Abnormal functional connectivity within the prefrontal cortex in interstitial cystitis/bladder pain syndrome (IC/BPS): A pilot study using resting state functional near‐infrared spectroscopy (rs‐fNIRS)

2021 ◽  
Author(s):  
Dongqing Pang ◽  
Limin Liao
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Pilot Study ◽  
Functional Connectivity ◽  
Resting State ◽  
Near Infrared ◽  
Pain Syndrome ◽  
Bladder Pain Syndrome ◽  
Functional Near Infrared Spectroscopy ◽  
Bladder Pain

scholarly journals What Does Sleeping Brain Tell About Stress? A Pilot Functional Near-Infrared Spectroscopy Study Into Stress-Related Cortical Hemodynamic Features During Sleep

2021 ◽  
Vol 3 ◽  
Author(s):  
Zilu Liang
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Pilot Study ◽  
Near Infrared Spectroscopy ◽  
Dorsolateral Prefrontal Cortex ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Stress Indicators ◽  
Measured Stress ◽  
Dorsolateral Prefrontal

People with mental stress often experience disturbed sleep, suggesting stress-related abnormalities in brain activity during sleep. However, no study has looked at the physiological oscillations in brain hemodynamics during sleep in relation to stress. In this pilot study, we aimed to explore the relationships between bedtime stress and the hemodynamics in the prefrontal cortex during the first sleep cycle. We tracked the stress biomarkers, salivary cortisol, and secretory immunoglobulin A (sIgA) on a daily basis and utilized the days of lower levels of measured stress as natural controls to the days of higher levels of measured stress. Cortical hemodynamics was measured using a cutting-edge wearable functional near-infrared spectroscopy (fNIRS) system. Time-domain, frequency-domain features as well as nonlinear features were derived from the cleaned hemodynamic signals. We proposed an original ensemble algorithm to generate an average importance score for each feature based on the assessment of six statistical and machine learning techniques. With all channels counted in, the top five most referred feature types are Hurst exponent, mean, the ratio of the major/minor axis standard deviation of the Poincaré plot of the signal, statistical complexity, and crest factor. The left rostral prefrontal cortex (RLPFC) was the most relevant sub-region. Significantly strong correlations were found between the hemodynamic features derived at this sub-region and all three stress indicators. The dorsolateral prefrontal cortex (DLPFC) is also a relevant cortical area. The areas of mid-DLPFC and caudal-DLPFC both demonstrated significant and moderate association to all three stress indicators. No relevance was found in the ventrolateral prefrontal cortex. The preliminary results shed light on the possible role of the RLPCF, especially the left RLPCF, in processing stress during sleep. In addition, our findings echoed the previous stress studies conducted during wake time and provides supplementary evidence on the relevance of the dorsolateral prefrontal cortex in stress responses during sleep. This pilot study serves as a proof-of-concept for a new research paradigm to stress research and identified exciting opportunities for future studies.

Performance Prediction for a Near-Infrared Spectroscopy-Brain–Computer Interface Using Resting-State Functional Connectivity of the Prefrontal Cortex

2018 ◽  
Vol 28 (10) ◽  
pp. 1850023 ◽  
Author(s):  
Jaeyoung Shin ◽  
Chang-Hwan Im
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Functional Connectivity ◽  
Near Infrared Spectroscopy ◽  
Resting State ◽  
Near Infrared ◽  
Mental Arithmetic ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Resting State Functional Connectivity

One of the most important issues in current brain–computer interface (BCI) research is the prediction of a user’s BCI performance prior to the main BCI session because it would be useful to reduce the time required to determine the BCI paradigm best suited to that user. In electroencephalography (EEG)-BCI research, whether a user has low BCI performance toward a specific BCI paradigm has been estimated using a variety of resting-state EEG features. However, no previous study has attempted to predict the performance of near-infrared spectroscopy (NIRS)-BCI using resting-state NIRS data recorded before the main BCI experiment. In this study, we investigated whether the performance of an NIRS-BCI discriminating a mental arithmetic task from the baseline state could be predicted using resting-state functional connectivity (RSFC) of the prefrontal cortex. The investigation of NIRS signals recorded from 29 participants revealed that the RSFC between bilateral channels in the prefrontal area was negatively correlated with subsequent BCI performance (e.g. a fitted line for the RSFC between L2 and R2 channels explains 41% of BCI performance variation). We expect that our indicator can be used to predict BCI performance of an individual user prior to the main NIRS-BCI experiments, thereby facilitating implementation of more efficient NIRS-BCI systems.

scholarly journals Prefrontal cortex hemodynamics and age: a pilot study using functional near infrared spectroscopy in children

2014 ◽  
Vol 8 ◽  
Author(s):  
Afrouz A. Anderson ◽  
Elizabeth Smith ◽  
Victor Chernomordik ◽  
Yasaman Ardeshirpour ◽  
Fatima Chowdhry ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Pilot Study ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy

scholarly journals Abnormality of Resting-State Functional Connectivity in Major Depressive Disorder: A Study With Whole-Head Near-Infrared Spectroscopy

2021 ◽  
Vol 12 ◽  
Author(s):  
Eisuke Sakakibara ◽  
Yoshihiro Satomura ◽  
Jun Matsuoka ◽  
Shinsuke Koike ◽  
Naohiro Okada ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Functional Connectivity ◽  
Depressive Disorder ◽  
Resting State ◽  
Near Infrared ◽  
Healthy Controls ◽  
Resting State Functional Connectivity ◽  
Major Depressive

Near-infrared spectroscopy (NIRS) is a functional neuroimaging modality that has advantages in clinical usage. Previous functional magnetic resonance imaging (fMRI) studies have found that the resting-state functional connectivity (RSFC) of the default mode network (DMN) is increased, while the RSFC of the cognitive control network (CCN) is reduced in patients with major depressive disorder (MDD) compared with healthy controls. This study tested whether the NIRS-based RSFC measurements can detect the abnormalities in RSFC that have been associated with MDD in previous fMRI studies. We measured 8 min of resting-state brain activity in 34 individuals with MDD and 78 age- and gender-matched healthy controls using a whole-head NIRS system. We applied a previously established partial correlation analysis for estimating RSFCs between the 17 cortical regions. We found that MDD patients had a lower RSFC between the left dorsolateral prefrontal cortex and the parietal lobe that comprise the CCN, and a higher RSFC between the right orbitofrontal cortex and ventrolateral prefrontal cortex, compared to those in healthy controls. The RSFC strength of the left CCN was negatively correlated with the severity of depressive symptoms and the dose of antipsychotic medication and positively correlated with the level of social functioning. The results of this study suggest that NIRS-based measurements of RSFCs have potential clinical applications.

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