Abstract: Recently, resting-state functional near-infrared spectroscopy (rs-fNIRS) research has experienced tremendous
progress. Resting-state functional connectivity (RSFC) has been adopted as a pivotal biomarker in rs-fNIRS
studies. However, it is yet to be clear if the RSFC derived from rs-fNIRS is reliable. This concern impedes extensive
utilization of rs-fNIRS.We systematically address the issue of reliability. Sixteen subjects participate in two rs-fNIRS
sessions held one week apart. RSFC in sensorimotor system is calculated using the seed-correlation approach.
Then, test-retest reliability is evaluated at three different scales (map-, cluster-, and channelwise) for individualand
group-level RSFC derived from different types of fNIRS signals [oxygenated (HbO), deoxygenated (HbR), and
total hemoglobin (HbT)]. The results show that, for HbO signals, individual-level RSFC generally has good-toexcellent
map-/clusterwise reliability, while group-level RSFC has excellent reliability. For HbT signals, the results
are similar. For HbR signals, the clusterwise reliability is comparable to that for HbO while the mapwise reliability
is slightly lower (fair to good). Focusing on RSFC at a single channel, we report poor channelwise reliability for all
three types of signals. We hereby propose that fNIRS-derived RSFC is a reliable biomarker if interpreted in mapand
clusterwise manners. However, channelwise interpretation of individual RSFC should proceed with caution.
Abstract: Recent studies of resting-state functional near-infrared spectroscopy (fNIRS)
have emerged as a hot topic and revealed that resting-state functional connectivity
(RSFC) is an inherent characteristic of the resting brain. However, it is currently
unclear if fNIRS-based RSFC is test-retest reliable. In this study, we utilized
independent component analysis (ICA) as an effective RSFC detection tool to address
the reliability question. Sixteen subjects participated in two rs-fNIRS recording
sessions held 1 week (6.88±1.09 days) apart. Then, RSFC in the sensorimotor regions
was extracted using ICA. Test-retest reliability was assessed for intra- and intersessions,
at the individual- and group-level, and for different hemoglobin
concentration signals. Our results clearly demonstrated that map-wise reliability was
excellent at the group level (with Pearson r coefficients up to 0.88) and generally fair
at the individual level. The cluster-wise reliability was better at the group level
(having reproducibility indices of up to 0.97 for the size and up to 0.80 for the
location of the detected RSFC) and was weaker but still fair at the individual level
(0.56 and 0.46 for intra- and inter-session reliability, respectively). Cluster-wise
intra-class correlation coefficients (ICCs) also exhibited fair-to-good reliability (with
single-measure ICC up to 0.56), while channel-wise single-measure ICCs indicated
lower reliability. We conclude that fNIRS-based, ICA-derived RSFC is an essential
and reliable biomarker at the individual and group levels if interpreted in map- and
cluster-wise manners. Our results also suggested that channel-wise individual-level
RSFC results should be interpreted with caution if no optode co-registration
procedure had been conducted and indicated that “cluster” should be treated as a
minimal analytical unit in further RSFC studies using fNIRS.
Abstract: As a promising non-invasive imaging technique, functional near infrared spectroscopy (fNIRS) has recently
earned increasing attention in resting-state functional connectivity (RSFC) studies. Preliminary fNIRS-based
RSFC studies adopted a seed correlation approach and yielded interesting results. However, the seed
correlation approach has several inherent problems, such as neglecting of interactions among multiple
regions and a dependence on seed region selection. Moreover, ineffectively reduced noise and artifacts in
fNIRS measurements also negatively affect RSFC results. In this study, independent component analysis (ICA)
was introduced to meet these challenges in RSFC detection based on resting-state fNIRS measurements. The
results of ICA on data from the sensorimotor and the visual systems both showed functional system-specific
RSFC maps. Results from comparison between ICA and the conventional seed correlation approach
demonstrated, both qualitatively and quantitatively, the superior performance of ICA with higher sensitivity
and specificity, especially in the case of higher noise level. The capability of ICA to separate noise and artifacts
from resting-state fNIRS data was also demonstrated, and the extracted noise and artifacts were illustrated.
Finally, some practical issues on performing ICA on resting-state fNIRS data were discussed.
Abstract: Independent component analysis (ICA) is a data-driven approach to study functional magnetic resonance
imaging (fMRI) data. Particularly, for group analysis on multiple subjects, temporally concatenation group
ICA (TC-GICA) is intensively used. However, due to the usually limited computational capability, data
reduction with principal component analysis (PCA: a standard preprocessing step of ICA decomposition) is
difficult to achieve for a large dataset. To overcome this, TC-GICA employs multiple-stage PCA data reduction.
Such multiple-stage PCA data reduction, however, leads to variable outputs due to different subject
concatenation orders. Consequently, the ICA algorithm uses the variable multiple-stage PCA outputs and
generates variable decompositions. In this study, a rigorous theoretical analysis was conducted to prove the
existence of such variability. Simulated and real fMRI experiments were used to demonstrate the subjectorder-
induced variability of TC-GICA results using multiple PCA data reductions. To solve this problem, we
propose a new subject order-independent group ICA (SOI-GICA). Both simulated and real fMRI data
experiments demonstrated the high robustness and accuracy of the SOI-GICA results compared to those of
traditional TC-GICA. Accordingly, we recommend SOI-GICA for group ICA-based fMRI studies, especially
those with large data sets.