Kaspar Schindler

Evolving functional network properties and synchronizability during human epileptic seizures

We assess electrical brain dynamics before, during, and after 100 human epileptic seizures with different anatomical onset locations by statistical and spectral properties of functionally defined networks. We observe a concave-like temporal evolution of characteristic path length and cluster coefficient indicative of a movement from a more random toward a more regular and then back toward a more random functional topology. Surprisingly, synchronizability was significantly decreased during the seizure state but increased already prior to seizure end. Our findings underline the high relevance of studying complex systems from the viewpoint of complex networks, which may help to gain deeper insights into the complicated dynamics underlying epileptic seizures.

Synchronization and desynchronization in epilepsy: controversies and hypotheses.

Abstract  Epilepsy has been historically seen as a functional brain disorder associated with excessive synchronization of large neuronal populations leading to a hypersynchronous state. Recent evidence showed that epileptiform phenomena, particularly seizures, result from complex interactions between neuronal networks characterized by heterogeneity of neuronal firing and dynamical evolution of synchronization. Desynchronization is often observed preceding seizures or during their early stages; in contrast, high levels of synchronization observed towards the end of seizures may facilitate termination. In this review we discuss cellular and network mechanisms responsible for such complex changes in synchronization. Recent work has identified cell‐type‐specific inhibitory and excitatory interactions, the dichotomy between neuronal firing and the non‐local measurement of local field potentials distant to that firing, and the reflection of the neuronal dark matter problem in non‐firing neurons active in seizures. These recent advances have challenged long‐established views and are leading to a more rigorous and realistic understanding of the pathophysiology of epilepsy.

EEG analysis with simulated neuronal cell models helps to detect pre-seizure changes

OBJECTIVES To test if a method for real-time detection of epileptic seizures based on electroencephalographic (EEG) analysis with simulated neuronal cell models can be modified to identify pre-seizure changes. METHODS Our EEG analysis method consists of two simulated leaky integrate and fire units (LIFU) connected to a signal preprocessing stage that marks parts of the EEG signals with slopes larger than a preset threshold Hth with unit pulses. The LIFUs change their spiking frequency depending on the rate and the synchrony of the impinging pulse trains. Here, we use our method in a high-sensitivity mode by setting Hth to low values, which causes the LIFUs to continuously spike during the interictal state. We test if the LIFUs spiking rates change before seizure onset. RESULTS We used 9 long-term EEGs (16+/-7 h) of 7 patients with drug resistant epilepsy. Fifteen seizures were analyzed and all were preceded by an increase of the time-averaged spiking rates SR(av) of the LIFUs. We defined a function F(Sz), which quantifies the changes of SR(av). F(Sz) increased and stayed above an individually set and fixed threshold 83+/-91 min (range: 4-330 min) before EEG seizure onset. Only two false alarms occurred. CONCLUSIONS We conclude that EEG analysis with simulated neuronal cell models may be used to detect pre-seizure changes with high sensitivity and specificity.

Intermittent spike-wave dynamics in a heterogeneous, spatially extended neural mass model.

Generalised epileptic seizures are frequently accompanied by sudden, reversible transitions from low amplitude, irregular background activity to high amplitude, regular spike-wave discharges (SWD) in the EEG. The underlying mechanisms responsible for SWD generation and for the apparently spontaneous transitions to SWD and back again are still not fully understood. Specifically, the role of spatial cortico-cortical interactions in ictogenesis is not well studied. We present a macroscopic, neural mass model of a cortical column which includes two distinct time scales of inhibition. This model can produce both an oscillatory background and a pathological SWD rhythm. We demonstrate that coupling two of these cortical columns can lead to a bistability between out-of-phase, low amplitude background dynamics and in-phase, high amplitude SWD activity. Stimuli can cause state-dependent transitions from background into SWD. In an extended local area of cortex, spatial heterogeneities in a model parameter can lead to spontaneous reversible transitions from a desynchronised background to synchronous SWD due to intermittency. The deterministic model is therefore capable of producing absence seizure-like events without any time dependent adjustment of model parameters. The emergence of such mechanisms due to spatial coupling demonstrates the importance of spatial interactions in modelling ictal dynamics, and in the study of ictogenesis.

Application of a multivariate seizure detection and prediction method to non-invasive and intracranial long-term EEG recordings

OBJECTIVE Retrospective evaluation and comparison of performances of a multivariate method for seizure detection and prediction on simultaneous long-term EEG recordings from scalp and intracranial electrodes. METHODS Two multivariate techniques based on simulated leaky integrate-and-fire neurons were investigated in order to detect and predict seizures. Both methods were applied and assessed on 423h of EEG and 26 seizures in total, recorded simultaneously from the scalp and intracranially continuously over several days from six patients with pharmacorefractory epilepsy. RESULTS Features generated from simultaneous scalp and intracranial EEG data showed a similar dynamical behavior. Significant performances with sensitivities of up to 73%/62% for scalp/invasive EEG recordings given an upper limit of 0.15 false detections per hour were obtained. Up to 59%/50% of all seizures could be predicted from scalp/invasive EEG, given a maximum number of 0.15 false predictions per hour. A tendency to better performances for scalp EEG was obtained for the detection algorithm. CONCLUSIONS The investigated methods originally developed for non-invasive EEG were successfully applied to intracranial EEG. Especially, concerning seizure detection the method shows a promising performance which is appropriate for practical applications in EEG monitoring. Concerning seizure prediction a significant prediction performance is indicated and a modification of the method is suggested. SIGNIFICANCE This study evaluates simultaneously recorded non-invasive and intracranial continuous long-term EEG data with respect to seizure detection and seizure prediction for the first time.

Self-organised transients in a neural mass model of epileptogenic tissue dynamics

Stimulation of human epileptic tissue can induce rhythmic, self-terminating responses on the EEG or ECoG. These responses play a potentially important role in localising tissue involved in the generation of seizure activity, yet the underlying mechanisms are unknown. However, in vitro evidence suggests that self-terminating oscillations in nervous tissue are underpinned by non-trivial spatio-temporal dynamics in an excitable medium. In this study, we investigate this hypothesis in spatial extensions to a neural mass model for epileptiform dynamics. We demonstrate that spatial extensions to this model in one and two dimensions display propagating travelling waves but also more complex transient dynamics in response to local perturbations. The neural mass formulation with local excitatory and inhibitory circuits, allows the direct incorporation of spatially distributed, functional heterogeneities into the model. We show that such heterogeneities can lead to prolonged reverberating responses to a single pulse perturbation, depending upon the location at which the stimulus is delivered. This leads to the hypothesis that prolonged rhythmic responses to local stimulation in epileptogenic tissue result from repeated self-excitation of regions of tissue with diminished inhibitory capabilities. Combined with previous models of the dynamics of focal seizures this macroscopic framework is a first step towards an explicit spatial formulation of the concept of the epileptogenic zone. Ultimately, an improved understanding of the pathophysiologic mechanisms of the epileptogenic zone will help to improve diagnostic and therapeutic measures for treating epilepsy.

Hyperperfusion of anterior cingulate gyrus in a case of paroxysmal nocturnal dystonia

The authors report the clinical, EEG, and SPECT findings of a patient with nocturnal paroxysmal dystonia. Ictal and interictal scalp EEG showed epileptiform activity over both frontal lobes. Subtraction ictal SPECT co-registered to MRI indicated a bilateral significant hyperperfusion in the anterior part of the cingulate gyrus. These results support earlier electrophysiologic investigations by others suggesting that anterior cingulate epilepsy may manifest as nocturnal paroxysmal dystonia, and illustrate the usefulness of computer-assisted SPECT analysis.

Detection of regional blood perfusion changes in epileptic seizures with dynamic brain perfusion CT--a pilot study.

BACKGROUND AND PURPOSE Perfusion CT (P-CT) is used for acute stroke management, not, however, for evaluating epilepsy. To test the hypothesis that P-CT may identify patients with increased regional cerebral blood flow during subtle status epilepticus (SSE), we compared P-CT in SSE to different postictal conditions. METHODS Fifteen patients (mean age 47 years, range 21-74) underwent P-CT immediately after evaluation in our emergency room. Asymmetry indices between affected and unaffected hemispheres were calculated for regional cerebral blood volume (rCBV), regional cerebral blood flow (rCBF), and mean transit time (MTT). Regional perfusion changes were compared to EEG findings. RESULTS Three patients in subtle status epilepticus (group 1) had increased regional perfusion with electro-clinical correlate. Six patients showed postictal slowing on EEG corresponding to an area of regional hypoperfusion (group 2). CT and EEG were normal in six patients with a first epileptic seizure (group 3). Cluster analysis of asymmetry indices separated SSE from the other two groups in all three parameters, while rCBF helped to distinguish between chronic focal epilepsies and single events. CONCLUSION Preliminary results indicate that P-CT may help to identify patients with SSE during emergency workup. This technique provides important information to neurologists or emergency physicians in the difficult clinical differential diagnosis of altered mental status due to subtle status epilepticus.

Forbidden ordinal patterns of periictal intracranial EEG indicate deterministic dynamics in human epileptic seizures

Purpose:  Epileptic seizures typically reveal a high degree of stereotypy, that is, for an individual patient they are characterized by an ordered and predictable sequence of symptoms and signs with typically little variability. Stereotypy implies that ictal neuronal dynamics might have deterministic characteristics, presumably most pronounced in the ictogenic parts of the brain, which may provide diagnostically and therapeutically important information. Therefore the goal of our study was to search for indications of determinism in periictal intracranial electroencephalography (EEG) studies recorded from patients with pharmacoresistent epilepsy.

Estimation of brain network ictogenicity predicts outcome from epilepsy surgery

Surgery is a valuable option for pharmacologically intractable epilepsy. However, significant post-operative improvements are not always attained. This is due in part to our incomplete understanding of the seizure generating (ictogenic) capabilities of brain networks. Here we introduce an in silico, model-based framework to study the effects of surgery within ictogenic brain networks. We find that factors conventionally determining the region of tissue to resect, such as the location of focal brain lesions or the presence of epileptiform rhythms, do not necessarily predict the best resection strategy. We validate our framework by analysing electrocorticogram (ECoG) recordings from patients who have undergone epilepsy surgery. We find that when post-operative outcome is good, model predictions for optimal strategies align better with the actual surgery undertaken than when post-operative outcome is poor. Crucially, this allows the prediction of optimal surgical strategies and the provision of quantitative prognoses for patients undergoing epilepsy surgery.