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Predicting changes to neural activity in asymptomatic cases following exposure to a chemical or biological (CB) threat agent can provide advanced warning to identify and treat at-risk individuals to minimize negative impact to human performance in operational environments. Recent advancements in human organ-on-a-chip technologies that mimic human biomechanics and the physiology of organ systems have enabled researchers to develop minimally invasive means to measure electrophysiological and electrochemical signal data from organoid devices. An organoid is a miniature 3D version of an organ grown in a lab to mimic the structure, function, and complexity of human organs and tissues.
The deliberate release of a CB agent on the battlefield poses a risk to the operational effectiveness, readiness, and lethality of the Joint Force. Rapid detection of CB threat exposure and the impacts to warfighter health and performance is critical in reducing time to recover, risk to mission effectiveness, and ultimately force lethality. Even nonlethal exposures to CB agents often limit warfighter performance by impacting cognitive neural activity.
Multiple organoids can be combined to form a multiorgan micro-physiological system (MPS) of the human body. Bioelectronic signals from MPS can be analyzed to determine potential adverse health effects of CB exposures to provide dynamic measurements to quantify human performance on the battlefield. When combined with novel computational tools in the domains of artificial intelligence and machine learning (AI/ML), there is an opportunity to classify and reconstruct high-fidelity organoid data to develop ML algorithms able to predict complex cognitive health effect responses within a human system.
This application is one of the drivers for the developing the Integrated Brain-on-Chip for Machine Learning-based Prediction of Warfighter Cognitive Performance (BRAINSTORM) by the Defense Threat Reduction Agency’s (DTRA) Chemical and Biological Technologies Department in its role as the Joint Science and Technology Office (JSTO) for Chemical and Biological Defense, an integral component of the Chemical and Biological Defense Program, in collaboration with the Air Force Research Laboratory (AFRL) 711th Human Performance Wing.
Existing brain-on-a-chip models provide an advanced platform for detecting and analyzing abnormal neural firing patterns, serving as early indicators of exposure to chemical or biological threats. The BRAINSTORM platform can identify subtle changes in neural firing patterns that may precede overt symptoms or acute toxicity for identifying potential chemical exposure at sublethal doses.
The microfluidics and high-resolution microelectrode array (MEA) data collected following exposure is also being used by the Naval Research Laboratory (NRL) to develop machine learning models to predict signatures of abnormal neural firing following exposures over time. These computational models derived from brain-on-a-chip data, combined with machine learning techniques, have the potential to predict future exposure scenarios and enable rapid and accurate identification of agents that may have an impact on human performance.
The rapid detection and prediction tools enabled by the integration of the BRAINSTORM platform, developed by AFRL and including ML approaches developed at NRL, offer significant benefits to the Joint Force, our nation, and our allies in terms of efficiency and cost-effectiveness for studying the effects of chemical agent exposures on representative human systems.
By harnessing the power of predictive capability, this technology has the potential to provide decision makers with timely and relevant information, minimize impact of chemical exposure to operations, and drive innovative mitigation strategies to maintain warfighter health, performance, and readiness. These advancements will improve the safety, preparedness, and operational effectiveness of military personnel, reducing the risk of cognitive impairment, and enhancing mission success.
POC: Sweta Batni, PhD, sweta.r.batni.civ@mail.mil
| Date Taken: | 02.11.2025 |
| Date Posted: | 02.11.2025 16:56 |
| Story ID: | 490576 |
| Location: | FT. BELVOIR, VIRGINIA, US |
| Web Views: | 115 |
| Downloads: | 1 |
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