Eight subjects (4 women; mean age 28 ± 10 years, range 20–45 years) participated in a crossover study, which consisted of three randomized ordered sessions, separated by at least 6 days (Figure 1 ). The order of physiological assessments before and after the application of cerebellar tDCS remained consistent across sessions. At the end of each session, subjects reported their attention, fatigue and perceived pain of tDCS using a self-scored visual analog scale where 1 represented poorest attention, maximal fatigue and pain, and 7 maximal attention, least fatigue and pain (Table 2 ) (Stefan et al., 2005 (link)).
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Transcranial Direct Current Stimulation
Transcranial Direct Current Stimulation
Transcranial Direct Current Stimulation (tDCS) is a non-invasive brain stimulation technique that uses low-intensity direct currents to modulate neuronal excitability and activity in the brain.
It has been widely used in research and clinical settings to study and treat a variety of neurological and psychiatric conditions, such as stroke, depression, and chronic pain. tDCS works by delivering a constant, weak electrical current through electrodes placed on the scalp, which can either increase or decrease the resting membrane potential of underlying neurons, leading to changes in their firing rates and synaptic efficacy.
This technique is relatively safe, well-tolerated, and has the potential to enhance the reproducibility and accuracy of research findings when applied with optimized protocols.
PubCompare.ai's AI-driven research tools can help researchers discover the best tDCS protocols and products by exploring the latest literature, pre-prints, and patents.
It has been widely used in research and clinical settings to study and treat a variety of neurological and psychiatric conditions, such as stroke, depression, and chronic pain. tDCS works by delivering a constant, weak electrical current through electrodes placed on the scalp, which can either increase or decrease the resting membrane potential of underlying neurons, leading to changes in their firing rates and synaptic efficacy.
This technique is relatively safe, well-tolerated, and has the potential to enhance the reproducibility and accuracy of research findings when applied with optimized protocols.
PubCompare.ai's AI-driven research tools can help researchers discover the best tDCS protocols and products by exploring the latest literature, pre-prints, and patents.
Most cited protocols related to «Transcranial Direct Current Stimulation»
Attention
Cerebellum
Fatigue
Pain
physiology
Transcranial Direct Current Stimulation
Visual Analog Pain Scale
Woman
Blood Vessel
Cortex, Cerebral
Cranium
Debility
Electric Conductivity
Electricity
Gray Matter
Head
Immune Tolerance
Microtubule-Associated Proteins
Muscle Tissue
Neck
Neurons
Orbitofrontal Cortex
Porifera
Process, Mastoid
Scalp
Shoulder
Tissues
Transcranial Direct Current Stimulation
White Matter
Brain
Cheek
Neoplasm Metastasis
Porifera
Process, Mastoid
Saline Solution
Scalp
Transcranial Direct Current Stimulation
For a tDCS simulation study, a realistic FE model of the head was generated from a T1-weighted, a T2-weighted and a diffusion-tensor (DT)-magnetic resonance image (MRI) of a healthy 26-year-old male subject. In a first step, the T2-MRI was rigidly registered onto the T1-MRI using a mutual information based cost-function (Jenkinson and Smith, 2001 (link)). Segmentation into tissue compartments skin, skull compacta, skull spongiosa, cerebrospinal fluid (CSF), brain gray (GM), and white matter (WM) was then performed using the FSL software1 (Zhang et al., 2001 (link); Smith, 2002 (link); Jenkinson et al., 2005 ). Segmentation started with the generation of initial masks for skin, inner and outer skull, and brain using both T1- and T2-MRI. In a second step, the T1-MRI served for GM and WM segmentation, while the T2-MRI allowed for a segmentation of skull compacta, skull spongiosa, and CSF space (see Figure 1 ). For the compartments of skin, skull compacta, skull spongiosa, and CSF, we used the isotropic conductivity values of 0.43, 0.007, 0.025, and 1.79 S/m, respectively (Baumann et al., 1997 (link); Akhtari et al., 2002 (link); Dannhauer et al., 2011 (link)). For the modeling of white matter conductivity anisotropy, the diffusion-weighted images were first artifact-corrected using our reversed-gradient approach introduced in Olesch et al. (2010 ). Diffusion tensors were then determined and the result was registered onto the structural images using the FSL routine vecreg2 . In a last step, the conductivity anisotropy in GM and WM was computed from the registered DTI using an effective medium approach (Tuch et al., 2001 (link); Rullmann et al., 2009 (link)). This resulted in mean conductivities of 0.19 and 0.24 S/m for WM and GM.
Two electrode patches with a size of 7 cm × 5 cm, thickness of 4 mm, and saline like conductivity of 1.4 S/m are modeled. A total current of 1 mA is injected at the red patch (anode) and removed at the blue one (cathode). For field modeling of this stimulation throughout the volume conductor, a quasistatic approximation of Maxwell’s equations (Plonsey and Heppner, 1967 (link)) was used, resulting in a Laplace equation for the electric potential Φ with inhomogeneous Neumann boundary conditions at the head surface. An isoparametric FE approach is used for the computation of Φ in a 1 mm geometry-adapted hexahedral mesh (Wolters et al., 2007a (link),b (link)), resulting in a large sparse linear equation system with 2.255 million unknowns, which is solved using an algebraic multigrid preconditioned conjugate gradient approach (Wolters et al., 2002 (link); Lew et al., 2009 (link)). In a last step, the current density J = σ grad Φ is computed with σ being the 3 × 3 conductivity tensor. For simulation, we used our software SimBio3 .
In all subsequent figures, slices of the current density distribution through the cortex are presented. The amplitude of J is coded by means of a linear color-scale. The current density distributions were computed in the 1 mm geometry-adapted hexahedral head model and the software SciRun4 was used for visualization.
Two electrode patches with a size of 7 cm × 5 cm, thickness of 4 mm, and saline like conductivity of 1.4 S/m are modeled. A total current of 1 mA is injected at the red patch (anode) and removed at the blue one (cathode). For field modeling of this stimulation throughout the volume conductor, a quasistatic approximation of Maxwell’s equations (Plonsey and Heppner, 1967 (link)) was used, resulting in a Laplace equation for the electric potential Φ with inhomogeneous Neumann boundary conditions at the head surface. An isoparametric FE approach is used for the computation of Φ in a 1 mm geometry-adapted hexahedral mesh (Wolters et al., 2007a (link),b (link)), resulting in a large sparse linear equation system with 2.255 million unknowns, which is solved using an algebraic multigrid preconditioned conjugate gradient approach (Wolters et al., 2002 (link); Lew et al., 2009 (link)). In a last step, the current density J = σ grad Φ is computed with σ being the 3 × 3 conductivity tensor. For simulation, we used our software SimBio
In all subsequent figures, slices of the current density distribution through the cortex are presented. The amplitude of J is coded by means of a linear color-scale. The current density distributions were computed in the 1 mm geometry-adapted hexahedral head model and the software SciRun
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Anisotropy
Brain
Cerebrospinal Fluid
Cortex, Cerebral
Cranium
Diffusion
Electric Conductivity
Electricity
Head
Healthy Volunteers
Males
Saline Solution
Skin
Transcranial Direct Current Stimulation
White Matter
Brain
Cortex, Cerebral
Discrimination, Psychology
Electric Stimulation Therapy
Evoked Potentials
Face
fMRI
Head
Nervousness
Parietal Lobe
Prefrontal Cortex
Radionuclide Imaging
Toxic Epidermal Necrolysis
Transcranial Direct Current Stimulation
Most recents protocols related to «Transcranial Direct Current Stimulation»
The experiment followed a randomized, sham-controlled, double-blind, crossover design (Schulz et al., 2010 (link)). It was designed based on the baseline stability assumption that the subjects could maintain a constant tremor manifestation if left unaffected. The aim of this experiment is to investigate the short-term (immediate) effect over Parkinsonian tremor, with three different active tDCS setups, namely the anodal, cathodal and bilateral setup. In order to exclude placebo effects, a control setup with sham tDCS was considered for comparison. Therefore, the complete experiment consisted of four different setups, with each corresponding to a session in the experiment (Figure 2 ). The sequence of carrying out the four sessions over each subject was generated by a random sequence generator programmed on Matlab R2015b (Mathworks Inc., USA). Each session was conducted around the same time of the day to minimize the circadian influences. It started with the pre-intervention evaluations involving three clinical scales and the continuous tremor signal assessment (CTSA), followed immediately by the tDCS intervention (sham/active) and the post-intervention evaluation. During the experiment, a specific physician served as the evaluator and finished all evaluations, while another experimenter performed the tDCS intervention. All subjects and the physician were blind to the current tDCS setup. Between each session, there was a long enough wash-out period of more than 2 days to clear up the effect of the previous intervention. In subsequent analysis, we evaluated the effect of the current setup of tDCS by comparing the performance of subjects before and after the tDCS intervention.
The three related clinical scales were: (1) UPDRS, (2) Fahn-Tolosa-Marin Tremor Rating Scale (FTMTRS) and (3) Purdue Pegboard Test (PPT). To lessen the time cost of the evaluation, full UPDRS and full FTMTRS were only used as a pre-intervention evaluation and a simplified version of them, namely the simplified version with only Part III of the UPDRS that relates motor function (UPDRS-III) and the modified FTMTRS (mFTMTRS) comprising Part A and Part B in FTMTRS, was used as the post-intervention evaluation. The assessment of the PPT remained the same before and after the intervention. To obtain more accurate and detailed description of tremor, we designed the procedure of CTSA where tremor acceleration and EMG signals were assessed. The sensors used for the CTSA were kept on subjects until the end of the post-intervention CTSA. In case that the accelerometer and EMG sensors might interfere with subjects' performance in chosen scales, we modified the sequence of the measures and arranged CTSA to be the last pre-intervention evaluation and the first post-intervention evaluation.
The three related clinical scales were: (1) UPDRS, (2) Fahn-Tolosa-Marin Tremor Rating Scale (FTMTRS) and (3) Purdue Pegboard Test (PPT). To lessen the time cost of the evaluation, full UPDRS and full FTMTRS were only used as a pre-intervention evaluation and a simplified version of them, namely the simplified version with only Part III of the UPDRS that relates motor function (UPDRS-III) and the modified FTMTRS (mFTMTRS) comprising Part A and Part B in FTMTRS, was used as the post-intervention evaluation. The assessment of the PPT remained the same before and after the intervention. To obtain more accurate and detailed description of tremor, we designed the procedure of CTSA where tremor acceleration and EMG signals were assessed. The sensors used for the CTSA were kept on subjects until the end of the post-intervention CTSA. In case that the accelerometer and EMG sensors might interfere with subjects' performance in chosen scales, we modified the sequence of the measures and arranged CTSA to be the last pre-intervention evaluation and the first post-intervention evaluation.
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Acceleration
Continuous Tremor
Fatty Acid Hydroxylase-Associated Neurodegeneration
Physicians
Transcranial Direct Current Stimulation
Tremor
Visually Impaired Persons
To apply tDCS, a commercial CE-certified device named DC-Stimulator (NeuroConn GmbH, Germany) was used. For each subject, we started by locating the primary motor cortex (M1) on the more-affected side through targeting the abductor pollicis brevis (APB) hot spot at rest with transcranial magnetic stimulation (TMS) with a device called Magstim Rapid 2 (Magstim Co., UK). A pair of sponge electrodes (6.5 cm*6.5 cm) moistened with 0.9% NaCl solution were placed regarding different tDCS setups, as shown in Figure 3A (assuming the MAS is on subject's left side): in the anodal setup, the anode was placed over the left M1 hotspot and the cathode was placed over the right supraorbital region. An opposite electrode placement setup was used in the cathodal setup. For bilateral setup, we placed the anode and the cathode over the right and the left M1 hotspot symmetrically. In all aforementioned active stimulations, a direct current of 1.5 mA was delivered constantly to the skull over 20 min with a ramp-up and ramp-down of 20 s. The parameters of the active stimulations were chosen in accordance with the most up-to-date safety guidelines for tDCS (Bikson et al., 2016 (link)). In sham tDCS, the electrode placement was the same as in the bilateral setup, however, in the 20-min protocol the direct current only lasted for a short time, followed by a serial pulse train of 110 uA (Figure 3B ) without any therapeutic effect (Palm et al., 2013 (link)). In either active or sham setup of tDCS, the subject was seated comfortably on a chair in a quiet state and waited until the end of the intervention.
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Arecaceae
Cranium
Medical Devices
Motor Cortex, Primary
Normal Saline
Porifera
Pulse Rate
Safety
Stimulation, Transcranial Magnetic
Therapeutic Effect
Transcranial Direct Current Stimulation
The post/pre ratios of each feature was grouped based on the factor of session (tDCS setup). For absolute post/pre ratios, the data was grouped with four levels (s-tDCS, a-tDCS, c-tDCS, and b-tDCS) while that of the relative post/pre ratios was grouped with three levels (a-tDCS, c-tDCS, and b-tDCS). The Gaussianity of each group and their homogeneity of variance were tested by the Lilliefors test and the Bartlett test. If the null hypothesis of both tests held, namely the groups were both Gaussian and homogeneous in variance, a one-way analysis of variance (ANOVA) with repeated measures was performed, followed by the Tukey-Kramer post-hoc analysis. Otherwise, a non-parametric statistical test called the Friedman test was performed, followed by the Nemenyi post-hoc analysis. Since the purpose of the analysis over the absolute post/pre ratios was to investigate the effectiveness of the active setups, we considered the performance in the control (s-tDCS) session as a baseline and compared only the pairs between the control (s-tDCS) session and the active tDCS (a-tDCS, c-tDCS, and b-tDCS) sessions in post-hoc. In contrast, the relative post/pre ratio was analyzed to investigate the difference between different active tDCS sessions without the baseline effect of the control (s-tDCS) setup. We considered all possible session pairs that were between different active tDCS setups in post-hoc.
The baseline stability assumption was verified from two aspects. First, for short-term baseline stability (within one single session), we inspected the pre- and the post-data of each subject and grouped them based on the factor of time with two levels (pre s-tDCS and post s-tDCS). If the null hypothesis of the Lilliefors test and the Bartlett test held, the paired student's t-test was performed. Otherwise, the Wilcoxon signed rank test was performed. Second, for long-term baseline stability (across different sessions), the pre-intervention data of different tDCS setups was targeted and grouped based on the factor of session with 4 levels (pre s-tDCS, pre a-tDCS, pre c-tDCS, and pre b-tDCS). The grouped data was analyzed with the same statistical procedure as in post/pre ratios.
All statistics were performed using Matlab (R2017a, MathWorks Inc., the USA) with the basic level of statistical significance set at p < 0.05.
The baseline stability assumption was verified from two aspects. First, for short-term baseline stability (within one single session), we inspected the pre- and the post-data of each subject and grouped them based on the factor of time with two levels (pre s-tDCS and post s-tDCS). If the null hypothesis of the Lilliefors test and the Bartlett test held, the paired student's t-test was performed. Otherwise, the Wilcoxon signed rank test was performed. Second, for long-term baseline stability (across different sessions), the pre-intervention data of different tDCS setups was targeted and grouped based on the factor of session with 4 levels (pre s-tDCS, pre a-tDCS, pre c-tDCS, and pre b-tDCS). The grouped data was analyzed with the same statistical procedure as in post/pre ratios.
All statistics were performed using Matlab (R2017a, MathWorks Inc., the USA) with the basic level of statistical significance set at p < 0.05.
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ARID1A protein, human
Transcranial Direct Current Stimulation
Statistics were performed using the SPSS 25.0 software (Statistical Package for Social Sciences, SPSS Inc, Chicago). We used three analyses of variance for repeated measures (rm-ANOVA) to examine if there was a difference between active HD-tDCS and the sham condition for our main region of interest (i.e., the prefrontal cortex). Data are reported as means and standard deviations. Mauchly's test was used to test for sphericity, and the Greenhouse–Geisser correction was applied if necessary. A p-value of < 0.05 was considered significant.
A three-way repeated-measures ANOVA was used to test for the effect of condition (active HD-tDCS vs. sham) x lead (Fp1, Fz, F3, F4, F7, F8, FC1, FC2) x frequency band (theta, alpha, beta) calculated in rsEEG absolute power (μV2).
For the calculation of the asymmetrical activity three repeated measures ANOVA for each frequency band (theta, alpha, beta) were conducted to test whether this activity changed from pre to posttest for the two groups (HD-tDCS vs. Sham).
To compute phase synchronization, we extracted the PLV using the method described by Lachaux et al. (1999) (link) and calculated the difference in symmetry between the electrode pairs that are part of the frontal region (Fz, Fp1, F3, F4, F7, F8, FC1, FC2) using a customized MATLAB script. We measured the connectivity between the electrodes using the Phase Locking Value (PLV). We conducted separate repeated-measures ANOVAs for the PLV of each chosen frontal electrode pair over Time (pretest vs posttest) with group (HD-tDCS vs Sham) as a between subjects factor.
A three-way repeated-measures ANOVA was used to test for the effect of condition (active HD-tDCS vs. sham) x lead (Fp1, Fz, F3, F4, F7, F8, FC1, FC2) x frequency band (theta, alpha, beta) calculated in rsEEG absolute power (μV2).
For the calculation of the asymmetrical activity three repeated measures ANOVA for each frequency band (theta, alpha, beta) were conducted to test whether this activity changed from pre to posttest for the two groups (HD-tDCS vs. Sham).
To compute phase synchronization, we extracted the PLV using the method described by Lachaux et al. (1999) (link) and calculated the difference in symmetry between the electrode pairs that are part of the frontal region (Fz, Fp1, F3, F4, F7, F8, FC1, FC2) using a customized MATLAB script. We measured the connectivity between the electrodes using the Phase Locking Value (PLV). We conducted separate repeated-measures ANOVAs for the PLV of each chosen frontal electrode pair over Time (pretest vs posttest) with group (HD-tDCS vs Sham) as a between subjects factor.
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Lobe, Frontal
neuro-oncological ventral antigen 2, human
Prefrontal Cortex
Transcranial Direct Current Stimulation
Data were collected as part of a study examining the effects of HD-tDCS in increasing empathy and reducing violent behavior in forensic patients with a substance dependence (see Sergiou et al., 2020a (link); Sergiou et al., 2022 (link)). A total of 50 male participants (mean age = 37.40 years, SD = 9.19 years, range: 22–62 years) were recruited from two departments of the division for forensic mental healthcare and addiction of Antes in Poortugaal, the Netherlands (see Table 1 for an overview). Twenty-one participants were recruited at the Forensic Addiction Clinic (FVK) and 29 from the Department of Forensic Care (AFZ). Inclusion criteria were: age 18–60, a good understanding of the Dutch language, diagnosed with an alcohol and/or cocaine substance use disorder (SUD) according to The Diagnostic and Statistical Manual of Mental Disorders (DSM–5; American Psychiatric Association, 2013), patients had to be abstinent and had to be sentenced for a violent offense as determined by one of the most used mandatory risk assessment tools in the Netherlands (HKT-R; Spreen et al., 2014). Exclusion criteria included: major neurological conditions, or major mental disorders, taking antipsychotics or other confounding medication (See supplementary materials S1 for CONSORT). All participants gave written consent to participate in the study. The study was conducted in accordance with the ethical standard of the Declaration of Helsinki (General Assembly of the World Medical Association, 2014 (link)) and was approved by the Medical and Ethical Review Board of the Erasmus Medical Center Rotterdam.Table 1
Demographic characteristics.
| tDCS group | Sham group | |||
|---|---|---|---|---|
| n | % | n | % | |
| Caucasian | 25 | 100 | 23 | 92 |
| Non- Caucasian | 0 | – | 2 | 8 |
| Primary education | 9 | 36 | 8 | 32 |
| High School | 7 | 28 | 6 | 24 |
| Secondary education (VET) | 9 | 36 | 11 | 44 |
| DSM-5 Axis I | 7 | 28 | 10 | 40 |
| DSM-5 Axis II | 8 | 32 | 10 | 40 |
| Mono substance use | 8 | 32 | 9 | 36 |
| Poly substance use | 17 | 68 | 16 | 64 |
*Note. Characteristics are displayed in percentage of participants per group, N = 50 (n = 25 for each condition). Participants were on average 36.4 years old (SD = 8.88) in the tDCS group and on average 38.4 years in the sham group (SD = 9.56), and participant age did not differ by condition. VET= Vocational Education and Training, DSM-5= Diagnostic Symptom Manual fifth edition.
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Addictive Behavior
Antipsychotic Agents
Cocaine
Diagnosis
Epistropheus
Ethanol
Ethical Review
Health Risk Assessment
Males
Mental Disorders
Nervous System Disorder
Patients
Pharmaceutical Preparations
Substance Dependence
Substance Use Disorders
Transcranial Direct Current Stimulation
Vocational Education
Top products related to «Transcranial Direct Current Stimulation»
Sourced in Germany
The DC-Stimulator Plus is a laboratory device used for electrical stimulation. It generates direct current (DC) electrical signals to be applied to research subjects or samples. The device provides adjustable intensity and duration settings to control the electrical output. It is a tool utilized in various research and scientific applications.
Sourced in Germany
The DC-STIMULATOR is a laboratory device designed to deliver direct current (DC) stimulation. It is a tool used for research and experimental purposes in controlled settings. The device's core function is to generate and apply DC electrical currents to facilitate the study of physiological and neurological processes.
Sourced in Germany
The Eldith DC stimulator is a laboratory device used to apply direct current (DC) stimulation. It generates controlled electrical currents for research and clinical applications. The device is capable of delivering precise and adjustable DC stimulation to participants or patients during various experimental or treatment protocols.
Sourced in Germany
The DC-STIMULATOR MC is a versatile laboratory equipment used for various research and clinical applications. It provides direct current (DC) stimulation with adjustable parameters, enabling controlled electrical stimulation. The core function of the device is to generate and deliver precise DC currents to experimental subjects or samples. This allows researchers to study the effects of electrical stimulation on biological systems in a controlled setting.
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MATLAB is a high-performance programming language and numerical computing environment used for scientific and engineering calculations, data analysis, and visualization. It provides a comprehensive set of tools for solving complex mathematical and computational problems.
Sourced in Germany
The Constant Current Stimulator is a laboratory device designed to deliver a consistent and controlled electrical current to a subject or sample. It maintains a stable current output regardless of changes in the resistance of the connected load. This device is intended for use in various research applications that require precise and reliable electrical stimulation.
Sourced in Germany
The NeuroConn DC-Stimulator Plus is a laboratory device designed for the application of direct current stimulation. It provides a controlled and precise delivery of direct current to research participants or patients. The device features adjustable current intensity and duration settings to facilitate various experimental or clinical protocols.
Sourced in United States
Signagel is an ultrasound gel produced by Parker Laboratories. It is designed to facilitate the transmission of ultrasound waves between the transducer and the patient's skin during diagnostic procedures.
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SPSS 25.0 is a statistical software package developed by IBM. It provides advanced analytical capabilities for data management, analysis, and visualization. The core function of SPSS 25.0 is to enable users to conduct a wide range of statistical tests and procedures, including regression analysis, hypothesis testing, and multivariate techniques.
Sourced in Germany
The Direct Current Stimulator is a laboratory device used to apply a controlled direct current to a subject or specimen. It generates a stable, adjustable direct current output that can be used for various research and experimental purposes. The device's core function is to provide a precise and consistent electrical stimulation, without any further interpretation or extrapolation on its intended use.
More about "Transcranial Direct Current Stimulation"
Transcranial Direct Current Stimulation (tDCS) is a non-invasive brain stimulation technique that utilizes low-intensity direct currents to modulate neuronal excitability and activity in the brain.
This neuromodulation technique has been extensively employed in research and clinical settings to investigate and treat a variety of neurological and psychiatric conditions, such as stroke, depression, and chronic pain.
The tDCS process involves delivering a constant, weak electrical current through electrodes placed on the scalp, which can either increase or decrease the resting membrane potential of underlying neurons.
This leads to changes in their firing rates and synaptic efficacy, ultimately affecting brain function.
The technique is relatively safe, well-tolerated, and has the potential to enhance the reproducibility and accuracy of research findings when applied with optimized protocols.
Devices like the DC-Stimulator Plus, DC-STIMULATOR, Eldith DC stimulator, DC-STIMULATOR MC, and NeuroConn DC-Stimulator Plus are commonly used for tDCS studies, along with electrode gels like Signagel.
Researchers can leverage PubCompare.ai's AI-driven tools to discover the best tDCS protocols and products by exploring the latest literature, pre-prints, and patents.
This can help improve the quality and consistency of tDCS research, which is often analyzed using statistical software like SPSS 25.0 and MATLAB.
This neuromodulation technique has been extensively employed in research and clinical settings to investigate and treat a variety of neurological and psychiatric conditions, such as stroke, depression, and chronic pain.
The tDCS process involves delivering a constant, weak electrical current through electrodes placed on the scalp, which can either increase or decrease the resting membrane potential of underlying neurons.
This leads to changes in their firing rates and synaptic efficacy, ultimately affecting brain function.
The technique is relatively safe, well-tolerated, and has the potential to enhance the reproducibility and accuracy of research findings when applied with optimized protocols.
Devices like the DC-Stimulator Plus, DC-STIMULATOR, Eldith DC stimulator, DC-STIMULATOR MC, and NeuroConn DC-Stimulator Plus are commonly used for tDCS studies, along with electrode gels like Signagel.
Researchers can leverage PubCompare.ai's AI-driven tools to discover the best tDCS protocols and products by exploring the latest literature, pre-prints, and patents.
This can help improve the quality and consistency of tDCS research, which is often analyzed using statistical software like SPSS 25.0 and MATLAB.