Characteristics of brain network topological properties in schizophrenic patients based on machine learning
10.3760/cma.j.cn371468-20231114-00197
- VernacularTitle:基于机器学习分析精神分裂症患者脑网络拓扑属性的特点
- Author:
Lunpu AI
1
;
Yangyang LIU
;
Ningning DING
;
Entu ZHANG
;
Yibo GENG
;
Qingjiang ZHAO
;
Haisan ZHANG
Author Information
1. 新乡医学院医学工程学院，新乡 453003
- Keywords:
Schizophrenia;
Functional magnetic resonance imaging;
Graph theory;
Machine learning;
Support vector machine
- From:
Chinese Journal of Behavioral Medicine and Brain Science
2024;33(5):419-424
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze brain topological property data through machine learning methods and explore changes in brain network topological properties in patients with schizophrenia.Methods:From January 2022 to August 2023, functional magnetic resonance imaging data of 60 patients with schizophrenia and 56 healthy controls were collected , and the data were preprocessed to construct brain functional networks and extract global and nodal topological properties. All subjects were divided into a training group and a testing group.The data of training group were fitted based on support vector machine, and the predictive performance was evaluated through cross-validation.The model was optimized by recursive feature elimination algorithm, then the indicators that contributed the most to predictive performance were extrated.The classification performance of the testing group was calculated based on the trained model with optimal predictive performance.SPSS 20.0 software was used for data analysis, the independent t-test and χ2 test were used for comparing the differences between the two groups. Results:The support vector machine achieved an accuracy of 75.00% in predicting the test group of schizophrenia patients based on all indicators. After removing redundant features and combining with the recursive feature elimination algorithm, the accuracy of the SVM model in predicting the test group increased to 90.00%. The nodal global efficiency(Ne)of the left superior temporal gyrus, right dorsal agranular insula, bilateral dorsal granular insula, bilateral caudal cingulate gyrus, and left lateral orbitofrontal cortex in the model contributed the most to classification.Compared to the control group, patients with schizophrenia had abnormal Ne values in these brain regions.Conclusion:There are multiple brain regions with abnormal Ne values in patients with schizophrenia, indicating that the abnormalities in information integration and transmission functions may be related to the imbalance in the dynamic equilibrium of the patients' brain networks.
