Existing neuroregulatory techniques can achieve precise stimulation of the whole brain or cortex, but high-focus deep brain stimulation has been a technical bottleneck in this field. In this paper, based on the theory of negative permeability emerged in recent years, a simulation model of magnetic replicator is established to study the distribution of the induced electric field in the deep brain and explore the possibility of deep focusing, which is compared with the traditional magnetic stimulation method. Simulation results show that a single magnetic replicator realized remote magnetic source. Under the condition of the same position and compared with the traditional method of stimulating, the former generated smaller induced electric field which sharply reduced with distance. By superposition of the magnetic field replicator, the induced electric field intensity could be increased and the focus could be improved, reducing the number of peripheral wires while guaranteeing good focus. The magnetic replicator model established in this paper provides a new idea for precise deep brain stimulation, which can be combined with neuroregulatory techniques in the future to lay a foundation for clinical application.
Transcranial electrical stimulation (TES) is a non-invasive neuromodulation technique with great potential. Electrode optimization methods based on simulation models of individual TES field could provide personalized stimulation parameters according to individual variations in head tissue structure, significantly enhancing the stimulation accuracy of TES. However, the existing electrode optimization methods suffer from prolonged computation times (typically exceeding 1 d) and limitations such as disregarding the restricted number of output channels from the stimulator, further impeding their clinical applicability. Hence, this paper proposes an efficient and practical electrode optimization method. The proposed method simultaneously optimizes both the intensity and focality of TES within the target brain area while constraining the number of electrodes used, and it achieves faster computational speed. Compared to commonly used electrode optimization methods, the proposed method significantly reduces computation time by 85.9% while maintaining optimization effectiveness. Moreover, our method considered the number of available channels for the stimulator to distribute the current across multiple electrodes, further improving the tolerability of TES. The electrode optimization method proposed in this paper has the characteristics of high efficiency and easy operation, potentially providing valuable supporting data and references for the implementation of individualized TES.