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Seizure duration predicts postictal electroencephalographic recovery after electroconvulsive therapy-induced seizures. Clin Neurophysiol 2023; 148:1-8. [PMID: 36773503 DOI: 10.1016/j.clinph.2023.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/20/2022] [Accepted: 01/11/2023] [Indexed: 01/27/2023]
Abstract
OBJECTIVE We aim to provide a quantitative description of the relation between seizure duration and the postictal state using features extracted from the postictal electroencephalogram (EEG). METHODS Thirty patients with major depressive disorder treated with electroconvulsive therapy (ECT) were studied with continuous EEG before, during, and after ECT-induced seizures. EEG recovery was quantified as the spectral difference between postictal and baseline EEG using the temporal brain symmetry index (BSI). The postictal temporal EEG evolution was modeled with a single exponential. The parameters of the model, including the time constant τ, describe the change and speed of postictal EEG recovery. The change from baseline EEG at t = 60 minutes post-seizure (ΔBSI) was calculated from the exponential fit. Postictal clinical reorientation time (ROT) was clinically established. A multivariate generalized multi-level Bayesian model was estimated with seizure duration and ROT as predictors of τ and ΔBSI. RESULTS EEG features of 290 seizures and postictal states were used for analyses. The model faithfully described the dynamics of the postictal EEG in nearly all patients. Seizure duration was associated with the recovery time constant, τ, and ΔBSI. ROT was associated with τ, but not with ΔBSI. CONCLUSIONS Longer seizures are associated with slower postictal EEG recovery and more enduring EEG changes compared to baseline. SIGNIFICANCE Quantitative EEG allows objective assessment of the postictal state.
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Kafashan M, Brian Hickman L, Labonte AK, Huels ER, Maybrier H, Guay CS, Subramanian S, Farber NB, Ching S, Hogan RE, Kelz MB, Avidan MS, Mashour GA, Palanca BJA. Quiescence during burst suppression and postictal generalized EEG suppression are distinct patterns of activity. Clin Neurophysiol 2022; 142:125-132. [PMID: 36030576 PMCID: PMC10287541 DOI: 10.1016/j.clinph.2022.07.493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 06/15/2022] [Accepted: 07/17/2022] [Indexed: 02/01/2023]
Abstract
OBJECTIVE Periods of low-amplitude electroencephalographic (EEG) signal (quiescence) are present during both anesthetic-induced burst suppression (BS) and postictal generalized electroencephalographic suppression (PGES). PGES following generalized seizures induced by electroconvulsive therapy (ECT) has been previously linked to antidepressant response. The commonality of quiescence during both BS and PGES motivated trials to recapitulate the antidepressant effects of ECT using high doses of anesthetics. However, there have been no direct electrographic comparisons of these quiescent periods to address whether these are distinct entities. METHODS We compared periods of EEG quiescence recorded from two human studies: BS induced in 29 healthy adult volunteers by isoflurane general anesthesia and PGES in 11 patients undergoing right unilateral ECT for treatment-resistant depression. An automated algorithm allowed detection of EEG quiescence based on a 10-microvolt amplitude threshold. Spatial, spectral, and temporal analyses compared quiescent epochs during BS and PGES. RESULTS The median (interquartile range) voltage for quiescent periods during PGES was greater than during BS (1.81 (0.22) microvolts vs 1.22 (0.33) microvolts, p < 0.001). Relative power was greater for quiescence during PGES than BS for the 1-4 Hz delta band (p < 0.001), at the expense of power in the theta (4-8 Hz, p < 0.001), beta (13-30 Hz, p = 0.04) and gamma (30-70 Hz, p = 0.006) frequency bands. Topographic analyses revealed that amplitude across the scalp was consistently higher for quiescent periods during PGES than BS, whose voltage was within the noise floor. CONCLUSIONS Quiescent epochs during PGES and BS have distinct patterns of EEG signals across voltage, frequency, and spatial domains. SIGNIFICANCE Quiescent epochs during PGES and BS, important neurophysiological markers for clinical outcomes, are shown to have distinct voltage and frequency characteristics.
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Affiliation(s)
- MohammadMehdi Kafashan
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - L Brian Hickman
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Department of Neurology, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA, USA
| | - Alyssa K Labonte
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Neuroscience Graduate Program, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Emma R Huels
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA; Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA
| | - Hannah Maybrier
- Psychological & Brain Sciences Department, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Christian S Guay
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Subha Subramanian
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Nuri B Farber
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - ShiNung Ching
- Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA; Division of Biology and Biomedical Sciences, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - R Edward Hogan
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Max B Kelz
- Department of Anesthesiology and Critical Care, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Michael S Avidan
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - George A Mashour
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA
| | - Ben J A Palanca
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA; Division of Biology and Biomedical Sciences, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
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