Abstract
As an emerging brain imaging technique, functional near infrared spectroscopy (fNIRS) has attracted widespread attention for advancing resting-state functional connectivity (FC) and graph theoretical analyses of brain networks. However, it remains largely unknown how the duration of the fNIRS signal scanning is related to stable and reproducible functional brain network features. To answer this question, we collected resting-state fNIRS signals (10-min duration, two runs) from 18 participants and then truncated the hemodynamic time series into 30-s time bins that ranged from 1 to 10 min. Measures of nodal efficiency, nodal betweenness, network local efficiency, global efficiency, and clustering coefficient were computed for each subject at each fNIRS signal acquisition duration. Analyses of the stability and between-run reproducibility were performed to identify optimal time length for each measure. We found that the FC, nodal efficiency and nodal betweenness stabilized and were reproducible after 1 min of fNIRS signal acquisition, whereas network clustering coefficient, local and global efficiencies stabilized after 1 min and were reproducible after 5 min of fNIRS signal acquisition for only local and global efficiencies. These quantitative results provide direct evidence regarding the choice of the resting-state fNIRS scanning duration for functional brain connectivity and topological metric stability of brain network connectivity.
Highlights
As an emerging brain imaging technique, functional near infrared spectroscopy is attracting increasing interests for studying human brain functional organization
For functional network connectivity methods to be useful in practical applications, non-invasive functional near infrared spectroscopy (fNIRS) brain imaging techniques need provide direct evidence to confirm that sufficient fNIRS imaging data with shortest reasonable scanning time was used for data analysis, which—to our knowledge—has not been investigated for functional connectivity (FC) and graph theoretical study
A series of fNIRS data with different collection durations for graph metric stabilization is contrasted with data recorded with relatively longer durations of 10 min, and the results revealed high similarity in the FC and graph theory metrics between short and long acquisition durations
Summary
As an emerging brain imaging technique, functional near infrared spectroscopy (fNIRS) is attracting increasing interests for studying human brain functional organization. In Fekete et al.’s study, the authors have noted that the small-world properties of the prefrontal network derived from fNIRS-based data are associated with variability in young children’s risk of developmental psychopathology (Fekete et al, 2014). To extend these studies to much wider applications, such as brain development and disease-associated studies, it is important for fNIRS data to be able to identify development/disease-associated changes in brain connectivity and topological metrics. Such changes may reflect functional markers of development/disease that could advance our understanding into brain nervous system function/dysfunction in the future
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