Control of Transcranial Direct Current Stimulation Duration by Assessing Functional Connectivity of Near-Infrared Spectroscopy Signals

2021 ◽  
Author(s):  
M. Atif Yaqub ◽  
Keum-Shik Hong ◽  
Amad Zafar ◽  
Chang-Seok Kim
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Direct Current ◽  
Network Theory ◽  
Short Range ◽  
Near Infrared ◽  
Brain State ◽  
Stimulation Period ◽  
The Brain ◽  
Connection Density

Transcranial direct current stimulation (tDCS) has been shown to create neuroplasticity in healthy and diseased populations. The control of stimulation duration by providing real-time brain state feedback using neuroimaging is a topic of great interest. This study presents the feasibility of a closed-loop modulation for the targeted functional network in the prefrontal cortex. We hypothesize that we cannot improve the brain state further after reaching a specific state during a stimulation therapy session. A high-definition tDCS of 1[Formula: see text]mA arranged in a ring configuration was applied at the targeted right prefrontal cortex of 15 healthy male subjects for 10[Formula: see text]min. Functional near-infrared spectroscopy was used to monitor hemoglobin chromophores during the stimulation period continuously. The correlation matrices obtained from filtered oxyhemoglobin were binarized to form subnetworks of short- and long-range connections. The connectivity in all subnetworks was analyzed individually using a new quantification measure of connectivity percentage based on the correlation matrix. The short-range network in the stimulated hemisphere showed increased connectivity in the initial stimulation phase. However, the increase in connection density reduced significantly after 6[Formula: see text]min of stimulation. The short-range network of the left hemisphere and the long-range network gradually increased throughout the stimulation period. The connectivity percentage measure showed a similar response with network theory parameters. The connectivity percentage and network theory metrics represent the brain state during the stimulation therapy. The results from the network theory metrics, including degree centrality, efficiency, and connection density, support our hypothesis and provide a guideline for feedback on the brain state. The proposed neuro-feedback scheme is feasible to control the stimulation duration to avoid overdosage.

Functional near-infrared spectroscopy in conjunction with electroencephalography of cerebellar transcranial direct current stimulation responses in the latent neurovascular coupling space – a chronic stroke study

2020 ◽  
Author(s):  
Zeynab Rezaee ◽  
Shashi Ranjan ◽  
Dhaval Solanki ◽  
Mahasweta Bhattacharya ◽  
MV Padma Srivastava ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Field Strength ◽  
Near Infrared Spectroscopy ◽  
Direct Current ◽  
Near Infrared ◽  
Neurovascular Coupling ◽  
Chronic Stroke ◽  
Functional Near Infrared Spectroscopy ◽  
Subject Specific ◽  
The Brain

AbstractCerebellar transcranial direct current stimulation (ctDCS) can facilitate motor learning; however, ctDCS effects have not been investigated using portable neuroimaging vis-à-vis lobular electric field strength. This is important since the subject-specific residual architecture for cerebellar interconnections with the cerebral cortex, including the prefrontal cortex (PFC) and the sensorimotor cortex (SMC), can influence the ctDCS effects on the cerebral functional activation. In this study, we investigated functional near-infrared spectroscopy (fNIRS) in conjunction with electroencephalography (EEG) to measure the changes in the brain activation at the PFC and the SMC following virtual reality (VR)-based Balance Training (VBaT), before and after ctDCS treatment in 12 hemiparetic chronic stroke survivors. Furthermore, we performed general linear modeling (GLM) that can putatively associate the lobular electric field strength due to ctDCS priming with the changes in the fNIRS-EEG measures in the chronic stroke survivors. Here, fNIRS-EEG based measures were investigated in their latent space found using canonical correlation analysis (CCA) that is postulated to capture neurovascular coupling. We found that the ctDCS electrode montage, as well as the state (pre-intervention, during intervention, post-intervention), had a significant (p<0.05) effect on the changes in the canonical scores of oxy-hemoglobin (O2Hb) signal measured with fNIRS. Also, skill acquisition during first exposure to VBaT decreased the activation (canonical score of O2Hb) of PFC of the non-lesioned hemisphere in the novices at their first exposure before the ctDCS intervention. Moreover, ctDCS intervention targeting the leg representation in the cerebellum led to a decrease in the canonical scores of O2Hb at the lesioned SMC, which is postulated to be related to the cerebellar brain inhibition. Furthermore, ctDCS electrode montage, as well as the state, had a significant (p<0.05) interaction effect on the canonical scores of log10-transformed EEG bandpower. Our current study showed the feasibility of fNIRS-EEG imaging of the ctDCS responses in the latent neurovascular coupling space that can not only be used for monitoring the dynamical changes in the brain activation associated with ctDCS-facilitated VBaT, but may also be useful in subject-specific current steering for tDCS to target the cerebral fNIRS-EEG sources to reduce inter-individual variability.

scholarly journals Functional Effects of Bilateral Dorsolateral Prefrontal Cortex Modulation During Sequential Decision-Making: A Functional Near-Infrared Spectroscopy Study With Offline Transcranial Direct Current Stimulation

2021 ◽  
Vol 14 ◽  
Author(s):  
Iryna Schommartz ◽  
Annika Dix ◽  
Susanne Passow ◽  
Shu-Chen Li
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Direct Current ◽  
Dorsolateral Prefrontal Cortex ◽  
Near Infrared ◽  
Sequential Decision Making ◽  
Sequential Decision ◽  
Functional Near Infrared Spectroscopy ◽  
Dorsolateral Prefrontal

The ability to learn sequential contingencies of actions for predicting future outcomes is indispensable for flexible behavior in many daily decision-making contexts. It remains open whether such ability may be enhanced by transcranial direct current stimulation (tDCS). The present study combined tDCS with functional near-infrared spectroscopy (fNIRS) to investigate potential tDCS-induced effects on sequential decision-making and the neural mechanisms underlying such modulations. Offline tDCS and sham stimulation were applied over the left and right dorsolateral prefrontal cortex (dlPFC) in young male adults (N = 29, mean age = 23.4 years, SD = 3.2) in a double-blind between-subject design using a three-state Markov decision task. The results showed (i) an enhanced dlPFC hemodynamic response during the acquisition of sequential state transitions that is consistent with the findings from a previous functional magnetic resonance imaging (fMRI) study; (ii) a tDCS-induced increase of the hemodynamic response in the dlPFC, but without accompanying performance-enhancing effects at the behavioral level; and (iii) a greater tDCS-induced upregulation of hemodynamic responses in the delayed reward condition that seems to be associated with faster decision speed. Taken together, these findings provide empirical evidence for fNIRS as a suitable method for investigating hemodynamic correlates of sequential decision-making as well as functional brain correlates underlying tDCS-induced modulation. Future research with larger sample sizes for carrying out subgroup analysis is necessary in order to decipher interindividual differences in tDCS-induced effects on sequential decision-making process at the behavioral and brain levels.

scholarly journals Design of Brain-Machine Interface Using Near-Infrared Spectroscopy

2013 ◽  
Vol 25 (6) ◽  
pp. 1000-1010
Author(s):  
Tomotaka Ito ◽  
◽  
Satoshi Ushii ◽  
Takafumi Sameshima ◽  
Yoshihiro Mitsui ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Medical Science ◽  
Brain State ◽  
State Estimate ◽  
Machine Interface ◽  
Blood Flow Patterns ◽  
The Brain

In recent years, the fields of robotics and medical science have been paying close attention to brainmachine interface (BMI) systems. BMI observes human cerebral activity and use the collected data as the input to various instruments. If such a systemcould be effectively realized, it could be used as a new intuitive input interface for application to human-robot interactions, welfare scenarios, etc. In this paper, we discussed a design problem related to a BMI system using near-infrared spectroscopy (NIRS). We developed a brain state classifier based on the learning vector quantization (LVQ) method. The proposed method classifies the cerebral blood flow patterns and outputs the brain state estimate. The classification experiments showed that the proposed method can successfully classify not only human physical motions and motor imageries, but also human emotions and human mental commands issued to a robot. Especially, in the classification of “the mental commands to a robot,” we successfully realized the imagery classification of five different mental commands. The results point to the potential of NIRS-based brain machine interfaces.

scholarly journals Mismatch between Tissue Partial Oxygen Pressure and Near-Infrared Spectroscopy Neuromonitoring of Tissue Respiration in Acute Brain Trauma: The Rationale for Implementing a Multimodal Monitoring Strategy

2021 ◽  
Vol 22 (3) ◽  
pp. 1122
Author(s):  
Mario Forcione ◽  
Mario Ganau ◽  
Lara Prisco ◽  
Antonio Maria Chiarelli ◽  
Andrea Bellelli ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Oxygen Pressure ◽  
Near Infrared ◽  
Optical Signal ◽  
Brain Trauma ◽  
Partial Oxygen Pressure ◽  
Tissue Respiration ◽  
The Brain ◽  
Partial Oxygen

The brain tissue partial oxygen pressure (PbtO2) and near-infrared spectroscopy (NIRS) neuromonitoring are frequently compared in the management of acute moderate and severe traumatic brain injury patients; however, the relationship between their respective output parameters flows from the complex pathogenesis of tissue respiration after brain trauma. NIRS neuromonitoring overcomes certain limitations related to the heterogeneity of the pathology across the brain that cannot be adequately addressed by local-sample invasive neuromonitoring (e.g., PbtO2 neuromonitoring, microdialysis), and it allows clinicians to assess parameters that cannot otherwise be scanned. The anatomical co-registration of an NIRS signal with axial imaging (e.g., computerized tomography scan) enhances the optical signal, which can be changed by the anatomy of the lesions and the significance of the radiological assessment. These arguments led us to conclude that rather than aiming to substitute PbtO2 with tissue saturation, multiple types of NIRS should be included via multimodal systemic- and neuro-monitoring, whose values then are incorporated into biosignatures linked to patient status and prognosis. Discussion on the abnormalities in tissue respiration due to brain trauma and how they affect the PbtO2 and NIRS neuromonitoring is given.

scholarly journals Classification of Prefrontal Cortex Activity Based on Functional Near-Infrared Spectroscopy Data upon Olfactory Stimulation

2021 ◽  
Vol 11 (6) ◽  
pp. 701
Author(s):  
Cheng-Hsuan Chen ◽  
Kuo-Kai Shyu ◽  
Cheng-Kai Lu ◽  
Chi-Wen Jao ◽  
Po-Lei Lee
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Kernel Functions ◽  
Olfactory Stimulation ◽  
Support Vector ◽  
Hemodynamic Response Function ◽  
Functional Near Infrared Spectroscopy ◽  
Data Set

The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained from the prefrontal cortex. These data included odor stimuli and air state, which triggered the hemodynamic response function (HRF), determined from variations in oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) levels; photoplethysmography (PPG) of two wavelengths (raw optical red and near-infrared data); and the ratios of data from two optical datasets. We adopted three SVM kernel functions (i.e., linear, quadratic, and cubic) to analyze signals and compare their performance with the HRF and PPG signals. The results revealed that oxyHb yielded the most efficient single-signal data with a quadratic kernel function, and a combination of HRF and PPG signals yielded the most efficient multi-signal data with the cubic function. Our results revealed superior SVM analysis of HRFs for classifying odor and air status using fNIRS data during olfaction in humans. Furthermore, the olfactory stimulation can be accurately classified by using quadratic and cubic kernel functions in SVM, even for an individual participant data set.

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