School of Medical Imaging, Bengbu Medical College, Bengbu, Anhui, China.
School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK.
Hum Brain Mapp. 2020 Sep;41(13):3620-3636. doi: 10.1002/hbm.25035. Epub 2020 May 29.
To reveal transition dynamics of global neuronal networks of math-gifted adolescents in handling long-chain reasoning, this study explores momentary phase-synchronized patterns, that is, electroencephalogram (EEG) synchrostates, of intracerebral sources sustained in successive 50 ms time windows during a reasoning task and non-task idle process. Through agglomerative hierarchical clustering for functional connectivity graphs and nested iterative cosine similarity tests, this study identifies seven general and one reasoning-specific prototypical functional connectivity patterns from all synchrostates. Markov modeling is performed for the time-sequential synchrostates of each trial to characterize the interstate transitions. The analysis reveals that default mode network, central executive network (CEN), dorsal attention network, cingulo-opercular network, left/right ventral frontoparietal network, and ventral visual network aperiodically recur over non-task or reasoning process, exhibiting high predictability in interactively reachable transitions. Compared to non-gifted subjects, math-gifted adolescents show higher fractional occupancy and mean duration in CEN and reasoning-triggered transient right frontotemporal network (rFTN) in the time course of the reasoning process. Statistical modeling of Markov chains reveals that there are more self-loops in CEN and rFTN of the math-gifted brain, suggesting robust state durability in temporally maintaining the topological structures. Besides, math-gifted subjects show higher probabilities in switching from the other types of synchrostates to CEN and rFTN, which represents more adaptive reconfiguration of connectivity pattern in the large-scale cortical network for focused task-related information processing, which underlies superior executive functions in controlling goal-directed persistence and high predictability of implementing imagination and creative thinking during long-chain reasoning.
为了揭示数学天赋青少年在处理长链推理时全球神经元网络的转变动态，本研究探索了瞬间相位同步模式，即脑内源在推理任务和非任务空闲过程中连续 50ms 时间窗口内的脑电图（EEG）同步状态。通过对功能连接图进行凝聚层次聚类和嵌套迭代余弦相似性测试，本研究从所有同步状态中识别出七种通用和一种推理特定的典型功能连接模式。对每个试验的时间序列同步状态进行马尔可夫建模，以表征状态间的转换。分析表明，默认模式网络、中央执行网络（CEN）、背侧注意网络、扣带回-顶叶网络、左右腹侧额顶网络和腹侧视觉网络在非任务或推理过程中周期性地出现，在交互可达的转换中表现出较高的可预测性。与非天赋组相比，数学天赋青少年在推理过程中 CEN 和推理触发的瞬态右侧额颞网络（rFTN）的分数占据和平均持续时间更高。马尔可夫链的统计建模表明，CEN 和 rFTN 中的自环更多，这表明在暂时保持拓扑结构方面，状态耐久性更强。此外，数学天赋组从其他类型的同步状态切换到 CEN 和 rFTN 的概率更高，这代表了在大型皮质网络中连接模式的适应性重构，这是在长链推理中控制目标导向坚持和高可预测性地实施想象和创造性思维的执行功能的基础。
