scholarly journals A computational study on the optimization of transcranial temporal interfering stimulation with high-definition electrode using unsupervised neural network

2021 ◽  
Author(s):  
Sang-kyu Bahn ◽  
Bo-Yeong Kang ◽  
Chany Lee
Keyword(s):  
Neural Network ◽  
Computational Study ◽  
Transcranial Electrical Stimulation ◽  
Deep Part ◽  
High Definition ◽  
Unsupervised Neural Network ◽  
Transcranial Alternating Current Stimulation ◽  
Current Value ◽  
The Brain ◽  
Target Depth

Transcranial temporal interfering stimulation (tTIS) can focally stimulate deep parts of the brain, which are related to specific functions, by using beats at two high AC frequencies that do not affect the human brain. However, it has limitations in terms of calculation time and precision for optimization because of its complexity and non-linearity. We aimed to propose a method using an unsupervised neural network (USNN) for tTIS to optimize quickly the interfering current value of high-definition electrodes, which can finely stimulate the deep part of the brain, and analyze the performance and characteristics of tTIS. A computational study was conducted using 16 realistic head models. This method generated the strongest stimulation on the target, even when targeting deep areas or multi-target stimulation. The tTIS was robust with target depth compared with transcranial alternating current stimulation, and mis-stimulation could be reduced compared with the case of using two-pair inferential stimulation. Optimization of a target could be performed in 3 min. By proposing the USNN for tTIS, we showed that the electrode currents of tTIS can be optimized quickly and accurately, and the possibility of stimulating the deep part of the brain precisely with transcranial electrical stimulation was confirmed.

scholarly journals A Computational Study On The Optimization of Transcranial Temporal Interfering Stimulation With High-Definition Electrode Using Unsupervised Neural Network

2021 ◽  
Author(s):  
Sang-kyu Bahn ◽  
Bo-Yeong Kang ◽  
Chany Lee
Keyword(s):  
Neural Network ◽  
Computational Study ◽  
Transcranial Electrical Stimulation ◽  
Deep Part ◽  
High Definition ◽  
Unsupervised Neural Network ◽  
Transcranial Alternating Current Stimulation ◽  
Current Value ◽  
The Brain ◽  
Target Depth

Abstract Transcranial temporal interfering stimulation (tTIS) can focally stimulate deep parts of the brain, which are related to specific functions, by using beats at two high AC frequencies that do not affect the human brain. However, it has limitations in terms of calculation time and precision for optimization because of its complexity and non-linearity. We aimed to propose a method using an unsupervised neural network (USNN) for tTIS to optimize quickly the interfering current value of high-definition electrodes, which can finely stimulate the deep part of the brain, and analyze the performance and characteristics of tTIS. A computational study was conducted using 16 realistic head models. This method generated the strongest stimulation on the target, even when targeting deep areas or multi-target stimulation. The tTIS was robust with target depth compared with transcranial alternating current stimulation, and mis-stimulation could be reduced compared with the case of using two-pair inferential stimulation. Optimization of a target could be performed in 3 min. By proposing the USNN for tTIS, we showed that the electrode currents of tTIS can be optimized quickly and accurately, and the possibility of stimulating the deep part of the brain precisely with transcranial electrical stimulation was confirmed.

scholarly journals Optimization of interferential stimulation of the human brain with electrode arrays

2019 ◽  
Author(s):  
Yu Huang ◽  
Abhishek Datta ◽  
Lucas C. Parra
Keyword(s):  
Modulation Depth ◽  
Current Source ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Transcranial Electrical Stimulation ◽  
Electrode Arrays ◽  
High Definition ◽  
Algorithmic Optimization ◽  
The Brain ◽  
Deep Brain

AbstractObjectiveInterferential stimulation (IFS) has generated considerable interest recently because of its potential to achieve focal electric fields in deep brain areas with transcranial currents. Conventionally, IFS applies sinusoidal currents through two electrode pairs with close-by frequencies. Here we propose to use an array of electrodes instead of just two electrode pairs; and to use algorithmic optimization to identify the currents required at each electrode to target a desired location in the brain.ApproachWe formulate rigorous optimization criteria for IFS to achieve either maximal modulation-depth or maximally focal stimulation. We find the solution for optimal modulation-depth analytically and maximize for focal stimulation numerically.Main resultsMaximal modulation is achieved when IFS equals conventional high-definition multi-electrode transcranial electrical stimulation (HD-TES) with a modulated current source. This optimal solution can be found directly from a current-flow model, i.e. the “lead field” without the need for algorithmic optimization. Once currents are optimized numerically to achieve optimal focal stimulation, we find that IFS can indeed be more focal than conventional HD-TES, both at the cortical surface and deep in the brain. Generally, however, stimulation intensity of IFS is weak and the locus of highest intensity does not match the locus of highest modulation.SignificanceThis proof-of-principle study shows the potential of IFS over HD-TES for focal non-invasive deep brain stimulation. Future work will be needed to improve on intensity of stimulation and convergence of the optimization procedure.

scholarly journals Integrating electric field modelling and neuroimaging to explain inter-individual variability of tACS effects

2019 ◽  
Author(s):  
Florian H. Kasten ◽  
Katharina Duecker ◽  
Marike C. Maack ◽  
Arnd Meiser ◽  
Christoph S. Herrmann
Keyword(s):  
Electric Field ◽  
Electric Fields ◽  
Individual Variability ◽  
Transcranial Electrical Stimulation ◽  
Field Modelling ◽  
Novel Approach ◽  
Before And After ◽  
Transcranial Alternating Current Stimulation ◽  
The Brain ◽  
Field Variability

AbstractUnderstanding variability of transcranial electrical stimulation (tES) effects is one of the major challenges in the brain stimulation community. Promising candidates to explain this variability are individual anatomy and the resulting differences of electric fields inside the brain. We integrated individual simulations of electric fields during tES with source-localization to predict variability of transcranial alternating current stimulation (tACS) aftereffects on α-oscillations. In two experiments, participants received 20 minutes of either α-tACS (1 mA) or sham stimulation. Magnetoencephalogram was recorded for 10 minutes before and after stimulation. tACS caused a larger power increase in the α-band as compared to sham. The variability of this effect was significantly predicted by measures derived from individual electric field modelling. Our results directly link electric field variability to variability of tACS outcomes, stressing the importance of individualizing stimulation protocols and providing a novel approach to analyze tACS effects in terms of dose-response relationships.

Transcranial electrical stimulation devices

2021 ◽  
Author(s):  
Dennis Q. Truong ◽  
Niranjan Khadka ◽  
Angel V. Peterchev ◽  
Marom Bikson
Keyword(s):  
Electrical Stimulation ◽  
Brain Function ◽  
Computational Models ◽  
Solid Metal ◽  
Transcranial Electrical Stimulation ◽  
Waveform Generator ◽  
High Definition ◽  
Low Intensity ◽  
Support Device ◽  
Transcranial Alternating Current Stimulation

Transcranial electrical stimulation (tES) devices apply electrical waveforms through electrodes placed on the scalp to modulate brain function. This chapter describes the principles, types, and components of tES devices as well as practical considerations for their use. All tES devices include a waveform generator, electrodes, and an adhesive or headgear to position the electrodes. tES dose is defined by the size and position of electrodes, and the waveform, duration, and intensity of the current. Many sub-classes of tES are named based on dose. This chapter focuses on low intensity tES, which includes transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), and transcranial pulsed current stimulation (tPCS). tES electrode types are reviewed, including electrolyte-soaked sponge, adhesive hydrogel, high-definition, hand-held solid metal, free paste on electrode, and dry. Computational models support device design and individual targeting. The tolerability of tES is protocol specific, and medical grade devices minimize risk.

scholarly journals Integrating electric field modeling and neuroimaging to explain inter-individual variability of tACS effects

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Florian H. Kasten ◽  
Katharina Duecker ◽  
Marike C. Maack ◽  
Arnd Meiser ◽  
Christoph S. Herrmann
Keyword(s):  
Electric Field ◽  
Electric Fields ◽  
Individual Variability ◽  
Transcranial Electrical Stimulation ◽  
Field Modeling ◽  
Novel Approach ◽  
Before And After ◽  
Transcranial Alternating Current Stimulation ◽  
The Brain ◽  
Field Variability

AbstractTranscranial electrical stimulation (tES) of the brain can have variable effects, plausibly driven by individual differences in neuroanatomy and resulting differences of the electric fields inside the brain. Here, we integrated individual simulations of electric fields during tES with source localization to predict variability of transcranial alternating current stimulation (tACS) aftereffects on α-oscillations. In two experiments, participants received 20-min of either α-tACS (1 mA) or sham stimulation. Magnetoencephalogram (MEG) was recorded for 10-min before and after stimulation. tACS caused a larger power increase in the α-band compared to sham. The variability of this effect was significantly predicted by measures derived from individual electric field modeling. Our results directly link electric field variability to variability of tACS outcomes, underline the importance of individualizing stimulation protocols, and provide a novel approach to analyze tACS effects in terms of dose-response relationships.

scholarly journals Generalization of Patterns by Identification with Polynomial Neural Network

2010 ◽  
Vol 61 (2) ◽  
pp. 120-124 ◽  
Author(s):  
Ladislav Zjavka
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Functional Dependence ◽  
Polynomial Neural Network ◽  
New Type ◽  
Input Variables ◽  
The Brain ◽  
Functional Output ◽  
Simplified Form

Generalization of Patterns by Identification with Polynomial Neural Network Artificial neural networks (ANN) in general classify patterns according to their relationship, they are responding to related patterns with a similar output. Polynomial neural networks (PNN) are capable of organizing themselves in response to some features (relations) of the data. Polynomial neural network for dependence of variables identification (D-PNN) describes a functional dependence of input variables (not entire patterns). It approximates a hyper-surface of this function with multi-parametric particular polynomials forming its functional output as a generalization of input patterns. This new type of neural network is based on GMDH polynomial neural network and was designed by author. D-PNN operates in a way closer to the brain learning as the ANN does. The ANN is in principle a simplified form of the PNN, where the combinations of input variables are missing.

scholarly journals Tracheal occlusion-evoked respiratory load compensation and inhibitory neurotransmitter expression in rats

2014 ◽  
Vol 116 (8) ◽  
pp. 1006-1016 ◽  
Author(s):  
Hsiu-Wen Tsai ◽  
Paul W. Davenport
Keyword(s):  
Neural Network ◽  
Brain Stem ◽  
Breathing Pattern ◽  
Respiratory Rhythm ◽  
Inhibitory Neurotransmitter ◽  
Load Compensation ◽  
Efferent Projections ◽  
Motor Reflex ◽  
Sensory Pathway ◽  
The Brain

Respiratory load compensation is a sensory-motor reflex generated in the brain stem respiratory neural network. The nucleus of the solitary tract (NTS) is thought to be the primary structure to process the respiratory load-related afferent activity and contribute to the modification of the breathing pattern by sending efferent projections to other structures in the brain stem respiratory neural network. The sensory pathway and motor responses of respiratory load compensation have been studied extensively; however, the mechanism of neurogenesis of load compensation is still unknown. A variety of studies has shown that inhibitory interconnections among the brain stem respiratory groups play critical roles for the genesis of respiratory rhythm and pattern. The purpose of this study was to examine whether inhibitory glycinergic neurons in the NTS were activated by external and transient tracheal occlusions (ETTO) in anesthetized animals. The results showed that ETTO produced load compensation responses with increased inspiratory, expiratory, and total breath time, as well as elevated activation of inhibitory glycinergic neurons in the caudal NTS (cNTS) and intermediate NTS (iNTS). Vagotomized animals receiving transient respiratory loads did not exhibit these load compensation responses. In addition, vagotomy significantly reduced the activation of inhibitory glycinergic neurons in the cNTS and iNTS. The results suggest that these activated inhibitory glycinergic neurons in the NTS might be essential for the neurogenesis of load compensation responses in anesthetized animals.

Neural-Network-Based Cross-Channel Intra Prediction

2021 ◽  
Vol 17 (3) ◽  
pp. 1-23
Author(s):  
Yue Li ◽  
Yan Yi ◽  
Dong Liu ◽  
Li Li ◽  
Zhu Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Intra Prediction ◽  
Transform Domain ◽  
High Definition ◽  
Compression Performance ◽  
Compact Representations ◽  
Test Models ◽  
Domain Loss ◽  
Different Color

To reduce the redundancy among different color channels, e.g., YUV, previous methods usually adopt a linear model that tends to be oversimple for complex image content. We propose a neural-network-based method for cross-channel prediction in intra frame coding. The proposed network utilizes twofold cues, i.e., the neighboring reconstructed samples with all channels, and the co-located reconstructed samples with partial channels. Specifically, for YUV video coding, the neighboring samples with YUV are processed by several fully connected layers; the co-located samples with Y are processed by convolutional layers; and the proposed network fuses the twofold cues. We observe that the integration of twofold information is crucial to the performance of intra prediction of the chroma components. We have designed the network architecture to achieve a good balance between compression performance and computational efficiency. Moreover, we propose a transform domain loss for the training of the network. The transform domain loss helps obtain more compact representations of residues in the transform domain, leading to higher compression efficiency. The proposed method is plugged into HEVC and VVC test models to evaluate its effectiveness. Experimental results show that our method provides more accurate cross-channel intra prediction compared with previous methods. On top of HEVC, our method achieves on average 1.3%, 5.4%, and 3.8% BD-rate reductions for Y, Cb, and Cr on common test sequences, and on average 3.8%, 11.3%, and 9.0% BD-rate reductions for Y, Cb, and Cr on ultra-high-definition test sequences. On top of VVC, our method achieves on average 0.5%, 1.7%, and 1.3% BD-rate reductions for Y, Cb, and Cr on common test sequences.

Sham transcranial electrical stimulation and its effects on corticospinal excitability: a systematic review and meta-analysis

2018 ◽  
Vol 29 (2) ◽  
pp. 223-232 ◽  
Author(s):  
Thusharika D. Dissanayaka ◽  
Maryam Zoghi ◽  
Michael Farrell ◽  
Gary F. Egan ◽  
Shapour Jaberzadeh
Keyword(s):  
Systematic Review ◽  
Electrical Stimulation ◽  
Meta Analysis ◽  
Random Noise ◽  
Corticospinal Excitability ◽  
Transcranial Electrical Stimulation ◽  
Placebo Effects ◽  
Randomized Controlled ◽  
Transcranial Random Noise Stimulation ◽  
Transcranial Alternating Current Stimulation

AbstractSham stimulation is used in randomized controlled trials (RCTs) to assess the efficacy of active stimulation and placebo effects. It should mimic the characteristics of active stimulation to achieve blinding integrity. The present study was a systematic review and meta-analysis of the published literature to identify the effects of sham transcranial electrical stimulation (tES) – including anodal and cathodal transcranial direct current stimulation (a-tDCS, c-tDCS), transcranial alternating current stimulation (tACS), transcranial random noise stimulation (tRNS) and transcranial pulsed current stimulation (tPCS) – on corticospinal excitability (CSE), compared to baseline in healthy individuals. Electronic databases – PubMed, CINAHL, Scopus, Science Direct and MEDLINE (Ovid) – were searched for RCTs of tES from 1990 to March 2017. Thirty RCTs were identified. Using a random-effects model, meta-analysis of a-tDCS, c-tDCS, tACS, tRNS and tPCS studies showed statistically non-significant pre-post effects of sham interventions on CSE. This review found evidence for statically non-significant effects of sham tES on CSE.

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