Support Vector Machine for Analyzing Contributions of Brain Regions During Task-State fMRI - Wang, Mengyue; Li, Chunlin; Zhang, Wenjing; Wang, Yonghao; Feng, Yuan; Liang, Ying; Wei, Jing; Zhang, Xu; Li, Xia; Chen, Renji (Frontiers in Neuroinformatics, 2019)
For the analysis of task functional Magnetic Resonance Imaging (fMRI) data, the mainstream method is to obtain task-related active brain regions based on generalized linear models. Machine learning as a data-driven technical method is increasingly used in fMRI data analysis. the language task data, including math task and story task, of the Human Connectome Project (HCP) used in this work. We chose a linear support vector machine as a classifier to classify math and story tasks and compared them with the activated brain regions of SPM statistical analysis. As a result, 13 of the 25 regions used for classification in SVM were activated regions, and 12 were non-activity regions. In particular, right paracentral lobule and right rolandic operculum which belong to non-activity regions showed the most contribution for classification. Therefore, the differences found in machine learning can provide a new understanding of the physiological mechanisms of brain regions under different tasks.