New observations may inform seizure models: very fast and very slow oscillations

Link Prediction Investigation of Dynamic Information Flow in Epilepsy

As a brain disorder, epilepsy is characterized with abnormal hypersynchronous neural firings. It is known that seizures initiate and propagate in different brain regions. Long-term intracranial multichannel electroencephalography (EEG) reflects broadband ictal activity under seizure occurrence. Network-based techniques are efficient in discovering brain dynamics and offering finger-print features for specific individuals. In this study, we adopt link prediction for proposing a novel workflow aiming to quantify seizure dynamics and uncover pathological mechanisms of epilepsy. A dataset of EEG signals was enrolled that recorded from 8 patients with 3 different types of pharmocoresistant focal epilepsy. Weighted networks are obtained from phase locking value (PLV) in subband EEG oscillations. Common neighbor (CN), resource allocation (RA), Adamic-Adar (AA), and Sorenson algorithms are brought in for link prediction performance comparison. Results demonstrate that RA outperforms its rivals. Similarity, matrix was produced from the RA technique performing on EEG networks later. Nodes are gathered to form sequences by selecting the ones with the highest similarity. It is demonstrated that variations are in accordance with seizure attack in node sequences of gamma band EEG oscillations. What is more, variations in node sequences monitor the total seizure journey including its initiation and termination.

An optimal strategy for epilepsy surgery: Disruption of the rich-club?

Surgery is a therapeutic option for people with epilepsy whose seizures are not controlled by anti-epilepsy drugs. In pre-surgical planning, an array of data modalities, often including intra-cranial EEG, is used in an attempt to map regions of the brain thought to be crucial for the generation of seizures. These regions are then resected with the hope that the individual is rendered seizure free as a consequence. However, post-operative seizure freedom is currently sub-optimal, suggesting that the pre-surgical assessment may be improved by taking advantage of a mechanistic understanding of seizure generation in large brain networks. Herein we use mathematical models to uncover the relative contribution of regions of the brain to seizure generation and consequently which brain regions should be considered for resection. A critical advantage of this modeling approach is that the effect of different surgical strategies can be predicted and quantitatively compared in advance of surgery. Herein we seek to understand seizure generation in networks with different topologies and study how the removal of different nodes in these networks reduces the occurrence of seizures. Since this a computationally demanding problem, a first step for this aim is to facilitate tractability of this approach for large networks. To do this, we demonstrate that predictions arising from a neural mass model are preserved in a lower dimensional, canonical model that is quicker to simulate. We then use this simpler model to study the emergence of seizures in artificial networks with different topologies, and calculate which nodes should be removed to render the network seizure free. We find that for scale-free and rich-club networks there exist specific nodes that are critical for seizure generation and should therefore be removed, whereas for small-world networks the strategy should instead focus on removing sufficient brain tissue. We demonstrate the validity of our approach by analysing intra-cranial EEG recordings from a database comprising 16 patients who have undergone epilepsy surgery, revealing rich-club structures within the obtained functional networks. We show that the postsurgical outcome for these patients was better when a greater proportion of the rich club was removed, in agreement with our theoretical predictions.

Spatial Coherence Profiles of Ictal High-Frequency Oscillations Correspond to Those of Interictal Low-Frequency Oscillations in the ECoG of Epileptic Patients

GOAL

We have previously demonstrated that the coherence of high-frequency oscillations (HFOs; 80-300 Hz) increased during extratemporal lobe seizures in a consistent and spatially focused electrode cluster. In this study, we have investigated the relationship between cohered HFO intracranial EEG (iEEG) activity with that of slower low-frequency oscillations (LFOs; <80 Hz).

METHODS

We applied wavelet phase coherence analysis to the iEEGs of patients with intractable extratemporal lobe epilepsy (ETLE).

RESULTS

It was observed that areas on the implanted patient subdural grids, which exhibited strong ictal HFO coherence were similar to tissue regions displaying strong interictal LFO coherence in the 5-12 Hz frequency range, relative to all other electrodes. A positive surgical outcome was correlated with having the clinically marked seizure onset zone(s) in close proximity to HFO/LFO coherence highlighted regions of interest (ROIs).

CONCLUSION

Recent studies have suggested that LFOs (in the 8-12 Hz frequency range) play an important role in controlling cortical excitability, by exerting an inhibitory effect on cortical processing, and that the presence of strong theta activity (4-8 Hz) in awake adults is suggestive of abnormal and/or pathological activity. We speculate that the overlapping spatial regions exhibiting increased coherence in both ictal HFOs and interictal LFOs identified local abnormalities that underlie epileptogenic networks.

SIGNIFICANCE

Whereas it is worthwhile to note that the small patient group ( n = 7) studied here, somewhat limits the clinical significance of our study, the results presented here suggest targeting HFO activity in the 80-300 Hz frequency range and/or interictal LFO activity in the 5-12 Hz frequency range, when defining seizure-related ROIs in the iEEGs of patients with ETLE.

Quantification of Interictal Neuromagnetic Activity in Absence Epilepsy with Accumulated Source Imaging

Aberrant brain activity in childhood absence epilepsy (CAE) during seizures has been well recognized as synchronous 3 Hz spike-and-wave discharges on electroencephalography. However, brain activity from low- to very high-frequency ranges in subjects with CAE between seizures (interictal) has rarely been studied. Using a high-sampling rate magnetoencephalography (MEG) system, we studied ten subjects with clinically diagnosed but untreated CAE in comparison with age- and gender-matched controls. MEG data were recorded from all subjects during the resting state. MEG sources were assessed with accumulated source imaging, a new method optimized for localizing and quantifying spontaneous brain activity. MEG data were analyzed in nine frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), low-gamma (30-55 Hz), high-gamma (65-90 Hz), ripple (90-200 Hz), high-frequency oscillation (HFO, 200-1,000 Hz), and very high-frequency oscillation (VHFO, 1,000-2,000 Hz). MEG source imaging revealed that subjects with CAE had higher odds of interictal brain activity in 200-1,000 and 1,000-2,000 Hz in the parieto-occipito-temporal junction and the medial frontal cortices as compared with controls. The strength of the interictal brain activity in these regions was significantly elevated in the frequency bands of 90-200, 200-1,000 and 1,000-2,000 Hz for subjects with CAE as compared with controls. The results indicate that CAE has significantly aberrant brain activity between seizures that can be noninvasively detected. The measurements of high-frequency neuromagnetic oscillations may open a new window for investigating the cerebral mechanisms of interictal abnormalities in CAE.

Focus on desynchronization rather than excitability: a new strategy for intraencephalic electrical stimulation

Epilepsy is a severely debilitating brain disease, often associated with premature death, which has an urgent need for alternative methods of treatment. In fact, roughly 25% of patients with epilepsy do not have seizures satisfactorily controlled by pharmacological treatment, and 30% of these patients with treatment-refractory seizures are not even eligible for ablative surgery. Epilepsy is most readily identifiable by its seizures and/or paroxysmal events, mostly viewed as spontaneously recurrent and unpredictable, which are caused by stereotyped changes in neurological function associated with hyperexcitability and hypersynchronicity of the underlying neural networks. Treatment has strongly been based on the fixed goal of depressing neuronal activity, working under the veiled assumption that hyperexcitability would lead to synchronous neuronal activity and, therefore, to seizure. Over the last 20-30 years, the emergence of electrical (ES) of deep brain structures, a practicable option for treating patients with otherwise untreatable seizures, has broadened our understanding of anticonvulsant mechanisms that conceptually differ from those of pharmacological treatment. Conversely, the research on ES therapy applied to epilepsy is contributing significantly to untwine the phenomena of excitation from that of synchronization as potential target mechanisms for abolishing seizures and predicting paroxysmal events. This paper is, thus, an addendum to other reviews on the subject of ES therapy in epilepsy which focuses on the desynchronization effect ES has on epileptogenic neural networks rather than its effect on overall brain excitability.

The dynamics of neurological disease: integrating computational, experimental and clinical neuroscience

There is a vast (and rapidly growing) amount of experimental and clinical data of the nervous system at very diverse spatial scales of activity (e.g. from sub-cellular through to whole organ), with many neurological disorders characterized by oscillations in neural activity across these disparate scales. Computer modelling and the development of associated mathematical theories provide us with a unique opportunity to integrate information from across these diverse scales of activity; leading to explanations of the potential mechanisms underlying the time-evolving dynamics and, more importantly, allowing the development of new hypotheses regarding neural function that may be tested experimentally and ultimately translated into the clinic. The purpose of this special issue is to present an overview of current integrative research in the areas of epilepsy, Parkinson's disease and schizophrenia, where multidisciplinary relationships involving theory, experimental and clinical research are becoming increasingly established.

Scaling effects and spatio-temporal multilevel dynamics in epileptic seizures

Epileptic seizures are one of the most well-known dysfunctions of the nervous system. During a seizure, a highly synchronized behavior of neural activity is observed that can cause symptoms ranging from mild sensual malfunctions to the complete loss of body control. In this paper, we aim to contribute towards a better understanding of the dynamical systems phenomena that cause seizures. Based on data analysis and modelling, seizure dynamics can be identified to possess multiple spatial scales and on each spatial scale also multiple time scales. At each scale, we reach several novel insights. On the smallest spatial scale we consider single model neurons and investigate early-warning signs of spiking. This introduces the theory of critical transitions to excitable systems. For clusters of neurons (or neuronal regions) we use patient data and find oscillatory behavior and new scaling laws near the seizure onset. These scalings lead to substantiate the conjecture obtained from mean-field models that a Hopf bifurcation could be involved near seizure onset. On the largest spatial scale we introduce a measure based on phase-locking intervals and wavelets into seizure modelling. It is used to resolve synchronization between different regions in the brain and identifies time-shifted scaling laws at different wavelet scales. We also compare our wavelet-based multiscale approach with maximum linear cross-correlation and mean-phase coherence measures.

A phenomenological model of seizure initiation suggests network structure may explain seizure frequency in idiopathic generalised epilepsy

We describe a phenomenological model of seizure initiation, consisting of a bistable switch between stable fixed point and stable limit-cycle attractors. We determine a quasi-analytic formula for the exit time problem for our model in the presence of noise. This formula--which we equate to seizure frequency--is then validated numerically, before we extend our study to explore the combined effects of noise and network structure on escape times. Here, we observe that weakly connected networks of 2, 3 and 4 nodes with equivalent first transitive components all have the same asymptotic escape times. We finally extend this work to larger networks, inferred from electroencephalographic recordings from 35 patients with idiopathic generalised epilepsies and 40 controls. Here, we find that network structure in patients correlates with smaller escape times relative to network structures from controls. These initial findings are suggestive that network structure may play an important role in seizure initiation and seizure frequency.

BrainModes: the role of neuronal oscillations in health and disease

The core idea of complexity science--namely how macroscopic phenomena emerge from the interactions between microscopic quantities--is particularly relevant to the study of the human brain. It is in this context that the term "BrainModes" was adopted to explore how cooperative phenomena (or 'modes' of activity) occurring at one spatial or temporal scale give rise to coherent structures at other scales. This Special Issue reports the 2009 BrainModes Workshop, held in Bristol (December 2009) which focussed on the fusion of theoretical, computational, experimental and clinical methods for enhancing our understanding of the role played by neuronal oscillations in healthy and diseased brain states.