Dataset Information

Transcranial Direct Current Stimulation Optimization - From Physics-Based Computer Simulations to High-Fidelity Head Phantom Fabrication and Measurements.

ABSTRACT:

Background

Transcranial direct current stimulation (tDCS) modulates neural networks. Computer simulations, while used to identify how currents behave within tissues of different conductivity properties, still need to be complemented by physical models.

Objective/hypothesis

To better understand tDCS effects on biology-mimicking tissues by developing and testing the feasibility of a high-fidelity 3D head phantom model that has sensing capabilities at different compartmental levels.

Methods

Models obtained from MRI images generated 3D printed molds. Agar phantoms were fabricated, and 18 monitoring electrodes were placed on specific phantom brain areas.

Results

When using rectangular electrodes, the measured and simulated voltages at the monitoring electrodes agreed reasonably well, except at excitation locations. The electric field distribution in different phantom layers appeared better confined with circular electrodes compared to rectangular electrodes.

Conclusion

The high-fidelity 3D head model was found to be feasible and comparable with computer-based electrical simulations, with high correlation between simulated and measured brain voltages. This feasibility study supports testing to further assess the reliability of this model.

SUBMITTER: Morales-Quezada L

PROVIDER: S-EPMC6837166 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Transcranial Direct Current Stimulation Optimization - From Physics-Based Computer Simulations to High-Fidelity Head Phantom Fabrication and Measurements.

Morales-Quezada Leon L El-Hagrassy Mirret M MM Costa Beatriz B McKinley R Andy RA Lv Pengcheng P Fregni Felipe F

Frontiers in human neuroscience 20191031

<h4>Background</h4>Transcranial direct current stimulation (tDCS) modulates neural networks. Computer simulations, while used to identify how currents behave within tissues of different conductivity properties, still need to be complemented by physical models.<h4>Objective/hypothesis</h4>To better understand tDCS effects on biology-mimicking tissues by developing and testing the feasibility of a high-fidelity 3D head phantom model that has sensing capabilities at different compartmental levels.< ...[more]

PMID: 31736732

Similar Datasets

Project description:In scientific disciplines, benchmarks play a vital role in driving progress forward. For a benchmark to be effective, it must closely resemble real-world tasks. If the level of difficulty or relevance is inadequate, it can impede progress in the field. Moreover, benchmarks should have low computational overhead to ensure accessibility and repeatability. The objective is to achieve a kind of ``Turing test'' by creating a surrogate model that is practically indistinguishable from the ground truth observation, at least within the dataset's explored boundaries. This objective necessitates a large quantity of data. This data encompasses numerous features that are characteristic of chemistry and materials science optimization tasks that are relevant to industry. These features include high levels of noise, multiple fidelities, multiple objectives, linear constraints, non-linear correlations, and failure regions. We performed 494498 random hard-sphere packing simulations representing 206 CPU days' worth of computational overhead. Simulations required nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters. The data was logged in a free-tier shared MongoDB Atlas database, producing two core tabular datasets: a failure probability dataset and a regression dataset. The failure probability dataset maps unique input parameter sets to the estimated probabilities that the simulation will fail. The regression dataset maps input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets were used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. In the regression dataset, percentile ranks were calculated for each group of identical parameter sets to account for heteroskedastic noise, thereby ensuring reliable and accurate data. This differs from the conventional approach that imposes a-priori assumptions, such as Gaussian noise, by specifying mean and standard deviation. This technique can be extended to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and the complex optimization scenarios encountered in the real world.

Project description:Biological processes require DNA and RNA helices to pack together in specific interhelical orientations. While electrostatic repulsion between backbone charges is expected to be maximized when helices are in parallel alignment, such orientations are commonplace in nature. To better understand how the repulsion is overcome, we used experimental and computational approaches to investigate how the orientational preferences of DNA helices depend on the concentration and valence of mobile cations. We used Förster resonance energy transfer (FRET) to probe the relative orientations of two 24-bp helices held together via a freely rotating PEG linker. At low cation concentrations, the helices preferred more "cross"-like orientations over those closer to parallel, and this preference was reduced with increasing salt concentrations. The results were in good quantitative agreement with Poisson-Boltzmann (PB) calculations for monovalent salt (Na+). However, PB underestimated the ability of mixtures of monovalent and divalent ions (Mg2+) to reduce the conformational preference. As a complementary approach, we performed all-atom molecular dynamics (MD) simulations and found better agreement with the experimental results. While MD and PB predict similar electrostatic forces, MD predicts a greater accumulation of Mg2+ in the ion atmosphere surrounding the DNA. Mg2+ occupancy is predicted to be greater in conformations close to the parallel orientation than in conformations close to the crossed orientation, enabling a greater release of Na+ ions and providing an entropic gain (one bound ion for two released). MD predicts an entropy gain larger than that of PB because of the increased Mg2+ occupancy. The entropy changes have a negligible effect at low Mg2+ concentrations because the free energies are dominated by electrostatic repulsion. However, as the Mg2+ concentration increases, charge screening is more effective and the mixing entropy produces readily detectable changes in packing preferences. Our results underline the importance of mixing entropy of counterions in nucleic acid interactions and provide a new understanding on the impact of a mixed ion atmosphere on the packing of DNA helices.

Dataset Information

Transcranial Direct Current Stimulation Optimization - From Physics-Based Computer Simulations to High-Fidelity Head Phantom Fabrication and Measurements.

Background

Objective/hypothesis

Methods

Results

Conclusion

Publications

Transcranial Direct Current Stimulation Optimization - From Physics-Based Computer Simulations to High-Fidelity Head Phantom Fabrication and Measurements.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets