Conference Agenda

Session chair: Prof. Serge Vulliemoz

Reason for choosing this topic: There is increasing evidence about the usefulness of functional brain connectivity to help diagnose epilepsy, to localize the epileptogenic focus or to unravel brain mechanisms that impact the patient’s life. However, these techniques have limitations and are currently not used in clinical practice. In this session we want to emphasize the methodologies that are used to calculate the functional connectivity pattern, define the added value of functional brain networks derived from EEG signals and discuss important issues in this field to pave the way for their implementation in clinical practice.

Functional MRI has been used increasingly to characterize brain networks in epilepsy and typically shows patterns of decreased connectivity within and increased connectivity between intrinsic cognitive networks (ICN), which in turn has been shown to be associated with cognitive performance. At the same time we know that focal epilepsy is associated with networks that show enhanced connectivity and produce periods of high EEG synchronicity in the form of epileptic discharges which can have a transient impact on cognition and cognitive networks. We have used simultaneous EEG and fMRI to try to tease apart the transient and longer term connectivity differences between focal epilepsy patients and controls. Perhaps surprisingly, ICNs are commonly perturbed by epileptic discharges arising from different brain areas and they explain a large degree of ICN connectivity abnormalities. Functional connectivity measured with fMRI is therefore very sensitivity to transient epileptic activity.

High frequency oscillations (Ripples: 80-250Hz, Fast Ripples (FR): 250-500Hz) are novel biomarkers for epileptogenic tissue. The pathophysiology suggests enhanced functional connectivity within FR-generating tissue. Our aim was to determine the relation between brain areas showing FRs and ‘baseline’ high frequency functional network characteristics. We compared the Eigenvector Centrality between channels that did and did not show events in epilepsy patients. We found functional isolation in the gamma-band and a suggestion of functional integration in the FR-band network of channels covering epileptogenic tissue. ‘Baseline’ high-frequency network parameters might help intra-operative recognition of epileptogenic tissue without the need for waiting for events.

<p>The brain is a complex system of neuronal populations, which establish functional connections during task execution and at rest. BOLD fMRI allows studying the spatial properties of these functional networks; however, it cannot reveal fast interactions which are needed to explain sub-second neural processing. Recent studies have shown that Hidden Markov Modelling (HMM) of MEG data can reveal consistent patterns of brain states fluctuating at small time scales. In this talk we will show that HMM analysis of EEG reveals a rich collection of such fast transient networks. Moreover, we investigate the BOLD correlates of these networks using simultaneously recorded fMRI data. Finally, we extend this methodology to epilepsy patients and characterize fast interactions between resting-state networks during interictal activity.</p>

Directed functional brain connectivity has been shown useful to localize the seizure onset zone in refractory epilepsy patients from intracranial EEG. In this presentation this methodology is extended so it can be applied to scalp EEG. First EEG source imaging is performed and later Granger causality measures such as the partial directed coherence and directed transfer function are calculated to unravel the network at source level. The proposed method is used to successfully localize the seizure onset zone from clinical EEG recordings in 23 patients. Furthermore, we show the capability of the method to classify 20 left from 20 right temporal lobe epilepsy patients based from the networks derived from high density EEG acquired during resting state.