Optimization of montages and electric currents in tDCS

Khorrampanah, Mahsa; Seyedarabi, Hadi; Daneshvar, Sabalan; Farhoudi, Mehdi

doi:10.1016/j.compbiomed.2020.103998

Cited by 13 publications

(10 citation statements)

References 28 publications

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“…As proposals for future studies, the use of modeling for multielectrode stimulation approaches, which may induce more spatially restricted electrical fields [ 105 ], may be interesting to explore the focality of different electrode configurations to a larger degree, although this was beyond the scope of this contribution. Another factor that requires to be considered in modeling tools is the reliability of modeling in specific populations, such as children [ 24 , 106 ] or patients with neurodegeneration or brain damage [ 107 , 108 ].…”

Section: Discussionmentioning

confidence: 99%

Standard Non-Personalized Electric Field Modeling of Twenty Typical tDCS Electrode Configurations via the Computational Finite Element Method: Contributions and Limitations of Two Different Approaches

Molero‐Chamizo

Nitsche

Lérida

et al. 2021

Biology

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Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation procedure to modulate cortical excitability and related brain functions. tDCS can effectively alter multiple brain functions in healthy humans and is suggested as a therapeutic tool in several neurological and psychiatric diseases. However, variability of results is an important limitation of this method. This variability may be due to multiple factors, including age, head and brain anatomy (including skull, skin, CSF and meninges), cognitive reserve and baseline performance level, specific task demands, as well as comorbidities in clinical settings. Different electrode montages are a further source of variability between tDCS studies. A procedure to estimate the electric field generated by specific tDCS electrode configurations, which can be helpful to adapt stimulation protocols, is the computational finite element method. This approach is useful to provide a priori modeling of the current spread and electric field intensity that will be generated according to the implemented electrode montage. Here, we present standard, non-personalized model-based electric field simulations for motor, dorsolateral prefrontal, and posterior parietal cortex stimulation according to twenty typical tDCS electrode configurations using two different current flow modeling software packages. The resulting simulated maximum intensity of the electric field, focality, and current spread were similar, but not identical, between models. The advantages and limitations of both mathematical simulations of the electric field are presented and discussed systematically, including aspects that, at present, prevent more widespread application of respective simulation approaches in the field of non-invasive brain stimulation.

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Section: Discussionmentioning

confidence: 99%

Standard Non-Personalized Electric Field Modeling of Twenty Typical tDCS Electrode Configurations via the Computational Finite Element Method: Contributions and Limitations of Two Different Approaches

Molero‐Chamizo

Nitsche

Lérida

et al. 2021

Biology

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“…Electrode position and current intensity optimization have been well studied in tDCS [25], [26], although the problem in tTIS is not convex. Owing to the nonlinearity and non-convex nature of the tTIS method, exhaustive search methods [9], [10] are usually employed to achieve personalized optimization.…”

Section: Optimizing Electrode Montages and Currentsmentioning

confidence: 99%

Non-invasive stimulation with temporal interference: optimization of the electric field deep in the brain with the use of a genetic algorithm

Stoupis

Samaras

2022

J. Neural Eng.

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Objective. Since the introduction of transcranial temporal interference stimulation (tTIS), there has been an ever-growing interest in this novel method, as it theoretically allows non-invasive stimulation of deep brain target regions. To date, attempts have been made to optimize the electrode montages and injected current to achieve personalized area targeting using two electrode pairs. Most of these methods use exhaustive search to find the best match, but faster and, at the same time, reliable solutions are required. In this study, the electrode combinations as well as the injected current for a two-electrode pair stimulation were optimized using a genetic algorithm, considering the right hippocampus as the region of interest (ROI). Methods. Simulations were performed on head models from the Population Head Model (PHM) repository. First, each model was fitted with an electrode array based on the 10-10 international EEG electrode placement system. Following electrode placement, the models were meshed and solved for all single-pair electrode combinations, using an electrode on the left mastoid as a reference (ground). At the optimization stage, different electrode pairs and injection currents were tested using a genetic algorithm to obtain the optimal combination for each model, by setting three different maximum electric field thresholds (0.2, 0.5, and 0.8 V/m) in the ROI. The combinations below the set threshold were given a high penalty. Results. Greater focality was achieved with our optimization, specifically in the ROI, with a significant decrease in the surrounding electric field intensity. In the non-optimized case, the mean brain volumes stimulated above 0.2 V/m were 99.9% in the ROI, and 76.4% in the rest of the gray matter. In contrast, the stimulated mean volumes were 91.4% and 29.6%, respectively, for the best optimization case with a threshold of 0.8 V/m. Additionally, the maximum electric field intensity inside the ROI was consistently higher than that outside of the ROI for all optimized cases. Significance. Given that the accomplishment of a globally optimal solution requires a brute-force approach, the use of a genetic algorithm can significantly decrease the optimization time, while achieving personalized deep brain stimulation. The results of this work can be used to facilitate further studies that are more clinically oriented; thus, enabling faster and at the same time accurate treatment planning for the stimulation sessions.

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“…However, systematic analysis of EF distribution patterns and finding the association between tES-induced EFs and stimulation outcomes requires a decision about which EF measures (e.g., maximum (99 th percentile to remove outliers), mean, median, or a binary thresholded EF map) are appropriate to use. Here, inspired by previously published studies in the field [34,35], we focused on EF peaks to provide an indicator of the location at which the target activity is most likely to be perturbed [36]. Then, peak EF location and strength in the peak location were compared with the cortical area under the center of target electrodes as the main "intended" anatomical target.…”

Section: Importance Of Peak Efs In Brain Stimulation Studiesmentioning

confidence: 99%

Are we really targeting and stimulating DLPFC by placing transcranial electrical stimulation (tES) electrodes over F3/F4?

Soleimani

Kuplicki

Camchong

et al. 2022

Preprint

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Background: Most transcranial electrical stimulation (tES) clinical trials place target electrodes over DLPFC based on the assumption that it would mainly stimulate the underlying brain region. Here, we assessed delivered electric fields (EF) using a symmetric and asymmetric DLPFC stimulation montage to identify additional prefrontal regions that are inadvertently targeted beyond DLPFC. Methods: Head models were generated from the human connectome project database's T1+T2-weighted MRIs of 80 healthy adults. Two common DLPFC montages (symmetric: F4/F3, asymmetric: F4/Fp1 with 5x7cm electrodes, 2mA intensity) were simulated. Averaged EF was extracted from (1) the center of the target electrode (F4), and (2) the top 1% of voxels that showed the strongest EF in individualized EF maps. Inter-individual variabilities were quantified with standard deviation (SD) of EF peak location and value. These steps were replicated with 66 participants with methamphetamine use disorder (MUD) as an independent clinical population. Results: In the healthy adults, EFs in the frontopolar area were significantly higher than EF under the target electrode in both symmetric (peak:0.41+-0.06, F4:0.22+-0.04) and asymmetric (peak:0.38+-0.04, F4:0.2+-0.04) montages (Heges g>0.7). Group-level location for EF peaks in MNI space was located in medial-frontopolar cortex, such that individualized EF peaks were placed in a cube with a volume of symmetric/asymmetric: 29cm3/46cm3. Similar results (with slight between-group differences) were found for MUDs that highlighted the role of the medial frontopolar cortex in both healthy and clinical populations. Conclusions: We highlighted that in common DLPFC tES montages, DLPFC was not maximally targeted and the frontopolar area was the area that received the highest EFs. Considering inter-individual and inter-groups variability, we specifically recommended that the frontopolar role should be considered as a potential mechanism underlying the clinical efficacy of DLPFC stimulation.

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Optimization of montages and electric currents in tDCS

Cited by 13 publications

References 28 publications

Standard Non-Personalized Electric Field Modeling of Twenty Typical tDCS Electrode Configurations via the Computational Finite Element Method: Contributions and Limitations of Two Different Approaches

Standard Non-Personalized Electric Field Modeling of Twenty Typical tDCS Electrode Configurations via the Computational Finite Element Method: Contributions and Limitations of Two Different Approaches

Non-invasive stimulation with temporal interference: optimization of the electric field deep in the brain with the use of a genetic algorithm

Are we really targeting and stimulating DLPFC by placing transcranial electrical stimulation (tES) electrodes over F3/F4?

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