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• Original Article •
Research on the recognition of brain functional connections in flight students based on multivariate pattern analysis
YE Lu  LIU Mengxuan  YAN Dongfeng  CHEN Xi  MA Shan 

Cite this article as: YE L, LIU M X, YAN D F, et al. Research on the recognition of brain functional connections in flight students based on multivariate pattern analysis[J]. Chin J Magn Reson Imaging, 2024, 15(2): 108-114. DOI:10.12015/issn.1674-8034.2024.02.016.


[Abstract] Objective Based on multivariate pattern analysis (MVPA), effectively identify the brain functional connections between flight cadets and healthy individuals.Materials and Methods Functional magnetic resonance data were collected from 40 licensed flight major students and 39 ground major students. The functional connectivity matrix was obtained through network functional connectivity analysis as a feature, and the feature dimensionality was reduced using the least absolute shrinkage and selection operator (LASSO) algorithm and independent sample t-test method, respectively. Support vector machines (SVM) with different kernel functions were used for training and prediction, and the performance of the model was evaluated using the left one cross validation method. Finally, the functional connections between corresponding brain regions were located based on the weight information in the trained SVM model.Results The linear kernel SVM model using LASSO feature screening had an accuracy of 81.82%, sensitivity of 82.05%, specificity of 81.58%, and area under the curve (AUC) of 0.88. The kernel function had little effect on the accuracy of the model. In the model, the right paracentral lobule, bilateral posterior central gyrus, bilateral inferior parietal angular gyrus, right fusiform gyrus, left orbital frontal gyrus, left superior parietal gyrus, and right orbital inferior frontal gyrus had higher weights. The weights in the model were concentrated in the somatomotor network (SMN) and default mode network (DMN), accounting for 25.62% and 25.27% of all weights, respectively.Conclusions SVM combined with LASSO algorithm for feature filtering can effectively recognize the brain of flight students, and has better interpretability and smaller overfitting. The weight information of the model reflects that flight students are mainly different from ordinary people in terms of motor and perceptual abilities.
[Keywords] flight cadets；magnetic resonance imaging；functional connectivity；minimum absolute contraction selection operator；support vector machine

YE Lu*   LIU Mengxuan   YAN Dongfeng   CHEN Xi   MA Shan  

Flight Technology College, Civil Aviation Flight University of China, Guanghan 618307, China

Corresponding author: YE L, E-mail: yelucafuc@163.com

Conflicts of interest   None.

Received  2023-09-20
Accepted  2024-01-21
DOI: 10.12015/issn.1674-8034.2024.02.016
Cite this article as: YE L, LIU M X, YAN D F, et al. Research on the recognition of brain functional connections in flight students based on multivariate pattern analysis[J]. Chin J Magn Reson Imaging, 2024, 15(2): 108-114. DOI:10.12015/issn.1674-8034.2024.02.016.

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