From Sideline to Sensor: A Dual-VOC Breath Signature for Objective, Non-Invasive Concussion Screening
Mild traumatic brain injury (mTBI), commonly referred to as concussion, presents a persistent clinical challenge due to its reliance on subjective symptom reporting, delayed onset of neurological deficits, and the low sensitivity of standard structural neuroimaging (CT/MRI) to microstructural axonal damage. This study presents a theoretical diagnostic framework and engineering concept for rapid, objective mTBI screening based on exhaled volatile organic compound (VOC) breathomics. Following an acoustic or mechanical impact to the brain, the resulting neurometabolic cascade is characterized by acute disruption of neuronal membranes, intracellular calcium influx, localized oxidative stress, and impaired cerebral glucose metabolism. By mapping these pathophysiology-driven metabolic shifts, this paper identifies acetone and isoprene as targeted, complementary breath biomarkers. Acetone serves as an indicator of cerebral energy failure and compensatory ketogenesis, whereas isoprene reflects broader systemic metabolic dysregulation and lipid peroxidation secondary to injury. We evaluate quantitative mass spectrometry analytical techniques -- focusing on Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) -- to define baseline physiological concentrations versus proposed post-mTBI volatile thresholds. Furthermore, we outline a dual-channel sensing architecture that pairs an activated alumina pre-filtration stage with a metal-oxide semiconductor (MOS) sensor array, designed to isolate targeted biomarkers while mitigating environmental and humidity cross-reactivity. This work establishes an analytical foundation for portable, non-invasive breath-based diagnostic tools intended to supplement existing clinical and sideline assessment protocols, such as the Sport Concussion Assessment Tool 6 (SCAT6).
Authors
- Nia Ou (ORCID: https://orcid.org/0009-0000-9029-5292)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-13
- DOI
- https://doi.org/10.5281/zenodo.22739542
- Primary Topic
- Advanced Chemical Sensor Technologies
- Type
- preprint