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

Autor: Ke M; School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China. keming@lut.edu.cn., Luo X; School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China., Guo Y; School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China., Zhang J; School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China., Ren X; School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China., Liu G; Department of Nuclear Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730030, China. keming@lut.edu.cn.
Jazyk: angličtina
Zdroj: Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology [Neurol Sci] 2024 Oct; Vol. 45 (10), pp. 4983-4996. Date of Electronic Publication: 2024 May 04.
DOI: 10.1007/s10072-024-07506-8
Abstrakt: Background: Juvenile 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.
Methods: Resting-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.
Results: Our 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.
Conclusions: Our 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.
(© 2024. Fondazione Società Italiana di Neurologia.)
Databáze: MEDLINE
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