Recognition of flight cadets brain functional magnetic resonance imaging data based on machine learning analysis

• Published in: PloS one Vol. 20; no. 6; p. e0324081
• Main Authors: Ye, Lu, Weng, Shuhao, Yan, Dongfeng, Ma, Shan, Chen, Xi
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.06.2025; Public Library of Science (PLoS)
• Subjects: Adult; Aerospace industry; Algorithms; Analysis; Auditory system; Aviation; Bayes Theorem; Biology and Life Sciences; Blood levels; Brain; Brain - diagnostic imaging; Brain - physiology; Brain mapping; Brain Mapping - methods; Brain research; Civil aviation; Classification; Cognition; Cognitive ability; Comparative analysis; Computer and Information Sciences; Female; Flight; Flight crews; Flight training; Functional magnetic resonance imaging; Health aspects; Homogeneity; Humans; Information processing; Learning algorithms; Machine Learning; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medicine and Health Sciences; Mental disorders; Methods; Neural networks; Neuroimaging; Oxygenation; Physical fitness; Physical Sciences; Physiological aspects; Pilots; Radio communications; Research and Analysis Methods; Sensorimotor integration; Sensory integration; Social aspects; Social Sciences; Support Vector Machine; Support vector machines; Training; Visual flight; Visual perception; Young Adult; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0324081

The rapid advancement of the civil aviation industry has attracted significant attention to research on pilots. However, the brain changes experienced by flight cadets following their training remain, to some extent, an unexplored territory compared to those of the general population. The aim of this study was to examine the impact of flight training on brain function by employing machine learning(ML) techniques. We collected resting-state functional magnetic resonance imaging (resting-state fMRI) data from 79 flight cadets and ground program cadets, extracting blood oxygenation level-dependent (BOLD) signal, amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) metrics as feature inputs for ML models. After conducting feature selection using a two-sample t-test, we established various ML classification models, including Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB). Comparative analysis of the model results revealed that the LR classifier based on BOLD signals could accurately distinguish flight cadets from the general population, achieving an AUC of 83.75% and an accuracy of 0.93. Furthermore, an analysis of the features contributing significantly to the ML classification models indicated that these features were predominantly located in brain regions associated with auditory-visual processing, motor function, emotional regulation, and cognition, primarily within the Default Mode Network (DMN), Visual Network (VN), and SomatoMotor Network (SMN). These findings suggest that flight-trained cadets may exhibit enhanced functional dynamics and cognitive flexibility.