Open-source magnetic system for wireless neuromodulations in vitro and for untethered brain stimulation in vivo

• Published in: Scientific reports Vol. 15; no. 1; pp. 17814 - 17
• Main Authors: Huang, Jun-Xuan, Yen, Ping-Hsiang, Cheng, Chao-Chun, Fang, Yi-Cheng, Chiang, Po-Han
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 22.05.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378; 639/925/350/1057; Animals; Brain - physiology; Cell culture; Deep Brain Stimulation - instrumentation; Deep Brain Stimulation - methods; Energy efficiency; Environmental monitoring; Feedback; Fluorescence microscopy; Humanities and Social Sciences; Magnetic Fields; Magnetic neuromodulation; Magnetic system; Magnetogenetics; Magnetomechanical stimulation; Mice; multidisciplinary; Nanoparticles; Neuromodulation; Neurosciences; Science; Science (multidisciplinary); Wireless deep brain stimulation; Wireless Technology - instrumentation; Open-source; Magnetogenetics; Wireless deep brain stimulation; Magnetomechanical stimulation; Magnetic neuromodulation; Magnetic system; Arduino; Graphic user interface; Python
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-03076-7

In recent years, significant advances have been made in magnetic neuromodulation technologies, enabling the manipulation of deep brain neurons without invasive implants. Wireless approaches, such as those leveraging magnetic nanoparticles and magnetosensitive proteins, have gained considerable attention. Among these, methods requiring low magnetic field density (< 50 mT) and low frequencies (< 20 Hz) show promise for broader applications due to their scalability and energy efficiency. However, the lack of cost-effective, user-friendly instruments for in vitro and in vivo experiments has hindered broader adoption. To address this, we demonstrate an open-source magnetic stimulation system that integrates Arduino-based hardware, electromagnetic coils, and real-time feedback sensors to monitor environmental parameters, including temperature, sound, vibration, and magnetic field density. Additionally, the system employs a closed-loop design, enabling adaptive control of magnetic stimulation based on tracking the subject’s position and environmental feedback. A Python-based graphical user interface (GUI) allows researchers to design and control stimulation protocols while monitoring feedback signals in real-time. The system includes multiple solenoid designs optimized for diverse applications, such as cell culture studies, fluorescence microscopy, and in vivo behavioral experiments, ensuring compatibility across experimental scales. The stability and versatility of the system were evaluated in multiple behavioral paradigms, including light-dark box and place preference tests. This low-cost, easy-access, and flexible platform can facilitate magnetic neuromodulation research and promote accessibility for basic and translational studies in neuroscience and bioelectronics.