4.6

CiteScore

2.2

Impact Factor
  • ISSN 1674-8301
  • CN 32-1810/R
Michael J Russell, Theodore Goodman, Ronald Pierson, Shane Shepherd, Qiang Wang, Bennett Groshong, David F Wiley. Individual differences in transcranial electrical stimulation current density[J]. The Journal of Biomedical Research, 2013, 27(6): 495-508. DOI: 10.7555/JBR.27.20130074
Citation: Michael J Russell, Theodore Goodman, Ronald Pierson, Shane Shepherd, Qiang Wang, Bennett Groshong, David F Wiley. Individual differences in transcranial electrical stimulation current density[J]. The Journal of Biomedical Research, 2013, 27(6): 495-508. DOI: 10.7555/JBR.27.20130074

Individual differences in transcranial electrical stimulation current density

More Information
  • Received Date: May 07, 2013
  • Transcranial electrical stimulation (TCES) is effective in treating many conditions, but it has not been possible to accurately forecast current density within the complex anatomy of a given subject's head. We sought to predict and verify TCES current densities and determine the variability of these current distributions in patient-specific models based on magnetic resonance imaging (MRI) data. Two experiments were performed. The first experiment estimated conductivity from MRIs and compared the current density results against actual measurements from the scalp surface of 3 subjects. In the second experiment, virtual electrodes were placed on the scalps of 18 subjects to model simulated current densities with 2 mA of virtually applied stimulation. This procedure was repeated for 4 electrode locations. Current densities were then calculated for 75 brain regions. Comparison of modeled and measured external current in experiment 1 yielded a correlation of r = .93. In experiment 2, modeled individual differences were greatest near the electrodes (ten-fold differences were common), but simulated current was found in all regions of the brain. Sites that were distant from the electrodes (e.g. hypothalamus) typically showed two-fold individual differences. MRI-based modeling can effectively predict current densities in individual brains. Significant variation occurs between subjects with the same applied electrode configuration. Individualized MRI-based modeling should be considered in place of the 10-20 system when accurate TCES is needed.
  • Related Articles

    [1]Ziyu Zhao, Yi Zhou, Jinxing Guan, Yan Yan, Jing Zhao, Zhihang Peng, Feng Chen, Yang Zhao, Fang Shao. The relationship between compartment models and their stochastic counterparts: A comparative study with examples of the COVID-19 epidemic modeling[J]. The Journal of Biomedical Research, 2024, 38(2): 175-188. DOI: 10.7555/JBR.37.20230137
    [2]Gurumurthy Channabasavaiah B., Saunders Thomas L., Ohtsuka Masato. Designing and generating a mouse model: frequently asked questions[J]. The Journal of Biomedical Research, 2021, 35(2): 76-90. DOI: 10.7555/JBR.35.20200197
    [3]Quintero-Rincón Antonio, D'Giano Carlos, Batatia Hadj. A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures[J]. The Journal of Biomedical Research, 2020, 34(3): 205-212. DOI: 10.7555/JBR.33.20190012
    [4]Wang Le Yi, McKelvey George M., Wang Hong. Multi-outcome predictive modelling of anesthesia patients[J]. The Journal of Biomedical Research, 2019, 33(6): 430-434. DOI: 10.7555/JBR.33.20180088
    [5]Maglione Michele, Salvador Enrico, Ruaro Maria E., Melato Mauro, Tromba Giuliana, Angerame Daniele, Bevilacqua Lorenzo. Bone regeneration with adipose derived stem cells in a rabbit model[J]. The Journal of Biomedical Research, 2019, 33(1): 38-45. DOI: 10.7555/JBR.32.20160066
    [6]Robert Scott Kiss, Allan Sniderman. Shunts, channels and lipoprotein endosomal traffic: a new model of cholesterol homeostasis in the hepatocyte[J]. The Journal of Biomedical Research, 2017, 31(2): 95-107. DOI: 10.7555/JBR.31.20160139
    [7]Mingming Gao, Guo Xin, Xu Qiu, Yuhui Wang, George Liu. Establishment of a rat model with diet-induced coronary atherosclerosis[J]. The Journal of Biomedical Research, 2017, 31(1): 47-55. DOI: 10.7555/JBR.31.20160020
    [8]Jiawei Liao, Wei Huang, George Liu. Animal models of coronary heart disease[J]. The Journal of Biomedical Research, 2017, 31(1): 3-10. DOI: 10.7555/JBR.30.20150051
    [9]Zengdi Zhang, Sanen Li, Steven Y Cheng. The miR-183~96~182 cluster promotes tumorigenesis in a mouse model of medulloblastoma[J]. The Journal of Biomedical Research, 2013, 27(6): 486-494. DOI: 10.7555/JBR.27.20130010
    [10]Keh-Dong Shiang, Fouad Kandeel. A computational model of the human glucose-insulin regulatory system[J]. The Journal of Biomedical Research, 2010, 24(5): 347-364. DOI: 10.1016/S1674-8301(10)60048-6
  • Cited by

    Periodical cited type(5)

    1. Langyan S, Bhardwaj R, Kumari J, et al. Nutritional Diversity in Native Germplasm of Maize Collected From Three Different Fragile Ecosystems of India. Front Nutr, 2022, 9: 812599. DOI:10.3389/fnut.2022.812599
    2. Juvinao-Quintero DL, Cardenas A, Perron P, et al. Associations between an integrated component of maternal glycemic regulation in pregnancy and cord blood DNA methylation. Epigenomics, 2021, 13(18): 1459-1472. DOI:10.2217/epi-2021-0220
    3. Zhang J, Wu X. Predict Health Care Accessibility for Texas Medicaid Gap. Healthcare (Basel), 2021, 9(9): 1214. DOI:10.3390/healthcare9091214
    4. Ayati M, Koyutürk M. PoCos: Population Covering Locus Sets for Risk Assessment in Complex Diseases. PLoS Comput Biol, 2016, 12(11): e1005195. DOI:10.1371/journal.pcbi.1005195
    5. Zhang Q, Zhao Y, Zhang R, et al. A Comparative Study of Five Association Tests Based on CpG Set for Epigenome-Wide Association Studies. PLoS One, 2016, 11(6): e0156895. DOI:10.1371/journal.pone.0156895

    Other cited types(0)

Catalog

    Article Metrics

    Article views (4407) PDF downloads (929) Cited by(5)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return