基于一般线性模型的机器学习方法在BOLD-fMRI 脑胶质瘤患者个体化运动功能区定位中的应用价值

Objective The study aimed to evaluate the application value of a machine learning method based on general linear model (GLM) in the localization of individual motor function in patients with glioma after blood oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI). Methods A retrospective study was conducted, and strict clinical screening was performed in the Neurosurgery Department of the First Affiliated Hospital of Xi'an Jiaotong University from November 2017 to November 2021. A total of 38 pathologically confirmed patients with glioma located in the motor area were selected and included in the validation set of the machine learning model (25 males, 13 females; aged 24−69), and 50 healthy volunteers were recruited and included in the training set (26 males, 14 females; aged 22−68). Extracting the resting-state fMRI (rs-fMRI) features from 98 subjects in the Human Connectome Project (HCP) using the independent component analysis (ICA). A machine learning model based on GLM was trained using the correlation between the rs-fMRI and task-based fMRI (tb-fMRI) features of healthy subjects. (1) GLM-predicted activation and actual activation were compared by Pearson correlation coefficient (CC) analysis; (2) the dice coefficient (DC) was used as a quantitative indicator of the prediction efficiency of the model and used in comparing the prediction efficiency of GLM and ICA methods. Results (1) GLM-prediction activation in glioma patients was highly similar to task-state function activation (CC>0.30 in 89.47% [34/38] of patients). (2) GLM was better than ICA in predicting task-state motor function activation. The DC was 0.34 (0.27, 0.42), and 0.26 (0.16, 0.30), respectively, the difference was statistically significant (Z= − 3.88; P<0.001). In the tumor-containing hemisphere, GLM was better than ICA in predicting task-state activation, with DCs of 0.36 (0.17, 0.48) and 0.34 (0.04, 0.45), respectively (Z=−2.43, P=0.015). The prediction effects of the two methods in the nontumor hemisphere was significantly higher than that in the tumor hemisphere (Z=−4.33, −3.59; all P values<0.001). Conclusion GLM-based machine learning can predict tb-fMRI motor activation in patients with glioma after rs-fMRI and before surgery and is more efficient than ICA.