Network Connectivity in Epilepsy: Resting State fMRI and EEG-fMRI Contributions

Abstract

There is a growing body of evidence pointing toward large-scale networks underlying the core phenomena in epilepsy, from seizure generation to cognitive dysfunction or response to treatment. The investigation of networks in epilepsy has become a key concept to unlock a deeper understanding of the disease. Functional imaging can provide valuable information to characterize network dysfunction; in particular resting state fMRI (RS-fMRI), which is increasingly being applied to study brain networks in a number of diseases. In patients with epilepsy, network connectivity derived from RS-fMRI has found connectivity abnormalities in a number of networks; these include the epileptogenic, cognitive and sensory processing networks. However, in majority of these studies, the effect of epileptic transients in the connectivity of networks has been neglected. EEG-fMRI has frequently shown networks related to epileptic transients that in many cases are concordant with the abnormalities shown in RS studies. This points toward a relevant role of epileptic transients in the network abnormalities detected in RS-fMRI studies. In this review, we summarize the network abnormalities reported by these two techniques side by side, provide evidence of their overlapping findings, and discuss their significance in the context of the methodology of each technique. A number of clinically relevant factors that have been associated with connectivity changes are in turn associated with changes in the frequency of epileptic transients. These factors include different aspects of epilepsy ranging from treatment effects, cognitive processes, or transition between different alertness states (i.e., awake-sleep transition). For RS-fMRI to become a more effective tool to investigate clinically relevant aspects of epilepsy it is necessary to understand connectivity changes associated with epileptic transients, those associated with other clinically relevant factors and the interaction between them, which represents a gap in the current literature. We propose a framework for the investigation of network connectivity in patients with epilepsy that can integrate epileptic processes that occur across different time scales such as epileptic transients and disease duration and the implications of this approach are discussed.

Keywords: EEG–fMRI; RS-fMRI; epilepsy; functional connectivity; resting state; resting state networks.

Figure 1

(A) Representing state dependant connectivity. If we consider a simple brain network with three linearly connected nodes (top), then connectivity between pairs of regions (A,B) or (A,C) can be graphically represented as a function of time (upper row graphics-red line). During: (1) cognitive tasks; (2) resting state in healthy population; and (3) resting state in patients with epilepsy. The fluctuations of this network’s connectivity in time could be measured at points illustrated by the black crosses. The brain’s connectivity state during rest and activity can then be summarized by the mean and range represented by the “+” and circles areas, respectively, in the lowest row of “Time average” plots. Epileptic transients are associated with changes in network connectivity (peaks in top right graphic) that account for a proportion of the connectivity (red area) expected to lie outside of the range associated with resting state activity in controls. The contribution of epileptic transients (red area) to the RS connectivity differences found patients with epilepsy (cross in the yellow area) and how this relates to normal connectivity (blue area in adjacent plot) still remains to be well characterized. (B) Connectivity differences between controls and epilepsy. Resting connectivity in patients with epilepsy (yellow area) falls out of the range (blue area) seen in controls for certain networks as reported by RS studies. Several hypotheses can be derived from this observation, e.g., (1) Connectivity changes are permanent abnormalities. (2) Connectivity changes are driven by transient epileptic activity. (3) They are a combination of both permanent and transient abnormalities. There are a number of factors that are know to modify connectivity and frequency of epileptic transients at different time scales: from cognitive process, sleep/awake transitions, or treatment effects in the short–medium term through to brain maturation and aging occurring at a long term scale. An example change in connectivity due to such factors is shown as trajectory in time (red line) through a space defined by the connectivity that can be measured using RS-fMRI. However, to understand connectivity changes associated with these factors, it is crucial to obtain measurements at different time points along the trajectory and associated them with clinically relevant factors. If RS connectivity dynamics and the role of epileptic transients in altering measured RS-fMRI connectivity are understood, RS connectivity measurements may be a potential biomarker of a number of clinically relevant aspects in epilepsy such as prediction of response to treatment, cognitive dysfunction associated to epilepsy or change to seizure patterns due to hormones, sleep, brain maturation, etc. (C) Example of a model of connectivity changes applied to investigate drug treatment in epilepsy. Any factor that modifies the rate of epileptic transients will result in changes to the RS connectivity. In the case of medical treatment, different degrees of response (partial or complete) would be associated with different connectivity states in a patient; more epileptic transients, means the network would spend more time with connectivity values in the “epileptic transient connectivity region” (red area) as illustrated in the first row graphic. How these changes to connectivity affect the mean connectivity of a patient with epilepsy is dependant on the proportion of abnormal connectivity explained by the epileptic transients. In this case, two scenarios are possible. Hypothesis 1: connectivity abnormalities in patients with epilepsy are mainly due to the abnormalities associated to epileptic transients, in which case, the gradual reduction of transients in time will result in the mean connectivity of a patient with epilepsy (represented by a red +) progressively moving towards connectivity found within the healthy population (represented by the blue circle with the mean on the black cross position). Alternatively, hypothesis 2 illustrates how if only a proportion of connectivity abnormalities are due to epileptic transients, connectivity may change in time due to treatment with a reduction in epileptic transients, however, the connectivity remains significantly different to the healthy population with potential therapeutic and cognitive consequences.