BOLD-fMRI activity informed by network variation of scalp EEG in juvenile myoclonic epilepsy

Abstract

Epilepsy is marked by hypersynchronous bursts of neuronal activity, and seizures can propagate variably to any and all areas, leading to brain network dynamic organization. However, the relationship between the network characteristics of scalp EEG and blood oxygenation level-dependent (BOLD) responses in epilepsy patients is still not well known. In this study, simultaneous EEG and fMRI data were acquired in 18 juvenile myoclonic epilepsy (JME) patients. Then, the adapted directed transfer function (ADTF) values between EEG electrodes were calculated to define the time-varying network. The variation of network information flow within sliding windows was used as a temporal regressor in fMRI analysis to predict the BOLD response. To investigate the EEG-dependent functional coupling among the responding regions, modulatory interactions were analyzed for network variation of scalp EEG and BOLD time courses. The results showed that BOLD activations associated with high network variation were mainly located in the thalamus, cerebellum, precuneus, inferior temporal lobe and sensorimotor-related areas, including the middle cingulate cortex (MCC), supplemental motor area (SMA), and paracentral lobule. BOLD deactivations associated with medium network variation were found in the frontal, parietal, and occipital areas. In addition, modulatory interaction analysis demonstrated predominantly directional negative modulation effects among the thalamus, cerebellum, frontal and sensorimotor-related areas. This study described a novel method to link BOLD response with simultaneous functional network organization of scalp EEG. These findings suggested the validity of predicting epileptic activity using functional connectivity variation between electrodes. The functional coupling among the thalamus, frontal regions, cerebellum and sensorimotor-related regions may be characteristically involved in epilepsy generation and propagation, which provides new insight into the pathophysiological mechanisms and intervene targets for JME.

Keywords: Functional coupling; Juvenile myoclonic epilepsy; Modulatory interaction; Network variation; Simultaneous EEG and fMRI.

Fig. 1

Overview of the proposed EEG-fMRI analysis based on the network variation of scalp EEG. (A): The time-varying scalp network was constructed from the preprocessed EEG using ADTF. (B): Variation of the information flow of the ADTF between electrodes in each 2 s time window was extracted to construct the network variation time series. (C): The values of network variation time series above the one standard deviation and the mean were used as the regressors in GLM-based EEG-informed fMRI analysis, respectively. (D): Statistical maps were obtained from the above GLM analysis.

Fig. 2

The BOLD response correlated with high EEG-network variation.

Fig. 3

The BOLD response correlated with medium EEG-network variation.

Fig. 4

Modulatory interaction for EEG-network variation and BOLD functional coupling. (A) Negative modulation effects exist from thalamus to frontal areas, from cerebellum to frontal and sensorimotor areas, as well as from frontal to sensorimotor areas. (B) Positive modulation effects exist within frontal areas, and from frontal to cerebellum. (C) Summarization of EEG-dependent functional coupling resulting from the modulatory interaction among four important epileptic central districts. Abbreviations of the regions according to AAL atlas: Front_S: Frontal_Sup; Front_M: Frontal_Med_Orb; ACC: anterior cingulate cortex; Front_Sm: Frontal_Sup_Medial; SMA: Supplementary motor area; Parac_l: Paracentral_lobule.

Fig. 5

Relationship between the modulation effects and clinical features. Significant negative correlation was found between negative modulation effects with age of epileptic onset, duration and seizure frequency (times per month). Note that the negative effect on y-axis represents reverse EEG modulation resulting to decreased functional coupling between regions, and the modulation becomes more prominent along with the negative effect enhancing.