volume | PIER Journals

Abstract

The transcranial magnetic stimulation (TMS) technology development becomes a painless, noninvasive, green treatment and detection method in recent years. However, because of the difference in efficiency of the stimulation system, the technology is not widely used. The focality of the magnetic field is one of the key issues that affect the efficiency of magnetic stimulation. If the focusing problem cannot be solved, the development of TMS technology will be restricted. Therefore, research of focusing has become a hot spot in recent years. In this paper, we mainly carry out three meaningful works. First, a hybrid algorithm is proposed based on a simplified particle swarm optimization algorithm (sPSO) and simulated annealing (SA) algorithm. The convergence of those algorithms is tested. The current through the coils is optimized and solved. Second, the influence of discharge circuit parameters on the magnetic field distribution in the head model is analyzed. Finally, five array coils are established, and the related parameters are configured by using the results of above research. The simulation results show that the hybrid algorithm can improve focality performance. The hybrid algorithm is made up of sPSO and SA. The proposed optimization algorithm and the study to the parameters of the discharge circuit are useful to enhance the focality of the TMS technology in the further development.

1. Barker, A. T., R. Jalinous, et al. "Non-invasive magnetic stimulation of human motor cortex," The Lancet, Vol. 1, 1106-1107, 1985.
doi:10.1016/S0140-6736(85)92413-4 Google Scholar

2. Zyss, T., A. Krawczyk, et al. "Transcranial magnetic stimulation TMS verus electroconvulsive therapy ECT in therapy of depressions - Advantages and disadvantages of computer modeling ,", De Gruyter, Vol. 6, No. 12, 67-73, 2010. Google Scholar

3. Ai, Q., J. Li, et al. "A new transcranial magnetic stimulation coil design to improve the focality," 2010 3rd International Conference on Biomedical Engineering and Informatics, Vol. 4, 1391-1395, 2010.
doi:10.1109/BMEI.2010.5639400 Google Scholar

4. Deng, Z. D., S. H. Lisanby, et al. "Electric field depth-focality tradeoff in transcranial magnetic stimulation: Simulation comparison of 50 coil designs," Brain Stimulation, Vol. 6, 1-13, 2013.
doi:10.1016/j.brs.2012.02.005 Google Scholar

5. Laakso, I. and A. Hirata, "Fast multigrid-based computation of the induced electric field for transcranial magnetic stimulation," Physics in Medicine & Biology, Vol. 57, 7753-7765, 2012.
doi:10.1088/0031-9155/57/23/7753 Google Scholar

6. Okada, A., A. Nishikawa, et al. "Magnetic navigation system for home use of repetitive transcranial magnetic stimulation (rTMS)," 2012 ICME International Conference on Complex Medical Engineering (CME), 112-118, 2012.
doi:10.1109/ICCME.2012.6275591 Google Scholar

7. Yang, S., G. Xu, L. Wang, and Q. Yang, "Analysis of double eight-figure coil for transcranial magnetic stimulation based on realistic head model," 2012 Sixth International Conference on Electromagnetic Field Problems and Applications, 1-4, 2012. Google Scholar

8. Yang, S., G. Xu, et al. "Circular coil array model for transcranial magnetic stimulation," IEEE Transactions on Applied Superconductivity, Vol. 20, 829-833, 2010.
doi:10.1109/TASC.2010.2040379 Google Scholar

9. Li, J., Z. Liang, et al. "Double butterfly coil for transcranial magnetic stimulation aiming at improving focality," IEEE Transactions on Magnetics, Vol. 48, 3509-3512, 2012.
doi:10.1109/TMAG.2012.2199474 Google Scholar

10. Zhang, S., Y. Tao, et al. "Experimental study to improve the focalization of a figure-eight coil of rTMS by using a highly conductive and highly permeable medium," IEEE Transactions on Neural Systems & Rehabilitation Engineering, Vol. 21, 364-369, 2013.
doi:10.1109/TNSRE.2012.2194723 Google Scholar

11. Gomez, L., F. Cajko, et al. "Numerical analysis and design of single-source multicoil TMS for deep and focused brain stimulation," IEEE Transactions on Bio-medical Engineering, Vol. 60, 2771-2782, 2013.
doi:10.1109/TBME.2013.2264632 Google Scholar

12. Lu, M. and S. Ueno, "Toward deep transcranial magnetic stimulation," 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS), 1-4, 2014. Google Scholar

13. Laudani, A., F. R. Fulginei, et al. "TMS array coils optimization by means of CFSO," IEEE Transactions on Magnetics, Vol. 51, 1-4, 2015.
doi:10.1109/TMAG.2014.2364176 Google Scholar

14. Tachas, N. J., K. G. Efthimiadis, et al. "The effect of coil modeling on the predicted induced electric field distribution during TMS," IEEE Transactions on Magnetics, Vol. 49, 1096-1100, 2013.
doi:10.1109/TMAG.2012.2219878 Google Scholar

15. Gomez, L., F. Cajko, et al. "Numerical analysis and design of single-source multicoil TMS for deep and focused brain stimulation," IEEE Transactions on Bio-medical Engineering, Vol. 60, 2771-2782, 2013.
doi:10.1109/TBME.2013.2264632 Google Scholar

16. Yang, S., G. Xu, et al. "Circular coil array model for transcranial magnetic stimulation," IEEE Transactions on Applied Superconductivity, Vol. 20, 829-833, 2010.
doi:10.1109/TASC.2010.2040379 Google Scholar

17. Clerc, M. and J. Kennedy, "The particle swarm - Explosion, stability, and convergence in a multidimensional complex space," IEEE Transactions on Evolutionary Computation, Vol. 6, 58-73, 2002.
doi:10.1109/4235.985692 Google Scholar

18. Holden, N. and A. A. Freitas, "A hybrid particle swarm/ant colony algorithm for the classification of hierarchical biological data," Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005, SIS 2005, 100-107, 2005.
doi:10.1109/SIS.2005.1501608 Google Scholar

19. Wang, H. and Z.-S. Li, "A simpler and more effective particle swarm optimization algorithm," Journal of Software, Vol. 18, 861-868, 2007.
doi:10.1360/jos180861 Google Scholar

20. Yu, L.-L. and Z. X. Cai, "Multiple optimization strategies for improving hybrid discrete particle swarm," Journal of Central South University, 2009. Google Scholar

21. Zyss, T., A. Krawczyk, et al. "Electroshock therapy (ECT) versus transcranial magnetic stimulation (TMS): Modeling of current flow in 3-D head structure," European Neuropsychopharmacology, Vol. 8, S317, 1998. Google Scholar

Dr. Hui Xiong

Dr. Jin Hua Shi

Dr. Xiao-Wei Hu

Dr. Jin-Zhen Liu