Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

• Published in: Frontiers in systems neuroscience Vol. 15; p. 713131
• Main Authors: Matar, Mona, Gokoglu, Suleyman A, Prelich, Matthew T, Gallo, Christopher A, Iqbal, Asad K., Britten, Richard A, Prabhu, R. A., Myers, Jerry G
• Format: Journal Article
• Language: English
• Published: Glenn Research Center Frontiers 13.09.2021; Frontiers Media S.A
• Subjects: behavioral decrement; Behavioral Sciences; cognitive impairment; impairment prediction; Neuroscience; radiation research; rodent studies; space radiation; Set-Shift; Behavioral Decrement; Impairment Prediction; Space Radiation; Artificial Neural Network; Cognitive Performance; Machine Learning; Rodent Model
• ISSN: 1662-5137; 1662-5137
• DOI: 10.3389/fnsys.2021.713131

This research uses machine-learned computational analyses to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is from a rodent model exposed to ≤ 15 cGy of individual Galactic Cosmic Radiation (GCR) ions: 4He, 16O, 28Si, 48Ti, or 56Fe, expected for a Lunar or Mars mission. This work investigates rats at a subject-based level and uses performance scores taken before irradiation to predict impairment in Attentional Set-shifting (ATSET) data post-irradiation. Here, the worst performing rats of the control group define the impairment thresholds based on population analyses via cumulative distribution functions, leading to the labeling of impairment for each subject. A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the Simple Discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the Compound Discrimination (CD) stage. On a subject-based level, implementing Machine Learning (ML) classifiers such as the Gaussian Naïve Bayes, Support Vector Machine, and Artificial Neural Networks identifies rats that have a higher tendency for impairment after GCR exposure. The algorithms employ the experimental prescreenperformance scores as multidimensional input features to predict each rodent’s susceptibility to cognitive impairment due to space radiation exposure. The receiver operating characteristic and the precision-recall curves of the ML models show a better prediction of impairment when 56Feis the ion in question in both SD and CD stages. They, however, do not depict impairment due to 4Hein SD and 28Siin CD, suggesting no dose-dependent impairment response in these cases. One key finding of our study is that prescreen performance scores can be used to predict the ATSET performance impairments. This result is significant to crewed space missions as it supports the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. Future research can focus on constructing ML ensemble methods to integrate the findings from the methodologies implemented in this study for morerobust predictionsof cognitive decrements due to space radiation exposure.