Controversies on the network theory of epilepsy : Debates held during the ICTALS 2019 conference

Acknowledgements We would like to acknowledge the contributions of the discussants to the exposition and discussion of the six debate topics. The discussants for debates 1-6 were Fabrice Wendling, Mark Cook, Mark Richardson, Thorsten Rings, Klaus Lehnertz and Piotr Suffczynski, respectively. Funding for ICTALS 2019 was received from the following foundations and industry partners: UCB S.A. (Belgium), American Epilepsy Society (AES), Epilepsy Innovation Institute (Ei2) and Epilepsy Foundation of America (EFA), NeuraLynx (Bozeman, MT, USA) and LivaNova (London, UK). The contribution of HZ was supported by award R01NS109062 from the National Institutes of Health, MG by the EPSRC via grants EP/P021417/1 and EP/N014391/1 and a Wellcome Trust Institutional Strategic Support Award (WT105618MA), and PJ by awards from the Ministry of Health of the Czech Republic AZV 17-28427A and the Czech Science Foundation 20-25298S. The opinions expressed in this article do not necessarily reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government.Peer reviewedPostprin

Reliable and Elastic Propagation of Cortical Seizures In Vivo

Mapping the fine-scale neural activity that underlies epilepsy is key to identifying potential control targets of this frequently intractable disease. Yet, the detailed in vivo dynamics of seizure progression in cortical microcircuits remain poorly understood. We combine fast (30-Hz) two-photon calcium imaging with local field potential (LFP) recordings to map, cell by cell, the spread of locally induced (4-AP or picrotoxin) seizures in anesthetized and awake mice. Using single-layer and microprism-assisted multilayer imaging in different cortical areas, we uncover reliable recruitment of local neural populations within and across cortical layers, and we find layer-specific temporal delays, suggesting an initial supra-granular invasion followed by deep-layer recruitment during lateral seizure spread. Intriguingly, despite consistent progression pathways, successive seizures show pronounced temporal variability that critically depends on GABAergic inhibition. We propose an epilepsy circuit model resembling an elastic meshwork, wherein ictal progression faithfully follows preexistent pathways but varies flexibly in time, depending on the local inhibitory restraint

The Enlightened Brain: Novel Imaging Methods Focus on Epileptic Networks at Multiple Scales

Epilepsy research is rapidly adopting novel fluorescence optical imaging methods to tackle unresolved questions on the cellular and circuit mechanisms of seizure generation and evolution. State of the art two-photon microscopy and wide-field fluorescence imaging can record the activity in epileptic networks at multiple scales, from neuronal microcircuits to brain-wide networks. These approaches exploit transgenic and viral technologies to target genetically encoded calcium and voltage sensitive indicators to subclasses of neurons, and achieve genetic specificity, spatial resolution and scalability that can complement electrophysiological recordings from awake animal models of epilepsy. Two-photon microscopy is well suited to study single neuron dynamics during interictal and ictal events, and highlight the differences between the activity of excitatory and inhibitory neuronal classes in the focus and propagation zone. In contrast, wide-field fluorescence imaging provides mesoscopic recordings from the entire cortical surface, necessary to investigate seizure propagation pathways, and how the unfolding of epileptic events depends on the topology of brain-wide functional connectivity. Answering these questions will inform pre-clinical studies attempting to suppress seizures with gene therapy, optogenetic or chemogenetic strategies. Dissecting which network nodes outside the seizure onset zone are important for seizure generation, propagation and termination can be used to optimize current and future evaluation methods to identify an optimal surgical strategy

Human seizures couple across spatial scales through travelling wave dynamics

Epilepsy—the propensity toward recurrent, unprovoked seizures—is a devastating disease affecting 65 million people worldwide. Understanding and treating this disease remains a challenge, as seizures manifest through mechanisms and features that span spatial and temporal scales. Here we address this challenge through the analysis and modelling of human brain voltage activity recorded simultaneously across microscopic and macroscopic spatial scales. We show that during seizure large-scale neural populations spanning centimetres of cortex coordinate with small neural groups spanning cortical columns, and provide evidence that rapidly propagating waves of activity underlie this increased inter-scale coupling. We develop a corresponding computational model to propose specific mechanisms—namely, the effects of an increased extracellular potassium concentration diffusing in space—that support the observed spatiotemporal dynamics. Understanding the multi-scale, spatiotemporal dynamics of human seizures—and connecting these dynamics to specific biological mechanisms—promises new insights to treat this devastating disease

Seizure pathways change on circadian and slower timescales in individual patients with focal epilepsy.

Personalized medicine requires that treatments adapt to not only the patient but also changing factors within each individual. Although epilepsy is a dynamic disorder characterized by pathological fluctuations in brain state, surprisingly little is known about whether and how seizures vary in the same patient. We quantitatively compared within-patient seizure network evolutions using intracranial electroencephalographic (iEEG) recordings of over 500 seizures from 31 patients with focal epilepsy (mean 16.5 seizures per patient). In all patients, we found variability in seizure paths through the space of possible network dynamics. Seizures with similar pathways tended to occur closer together in time, and a simple model suggested that seizure pathways change on circadian and/or slower timescales in the majority of patients. These temporal relationships occurred independent of whether the patient underwent antiepileptic medication reduction. Our results suggest that various modulatory processes, operating at different timescales, shape within-patient seizure evolutions, leading to variable seizure pathways that may require tailored treatment approaches

Epilepsia

SummaryObjective:Effective surgical treatment of drug resistant epilepsy depends on accurate localization of the epileptogenic zone (EZ). High frequency oscillations (HFOs) are potential biomarkers of the EZ. Previous research has shown that HFOs often occur within submillimeter areas of brain tissue and that the coarse spatial sampling of clinical intracranial electrode arrays may limit the accurate capture of HFO activity. In this study, we sought to characterize microscale HFO activity captured on thin, flexible micro-electrocorticographic (\u3bcECoG) arrays, which provide high spatial resolution over large cortical surface areas.Methods:We used novel liquid crystal polymer thin-film (LCP-TF) \u3bcECoG arrays (0.76\u20131.72 mm inter-contact spacing) to capture HFOs in eight intraoperative recordings from seven patients with epilepsy. We identified ripple (80 \u2013 250 Hz) and fast ripple (250 \u2013 600 Hz) HFOs using a common energy thresholding detection algorithm along with two stages of artifact rejection. We visualized microscale subregions of HFO activity using spatial maps of HFO rate, signal-to-noise ratio, and mean peak frequency. We quantified the spatial extent of HFO events by measuring covariance between detected HFOs and surrounding activity. We also compared HFO detection rates on microcontacts to simulated macrocontacts by spatially averaging data.Results:We found visually delineable subregions of elevated HFO activity within each \u3bcECoG recording. 47% of HFOs occurred on single 200 \u3bcm diameter recording contacts with minimal high frequency activity on surrounding contacts. Other HFO events occurred across multiple contacts simultaneously, with covarying activity most often limited to a 0.95 mm radius. Through spatial averaging, we estimated that macrocontacts with 2\u20133 mm diameter would only capture 44% of the HFOs detected in our \u3bcECoG recordings.Significance:These results demonstrate that thin-film microcontact surface arrays with both high resolution and large coverage accurately capture microscale HFO activity and may improve the utility of HFOs to localize the EZ for treatment of drug resistant epilepsy.UL 1TR002553/NH/NIH HHSUnited States/R01 DC019498/DC/NIDCD NIH HHSUnited States/R01 HL151490/NH/NIH HHSUnited States/U01 NS122123/NH/NIH HHSUnited States/UF1 NS122123/NS/NINDS NIH HHSUnited States/U01 NS090415/NS/NINDS NIH HHSUnited States/RF1 MH116978/NH/NIH HHSUnited States/R01 MH116978/NH/NIH HHSUnited States/UG3 NS120172/NS/NINDS NIH HHSUnited States/U01 NS099697/NH/NIH HHSUnited States/R01 NS062092/NH/NIH HHSUnited States/U01 NS099697/NS/NINDS NIH HHSUnited States/R01 NS109367/NS/NINDS NIH HHSUnited States/R01 NS104923/NH/NIH HHSUnited States/R01 NS06209207/NH/NIH HHSUnited States/T32 GM136573/GM/NIGMS NIH HHSUnited States/T32 GM136573/NH/NIH HHSUnited States/K12 NS080223/NH/NIH HHSUnited States/U01 NS099705/NS/NINDS NIH HHSUnited States/U01 NS103518/NS/NINDS NIH HHSUnited States/U48DP006396-01SIP 19-003/CC/CDC HHSUnited States/RF1 MH116978/MH/NIMH NIH HHSUnited States/R01 NS062092/NS/NINDS NIH HHSUnited States/1R01-DC019498-01A1/NH/NIH HHSUnited States/R01 MH111417/NH/NIH HHSUnited States/UL1 TR002553/TR/NCATS NIH HHSUnited States/R01 MH107396/MH/NIMH NIH HHSUnited States/R01 MH111417/MH/NIMH NIH HHSUnited States/U01 NS090415/NH/NIH HHSUnited States/R01 NS104923/NS/NINDS NIH HHSUnited States/U01 NS123668/NS/NINDS NIH HHSUnited States/UG3 NS120172/NH/NIH HHSUnited States/U01 NS103518/NH/NIH HHSUnited States/R01 DC019498/NH/NIH HHSUnited States/R01 NS109367/NH/NIH HHSUnited States/U01 NS099705/NH/NIH HHSUnited States/K12 NS080223/NS/NINDS NIH HHSUnited States/U01 NS123668/NH/NIH HHSUnited States/U01 NS099577/NS/NINDS NIH HHSUnited States/R01 MH107396/NH/NIH HHSUnited States/U01 NS099577/NH/NIH HHSUnited States

Predicting the spatiotemporal diversity of seizure propagation and termination in human focal epilepsy

Recent studies have shown that seizures can spread and terminate across brain areas via a rich diversity of spatiotemporal patterns. In particular, while the location of the seizure onset area is usually in-variant across seizures in a same patient, the source of traveling (2-3 Hz) spike-and-wave discharges (SWDs) during seizures can either move with the slower propagating ictal wavefront or remain stationary at the seizure onset area. In addition, although most focal seizures terminate quasi-synchronously across brain areas, some evolve into distinct ictal clusters and terminate asynchronously. To provide a unifying perspective on the observed diversity of spatiotemporal dynamics for seizure spread and termination, we introduce here the Epileptor neural field model. Two mechanisms play an essential role. First, while the slow ictal wavefront propagates as a front in excitable neural media, the faster SWDs propagation results from coupled-oscillator dynamics. Second, multiple time scales interact during seizure spread, allowing for low-voltage fast-activity (>10 Hz) to hamper seizure spread and for SWD propagation to affect the way a seizure terminates. These dynamics, together with variations in short and long-range connectivity strength, play a central role on seizure spread, maintenance and termination. We demonstrate how Epileptor field models incorporating the above mechanisms predict the previously reported diversity in seizure spread patterns. Furthermore, we confirm the predictions for synchronous or asynchronous (clustered) seizure termination in human seizures recorded via stereotactic EEG. Our new insights into seizure spatiotemporal dynamics may also contribute to the development of new closed-loop neuromodulation therapies for focal epilepsy.Comment: 10 pages + 9 pages Supporting Information (SI), 7 figures, 1 SI table, 7 SI figure

Spontaneous transitions to focal-onset epileptic seizures: A dynamical study

Given the complex temporal evolution of epileptic seizures, understanding their dynamic nature might be beneficial for clinical diagnosis and treatment. Yet, the mechanisms behind, for instance, the onset of seizures are still unknown. According to an existing classification, two basic types of dynamic onset patterns plus a number of more complex onset waveforms can be distinguished. Here, we introduce a basic three-variable model with two time scales to study potential mechanisms of spontaneous seizure onset. We expand the model to demonstrate how coupling of oscillators leads to more complex seizure onset waveforms. Finally, we test the response to pulse perturbation as a potential biomarker of interictal changes. Focal epileptic seizures are characterized by complex spatiotemporal rhythms of electric brain activity. Phenomenologically, there are different types of rhythms, namely, fast-small oscillations, slow-large spiking, and complex waveforms of the voltage. The dynamics of these types are, however, still poorly understood and their clinical characterization is, therefore, mostly descriptive. We use computational modeling of macro-level electrical brain activity to study the spontaneous onset and evolution of major clinical seizure rhythms. Looking at principal models of one and two coupled oscillators with a slow driving population, we show the dynamical characteristics and relationships between basic seizure onset types. In addition, we show that pulse perturbations of background activity can serve as a biomarker for seizure onset

Whole Brain Network Dynamics of Epileptic Seizures at Single Cell Resolution

Epileptic seizures are characterised by abnormal brain dynamics at multiple scales, engaging single neurons, neuronal ensembles and coarse brain regions. Key to understanding the cause of such emergent population dynamics, is capturing the collective behaviour of neuronal activity at multiple brain scales. In this thesis I make use of the larval zebrafish to capture single cell neuronal activity across the whole brain during epileptic seizures. Firstly, I make use of statistical physics methods to quantify the collective behaviour of single neuron dynamics during epileptic seizures. Here, I demonstrate a population mechanism through which single neuron dynamics organise into seizures: brain dynamics deviate from a phase transition. Secondly, I make use of single neuron network models to identify the synaptic mechanisms that actually cause this shift to occur. Here, I show that the density of neuronal connections in the network is key for driving generalised seizure dynamics. Interestingly, such changes also disrupt network response properties and flexible dynamics in brain networks, thus linking microscale neuronal changes with emergent brain dysfunction during seizures. Thirdly, I make use of non-linear causal inference methods to study the nature of the underlying neuronal interactions that enable seizures to occur. Here I show that seizures are driven by high synchrony but also by highly non-linear interactions between neurons. Interestingly, these non-linear signatures are filtered out at the macroscale, and therefore may represent a neuronal signature that could be used for microscale interventional strategies. This thesis demonstrates the utility of studying multi-scale dynamics in the larval zebrafish, to link neuronal activity at the microscale with emergent properties during seizures