Micromagnetic Neural Stimulation and Spintronic Neural Sensing

In this talk, I will report our team’s magnetic biomedical brain related research: 1) micromagnetic neural stimulation (μMS) [1]; 2) spintronic neural sensing [2]. First, designing, fabrication and testing of two micromagnetic implants – the Magnetic Pen (MagPen), a solenoid-shaped single µcoil prototype and the Magnetic Patch (MagPatch), a rectangular helix shaped planar µcoil array prototype, will be reported. The efficacy of micromagnetic activation using MagPen has been tested over the following rodent models [3,4,5]: on the rat hippocampal CA3-CA1 synaptic pathway in vitro; on the medial forebrain bundle (MFB) of rodents for the study of striatal dopamine release in vivo; on the rat sciatic nerve to demonstrate the dose-response relationship for µMS in vivo; and, on the vagus nerve to demonstrate fiber-specific activation of the nerve in vivo. The MagPen prototype had its own caveat in terms of mm-size, lack of multidimensional spatial control and activation at the cellular-level. To bridge this research gap, the MagPatch array was designed and fabricated with the goal to study μMS at the single cell resolution. Second, we used FEM exemplary models and open-source computational libraries and calculated the magnetic fields generated by individual neurons and neuronal networks at micrometer distances [2]. Our results show that the magnetic field generated by a single-neuron action potential can be detected by ultra-high sensitivity sub-pT magnetic field sensors. Room temperature, high endurance, small volume and low power make spintronic sensors one of the promising candidates for neural sensing. On this aspect, I will review recent experimental progress for the spintronic sensors for neural sensing [6], with a specific discussion on our effort on spintronic stack design, device fabrication and detection [7].

1. 1. R. Saha, et al, and JP Wang, Nanotechnology 33 (2022) 182004,
   
   2. D. Tonini, et al, Ann Biomed Sci Eng. 6 (2022) 019-029;
   
   3. R. Saha, et al, Journal of Neural Engineering 19 (2022) 016018,
   
   4. R. Saha, et al, J. Neural Eng. 20 (2023) 036022.
   
   5. R, Saha, et al, Biomedical Physics and Engineering Express (under revision)
   
   6. A. Kanno, et al, Sci Rep 12, 6106 (2022).
   
   7. D. Tonini, et al, AIP Advance (under review)