ABSTRACT
Background Traumatic brain injury (TBI) is a global cause of disability and mortality. Treatment depends on mitigation of secondary injury resulting in axonal injury, necrosis, brain dysfunction, and disruption of electrical and chemical signaling in neural circuits. To better understand TBI, translational models are required to study physiology, diagnostics, and treatments in homologous species, such as swine. Electromagnetic fields (EMFs) from altered neural circuits can be measured and historically have been reliant on expensive shielding and supercooling in magnetoencephalography. Using proprietary induction sensors, it has been found that a non-invasive, non-contact approach with an engineered Mu-metal and copper mesh-shielded helmet effectively measures EMFs. This has not yet been investigated in swine models. We wished to evaluate the efficacy of this technology to assess TBI-dependent EMF changes in swine to describe the efficacy of these sensors and this model using a gravity-dependent controlled cortical impact (CCI). Methods A Yucatan miniswine was evaluated using non-contact, non-invasive proprietary induction sensors with an engineered dual-layer Mu-metal and interlaced copper mesh helmet with sensors within EMF channels connected to a helmet. Swine EMF recordings were obtained prior to induced gravity-dependent CCI followed by post-TBI measurements. Behavioral changes and changes in EMF measurements were assessed. EMF measurements were evaluated with an artificial intelligence (AI) model. Results Differences between room "noise" EMF measurements and pre-TBI swine electromagnetic field measurements were identified. Morphological characteristics between pre-injury and post-injury measurements were noted. AI modeling differentiated pre-injury and post-injury patterns in the swine EMF. Frequently identified frequencies seen post-injury were peaks at 2.5 Hz and 6.5 Hz and a valley at 11 Hz. The AI model identified less changes in the slope and thus decreased variation of EMF measurements post-TBI between 4.5 Hz and 7 Hz. Conclusions For the first time, it was identified that cortical function in a swine can be appropriately measured using novel induction sensors and shielding isolated to a helmet and EMF channels. The swine model can be appropriately differentiated from the external noise signal with identifiably different pre-injury and post-injury EMFs. Patterns can be recognized within the post-injury EMF due to altered neural circuits that can be measured using these sensors continuously, non-invasively, and in real time.
ABSTRACT
Background and objective Traumatic brain injury (TBI) has been associated with aberrations in neural circuitry attributable to the pathology resulting in electromagnetic field (EMF) changes. These changes have been evaluated in a variety of settings including through novel induction sensors with an ultra-portable shielded helmet and EMF channels with differences identified by comparing pre-injury and post-injury states. Modulation of the EMF has undergone cursory evaluation in neurologic conditions but has not yet been fully evaluated for clinical effects in treatment. Target EMF stimulation using EMF-related changes preoperatively to postoperatively has not yet been attempted and has not been completed using induction sensor technology. Our objectives in this study were twofold: we wanted to test the hypothesis that targeted stimulation using an EMF signal generator and stimulator to abnormal thresholds identified by real-time measurement of EMFs may enable early resolution of EMF changes and treatment of the TBI as modeled through controlled cortical impact (CCI); we also aimed to assess the feasibility of attempting this using real-time measurements with an EMF shielded helmet with EMF channels and non-contact, non-invasive induction sensors with attached EMF transmitters in real-time. Methods A singular Yucatan miniswine was obtained and baseline EMF recordings were obtained. A CCI of TBI and postoperative assessment of cortical EMF in a non-invasive, non-contact fashion were completed. Alterations in EMF were evaluated and EMF stimulation using those abnormal frequencies was completed using multiple treatments involving three minutes of EMF stimulation at abnormal frequencies. Stimulation thresholds of 2.5 Hz, 3.5 Hz, and 5.5 Hz with 1 V signal intensity were evaluated using sinusoidal waves. Additionally, stimulation thresholds using differing offsets to the sine wave at -500 mV, 0 mV, and 500 mv were assessed. Daily EMF and post-stimulation EMF measurements were recorded. EMF patterns were then assessed using an artificial intelligence (AI) model. Results AI modeling appropriately identified differences in EMF signal in pre-injury, post-injury, and post-stimulation states. EMF stimulation using a positive offset of 500 mV appeared to have maximal beneficial effects in return to baseline. Similarly targeted stimulation using thresholds of 2.5 Hz and 5.5 Hz with a positive 500 mV offset at 1 V allowed for recovery of EMF patterns post-injury towards patterns seen in baseline EMF measurements on stimulation day seven (postoperative day 17). Conclusion Stimulation of neural circuits with targeted EMF in a sinusoidal pattern with targeted thresholds after measurement with induction sensors with shielding isolated to a Mu-metal and copper mesh helmet and EMF channels is efficacious in promoting neuronal circuit recovery to preoperative baselines in the TBI miniswine model. Similarly, our findings confirm the appropriateness of this translational model in the evaluation of brain neuronal circuit EMF and that preoperative and post-trauma differences can be appropriately assessed with this technology.