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

Abstract

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.

Application of functional near-infrared spectroscopy in the healthcare industry: A review

“…[30][31][32][33][34]79,121,236,266,272 Functional and e®ective connectivity analyses are useful to understand the processing that takes place in the human brain, and covering more brain areas will allow for better understanding based on the experimental conditions. 4,271,[295][296][297]352 The bundled optode approach that creates hundreds of channels at di®erent brain depths with high spatial resolution can assuredly assist in the creation of 3D images via fNIRS, which can be compared with those created via fMRI. 7 This technique involves spatially resolved spectroscopy that utilizes multidistance channel formation among groups of sources and detectors placed as close to each other as possible.…”

“…3 The rehabilitation of patients with impaired brain functions is essential; however, these modalities cannot be used simultaneously with rehabilitating techniques, such as electric stimulation, as these techniques are a®ected by electric and magnetic¯elds. 4 In contrast to established neuroimaging methods, fNIRS has proven its worthiness during the last decade. Most of the human tissues are comparatively more translucent than oxygenated (HbO) and deoxygenated hemoglobin (HbR) in a spectrum between 650 nm and 1000 nm.…”

“…7,8 fNIRS can reveal the underlying neuronal networks and their complex connections in the form of functional connectivity. 4,9 Various algorithms and techniques have been developed and explored using fNIRS to improve the brain-computer interfaces (BCIs) to help physically disabled persons. [10][11][12][13][14][15] This paper reviews the research works conducted to advance the understanding of the e®ects of various diseases on our brain using fNIRS.…”

Reduction of Onset Delay in Functional Near-Infrared Spectroscopy: Prediction of HbO/HbR Signals

“…It offers several advantages, including acceptable temporal and spatial resolution , portability, and low cost (Ferrari and Quaresima, 2012). With these advantages, fNIRS has successfully demonstrated its potential as a viable neuroimaging tool for applications to the health care industry (Hong and Yaqub, 2019), neurological disorders (Obrig, 2014;Ghafoor et al, 2019;Yang et al, 2019), psychiatric disorders (Ohi et al, 2017), behavioral and cognitive development (Watanabe et al, 2017;Yaqub et al, 2018), and brain-computer interfaces (BCIs) (Nicolas-Alonso and Gomez-Gil, 2012;Naseer and Hong, 2015;Schudlo and Chau, 2018;Shin and Im, 2018).…”

Early Detection of Hemodynamic Responses Using EEG: A Hybrid EEG-fNIRS Study

“…A recent hybrid EEG-fNIRS study also combined EEG and fNIRS (initial dip) features in a 0~2 s window to enhance the accuracy of a BCI system. However, the command generation time needs to be further reduced for practical applications (Li et al, 2017 ) by extracting differentiable features (Kim et al, 2016 ; Naseer et al, 2016a ; Huang et al, 2017 ; Song et al, 2018 ), or by using signal processing algorithms (Santosa et al, 2013 ; Gui et al, 2017 ; Hamadache and Lee, 2017 ; Yaqub et al, 2018 ), or by application of better classification approach (Bui et al, 2016 ; Choi et al, 2016 ; Naseer et al, 2016b ; Azizi et al, 2017 ). Therefore, a decision making scheme that can detect fNIRS signals in a window smaller than 2 s should be the primary focus of the current hybrid EEG-fNIRS research.…”