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EEG source-space synchrostate transitions and Markov modeling in the math-gifted brain during a long-chain reasoning task.
Hum Brain Mapp. 2020 09; 41(13):3620-3636.HB

Abstract

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.

Authors+Show Affiliations

School of Medical Imaging, Bengbu Medical College, Bengbu, Anhui, China.School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK.Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, China.School of Computer Science and Information Technology, Xinyang Normal University, Xinyang, Henan, China.Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, China.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

32469458

Citation

Zhang, Li, et al. "EEG Source-space Synchrostate Transitions and Markov Modeling in the Math-gifted Brain During a Long-chain Reasoning Task." Human Brain Mapping, vol. 41, no. 13, 2020, pp. 3620-3636.
Zhang L, Gan JQ, Zhu Y, et al. EEG source-space synchrostate transitions and Markov modeling in the math-gifted brain during a long-chain reasoning task. Hum Brain Mapp. 2020;41(13):3620-3636.
Zhang, L., Gan, J. Q., Zhu, Y., Wang, J., & Wang, H. (2020). EEG source-space synchrostate transitions and Markov modeling in the math-gifted brain during a long-chain reasoning task. Human Brain Mapping, 41(13), 3620-3636. https://doi.org/10.1002/hbm.25035
Zhang L, et al. EEG Source-space Synchrostate Transitions and Markov Modeling in the Math-gifted Brain During a Long-chain Reasoning Task. Hum Brain Mapp. 2020;41(13):3620-3636. PubMed PMID: 32469458.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - EEG source-space synchrostate transitions and Markov modeling in the math-gifted brain during a long-chain reasoning task. AU - Zhang,Li, AU - Gan,John Q, AU - Zhu,Yanmei, AU - Wang,Jing, AU - Wang,Haixian, Y1 - 2020/05/29/ PY - 2019/06/03/received PY - 2020/04/06/revised PY - 2020/04/26/accepted PY - 2020/5/30/pubmed PY - 2020/5/30/medline PY - 2020/5/30/entrez KW - EEG source-space synchrostate KW - Markov chain modeling KW - agglomerative hierarchical clustering KW - logical reasoning KW - math-gifted adolescents SP - 3620 EP - 3636 JF - Human brain mapping JO - Hum Brain Mapp VL - 41 IS - 13 N2 - 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. SN - 1097-0193 UR - https://www.unboundmedicine.com/medline/citation/32469458/EEG_source_space_synchrostate_transitions_and_Markov_modeling_in_the_math_gifted_brain_during_a_long_chain_reasoning_task_ L2 - https://doi.org/10.1002/hbm.25035 DB - PRIME DP - Unbound Medicine ER -
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