Dynamics of epileptic seizures: Evolution, spreading, and suppression

Kim, J.W.; Roberts, James A.; Robinson, P. A.

doi:10.1016/j.jtbi.2008.12.009

Cited by 51 publications

(60 citation statements)

References 29 publications

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“…Epileptic seizures typically show strong rhythmic content reflecting pathological synchronization, rather than the erratic alternating periods of bursting and quiescence seen in burst suppression. Seizures have been subjected to numerous computational analyses, which typically suggest they reflect a nonlinear bifurcation (Robinson et al, 2002;Lopes da Silva et al, 2003;Breakspear et al, 2006), offering novel opportunities for seizure control (Kramer et al, 2006;Kim et al, 2009). In contrast, there has been little computational treatment of burst suppression following hypoxia, leaving clinical innovation to be guided principally by trial and error.…”

Section: Discussionmentioning

confidence: 99%

Scale-Free Bursting in Human Cortex following Hypoxia at Birth

et al. 2014

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The human brain is fragile in the face of oxygen deprivation. Even a brief interruption of metabolic supply at birth challenges an otherwise healthy neonatal cortex, leading to a cascade of homeostatic responses. During recovery from hypoxia, cortical activity exhibits a period of highly irregular electrical fluctuations known as burst suppression. Here we show that these bursts have fractal properties, with power-law scaling of burst sizes across a remarkable 5 orders of magnitude and a scale-free relationship between burst sizes and durations. Although burst waveforms vary greatly, their average shape converges to a simple form that is asymmetric at long time scales. Using a simple computational model, we argue that this asymmetry reflects activity-dependent changes in the excitatory-inhibitory balance of cortical neurons. Bursts become more symmetric following the resumption of normal activity, with a corresponding reorganization of burst scaling relationships. These findings place burst suppression in the broad class of scale-free physical processes termed crackling noise and suggest that the resumption of healthy activity reflects a fundamental reorganization in the relationship between neuronal activity and its underlying metabolic constraints.

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Section: Discussionmentioning

confidence: 99%

Scale-Free Bursting in Human Cortex following Hypoxia at Birth

et al. 2014

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“…Mean-field models have a long history in computational neuroscience (e.g., [26][27][28][29], including many important applications for seizure modeling. These include, for example, mean-field models of absence seizures (30)(31)(32), of depth and surface electrode recordings from patients with temporal lobe epilepsy (33,34), seizure generalization (35), and anesthetic-induced seizures (36).…”

Section: Signatures Of a Critical Transition In A Mean-field Model Ofmentioning

confidence: 99%

Human seizures self-terminate across spatial scales via a critical transition

Kramer

Truccolo

Eden

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

191

147

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Why seizures spontaneously terminate remains an unanswered fundamental question of epileptology. Here we present evidence that seizures self-terminate via a discontinuous critical transition or bifurcation. We show that human brain electrical activity at various spatial scales exhibits common dynamical signatures of an impending critical transition-slowing, increased correlation, and flickering-in the approach to seizure termination. In contrast, prolonged seizures (status epilepticus) repeatedly approach, but do not cross, the critical transition. To support these results, we implement a computational model that demonstrates that alternative stable attractors, representing the ictal and postictal states, emulate the observed dynamics. These results suggest that self-terminating seizures end through a common dynamical mechanism. This description constrains the specific biophysical mechanisms underlying seizure termination, suggests a dynamical understanding of status epilepticus, and demonstrates an accessible system for studying critical transitions in nature.critical slowing down | epilepsy | electrocorticogram | local field potential A lthough extensive observations and research have elucidated some mechanism of seizure initiation and maintenance, how seizures spontaneously terminate remains one of the most important, but still unanswered, questions in epileptology (1). Some of the potential mechanisms of seizure termination (2) include glutamate depletion (3), dynamic changes in ion concentrations (4-6), persistent activation of a hyperpolarization-activated cation conductance (7), changing synaptic effectiveness (5, 8), modulatory effects from subcortical structures and the cerebellum (9), and reduced pH (10). Together, these studies suggest distinct pathways to seizure termination through specific biophysical mechanisms.We propose here a dynamical understanding of seizure termination that encompasses and constrains these and other hypothesized biophysical mechanisms. Specifically, we propose that focal seizures with secondary generalization terminate in a manner consistent with crossing a critical transition or bifurcation in which the system traverses a critical threshold and shifts suddenly to an alternative, attracting dynamical regime (11). Such regime shifts between alternative stable states have been described in many real-world systems (12-14) and exhibit a repertoire of early warning indicators (15-17). The seizing brain provides a unique living system for studying the signatures of an impending critical transition as well as one in which interventions can have profound practical import.We characterize the features of seizure termination in both multiscale in vivo data from patients with epilepsy and a computational model of cortical field activity. We show that population electrical activity-observed at macroscopic spatial scales-exhibits numerous signatures of an impending critical transition, whereas spatially localized recordings of neural spiking activity possess different dynamics. These insi...

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“…There is strong agreement that this is representative of the brain activity occurring prior to, at and during a seizure [39]. However, in modelling such synchronisation, the appropriateness of the Kuramoto model [118] has not been explicitly examined.…”

Section: E2 Simultaneous Eeg and Fmrimentioning

confidence: 99%

“…Biologically, it is the mechanism that controls the beating of the heart with the synchronisation of the signalling coming from the heart's pacemaker (the sinoatrial (sinus) node) [108,109]. Synchronisation is believe to be the process responsible for epileptic events and seizures [110] and has been utilised to model their dynamics [111,112,39].…”

Section: F1 the Phenomenon Of Synchronisationmentioning

confidence: 99%

“…Kim et al [112,111,39] used delay differential equations in their study of the evolution, spreading and suppression dynamics of epilepsy. Kim et al [39] made a passing comment about "Kuramato type all-in-all coupling" without specifically citing a publication by Kuramoto. However, they do cite papers related to Parkinson's disease where the Kuramoto model is specifically mentioned.…”

Section: F3 the Kuramoto Model And Epilepsymentioning

confidence: 99%

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Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

Dunn¹,

Abbott²,

Masterton³

et al. 2011

ANZIAMJ

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The Brain Research Institute (bri) uses various types of indirect measurements, including eeg and fmri, to understand and assess brain activity and function. As well as the recovery of generic information about brain function, research also focuses on the utilisation of such data and understanding to study the initiation, dynamics, spread and suppression of epileptic seizures. To assist with the future focussing of this aspect of their research, the bri asked the misg 2010 participants to examine how the available eeg and fmri data and current knowledge about epilepsy should be analysed and interpreted to yield an enhanced understanding about brain activity occurring before, at commencement of, during, and after a seizure. Though the deliberations of the study group were wide ranging in terms of the related matters considered and discussed, considerable progress was

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Dynamics of epileptic seizures: Evolution, spreading, and suppression

Cited by 51 publications

References 29 publications

Scale-Free Bursting in Human Cortex following Hypoxia at Birth

Scale-Free Bursting in Human Cortex following Hypoxia at Birth

Human seizures self-terminate across spatial scales via a critical transition

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

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