Distinguishing Dependent-Stage Secondary Epileptogenesis in a Complex Case of Giant Hypothalamic Hamartoma With Assistance of a Computational Method.

Besides gelastic seizures, hypothalamic hamartoma (HH) is also noted for its susceptibility to remote secondary epileptogenesis. Although clinical observations have demonstrated its existence, and a three-stage theory has been proposed, how to determine whether a remote symptom is spontaneous or dependent on epileptic activities of HH is difficult in some cases. Herein, we report a case of new non-gelastic seizures in a 9-year-old female associated with a postoperatively remaining HH. Electrophysiological examinations and stereo-electroencephalography (SEEG) demonstrated seizure onsets with slow-wave and fast activities on the outside of the HH. By using computational methodologies to calculate the network dynamic effective connectivities, the importance of HH in the epileptic network was revealed. After SEEG-guided thermal coagulation of the remaining HH, the patient finally was seizure-free at the 2-year follow-up. This case showed the ability of computational methods to reveal information underlying complex SEEG signals, and further demonstrated the dependent-stage secondary epileptogenesis, which has been rarely reported.

Localization of Epileptogenic Zone With the Correction of Pathological Networks.

Patients with focal drug-resistant epilepsy are potential candidates for surgery. Stereo-electroencephalograph (SEEG) is often considered as the "gold standard" to identify the epileptogenic zone (EZ) that accounts for the onset and propagation of epileptiform discharges. However, visual analysis of SEEG still prevails in clinical practice. In addition, epilepsy is increasingly understood to be the result of network disorder, but the specific organization of the epileptic network is still unclear. Therefore, it is necessary to quantitatively localize the EZ and investigate the nature of epileptogenic networks. In this study, intracranial recordings from 10 patients were analyzed through adaptive directed transfer function, and the out-degree of effective network was selected as the principal indicator to localize the epileptogenic area. Furthermore, a coupled neuronal population model was used to qualitatively simulate electrical activity in the brain. By removing individual populations, virtual surgery adjusting the network organization could be performed. Results suggested that the accuracy and detection rate of the EZ localization were 82.86 and 85.29%, respectively. In addition, the same stage shared a relatively stable connectivity pattern, while the patterns changed with transition to different processes. Meanwhile, eight cases of simulations indicated that networks in the ictal stage were more likely to generate rhythmic spikes. This indicated the existence of epileptogenic networks, which could enhance local excitability and facilitate synchronization. The removal of the EZ could correct these pathological networks and reduce the amount of spikes by at least 75%. This might be one reason why accurate resection could reduce or even suppress seizures. This study provides novel insights into epilepsy and surgical treatments from the network perspective.

On the origin of epileptic High Frequency Oscillations observed on clinical electrodes.

OBJECTIVE: In this study we aim to identify the key (patho)physiological mechanisms and biophysical factors which impact the observability and spectral features of High Frequency Oscillations (HFOs). METHODS: In order to accurately replicate HFOs we developed virtual-brain/virtual-electrode simulation environment combining novel neurophysiological models of neuronal populations with biophysical models for the source/sensor relationship. Both (patho)physiological mechanisms (synaptic transmission, depolarizing GABAA effect, hyperexcitability) and physical factors (geometry of extended cortical sources, size and position of electrodes) were taken into account. Simulated HFOs were compared to real HFOs extracted from intracerebral recordings of 2 patients. RESULTS: Our results revealed that HFO pathological activity is being generated by feed-forward activation of cortical interneurons that produce fast depolarizing GABAergic post-synaptic potentials (PSPs) onto pyramidal cells. Out of phase patterns of depolarizing GABAergic PSPs explained the shape, entropy and spatiotemporal features of real human HFOs. CONCLUSIONS: The terminology "high-frequency oscillation" (HFO) might be misleading as the fast ripple component (200-600 Hz) is more likely a "high-frequency activity" (HFA), the origin of which is independent from any oscillatory process. SIGNIFICANCE: New insights regarding the origins and observability of HFOs along depth-EEG electrodes were gained in terms of spatial extent and 3D geometry of neuronal sources.

From intracerebral EEG signals to brain connectivity: identification of epileptogenic networks in partial epilepsy.

Epilepsy is a complex neurological disorder characterized by recurring seizures. In 30% of patients, seizures are insufficiently reduced by anti-epileptic drugs. In the case where seizures originate from a relatively circumscribed region of the brain, epilepsy is said to be partial and surgery can be indicated. The success of epilepsy surgery depends on the accurate localization and delineation of the epileptogenic zone (which often involves several structures), responsible for seizures. It requires a comprehensive pre-surgical evaluation of patients that includes not only imaging data but also long-term monitoring of electrophysiological signals (scalp and intracerebral EEG). During the past decades, considerable effort has been devoted to the development of signal analysis techniques aimed at characterizing the functional connectivity among spatially distributed regions over interictal (outside seizures) or ictal (during seizures) periods from EEG data. Most of these methods rely on the measurement of statistical couplings among signals recorded from distinct brain sites. However, methods differ with respect to underlying theoretical principles (mostly coming from the field of statistics or the field of non-linear physics). The objectives of this paper are: (i) to provide an brief overview of methods aimed at characterizing functional brain connectivity from electrophysiological data, (ii) to provide concrete application examples in the context of drug-refractory partial epilepsies, and iii) to highlight some key points emerging from results obtained both on real intracerebral EEG signals and on signals simulated from physiologically plausible models in which the underlying connectivity patterns are known a priori (ground truth).