Abstract
BackgroundThe human brain is a highly complex system that can be represented as a structurally interconnected and functionally synchronized network, which assures both the segregation and integration of information processing. Recent studies have demonstrated that a variety of neuroimaging and neurophysiological techniques such as functional magnetic resonance imaging (MRI), diffusion MRI and electroencephalography/magnetoencephalography can be employed to explore the topological organization of human brain networks. However, little is known about whether functional near infrared spectroscopy (fNIRS), a relatively new optical imaging technology, can be used to map functional connectome of the human brain and reveal meaningful and reproducible topological characteristics.ResultsWe utilized resting-state fNIRS (R-fNIRS) to investigate the topological organization of human brain functional networks in 15 healthy adults. Brain networks were constructed by thresholding the temporal correlation matrices of 46 channels and analyzed using graph-theory approaches. We found that the functional brain network derived from R-fNIRS data had efficient small-world properties, significant hierarchical modular structure and highly connected hubs. These results were highly reproducible both across participants and over time and were consistent with previous findings based on other functional imaging techniques.ConclusionsOur results confirmed the feasibility and validity of using graph-theory approaches in conjunction with optical imaging techniques to explore the topological organization of human brain networks. These results may expand a methodological framework for utilizing fNIRS to study functional network changes that occur in association with development, aging and neurological and psychiatric disorders.
Highlights
The human brain is a highly complex network that is interconnected structurally by a dense of cortico-cortical axonal pathways [1] and functionally through synchronized or coherent neural activity [2]
Among sparsity values of 0.30,S,0.91, the mean hierarchy coefficient was 0.3360.05 and the corresponding mean z-score was 4.1061.11. These results demonstrate a significant non-random hierarchical organization of the functional human brain networks that were based on functional near infrared spectroscopy (fNIRS) data
We found that the resting-state fNIRS (R-fNIRS)-based functional brain network exhibited: 1) an optimal small-world configuration for both localized and distributed information processing; 2) a hierarchical organization that supports top-down relationships between nodes and minimizes wiring costs; 3) a modular architecture where the identified modules correspond to several well-known brain functions; and 4) heterogeneous nodal centrality of core hubs
Summary
The human brain is a highly complex network that is interconnected structurally by a dense of cortico-cortical axonal pathways [1] and functionally through synchronized or coherent neural activity [2]. Recent studies have shown that human brain networks can be constructed from a variety of neuroimaging and neurophysiological techniques (e.g., structural MRI, diffusion MRI, functional MRI and electroencephalography/magnetoencephalography) and further quantitatively analyzed with graph-theory methods. Graph-based network analysis approaches are straightforward but powerful in characterizing topological properties of the brain networks Using this theory, it has been shown that human brain networks possess many non-trivial topological properties such as small-world topology, modularity and highly connected hubs [3,4,5,6]. Several imaging techniques have been employed extensively to study connectivity patterns in the brain, it is still largely unknown whether functional near infrared spectroscopy (fNIRS), a relatively new optical imaging technology, can be used to map the functional connectome of the human brain and reveal its underlying infrastructure. Little is known about whether functional near infrared spectroscopy (fNIRS), a relatively new optical imaging technology, can be used to map functional connectome of the human brain and reveal meaningful and reproducible topological characteristics
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