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ArticleFrontiers in neuroscience2022

Dynamical Complexity Fingerprints of Occupation-Dependent Brain Functional Networks in Professional Seafarers.

Hongjie Yan, Huijun Wu, Yanyan Chen, Yang Yang, Min Xu, Weiming Zeng, Jian Zhang, Chunqi Chang, Nizhuan Wang

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Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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4 citing papers in PubMed.

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5 · Who and what money

Authors and funding

9 authors.

Hongjie YanDepartment of Neurology, Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China.
Huijun WuSchool of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Yanyan ChenSchool of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Yang YangKey Laboratory of Behavioral Science, Center for Brain Science and Learning Difficulties, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
Min XuCenter for Brain Disorders and Cognitive Science, Shenzhen University, Shenzhen, China.
Weiming ZengLab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China.
Jian ZhangSchool of Pharmacy, Health Science Center, Shenzhen University, Shenzhen, China.
Chunqi ChangSchool of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Nizhuan WangSchool of Biomedical Engineering, ShanghaiTech University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The complexity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data has been applied for exploring cognitive states and occupational neuroplasticity. However, there is little information about the influence of occupational factors on dynamic complexity and topological properties of the connectivity networks. In this paper, we proposed a novel dynamical brain complexity analysis (DBCA) framework to explore the changes in dynamical complexity of brain activity at the voxel level and complexity topology for professional seafarers caused by long-term working experience. The proposed DBCA is made up of dynamical brain entropy mapping analysis and complex network analysis based on brain entropy sequences, which generate the dynamical complexity of local brain areas and the topological complexity across brain areas, respectively. First, the transient complexity of voxel-wise brain map was calculated; compared with non-seafarers, seafarers showed decreased dynamic entropy values in the cerebellum and increased values in the left fusiform gyrus (BA20). Further, the complex network analysis based on brain entropy sequences revealed small-worldness in terms of topological complexity in both seafarers and non-seafarers, indicating that it is an inherent attribute of human the brain. In addition, seafarers showed a higher average path length and lower average clustering coefficient than non-seafarers, suggesting that the information processing ability is reduced in seafarers. Moreover, the reduction in efficiency of seafarers suggests that they have a less efficient processing network. To sum up, the proposed DBCA is effective for exploring the dynamic complexity changes in voxel-wise activity and region-wise connectivity, showing that occupational experience can reshape seafarers' dynamic brain complexity fingerprints.

Indexed as

brain entropydynamical complexityefficiencygraph theoryoccupational neuroplasticityseafarersmall-worldness

Identifiers

PMID35368265
PMCPMC8973415

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