ReviewMultiscale recordings reveal the dynamic spatial structure of human seizures
Introduction
The long-standing question of the nature of electrophysiological activity that drives and characterizes the transition to seizure, despite its obvious importance to the field of epilepsy, remains unresolved. Its importance cannot be overstated, as it impacts both diagnostic studies in epilepsy patients carried out for the purpose of guiding treatment decisions, and laboratory investigations of ictogenesis and seizure propagation.
Although EEG is the most direct technique for accessing neurophysiological function at a fine temporal scale, it provides only a limited view of brain activity, being dominated by the summated postsynaptic currents in the superficial neocortical layers (Trevelyan and Schevon, 2014). Such currents can spread rapidly over long distances, and can have variable amplitudes due to constructive interference or to variations in dipole orientation relative to the recording electrode (Einevoll et al., 2013). As a result, the view of seizures provided by EEG recordings is often confusing. For example, there is frequent inter-reader disagreement regarding the location and timing of seizure onset (Wilson et al., 2003). The apparent rapid, broad spread of ictal activity contributed to the emergence of the notion that large-area networks, rather than small foci, are responsible for generating seizures (Spencer, 2002). This has led to an active debate regarding the extent to which seizures involve large-area networks, or whether they truly begin at a single, spatially restricted focus. In short, despite over 50 years of basic science and clinical research, the processes by which seizures begin, spread, and terminate are still the subject of considerable controversy.
The availability of continuous microelectrode recordings from patients undergoing invasive seizure monitoring as part of surgical treatment for pharmacoresistant focal epilepsy has provided an unprecedented window into the cellular basis of the epileptiform EEG, and permits investigators to draw parallels with laboratory studies in animal models. This has led to the definition of key spatial and temporal properties of seizure activity in humans at both the microscale and macroscale. In this review, we discuss the spatial structure of human seizures and how it can provide a useful framework for addressing open questions in ictogenesis, while also providing a clinically useful model for identifying seizure foci for therapeutic purposes.
Section snippets
Recording single and multiunit activity in humans
Cerebral signals that are recorded in EEG are composed of superimposed oscillations in a range of frequencies. In the sub-gamma frequency bands (< 25–30 Hz), oscillations demonstrate a complex relationship with neuronal firing (Buzsaki et al., 2012; Lakatos et al., 2005), and oscillatory phase can carry independent information (O'Keefe, 1993). In the gamma bands, especially in the high gamma (> 80 Hz) range, field potentials become highly correlated with population firing (Ray and Maunsell, 2011
What is a seizure?
In interpreting studies of ictal neuronal activity and their contribution to our understanding of the pathophysiology of seizures, it is important to consider that the ictal transition can be viewed quite differently depending on the methods used to define its location and timing. The International League Against Epilepsy defines a seizure as “a transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the brain”, emphasizing a clinical change
Spatial structure of seizures in microelectrode recordings
A number of studies have characterized the single- or multi-unit activity signature during clinical seizures in humans (Babb and Crandall, 1976; Bower et al., 2012; Isokawa-Akesson et al., 1987; Schevon et al., 2012; Truccolo et al., 2011, 2014; Wyler et al., 1982). A striking result shared across these studies is that most of the cases reported demonstrated an absence of intense or synchronized neuronal activity during seizures. Increased firing rates at seizure onset has been reported in as
Spatial structure of seizures in macroelectrode recordings
Obtaining a large-area view of the spatial structure of seizures requires utilizing data from subdural or depth macroelectrodes, which cannot directly detect multiunit activity. However, high gamma activity, specifically high frequency oscillations (HFOs) can serve as a useful index of synchronized population firing (Eissa et al., 2016; Ray and Maunsell, 2011), and indeed high amplitude discharges in the ictal core are tightly coupled to bursts of high frequency activity and to corresponding
Identifying single units across the ictal transition
Thus far, we have discussed unit firing at seizure onset in terms of multi-unit activity, rather than single units. Spike sorting and clustering, the process of identifying single units from extracellular microelectrode recordings, and classifying by cell type, permits assessment of separate classes of neurons (Gold et al., 2006; Quiroga et al., 2004). Briefly, action potentials from neurons of the same cell type show similar features to one another, but the exact geometry between the neuron
Role of feedforward inhibition in seizure propagation
The cellular mechanisms responsible for the abrupt, dramatic transition in neural activity at the ictal wavefront remain a topic of debate, although much of the confusion almost certainly arises from conflating different types of epileptic discharge. During recruitment of new territories to an existing ictal event, it is reasonable to assume that the driving force is provided by the glutamatergic output of the seizing, “core” territories. Both parvalbumin and somatostatin interneurons
Network effects of the ictal wavefront
Long clinical experience with EEG recordings of human seizures as appearing to spread rapidly through large brain areas has led to the hypothesis that seizures are generated from large-scale pathological networks (Spencer, 2002). Network analyses of ictal and interictal EEG have demonstrated increased connectivity between seizure-generating areas and related structures (Kramer et al., 2010; Schevon et al., 2007), and computational modeling based on long-range interictal connectivity alone has
Conclusion
By considering the electrophysiological signatures of seizures across scales from single unit activity to local field potential, a spatiotemporal structure emerges in which seizures are generated from small discrete foci and constrained by a much larger surrounding area of feedforward inhibition. These small foci nevertheless can have large-area network effects. The existence of human microelectrode recordings of seizures, even with all the attendant limitations of such data, are critical for
Acknowledgements
This work was supported by NIH NINDSR01-NS084142 (Schevon) and NIH NINDS CRCNSR01 NS095368.
Competing interests
The authors have no competing interests to declare
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