A Swine Model of Changes in the Neuronal Electromagnetic Field After Traumatic Brain Injury: A Pilot Study.

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.

AI evidence extraction

Main findings

Using non-contact induction sensors with a Mu-metal and copper mesh-shielded helmet, the authors report measurable differences between room noise and pre-TBI swine EMF recordings, and morphological differences between pre-injury and post-injury recordings. An AI model differentiated pre-injury and post-injury EMF patterns; post-injury recordings frequently showed peaks at 2.5 Hz and 6.5 Hz and a valley at 11 Hz, with decreased variation between 4.5–7 Hz.

Outcomes measured

• Changes in measured neuronal electromagnetic field (EMF) patterns pre- vs post-traumatic brain injury (TBI)
• Ability to differentiate swine EMF signal from room noise
• AI-based classification/differentiation of pre-injury vs post-injury EMF patterns
• Frequency-domain features of post-injury EMF (e.g., peaks/valleys, slope/variation changes)

Limitations

• Pilot study with a single animal (n=1)
• Proprietary induction sensors (limited methodological transparency in abstract)
• No human data; translational relevance not established in abstract
• Exposure is measurement of endogenous EMF rather than environmental EMF exposure

{
    "study_type": "animal",
    "exposure": {
        "band": null,
        "source": "other",
        "frequency_mhz": null,
        "sar_wkg": null,
        "duration": null
    },
    "population": "Yucatan miniswine (swine model)",
    "sample_size": 1,
    "outcomes": [
        "Changes in measured neuronal electromagnetic field (EMF) patterns pre- vs post-traumatic brain injury (TBI)",
        "Ability to differentiate swine EMF signal from room noise",
        "AI-based classification/differentiation of pre-injury vs post-injury EMF patterns",
        "Frequency-domain features of post-injury EMF (e.g., peaks/valleys, slope/variation changes)"
    ],
    "main_findings": "Using non-contact induction sensors with a Mu-metal and copper mesh-shielded helmet, the authors report measurable differences between room noise and pre-TBI swine EMF recordings, and morphological differences between pre-injury and post-injury recordings. An AI model differentiated pre-injury and post-injury EMF patterns; post-injury recordings frequently showed peaks at 2.5 Hz and 6.5 Hz and a valley at 11 Hz, with decreased variation between 4.5–7 Hz.",
    "effect_direction": "unclear",
    "limitations": [
        "Pilot study with a single animal (n=1)",
        "Proprietary induction sensors (limited methodological transparency in abstract)",
        "No human data; translational relevance not established in abstract",
        "Exposure is measurement of endogenous EMF rather than environmental EMF exposure"
    ],
    "evidence_strength": "very_low",
    "confidence": 0.7399999999999999911182158029987476766109466552734375,
    "peer_reviewed_likely": "yes",
    "keywords": [
        "traumatic brain injury",
        "TBI",
        "swine model",
        "Yucatan miniswine",
        "controlled cortical impact",
        "neuronal electromagnetic field",
        "magnetoencephalography alternative",
        "induction sensors",
        "Mu-metal shielding",
        "copper mesh",
        "non-contact measurement",
        "artificial intelligence"
    ],
    "suggested_hubs": []
}

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