Factors Predicting Ictal Quality in Bilateral Electroconvulsive Therapy Sessions

In electroconvulsive therapy (ECT), ictal characteristics predict treatment response and can be modified by changes in seizure threshold and in the ECT technique. We aimed to study the impact of ECT procedure-related variables that interact during each session and might influence the seizure results. Two hundred and fifty sessions of bilateral ECT in forty-seven subjects were included. Seizure results were evaluated by two different scales of combined ictal EEG parameters (seizure quality index (SQI) and seizure adequacy markers sum (SAMS) scores) and postictal suppression rating. Repeated measurement regression analyses were performed to identify predictors of each session’s three outcome variables. Univariate models identified age, physical status, hyperventilation, basal oxygen saturation, days between sessions, benzodiazepines, lithium, and tricyclic antidepressants as predictors of seizure quality. Days elapsed between sessions, higher oxygen saturation and protocolized hyperventilation application were significant predictors of better seizure quality in both scales used in multivariate models. Additionally, lower ASA classification influenced SQI scores as well as benzodiazepine use and lithium daily doses were predictors of SAMS scores. Higher muscle relaxant doses and lower applied stimulus intensities significantly influenced the postictal suppression rating. The study found several modifiable procedural factors that impacted the obtained seizure characteristics; they could be adjusted to optimize ECT session results.

Delta-gamma phase-amplitude coupling as a biomarker of postictal generalized EEG suppression

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.

Factors Affecting Electroconvulsive Therapy Ictal Outcomes: Duration and Postictal Suppression

Electroconvulsive therapy (ECT) is an effective and rapid treatment for severe depression, however predictors of therapeutic outcomes remain insufficiently understood. Ictal duration and postictal suppression are two outcomes that may be correlated with patient response, yet patient and treatment variables which may influence these outcomes have not been thoroughly explored. We collected ECT stimulus metrics, EEG parameters, patient demographics, primary diagnosis, and anesthesia type for retrospective ECTs. Univariate and multivariate mixed-effects linear regression models were used to identify variables associated with ictal duration and postictal suppression index. For both outcomes, multivariate models which included all variables resulted in the best fit, reflecting the complex influences of a variety of factors on the ictal response. These results are an important step forward in elucidating patterns in retrospective ECT clinical data which may lead to new clinical knowledge of modifiable factors to optimize ECT treatment outcomes.