Dataset Information

Autonomic changes with seizures correlate with postictal EEG suppression.

ABSTRACT:

Objective

Sudden unexpected death in epilepsy (SUDEP) poses a poorly understood but considerable risk to people with uncontrolled epilepsy. There is controversy regarding the significance of postictal generalized EEG suppression as a biomarker for SUDEP risk, and it remains unknown whether postictal EEG suppression has a neurologic correlate. Here, we examined the profile of autonomic alterations accompanying seizures with a wrist-worn biosensor and explored the relationship between autonomic dysregulation and postictal EEG suppression.

Methods

We used custom-built wrist-worn sensors to continuously record the sympathetically mediated electrodermal activity (EDA) of patients with refractory epilepsy admitted to the long-term video-EEG monitoring unit. Parasympathetic-modulated high-frequency (HF) power of heart rate variability was measured from concurrent EKG recordings.

Results

A total of 34 seizures comprising 22 complex partial and 12 tonic-clonic seizures from 11 patients were analyzed. The postictal period was characterized by a surge in EDA and heightened heart rate coinciding with persistent suppression of HF power. An increase in the EDA response amplitude correlated with an increase in the duration of EEG suppression (r = 0.81, p = 0.003). Decreased HF power correlated with an increase in the duration of EEG suppression (r = -0.87, p = 0.002).

Conclusion

The magnitude of both sympathetic activation and parasympathetic suppression increases with duration of EEG suppression after tonic-clonic seizures. These results provide autonomic correlates of postictal EEG suppression and highlight a critical window of postictal autonomic dysregulation that may be relevant in the pathogenesis of SUDEP.

SUBMITTER: Poh MZ

PROVIDER: S-EPMC3369522 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Publications

Autonomic changes with seizures correlate with postictal EEG suppression.

Poh M-Z MZ Loddenkemper T T Reinsberger C C Swenson N C NC Goyal S S Madsen J R JR Picard R W RW

Neurology 20120425 23

<h4>Objective</h4>Sudden unexpected death in epilepsy (SUDEP) poses a poorly understood but considerable risk to people with uncontrolled epilepsy. There is controversy regarding the significance of postictal generalized EEG suppression as a biomarker for SUDEP risk, and it remains unknown whether postictal EEG suppression has a neurologic correlate. Here, we examined the profile of autonomic alterations accompanying seizures with a wrist-worn biosensor and explored the relationship between auto ...[more]

PMID: 22539579

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Project description:It is not fully understood how seizures terminate and why some seizures are followed by a period of complete brain activity suppression, postictal generalized EEG suppression. This is clinically relevant as there is a potential association between postictal generalized EEG suppression, cardiorespiratory arrest and sudden death following a seizure. We combined human encephalographic seizure data with data of a computational model of seizures to elucidate the neuronal network dynamics underlying seizure termination and the postictal generalized EEG suppression state. A multi-unit computational neural mass model of epileptic seizure termination and postictal recovery was developed. The model provided three predictions that were validated in EEG recordings of 48 convulsive seizures from 48 subjects with refractory focal epilepsy (20 females, age range 15-61 years). The duration of ictal and postictal generalized EEG suppression periods in human EEG followed a gamma probability distribution indicative of a deterministic process (shape parameter 2.6 and 1.5, respectively) as predicted by the model. In the model and in humans, the time between two clonic bursts increased exponentially from the start of the clonic phase of the seizure. The terminal interclonic interval, calculated using the projected terminal value of the log-linear fit of the clonic frequency decrease was correlated with the presence and duration of postictal suppression. The projected terminal interclonic interval explained 41% of the variation in postictal generalized EEG suppression duration (P < 0.02). Conversely, postictal generalized EEG suppression duration explained 34% of the variation in the last interclonic interval duration. Our findings suggest that postictal generalized EEG suppression is a separate brain state and that seizure termination is a plastic and autonomous process, reflected in increased duration of interclonic intervals that determine the duration of postictal generalized EEG suppression.

Project description:Postictal generalized EEG suppression is the state of suppression of electrical activity at the end of a seizure. Prolongation of this state has been associated with increased risk of sudden unexpected death in epilepsy, making characterization of underlying electrical rhythmic activity during postictal suppression an important step in improving epilepsy treatment. Phase-amplitude coupling in EEG reflects cognitive coding within brain networks and some of those codes highlight epileptic activity; therefore, we hypothesized that there are distinct phase-amplitude coupling features in the postictal suppression state that can provide an improved estimate of this state in the context of patient risk for sudden unexpected death in epilepsy. We used both intracranial and scalp EEG data from eleven patients (six male, five female; age range 21-41 years) containing 25 seizures, to identify frequency dynamics, both in the ictal and postictal EEG suppression states. Cross-frequency coupling analysis identified that during seizures there was a gradual decrease of phase frequency in the coupling between delta (0.5-4 Hz) and gamma (30+ Hz), which was followed by an increased coupling between the phase of 0.5-1.5 Hz signal and amplitude of 30-50 Hz signal in the postictal state as compared to the pre-seizure baseline. This marker was consistent across patients. Then, using these postictal-specific features, an unsupervised state classifier-a hidden Markov model-was able to reliably classify four distinct states of seizure episodes, including a postictal suppression state. Furthermore, a connectome analysis of the postictal suppression states showed increased information flow within the network during postictal suppression states as compared to the pre-seizure baseline, suggesting enhanced network communication. When the same tools were applied to the EEG of an epilepsy patient who died unexpectedly, ictal coupling dynamics disappeared and postictal phase-amplitude coupling remained constant throughout. Overall, our findings suggest that there are active postictal networks, as defined through coupling dynamics that can be used to objectively classify the postictal suppression state; furthermore, in a case study of sudden unexpected death in epilepsy, the network does not show ictal-like phase-amplitude coupling features despite the presence of convulsive seizures, and instead demonstrates activity similar to postictal. The postictal suppression state is a period of elevated network activity as compared to the baseline activity which can provide key insights into the epileptic pathology.

Project description:Abstract Autonomic dysfunction has been described in patients with Huntington’s disease, but it is unclear if these changes in autonomic tone are related to the central autonomic network. We performed a pilot study to investigate the relationship between the integrity of the central autonomic network and peripheral manifestiations of autonomic dysfunction in premanifest Huntington’s disease. We recruited male participants with pre-motor-manifest Huntington’s disease and a comparison group consisting of healthy, male participants of approximately the same age. As this was a pilot study, only males were included to reduce confounding. Participants underwent a resting-state functional magnetic resonance imaging study to quantify functional connectivity within the central autonomic network, as well as a resting 3-lead ECG to measure heart rate variability with a particular focus on the parasympathetic time-domain measures of root mean square of successive differences between normal heartbeats. The pre-motor-manifest Huntington’s disease participants had significantly decreased root mean square of successive differences between normal heartbeats values compared with the healthy comparison group. The pre-motor-manifest Huntington’s disease group had significantly lower functional connectivity within the central autonomic network, which was positively correlated with root mean square of successive differences between normal heartbeats. Patients with pre-motor-manifest Huntington’s disease have reduced functional connectivity within the central autonomic network, which is significantly associated with observed changes in autonomic function. Schultz et al. report that autonomic dysfunction in patients with presymptomatic Huntington’s disease was associated with decreased functional connectivity within the Central Autonomic Network. These results provide insight into the underlying pathology of autonomic dysfunction in Huntington’s disease and its possible use as a marker for disease progression. Graphical Abstract Graphical abstract

Dataset Information

Autonomic changes with seizures correlate with postictal EEG suppression.

Objective

Methods

Results

Conclusion

Publications

Autonomic changes with seizures correlate with postictal EEG suppression.

Similar Datasets

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