Default mode network abnormalities in idiopathic generalized epilepsy
ABSTRACT Idiopathic generalized epilepsy (IGE) is associated with widespread cortical network abnormalities on electroencephalography. Resting state functional connectivity (RSFC), based on fMRI, can assess the brain's global functional organization and its disruption in clinical conditions. We compared RSFC associated with the 'default mode network' (DMN) between people with IGE and healthy controls. Strength of functional connectivity within the DMN associated with seeds in the posterior cingulate cortex (PCC) and medial prefrontal cortices (MPFC) was compared between people with IGE and healthy controls and was correlated with seizure duration, age of seizure onset and age at scan. Those with IGE showed markedly reduced functional network connectivity between anterior and posterior cortical seed regions. Seizure duration positively correlates with RSFC between parahippocampal gyri and the PCC but negatively correlates with connectivity between the PCC and frontal lobe. The observed pattern of disruption provides evidence for integration- and segregation-type network abnormalities and supports aberrant network organization among people with IGE.
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ABSTRACT: The relationship between anatomic and resting state functional con-nectivity (FC) of large-scale brain networks has been of interest and has been investigated in a number of articles. In a recent article we introduced a graph diffusion model which predicts the functional network from the structural network in healthy brains. In this work we apply the graph diffusion model to two types of epilepsy, medial temporal sclerosis epilepsy (TLE-MTS), and MRI-normal temporal lobe epilepsy (TLE-no). We show that it is possible to estimate function from structure in non-healthy brains. We conclude that TLE-MTS on average requires a higher graph diffusion depth to estimate FC than both the healthy or the TLE-no groups. This suggests that an overly strong FC/SC relationship might be a sign of poor brain condition.International Symposium on Biomedical Imaging, New York, USA; 04/2015
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ABSTRACT: Juvenile myoclonic epilepsy (JME) is a common idiopathic (genetic) generalized epilepsy (IGE) syndrome characterized by impairments in executive and cognitive control, affecting independent living and psychosocial functioning. There is a growing consensus that JME is associated with abnormal function of diffuse brain networks, typically affecting frontal and fronto-thalamic areas.Methods Using diffusion MRI and a graph theoretical analysis, we examined bivariate (network-based statistic) and multivariate (global and local) properties of structural brain networks in patients with JME (N = 35) and matched controls. Neuropsychological assessment was performed in a subgroup of 14 patients.ResultsNeuropsychometry revealed impaired visual memory and naming in JME patients despite a normal full scale IQ (mean = 98.6). Both JME patients and controls exhibited a small world topology in their white matter networks, with no significant differences in the global multivariate network properties between the groups. The network-based statistic approach identified one subnetwork of hyperconnectivity in the JME group, involving primary motor, parietal and subcortical regions. Finally, there was a significant positive correlation in structural connectivity with cognitive task performance.Conclusions Our findings suggest that structural changes in JME patients are distributed at a network level, beyond the frontal lobes. The identified subnetwork includes key structures in spike wave generation, along with primary motor areas, which may contribute to myoclonic jerks. We conclude that analyzing the affected subnetworks may provide new insights into understanding seizure generation, as well as the cognitive deficits observed in JME patients.11/2014; 7. DOI:10.1016/j.nicl.2014.11.018
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ABSTRACT: Recent findings highlighted the non-stationarity of brain functional connectivity (FC) during resting-state functional magnetic resonance imaging (fMRI), encouraging the development of methods allowing to explore brain network dynamics. This appears particularly relevant when dealing with brain diseases involving dynamic neuronal processes, like epilepsy. In this study, we introduce a new method to pinpoint connectivity changes related to epileptic activity by integrating EEG and dynamic FC information. To our knowledge, no previous work has attempted to integrate dFC with the epileptic activity from EEG. The detailed results obtained from the analysis of two patients successfully detected specific patterns of connections/disconnections related to the epileptic activity and highlighted the potential of a dynamic analysis for a better understanding of network organisation in epilepsy.2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014); 04/2014