Speakers
Description
Deep space missions beyond Low Earth Orbit expose crews to significant doses of galactic cosmic radiation (GCR), composed of high-energy protons and high-atomic-number, high-energy (HZE) ions including ⁵⁶Fe, ²⁸Si, ⁴⁸Ti, ¹⁶O, and ⁴He. Unlike the protection offered by Earth's magnetosphere, GCR cannot be attenuated by available spacecraft materials, and its cumulative effects on the central nervous system (CNS) represent one of the most critical risks for crewed missions to the Moon and Mars. Data from the RAD instrument aboard the Curiosity rover indicate that a six-month transit to Mars exposes crews to approximately 60% of the recommended career limit. Solar minimum periods further intensify GCR exposure during long-duration missions.
Experimental rodent studies at NASA's Space Radiation Laboratory (NSRL) demonstrate that exposure to mission-relevant doses (≤15 cGy) produces persistent neurocognitive deficits, including impairment of spatial memory, executive function, and attentional shifting. These effects are ion-type- and dose-dependent: ⁵⁶Fe at 1–10 cGy produces impairment in the simple discrimination (SD) and compound discrimination (CD) stages of the Attentional Set-Shifting Test (ATSET), while ⁴He and ²⁸Si produce distinct cognitive profiles in different brain regions, suggesting cerebral circuits are differentially affected by radiation field composition.
This work synthesizes recent advances in applying supervised machine learning (ML), including support vector machines (SVM), Gaussian naïve Bayes (GNB), and artificial neural networks (ANN), for predicting individual-level cognitive impairment in rodents subjected to GCR. Using pre-irradiation behavioral scores as multidimensional input features, classifiers demonstrate better than chance predictive capability, surpassing conventional population level statistical analyses. ML models based on U-Net architectures calculate physical dose distributions of heavy ions with deviations under 2% from Monte Carlo simulations in milliseconds, highlighting ML potential for accurate modeling of HZE effects in physical planning and biological prediction. Neurobiological mechanisms, including microglial activation, reduced hippocampal neurogenesis, loss of dendritic spines, and disruption of synaptic plasticity, establish the biological plausibility of individual susceptibility to GCR. Genetic modifiers such as ATM heterozygosity and ApoE isoforms further modulate this sensitivity, reinforcing the need for personalized pre-mission risk stratification. ML tools trained with ground-based GCR data can screen astronaut candidates and predict cognitive impairment before departure, supporting permissible exposure limits and countermeasures integrated into space weather monitoring.