Key Factors in the Cortical Response to Transcranial Electrical Stimulations—A Multi-Scale Modeling Study

Transcranial electrode stimulation (tES), one of the techniques used to apply non-invasive brain stimulation (NIBS), modulates cortical activities by delivering weak electric currents through scalp-attached electrodes. This emerging technique has gained increasing attention recently; however, the results of tES vary greatly depending upon subjects and the stimulation paradigm, and its cellular mechanism remains uncertain. In particular, there is a controversy over the factors that determine the cortical response to tES. Some studies have reported that the electric field's (EF) orientation is the determining factor, while others have demonstrated that the EF magnitude itself is the crucial factor. In this work, we conducted an in-depth investigation of cortical activity in two types of electrode montages used widely-the conventional (C)-tES and high-definition (HD)-tES-as well as two stimulation waveforms-direct current (DC) and alternating current (AC). To do so, we constructed a multi-scale model by coupling an anatomically realistic human head model and morphologically realistic multi-compartmental models of three types of cortical neurons (layer 2/3 pyramidal neuron, layer 4 basket cell, layer 5 pyramidal neuron). Then, we quantified the neuronal response to the C-/HD-tDCS/tACS and explored the relation between the electric field (EF) and the radial field's (RF: radial component of EF) magnitude and the cortical neurons' threshold. The EF tES induced depended upon the electrode montage, and the neuronal responses were correlated with the EF rather than the RF's magnitude. The electrode montages and stimulation waveforms caused a small difference in threshold, but the higher correlation between the EF's magnitude and the threshold was consistent. Further, we observed that the neurons' morphological features affected the degree of the correlation highly. Thus, the EF magnitude was a key factor in the responses of neurons with arborized axons. Our results demonstrate that the crucial factor in neuronal excitability depends upon the neuron models' morphological and biophysical properties. Hence, to predict the cellular targets of NIBS precisely, it is necessary to adopt more advanced neuron models that mimic realistic morphological and biophysical features of actual human cells.

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Simulation of transcranial magnetic stimulation in head model with morphologically-realistic cortical neurons

10.1101/506204 ◽

2018 ◽

Cited By ~ 1

Author(s):

Aman Aberra ◽

Boshuo Wang ◽

Warren M Grill ◽

Angel V Peterchev

Keyword(s):

Transcranial Magnetic Stimulation ◽

Electric Fields ◽

Cortical Neurons ◽

Magnetic Stimulation ◽

Brain Activity ◽

Pyramidal Cells ◽

Human Head ◽

Mechanistic Explanation ◽

Head Model ◽

Neural Activation

Transcranial magnetic stimulation (TMS) enables non-invasive modulation of brain activity with both clinical and research applications, but fundamental questions remain about the neural types and elements it activates and how stimulation parameters affect the neural response. We integrated detailed neuronal models with TMS-induced electric fields in the human head to quantify the effects of TMS on cortical neurons. TMS activated with lowest intensity layer 5 pyramidal cells at their intracortical axonal terminations in the superficial gyral crown and lip regions. Layer 2/3 pyramidal cells and inhibitory basket cells may be activated too, whereas direct activation of layers 1 and 6 was unlikely. Neural activation was largely driven by the field magnitude, contrary to theories implicating the field component normal to the cortical surface. Varying the induced current's direction caused a waveform-dependent shift in the activation site and provided a mechanistic explanation for experimentally observed differences in thresholds and latencies of muscle responses. This biophysically-based simulation provides a novel method to elucidate mechanisms and inform parameter selection of TMS and other forms of cortical stimulation.

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Three-dimensional Reconstruction of Microscopic Image Based on Multi-ST Algorithm

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2020.64.2.020506 ◽

2020 ◽

Vol 64 (2) ◽

pp. 20506-1-20506-7

Author(s):

Min Zhu ◽

Rongfu Zhang ◽

Pei Ma ◽

Xuedian Zhang ◽

Qi Guo

Keyword(s):

3D Reconstruction ◽

Three Dimensional ◽

Scale Model ◽

Microscopic Image ◽

Multi Scale ◽

Gaussian Pyramid ◽

Cost Aggregation ◽

Dimensional Reconstruction ◽

Image Pairs ◽

Accurate Matching

Abstract Three-dimensional (3D) reconstruction is extensively used in microscopic applications. Reducing excessive error points and achieving accurate matching of weak texture regions have been the classical challenges for 3D microscopic vision. A Multi-ST algorithm was proposed to improve matching accuracy. The process is performed in two main stages: scaled microscopic images and regularized cost aggregation. First, microscopic image pairs with different scales were extracted according to the Gaussian pyramid criterion. Second, a novel cost aggregation approach based on the regularized multi-scale model was implemented into all scales to obtain the final cost. To evaluate the performances of the proposed Multi-ST algorithm and compare different algorithms, seven groups of images from the Middlebury dataset and four groups of experimental images obtained by a binocular microscopic system were analyzed. Disparity maps and reconstruction maps generated by the proposed approach contained more information and fewer outliers or artifacts. Furthermore, 3D reconstruction of the plug gauges using the Multi-ST algorithm showed that the error was less than 0.025 mm.

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Effects of parametric uncertainty on multi-scale model predictions of shock response of a pressed energetic material

Journal of Applied Physics ◽

10.1063/1.5098955 ◽

2019 ◽

Vol 125 (23) ◽

pp. 235104 ◽

Cited By ~ 6

Author(s):

Sangyup Lee ◽

Oishik Sen ◽

Nirmal Kumar Rai ◽

Nicholas J. Gaul ◽

K. K. Choi ◽

...

Keyword(s):

Energetic Material ◽

Parametric Uncertainty ◽

Scale Model ◽

Multi Scale ◽

Model Predictions ◽

Shock Response

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The Winter Habitat Selection of Red Deer (Cervus elaphus) Based on a Multi-Scale Model

Animals ◽

10.3390/ani10122454 ◽

2020 ◽

Vol 10 (12) ◽

pp. 2454

Author(s):

Yue Sun ◽

Yanze Yu ◽

Jinhao Guo ◽

Minghai Zhang

Keyword(s):

Habitat Selection ◽

Environmental Factors ◽

Cervus Elaphus ◽

Red Deer ◽

Scale Model ◽

Winter Habitat ◽

Multi Scale ◽

Optimal Scale ◽

Scale Models ◽

Selection Of

Single-scale frameworks are often used to analyze the habitat selections of species. Research on habitat selection can be significantly improved using multi-scale models that enable greater in-depth analyses of the scale dependence between species and specific environmental factors. In this study, the winter habitat selection of red deer in the Gogostaihanwula Nature Reserve, Inner Mongolia, was studied using a multi-scale model. Each selected covariate was included in multi-scale models at their “characteristic scale”, and we used an all subsets approach and model selection framework to assess habitat selection. The results showed that: (1) Univariate logistic regression analysis showed that the response scale of red deer to environmental factors was different among different covariate. The optimal scale of the single covariate was 800–3200 m, slope (SLP), altitude (ELE), and ratio of deciduous broad-leaved forests were 800 m in large scale, except that the farmland ratio was 200 m in fine scale. The optimal scale of road density and grassland ratio is both 1600 m, and the optimal scale of net forest production capacity is 3200 m; (2) distance to forest edges, distance to cement roads, distance to villages, altitude, distance to all road, and slope of the region were the most important factors affecting winter habitat selection. The outcomes of this study indicate that future studies on the effectiveness of habitat selections will benefit from multi-scale models. In addition to increasing interpretive and predictive capabilities, multi-scale habitat selection models enhance our understanding of how species respond to their environments and contribute to the formulation of effective conservation and management strategies for ungulata.

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An efficient multi-scale model for needle-punched Cf/SiCm composite materials with experimental validation

Composites Part B Engineering ◽

10.1016/j.compositesb.2021.108890 ◽

2021 ◽

Vol 217 ◽

pp. 108890

Author(s):

Hyoung Jun Lim ◽

Hoil Choi ◽

Min Jung Lee ◽

Gun Jin Yun

Keyword(s):

Composite Materials ◽

Experimental Validation ◽

Scale Model ◽

Multi Scale

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A spatio-temporal multi-scale model for Geyer saturation point process: Application to forest fire occurrences

Spatial Statistics ◽

10.1016/j.spasta.2021.100492 ◽

2021 ◽

Vol 41 ◽

pp. 100492

Author(s):

Morteza Raeisi ◽

Florent Bonneu ◽

Edith Gabriel

Keyword(s):

Point Process ◽

Forest Fire ◽

Scale Model ◽

Saturation Point ◽

Multi Scale ◽

Spatio Temporal

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Neural silences can be localized rapidly using noninvasive scalp EEG

Communications Biology ◽

10.1038/s42003-021-01768-0 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Alireza Chamanzar ◽

Marlene Behrmann ◽

Pulkit Grover

Keyword(s):

Center Of Mass ◽

Cost Effective ◽

Human Head ◽

Head Model ◽

Cortical Function ◽

Scalp Eeg ◽

Localization Algorithms ◽

Important Benefit ◽

Eeg Recordings ◽

Detection And Localization

AbstractA rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.

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Multi-Scale Anti-Occlusion Correlation Filters Object Tracking Method Based on Complementary Features

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421550028 ◽

2020 ◽

pp. 2155002

Author(s):

Xiuhua Hu ◽

Yuan Chen ◽

Yan Hui ◽

Yingyu Liang ◽

Guiping Li ◽

...

Keyword(s):

Object Tracking ◽

Scale Model ◽

Correlation Filters ◽

Motion Feature ◽

Tracking Method ◽

Object State ◽

Multi Scale ◽

Tracking Process ◽

Object Appearance ◽

Response Peak

Aiming to tackle the problem of tracking drift easily caused by complex factors during the tracking process, this paper proposes an improved object tracking method under the framework of kernel correlation filter. To achieve discriminative information that is not sensitive to object appearance change, it combines dimensionality-reduced Histogram of Oriented Gradients features and Lab color features, which can be used to exploit the complementary characteristics robustly. Based on the idea of multi-resolution pyramid theory, a multi-scale model of the object is constructed, and the optimal scale for tracking the object is found according to the confidence maps’ response peaks of different sizes. For the case that tracking failure can easily occur when there exists inappropriate updating in the model, it detects occlusion based on whether the occlusion rate of the response peak corresponding to the best object state is less than a set threshold. At the same time, Kalman filter is used to record the motion feature information of the object before occlusion, and predict the state of the object disturbed by occlusion, which can achieve robust tracking of the object affected by occlusion influence. Experimental results show the effectiveness of the proposed method in handling various internal and external interferences under challenging environments.

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A parametric prediction of the Young’s modulus of polymers enhanced with ΜWCNTs

MATEC Web of Conferences ◽

10.1051/matecconf/201823300025 ◽

2018 ◽

Vol 233 ◽

pp. 00025

Author(s):

P.V. Polydoropoulou ◽

K.I. Tserpes ◽

Sp.G. Pantelakis ◽

Ch.V. Katsiropoulos

Keyword(s):

Young’S Modulus ◽

Elastic Properties ◽

Young's Modulus ◽

Experimental Results ◽

Scale Model ◽

Fe Model ◽

Multi Scale ◽

Unit Cells ◽

Random Dispersion

In this work a multi-scale model simulating the effect of the dispersion, the waviness as well as the agglomerations of MWCNTs on the Young’s modulus of a polymer enhanced with 0.4% MWCNTs (v/v) has been developed. Representative Unit Cells (RUCs) have been employed for the determination of the homogenized elastic properties of the MWCNT/polymer. The elastic properties computed by the RUCs were assigned to the Finite Element (FE) model of a tension specimen which was used to predict the Young’s modulus of the enhanced material. Furthermore, a comparison with experimental results obtained by tensile testing according to ASTM 638 has been made. The results show a remarkable decrease of the Young’s modulus for the polymer enhanced with aligned MWCNTs due to the increase of the CNT agglomerations. On the other hand, slight differences on the Young’s modulus have been observed for the material enhanced with randomly-oriented MWCNTs by the increase of the MWCNTs agglomerations, which might be attributed to the low concentration of the MWCNTs into the polymer. Moreover, the increase of the MWCNTs waviness led to a significant decrease of the Young’s modulus of the polymer enhanced with aligned MWCNTs. The experimental results in terms of the Young’s modulus are predicted well by assuming a random dispersion of MWCNTs into the polymer.

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