Complex discharge-affecting networks in juvenile myoclonic epilepsy: A simultaneous EEG-fMRI study - PubMed (original) (raw)

. 2016 Oct;37(10):3515-29.

doi: 10.1002/hbm.23256. Epub 2016 May 9.

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Complex discharge-affecting networks in juvenile myoclonic epilepsy: A simultaneous EEG-fMRI study

Li Dong et al. Hum Brain Mapp. 2016 Oct.

Abstract

Juvenile myoclonic epilepsy (JME) is a common subtype of idiopathic generalized epilepsies (IGEs) and is characterized by myoclonic jerks, tonic-clonic seizures and infrequent absence seizures. The network notion has been proposed to better characterize epilepsy. However, many issues remain not fully understood in JME, such as the associations between discharge-affecting networks and the relationships among resting-state networks. In this project, eigenspace maximal information canonical correlation analysis (emiCCA) and functional network connectivity (FNC) analysis were applied to simultaneous EEG-fMRI data from JME patients. The main findings of our study are as follows: discharge-affecting networks comprising the default model (DMN), self-reference (SRN), basal ganglia (BGN) and frontal networks have linear and nonlinear relationships with epileptic discharge information in JME patients; the DMN, SRN and BGN have dense/specific associations with discharge-affecting networks as well as resting-state networks; and compared with controls, significantly increased FNCs between the salience network (SN) and resting-state networks are found in JME patients. These findings suggest that the BGN, DMN and SRN may play intermediary roles in the modulation and propagation of epileptic discharges. These roles further tend to disturb the switching function of the SN in JME patients. We also postulate that emiCCA and FNC analysis may provide a potential analysis platform to provide insights into our understanding of the pathophysiological mechanism of epilepsy subtypes such as JME. Hum Brain Mapp 37:3515-3529, 2016. © 2016 Wiley Periodicals, Inc.

Keywords: EEG-fMRI; Juvenile myoclonic epilepsy; complex discharge-affecting networks; functional network connectivity; nonlinearity.

© 2016 Wiley Periodicals, Inc.

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Figures

Figure 1

Figure 1

The framework of discharge‐affecting network analysis using _emi_CCA. A: For fMRI data, group ICA was first applied to extract the spatiotemporal features of the fMRI data; then, the IC time courses were concatenated across JME patients and defined as dataset Y. For EEG data, the onsets of GSWDs were first identified by neurologists. Then, the dataset X was defined by a design matrix containing the onsets of GSWDs, which were convolved with 4 SPM canonical HRFs (peaking at 3, 5, 7 and 9 s), 1 Glover HRF and 1 single Gamma HRF. B: _emi_CCA was applied to identify the linear and nonlinear discharge‐affecting components with weights (α) exceeding the 1.5 standard deviations of weight values corresponding to the significant maximal information eigen coefficients (MIECs). C: The maximal time‐lagged correlation method was used to examine the possible functional network connectivity between those discharge‐affecting networks identified by _emi_CCA. [Color figure can be viewed in the online issue, which is available at

http://wileyonlinelibrary.com

.]

Figure 2

Figure 2

Discharge‐affecting networks identified by _emi_CCA in JME patients. The T‐maps of these spatial components (P < 0.05, FWE‐corrected) are shown. The size of the yellow circle presents the weight of each time course in _emi_CCA, which represent the weightiness of each piece of dataset Y. Linear (blue border) or nonlinear (red border) relationships between the fMRI time courses of networks and the EEG discharges are also shown. [Color figure can be viewed in the online issue, which is available at

http://wileyonlinelibrary.com

.]

Figure 3

Figure 3

Discharge‐affecting FNCs in JME patients (P < 0.05, FDR‐corrected). The lines show the significant positive (red) and negative (blue) correlation connections in JME patients from the 55 possible correlation combinations. The size of circles in the bottom right represents the degree of the node in functional network connectivity (containing positive and negative), and the green color represents the networks, which are also the resting‐state networks. The lags, which are the amount of delay between component time courses, are also shown on the right (P < 0.05). A → B: component B lags component A by some calculated seconds. [Color figure can be viewed in the online issue, which is available at

http://wileyonlinelibrary.com

.]

Figure 4

Figure 4

Other 6 meaningful resting‐state networks in JME patients (P < 0.05, FWE‐corrected). L: left; R: right. [Color figure can be viewed in the online issue, which is available at

http://wileyonlinelibrary.com

.]

Figure 5

Figure 5

The FNCs of resting‐state networks in JME patients and controls (P < 0.05, FDR‐corrected) and the differences of FNCs between them. For FNCs in each group, the red lines represent positive connections, and blue lines represent negative connections. The lag (_P_ < 0.05), A →; B, represents that component B lags component A by some calculated seconds. For the differences between two groups, red lines represent that JME patients show greater correlations than controls, while blue lines representing that JME patients show lower connections. The yellow color represents the discharge‐affecting resting‐state networks. The delay (_P_ < 0.05), A → B (Patients > Controls), represents that the delay (component B lags component A) in JME patients is larger than controls. [Color figure can be viewed in the online issue, which is available at

http://wileyonlinelibrary.com

.]

Figure 6

Figure 6

Partial correlations between discharge‐affecting FNCs (Fisher's _z_‐score) and the age of epilepsy onset in JME patients, while controlling for the gender. R: partial correlation coefficient; P: _P_‐value.

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