Changes in Dynamics Within and Between Resting-State Subnetworks in Juvenile Myoclonic Epilepsy Occur at Multiple Frequency Bands

Abstract

Time-varying connectivity analyses have indicated idiopathic generalized epilepsy (IGE) could cause significant abnormalities in dynamic connective pattern within and between resting-state sub-networks (RSNs). However, previous studies mainly focused on the IGE-induced dynamic changes of functional connectivity (FC) in specific frequency band (0.01–0.08 Hz or 0.01–0.15 Hz), ignoring the changes across different frequency bands. Here, 24 patients with IGE characterized by juvenile myoclonic epilepsy (JME) and 24 matched healthy controls were studied using a data-driven frequency decomposition approach and a sliding window approach. The RSN dynamics, including intra-RSN dynamics and inter-RSN dynamics, was further calculated to investigate dynamic FC changes within and between RSNs in JME patients in each decomposed frequency band. Compared to healthy controls, JME patients not only showed frequency-dependent decrease in intra-RSN dynamics within multiple RSNs but also exhibited fluctuant alterations in inter-RSN dynamics among several RSNs over different frequency bands especially in the ventral/dorsal attention network and the subcortical network. Additionally, the disease severity had significantly negative correlations with both intra-RSN dynamics within the subcortical network and inter-RSN dynamics between the subcortical network and the default network at the lower frequency band (0.0095–0.0195 Hz). These results suggested that abnormal dynamic FC within and between RSNs in JME occurs at multiple frequency bands and the lower frequency band (0.0095–0.0195 Hz) was probably more sensitive to JME-caused dynamic FC abnormalities. The frequency subdivision and selection are potentially helpful for detecting particular changes of dynamic FC in JME.

Altered dynamic effective connectivity of the default mode network in newly diagnosed drug-naïve juvenile myoclonic epilepsy

“…A recent study reported that patients with GTCS showed enhanced temporal variability of FC in large-scale brain functional networks, with these excessive variability anti-correlated with the duration of disease ( Jia et al, 2020 ). Our previous study also suggested that reduced dynamics within and between RSNs were linked to greater disease severity in JME ( Zhang et al, 2018 ). Taken together, the established associations in this study indicate that the early identified abnormalities in brain networks may be implicated in the progress of the disease, and can be an indication of disrupted developmental processes in JME.…”

“…For patients with JME, hyper dynamic characteristics in temporally fluctuating functional networks have been found ( Wang et al, 2019 ). Our previous study also demonstrated altered FC dynamics within and between RSNs, which were associated with the disease severity of JME patients ( Zhang et al, 2018 ). These studies suggested aberrant dynamic connectivity patterns of RSNs, particularly the DMN, are remarkable characteristics in patients with IGE.…”

Causality Analysis to the Abnormal Subcortical–Cortical Connections in Idiopathic-Generalized Epilepsy

“…Furthermore, in the temporal dimension, the time-varying dynamics of the connections in thalamus-frontal_mid, thalamus-precuneus, precuneus-frontal_mid, and also the frontal_mid-post_central were significantly increased in IGE. The spatial dysconnectivity and the altered FNC dynamics in thalamocortical circuit in epileptic brain have been reported in many studies (O'Muircheartaigh et al, 2012;Kim et al, 2014;Zhang et al, 2018;Qin et al, 2020b). Previous studies have demonstrated the role of thalamus in leading in initiating and maintaining the epileptic activity, which may be involved early or late in the discharge (Tyvaert et al, 2009;Martin-Lopez et al, 2017).…”

Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity

“…Finally, as mentioned earlier, the eye condition is different in the COBRE and FBIRN datasets. A previous study reported that different eye conditions might modulate DMN dynamics (Zhang et al, 2018), which could explain some differences in the DMN dynamics between the two datasets. State 5 of FBIRN dataset was distinguished from all other states in both datasets by showing higher within-ACC connectivity.…”