Abstract
Recent research has demonstrated the feasibility of combining functional near-infrared spectroscopy (fNIRS) and graph theory approaches to explore the topological attributes of human brain networks. However, the test-retest (TRT) reliability of the application of graph metrics to these networks remains to be elucidated. Here, we used resting-state fNIRS and a graph-theoretical approach to systematically address TRT reliability as it applies to various features of human brain networks, including functional connectivity, global network metrics and regional nodal centrality metrics. Eighteen subjects participated in two resting-state fNIRS scan sessions held ∼20 min apart. Functional brain networks were constructed for each subject by computing temporal correlations on three types of hemoglobin concentration information (HbO, HbR, and HbT). This was followed by a graph-theoretical analysis, and then an intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of each network metric. We observed that a large proportion of resting-state functional connections (∼90%) exhibited good reliability (0.6< ICC <0.74). For global and nodal measures, reliability was generally threshold-sensitive and varied among both network metrics and hemoglobin concentration signals. Specifically, the majority of global metrics exhibited fair to excellent reliability, with notably higher ICC values for the clustering coefficient (HbO: 0.76; HbR: 0.78; HbT: 0.53) and global efficiency (HbO: 0.76; HbR: 0.70; HbT: 0.78). Similarly, both nodal degree and efficiency measures also showed fair to excellent reliability across nodes (degree: 0.52∼0.84; efficiency: 0.50∼0.84); reliability was concordant across HbO, HbR and HbT and was significantly higher than that of nodal betweenness (0.28∼0.68). Together, our results suggest that most graph-theoretical network metrics derived from fNIRS are TRT reliable and can be used effectively for brain network research. This study also provides important guidance on the choice of network metrics of interest for future applied research in developmental and clinical neuroscience.
Highlights
The human brain is a highly complex system that can be represented as a structurally or functionally interconnected network that assures rapid segregation and integration of information processing
A comprehensive assessment of network reliability was carried out on three sets of network measures (RSFC, global network metrics and regional nodal metrics). These network metrics were calculated based on two sets of noisereduction data using frequency-based and ICA-based denoising approaches, respectively, and the results consistently demonstrated that the R-functional near-infrared spectroscopy (fNIRS) brain networks possessed good reliability on resting-state functional connectivity (RSFC) and fair to excellent reliability on most global and nodal network metrics
TRT reliability of RSFC maps Based on a correlational analysis and a reliability assessment, we demonstrated that the fNIRS-based RSFC maps have high similarity (Pearson correlation: r = 0.9160.03, averaged across three concentration signals) and high reliability (ICC = 0.6960.03, averaged across three concentration signals) across sessions
Summary
The human brain is a highly complex system that can be represented as a structurally or functionally interconnected network that assures rapid segregation and integration of information processing. In the R-fNIRS based network analysis framework, the channels are considered vertices, and RSFCs between channels are considered edges Using this approach, we previously demonstrated that R-fNIRS can effectively characterize the topological attributes of human brain networks, such as smallworld efficiency, modularity and highly connected hubs [12]. The results of this study were consistent with recent findings from BOLD-fMRI research, suggesting the feasibility and validity of combining R-fNIRS and graph theory analysis to identify the functional properties of human brain networks. It still remains largely unknown whether the topological brain network measures derived from R-fNIRS data are repeatable or test-retest (TRT) reliable. It is an important and necessary task to explore the TRT reliability of functional brain networks derived from human R-fNIRS data
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