Connectivity Shifted Cerebral Regions as Seeds for Mental Arithmetic Cognitive State Classification

Machine learning provides a potential modeling framework for better revealing the cognitive state of the human brain during mental arithmetic, but how to choose the features of mental arithmetic for model training remains to be studied. However, many regions in the cerebral cortex are closely related to mental arithmetic, and they are important seeds for distinguishing cognitive states of the mental arithmetic. In this paper, with the network analysis on the task-evoked fMRI data, the brain regions with significant differences on the brain functional connectivity properties in mental arithmetic are found. These brain regions are used as the features for mental arithmetic cognitive state classification, and the trained model is constructed by using multi-voxel pattern analysis. The results show that the performance of the model trained by the brain regions with significant differences in network properties as the modeling features is significantly higher than that of the model constructed by the brain regions without significant differences in network properties. After further reducing the dimensions of these seeds with linear discriminant analysis, the average accuracy of the trained models for all subjects reached 60.2%, which was statistically significantly higher than the chance level. Therefore, the trained model can be effectively used to classify mental arithmetic cognitive states.