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ArticleNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2024

Alterations in spatiotemporal characteristics of dynamic networks in juvenile myoclonic epilepsy.

Ming Ke, Xiaofei Luo, Yi Guo, Juli Zhang, Xupeng Ren, Guangyao Liu

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Article in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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3 · Its place in the literature

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1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Ming KeSchool of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China. keming@lut.edu.cn.ORCID http://orcid.org/0000-0002-9690-1529
Xiaofei LuoSchool of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China.
Yi GuoSchool of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China.
Juli ZhangSchool of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China.
Xupeng RenSchool of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China.
Guangyao LiuDepartment of Nuclear Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730030, China. keming@lut.edu.cn.

Funding

National Natural Science Foundation of China 61966023National Natural Science Foundation of China 82160326
6 · The paper itself

Abstract

backgroundJuvenile myoclonic epilepsy (JME) is characterized by altered patterns of brain functional connectivity (FC). However, the nature and extent of alterations in the spatiotemporal characteristics of dynamic FC in JME patients remain elusive. Dynamic networks effectively encapsulate temporal variations in brain imaging data, offering insights into brain network abnormalities and contributing to our understanding of the seizure mechanisms and origins.

methodsResting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 37 JME patients and 37 healthy counterparts. Forty-seven network nodes were identified by group-independent component analysis (ICA) to construct the dynamic network. Ultimately, patients' and controls' spatiotemporal characteristics, encompassing temporal clustering and variability, were contrasted at the whole-brain, large-scale network, and regional levels.

resultsOur findings reveal a marked reduction in temporal clustering and an elevation in temporal variability in JME patients at the whole-brain echelon. Perturbations were notably pronounced in the default mode network (DMN) and visual network (VN) at the large-scale level. Nodes exhibiting anomalous were predominantly situated within the DMN and VN. Additionally, there was a significant correlation between the severity of JME symptoms and the temporal clustering of the VN.

conclusionsOur findings suggest that excessive temporal changes in brain FC may affect the temporal structure of dynamic brain networks, leading to disturbances in brain function in patients with JME. The DMN and VN play an important role in the dynamics of brain networks in patients, and their abnormal spatiotemporal properties may underlie abnormal brain function in patients with JME in the early stages of the disease.

Indexed as

BrainMagnetic Resonance ImagingMyoclonic Epilepsy, JuvenileNerve NetAdolescentAdultBrain MappingDefault Mode NetworkFemaleHumansMaleNeural PathwaysYoung AdultDynamic networkfMRIFunctional connectivityFunctional networksIndependent component analysisJuvenile myoclonic epilepsy

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.