PDF(Pubmed)
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.
摘要：
从静息状态功能磁共振成像（rs-fMRI）数据得出的复杂性已用于探索认知状态和职业神经可塑性。然而,关于职业因素对连接网络的动态复杂性和拓扑特性的影响的信息很少。在本文中,我们提出了一种新颖的动态脑复杂性分析（DBCA）框架，以探索长期工作经验导致的体素水平和复杂拓扑的大脑活动的动态复杂性变化。所提出的DBCA由动态脑熵映射分析和基于脑熵序列的复杂网络分析组成，产生局部大脑区域的动态复杂性和整个大脑区域的拓扑复杂性，分别。首先,计算了逐体素脑图的瞬时复杂度；与非海员相比，海员显示小脑的动态熵值降低，左梭形回（BA20）的值增加。Further,基于脑熵序列的复杂网络分析揭示了海员和非海员在拓扑复杂性方面的小世界性，这表明它是人类大脑的固有属性。此外,海员比非海员表现出更高的平均路径长度和更低的平均聚类系数，这表明海员的信息处理能力降低。此外，海员效率的降低表明他们的处理网络效率较低。总而言之,所提出的DBCA对于探索体素活动和区域连通性的动态复杂性变化是有效的，表明职业经验可以重塑海员的动态大脑复杂性指纹。
