Abstract
The mainstream method used for the analysis of task functional Magnetic Resonance Imaging (fMRI) data, 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) was 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 a SPM statistical analysis. As a result, 13 of the 25 regions used for classification in SVM were activated regions, and 12 were non-activated regions. In particular, the right Paracentral Lobule and right Rolandic Operculum which belong to non-activated regions, contributed most to the classification. Therefore, the differences found in machine learning can provide a new understanding of the physiological mechanisms of brain regions under different tasks.
Highlights
In functional magnetic resonance data analysis, GLM are one of the most common model-based methods that correlate measured hemodynamic signals with controlled experimental variables (Friston et al, 1994; Holmes and Friston, 1998)
Each voxel of the functional Magnetic Resonance Imaging image and the experimental paradigm are analyzed by a generalized linear model, and each voxel corresponds to a coefficient Bata of a regression equation, and all coefficients are combined to form a statistical parameter map (Yan et al, 2011; Wu et al, 2012)
The behavioral data were collected from 1046 participants during the functional Magnetic Resonance Imaging (fMRI) experiments
Summary
In functional magnetic resonance data analysis, GLM (generalized linear models) are one of the most common model-based methods that correlate measured hemodynamic signals with controlled experimental variables (Friston et al, 1994; Holmes and Friston, 1998). Each voxel of the functional Magnetic Resonance Imaging (fMRI) image and the experimental paradigm are analyzed by a generalized linear model, and each voxel corresponds to a coefficient Bata of a regression equation, and all coefficients are combined to form a statistical parameter map (Yan et al, 2011; Wu et al, 2012). A one sample t-test is performed on the statistical parameter maps of all subjects to determine the activation region of the group (Beckmann et al, 2003). Since machine learning can find features that contribute most to classification (Meier et al, 2012; Lv et al, 2015), differences found can provide a new understanding of the physiological mechanisms of brain regions under different tasks
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