Comparison of short-channel separation and spatial domain filtering for removal of non-neural components in functional near-infrared spectroscopy signals

Noah, J. Adam; Zhang, Xian Z.; Dravida, Swethasri; DiCocco, Courtney; Suzuki, Tatsuya; Aslin, Richard Ν.; Tachtsidis, Ilias; Hirsch, Joy

doi:10.1117/1.nph.8.1.015004

Cited by 37 publications

(43 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Discussionmentioning

confidence: 99%

“…We have not systematically investigated methods such as global signal regression, 24 independent/principal component analysis on long channels 20,37,38 or anticorrelation separation, 23,27 which are likely be somewhat effective in removing physiological noise when short channels are not available or infeasible to implement. Zhou et al 23 reported in a previous study that the anticorrelation separation method is not as effective as the short channel correction based on principal component analysis.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Reducing false discoveries in resting-state functional connectivity using short channel correction: an fNIRS study

et al. 2022

View full text Add to dashboard Cite

. Significance: Functional near-infrared spectroscopy (fNIRS) is a neuroimaging tool that can measure resting-state functional connectivity; however, non-neuronal components present in fNIRS signals introduce false discoveries in connectivity, which can impact interpretation of functional networks. Aim: We investigated the effect of short channel correction on resting-state connectivity by removing non-neuronal signals from fNIRS long channel data. We hypothesized that false discoveries in connectivity can be reduced, hence improving the discriminability of functional networks of known, different connectivity strengths. Approach: A principal component analysis-based short channel correction technique was applied to resting-state data of 10 healthy adult subjects. Connectivity was analyzed using magnitude-squared coherence of channel pairs in connectivity groups of homologous and control brain regions, which are known to differ in connectivity. Results: By removing non-neuronal components using short channel correction, significant reduction of coherence was observed for oxy-hemoglobin concentration changes in frequency bands associated with resting-state connectivity that overlap with the Mayer wave frequencies. The results showed that short channel correction reduced spurious correlations in connectivity measures and improved the discriminability between homologous and control groups. Conclusions: Resting-state functional connectivity analysis with short channel correction performs better than without correction in its ability to distinguish functional networks with distinct connectivity characteristics.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Reducing false discoveries in resting-state functional connectivity using short channel correction: an fNIRS study

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The central assumption in PCA neuroimaging analysis is that global physiological noise is the primary source of spatial covariance. Therefore, removing the first principal component of each hemoglobin time series could remove systemic physiological noise ( Zhang et al, 2005 ; Mesquita et al, 2010 ; Carbonell et al, 2011 ; Novi et al, 2016 ; Santosa et al, 2020 ; Noah et al, 2021 ). To filter the fNIRS signal with PCA, singular value decomposition was performed on the covariance matrix of the fNIRS signal (

), and the channel filtering was achieved by subtracting the n -first principal spatial components.…”

Section: Methodsmentioning

confidence: 99%

“…First, since the SC regression aims to remove systemic physiology, we prefer to perform it on physiological data, which is the hemoglobin time series in the case of fNIRS. Second, SC regression on the hemoglobin time series facilitates the comparison of SC regression with the other procedures (additional physiological measurements and PCA) to remove systemic physiology ( Santosa et al, 2020 ; Noah et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Effects of Systemic Physiology on Mapping Resting-State Networks Using Functional Near-Infrared Spectroscopy

et al. 2022

View full text Add to dashboard Cite

Resting-state functional connectivity (rsFC) has gained popularity mainly due to its simplicity and potential for providing insights into various brain disorders. In this vein, functional near-infrared spectroscopy (fNIRS) is an attractive choice due to its portability, flexibility, and low cost, allowing for bedside imaging of brain function. While promising, fNIRS suffers from non-neural signal contaminations (i.e., systemic physiological noise), which can increase correlation across fNIRS channels, leading to spurious rsFC networks. In the present work, we hypothesized that additional measurements with short channels, heart rate, mean arterial pressure, and end-tidal CO2 could provide a better understanding of the effects of systemic physiology on fNIRS-based resting-state networks. To test our hypothesis, we acquired 12 min of resting-state data from 10 healthy participants. Unlike previous studies, we investigated the efficacy of different pre-processing approaches in extracting resting-state networks. Our results are in agreement with previous studies and reinforce the fact that systemic physiology can overestimate rsFC. We expanded on previous work by showing that removal of systemic physiology decreases intra- and inter-subject variability, increasing the ability to detect neural changes in rsFC across groups and over longitudinal studies. Our results show that by removing systemic physiology, fNIRS can reproduce resting-state networks often reported with functional magnetic resonance imaging (fMRI). Finally, the present work details the effects of systemic physiology and outlines how to remove (or at least ameliorate) their contributions to fNIRS signals acquired at rest.

show abstract

“…Baseline drift was removed using wavelet detrending provided in NIRS-SPM (77). In accordance with recommendations for best practices using fNIRS data (83), global components attributable to blood pressure and other systemic effects (84) were removed using a principal component analysis (PCA) spatial global mean filter (62, 64, 85) prior to general linear model (GLM) analysis. All analyses are reported using the combined OxyHb and deOxyHb signals.…”

Section: Methodsmentioning

confidence: 99%

Neural Correlates of Eye Contact and Social Function in Autism Spectrum Disorder

Hirsch

Zhang

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Reluctant eye contact and reduced social interactions characteristic of autism spectrum disorder (ASD) are consistent with deficits in oculomotor and face processing systems. We test the hypothesis that these deficits are interrelated ASD. Eye-tracking and hyperscanning with functional near-infrared spectroscopy (fNIRS) were used to acquire neuroimaging data during live, dynamic eye-to-eye contacts in 17 ASD and 19 typically-developed (TD) adults. Real eye contact conditions were contrasted with conditions where eye gaze was directed at a comparable dynamic face video. These findings were regressed with eye-contact dwell-time, i.e., times when gaze of both partners was in the eye-box of the other, to confirm the relationship between visual sensing and neural coding. Visual fixations and positional variance were also determined. Average gaze dwell-times in the "eye-box" did not vary between ASD and TD participants but were longer for the Real Eye than Video Eye condition for both groups. However, positional gaze variability, "jitter", was higher for ASD in both conditions. Neural findings for TD [Real Eye > Video Eye] were consistent with previous findings for interactive face-gaze with activity in right temporal and dorsal parietal regions. However, in ASD ventral temporal regions were observed for this contrast without evidence for dorsal parietal activity. This neural difference was enhanced when regressed by eye-contact dwell-times. Together findings are consistent with the hypothesis that unstable bottom-up oculomotor signals contribute to deficits in live face processing and reduced dorsal stream activity in ASD.

show abstract

Comparison of short-channel separation and spatial domain filtering for removal of non-neural components in functional near-infrared spectroscopy signals

Cited by 37 publications

References 77 publications

Reducing false discoveries in resting-state functional connectivity using short channel correction: an fNIRS study

Reducing false discoveries in resting-state functional connectivity using short channel correction: an fNIRS study

Effects of Systemic Physiology on Mapping Resting-State Networks Using Functional Near-Infrared Spectroscopy

Neural Correlates of Eye Contact and Social Function in Autism Spectrum Disorder

Contact Info

Product

Resources

About