Abstract
Neural feedback technology based on rt-fMRI (real-time functional Magnetic Resonance Imaging) provides a new non-invasive method to improve the cognitive function of the human brain, which achieves by training the human brain to regulate emotion. At the same time, brain network approaches based on graph theory is a hot spot. In this paper, we focus on the changes in the human brain’s small-world topology and network efficiency in graph theory before and after neurofeedback experiments. We designed an emotion self-regulation training with rt-fMRI, and acquired data from 20 participants, divided into the experimental group (EG) and the control group (CG). Subsequently, we constructed the brain network through the Anatomic Automatic Labelling (AAL) atlas, compared the topological changes of brain network between the EG and the CG in emotion self-regulation training. Our results show that both the EG and the CG have small-world topology, there are differences in small-world topology with emotion self-regulation training. Additionally, local efficiency is significantly different under certain sparsity, which suggests that emotional regulation has a positive effect on local networks. However, there is no significant difference in global efficiency, which means that the global network property does not change in emotion regulation training.
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Acknowledgment
This work was supported by the National Key Research and Development Plan of China under grant 2017YFB1002502, the National Natural Science Foundation of China (No. 61701089), and the Natural Science Foundation of Henan Province of China (No. 162300410333).
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Hu, L. et al. (2019). The Changes of Brain Networks Topology in Graph Theory of rt-fMRI Emotion Self-regulation Training. In: Liang, P., Goel, V., Shan, C. (eds) Brain Informatics. BI 2019. Lecture Notes in Computer Science(), vol 11976. Springer, Cham. https://doi.org/10.1007/978-3-030-37078-7_13
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DOI: https://doi.org/10.1007/978-3-030-37078-7_13
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