Nano-scale simulation of neuronal damage by galactic cosmic rays

TL;DR

This study uses nano-scale Monte Carlo simulations to analyze how galactic cosmic rays affect neuronal structures, providing detailed insights into energy deposition processes relevant for space radiation risk assessment.

Contribution

First nano-scale simulation of realistic neuron geometry with GCRSim and SimGCRSim distributions, revealing detailed ionization and energy deposition patterns in neurons.

Findings

Ionizations account for 68% of energy deposition.

Vibrational excitations are the most common energy events.

Energy deposition declines hyperbolically with particle energy.

Abstract

The effects of complex, mixed-ion radiation fields on neuronal function remain largely unexplored. Here, we present a complete analysis of the nano-scale physics associated with broad-spectrum galactic cosmic ray (GCR) irradiation in a realistic cornu ammonis 1 (CA1) pyramidal neuron geometry. We simulate the entire 33 ion-energy beam fluence distribution currently in use at the NASA Space Radiation Laboratory galactic cosmic ray simulator (GCRSim). We use the TOol for PArticle Simulation (TOPAS) and TOPAS-nBio Monte Carlo-based track structure simulation toolkits to assess the dosimetry, physics processes, and fluence statistics of different neuronal compartments at the nanometer scale. We also make comparisons between the full GCRSim distribution and a simplified 6 ion-energy spectrum (SimGCRSim). We show that across all physics processes, ionizations mediate the majority of the energy deposition $(68 \pm 1\%)$, though vibrational excitations are the most abundant ($70 \pm 2\%$ of all energy deposition events). We report that neuronal energy deposition by proton and $\alpha$-particle tracks declines approximately hyperbolically with increasing primary particle energy at mission-relevant energies. We also demonstrate an inverted exponential relationship between dendritic segment irradiation probability and neuronal absorbed dose. Finally, we find that there are no significant differences in the average physical responses between the GCRSim and SimGCRSim fluence distributions. To our knowledge, this is the first nano-scale simulation study of a realistic neuron geometry using the GCRSim and SimGCRSim fluence distributions. The results presented here are expected to aid in the interpretation of future experimental results and help guide future study designs.