scholarly journals Effects of HD-tDCS on Resting-State Functional Connectivity in the Prefrontal Cortex: An fNIRS Study

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
M. Atif Yaqub ◽  
Seong-Woo Woo ◽  
Keum-Shik Hong
Keyword(s):  
Graph Theory ◽  
Prefrontal Cortex ◽  
Functional Connectivity ◽  
Resting State ◽  
Near Infrared ◽  
Pearson Correlation ◽  
Brain Diseases ◽  
Resting State Functional Connectivity ◽  
High Definition ◽  
Functional Near Infrared Spectroscopy

Functional connectivity is linked to several degenerative brain diseases prevalent in our aging society. Electrical stimulation is used for the clinical treatment and rehabilitation of patients with many cognitive disorders. In this study, the effects of high-definition transcranial direct current stimulation (HD-tDCS) on resting-state brain networks in the human prefrontal cortex were investigated by using functional near-infrared spectroscopy (fNIRS). The intrahemispheric as well as interhemispheric connectivity changes induced by 1 mA HD-tDCS were examined in 15 healthy subjects. Pearson correlation coefficient-based correlation matrices were generated from filtered time series oxyhemoglobin (ΔHbO) signals and converted into binary matrices. Common graph theory metrics were computed to evaluate the network changes. Systematic interhemispheric, intrahemispheric, and intraregional connectivity analyses demonstrated that the stimulation positively affected the resting-state connectivity in the prefrontal cortex. The poststimulation connectivity was increased throughout the prefrontal region, while focal HD-tDCS effects induced an increased rate of connectivity in the stimulated hemisphere. The graph theory metrics clearly distinguished the prestimulation and poststimulation networks for a range of thresholds. The results of this study suggest that HD-tDCS can be used to increase functional connectivity in the prefrontal cortex. The increase in functional connectivity can be explored clinically for neurorehabilitation of patients with degenerative brain diseases.

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 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.

scholarly journals Narrowband Resting-State fNIRS Functional Connectivity in Autism Spectrum Disorder

2021 ◽  
Vol 15 ◽  
Author(s):  
Weiting Sun ◽  
Xiaoyin Wu ◽  
Tingzhen Zhang ◽  
Fang Lin ◽  
Huiwen Sun ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Near Infrared ◽  
Low Frequency ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Resting State Functional Connectivity ◽  
Characteristic Analysis ◽  
Functional Near Infrared Spectroscopy

Hemispheric asymmetry in the power spectrum of low-frequency spontaneous hemodynamic fluctuations has been previously observed in autism spectrum disorder (ASD). This observation may imply a specific narrow-frequency band in which individuals with ASD could show more significant alteration in resting-state functional connectivity (RSFC). To test this assumption, we evaluated narrowband RSFC at several frequencies for functional near-infrared spectroscopy signals recorded from the bilateral temporal lobes on 25 children with ASD and 22 typically developing (TD) children. In several narrow-frequency bands, we observed altered interhemispheric RSFC in ASD. However, in the band of 0.01–0.02 Hz, more mirrored channel pairs (or cortical sites) showed significantly weaker RSFC in the ASD group. Receiver operating characteristic analysis further demonstrated that RSFC in the narrowband of 0.01–0.02 Hz might have better differentiation ability between the ASD and TD groups. This may indicate that the narrowband RSFC could serve as a characteristic for the prediction of ASD.

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