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Karl S, Sartorius A, Aksay SS. No Effect of Serum Electrolyte Levels on Electroconvulsive Therapy Seizure Quality Parameters. J ECT 2024; 40:47-50. [PMID: 38411578 DOI: 10.1097/yct.0000000000000966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
INTRODUCTION Seizure quality is considered to be associated with treatment outcomes of electroconvulsive therapy (ECT). A wide range of treatment parameters and patient characteristics are known to influence seizure quality. However, conflicting results exist for the role of serum electrolyte levels and seizure quality. METHODS We retrospectively analyzed a total of 454 patients and a total of 2119 individual acute ECT sessions irrespective of diagnosis where a clinical evaluation of serum levels of sodium, potassium, and calcium took place routinely up to 2 days before the ECT session. To assess the impact of serum electrolyte levels on seizure quality parameters, we used mixed-effects linear regression analysis with Bonferroni correction for multiple testing. RESULTS Serum sodium, potassium, and calcium levels were not associated with seizure quality markers after correcting the significance level for multiple testing. Younger age was consistently associated with higher postictal suppression, interhemispheric coherence, midictal amplitude, and peak heart rate. Lower dose was consistently associated with longer electroencephalogram and motor seizure duration. CONCLUSIONS Our results suggest that there is no clinically relevant effect of serum electrolyte levels on seizure quality, at least within clinically commonly observed ranges of serum electrolyte concentrations.
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Affiliation(s)
- Sebastian Karl
- From the Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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Dynamic impedance is correlated with static impedance and seizure quality parameters in bifrontal electroconvulsive therapy. Acta Neuropsychiatr 2023; 35:177-185. [PMID: 36803888 DOI: 10.1017/neu.2023.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
BACKGROUND To evoke a therapeutically effective seizure, electrical stimulation in electroconvulsive therapy (ECT) has to overcome the combined resistivity of scalp, skull and other tissues. Static impedances are measured prior to stimulation using high-frequency electrical alternating pulses, dynamic impedances during passage of the stimulation current. Static impedance can partially be influenced by skin preparation techniques. Earlier studies showed a correlation between dynamic and static impedance in bitemporal and right unilateral ECT. OBJECTIVE This study aims at assessing the correlation of dynamic and static impedance with patient characteristics and seizure quality criteria in bifrontal ECT. METHODS We performed a cross-sectional single-centre retrospective analysis of ECT treatments at the Psychiatric University Hospital Zurich between May 2012 and March 2020 and used linear mixed-effects regression models in 78 patients with a total of 1757 ECT sessions. RESULTS Dynamic and static impedance were strongly correlated. Dynamic impedance was significantly correlated with age and higher in women. Energy set and factors positively (caffeine) and negatively (propofol) affecting seizure at the neuronal level were not associated with dynamic impedance. For secondary outcomes, dynamic impedance was significantly related to Maximum Sustained Power and Average Seizure Energy Index. Other seizure quality criteria showed no significant correlation with dynamic impedance. CONCLUSION Aiming for low static impedance might reduce dynamic impedance, which is correlated with positive seizure quality parameters. Therefore, good skin preparation to achieve low static impedance is recommended.
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Daniel PM, Schälte G, Grözinger M. Cerebral oxygen saturation in the prefrontal cortex during electroconvulsive therapy and its relation with the postictal reorientation time. J Psychiatr Res 2022; 155:10-16. [PMID: 35969960 DOI: 10.1016/j.jpsychires.2022.07.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/08/2022] [Accepted: 07/20/2022] [Indexed: 10/31/2022]
Abstract
The therapeutic effect of Electroconvulsive Therapy (ECT) has been attributed to generalised seizure. Although patients are well oxygenated prior to and during treatment, critics have associated ECT with brain tissue hypoxemia. In this study, the regional oxygen saturation (rSO2) was measured continuously during ECT in the prefrontal cortex (PFC) of both hemispheres using 2-channel Near Infrared Spectroscopy (NIRS). Additionally, the postictal reorientation time (PRT) was determined and related to the rSO2 course. We evaluated 72 ECT treatments in 22 adult patients who were treated for a therapy-resistant depressive syndrome. The therapy was performed according to our standard clinical procedures deploying right unilateral (RUL) and left anterior versus right temporal (LART) electrode placements. According to our results, the rSO2 courses showed an increase during hyperventilation, a sharp drop immediately after the stimulus, and a long recovery period with values far exceeding the baseline. In 55,6% of treatments the rSO2 course stayed above the baseline. In the others, the drop fell below it for an average of 12.6 s. According to a cardio surgical standard no signs of hypoxemia occurred during ECT treatments. The rSO2 drop at seizure onset was the only parameter of the oxygen course related to the PRT in the multivariate analysis and might therefore be a characteristic feature of the seizure. It could reflect its physiological intensity and thereby be involved in the mechanism of action of ECT. NIRS seems to be an interesting non-invasive tool for monitoring and studying ECT.
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Affiliation(s)
- Pascal Michael Daniel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
| | - Gereon Schälte
- Department of Anaesthesiology, Medical Faculty, RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
| | - Michael Grözinger
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
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Simon L, Blay M, Galvao F, Brunelin J. Using EEG to Predict Clinical Response to Electroconvulsive Therapy in Patients With Major Depression: A Comprehensive Review. Front Psychiatry 2021; 12:643710. [PMID: 34248695 PMCID: PMC8264052 DOI: 10.3389/fpsyt.2021.643710] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 05/18/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: An important approach to improve the therapeutic effect of electroconvulsive therapy (ECT) may be to early characterize patients who are more likely to respond. Our objective was to explore whether baseline electroencephalography (EEG) settings before the beginning of ECT treatment can predict future clinical response to ECT in patients with depressive disorder. Methods: We conducted a systematic search in the MEDLINE, EMBASE, PsycINFO, Web of Science, and Cochrane Central Register of Controlled Trials (CENTRAL) databases to identify studies using EEG in adults with depressive disorder treated by ECT. To investigate the predictive value of baseline EEG on clinical outcomes of ECT, we extracted from the retrieved studies and qualitatively described the association between the baseline EEG markers characteristics and the rates of future responders and/or remitters to ECT. Results: The primary search yielded 2,531 potentially relevant citations, and 12 articles were selected according to inclusion criteria. Most of the studies were prospective studies with small sample size. Sociodemographic and clinical characteristics of patients, ECT settings, EEG settings, and outcomes were heterogeneous. Event-related potential (ERP) paradigms were used in three studies, polysomnography was used in three studies, and the six other studies used EEG to measure cerebral connectivity and activity. Conclusions: P300 amplitude, coherence, and connectivity measures were correlated with remission in patients with depression treated by ECT. Sleep EEG recordings seemed not to be correlated with remission after ECT. Further prospective studies with large sample size are needed to determine optimal EEG parameters associated with clinical response to ECT in depressive disorder. Systematic Review Registration: PROSPERO CRD42020181978.
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Affiliation(s)
- Louis Simon
- Centre Hospitalier Le Vinatier, Bron, France.,INSERM, U1028, CNRS, UMR5292, Lyon Neuroscience Research Center, PSYR2 Team, Lyon, France.,Lyon University, Université Lyon 1, Villeurbanne, France
| | - Martin Blay
- Centre Hospitalier Le Vinatier, Bron, France.,Lyon University, Université Lyon 1, Villeurbanne, France
| | | | - Jerome Brunelin
- Centre Hospitalier Le Vinatier, Bron, France.,INSERM, U1028, CNRS, UMR5292, Lyon Neuroscience Research Center, PSYR2 Team, Lyon, France.,Lyon University, Université Lyon 1, Villeurbanne, France
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Hu Q, Huang H, Jiang Y, Jiao X, Zhou J, Tang Y, Zhang T, Sun J, Yao D, Luo C, Li C, Wang J. Temporoparietal Connectivity Within Default Mode Network Associates With Clinical Improvements in Schizophrenia Following Modified Electroconvulsive Therapy. Front Psychiatry 2021; 12:768279. [PMID: 35058815 PMCID: PMC8763790 DOI: 10.3389/fpsyt.2021.768279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/02/2021] [Indexed: 12/19/2022] Open
Abstract
Although modified electroconvulsive therapy (ECT) has been reported to be effective for the treatment of schizophrenia (SCZ), its action mechanism is unclear. To elucidate the underlying ECT mechanisms of SCZ, this study used a longitudinal cohort including 21 SCZ patients receiving only antipsychotics (DSZ group) and 21 SCZ patients receiving a regular course of ECT combining with antipsychotics (MSZ group) for 4 weeks. All patients underwent magnetic resonance imaging (MRI) scans at baseline (t1) and follow-up (t2) time points. A matched healthy control (HC) group included 23 individuals who were only scanned at baseline. Functional connectivity (FC) within the default mode network (DMN) was evaluated before and after ECT. Significant interaction of the group over time was found in FC between angular gyrus (AG) and middle temporal gyrus (MTG). Post-hoc analysis showed a significantly enhanced FC of left AG(AG.L) and right MTG (MTG.R) in the MSZ group relative to the DSZ group. In addition, the right AG (AG.R) showed significantly enhanced FC between MTG.R and left MTG (MTG.L) after ECT in the MSZ group, but no in the DSZ group. In particular, the FCs change in AG.L-MTG.R and AG.R-MTG.R were positively correlated with the Positive and Negative Syndrome Scale (PANSS) negative score reduction. Furthermore, the FC change in AG.L-MTG.R was also positively correlated with the PANSS general psychopathology score reduction. These findings confirmed a potential relationship between ECT inducing hyperconnectivity within DMN and improvements in symptomatology of SCZ, suggesting that ECT controls mental symptoms by regulating the temporoparietal connectivity within DMN.
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Affiliation(s)
- Qiang Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuchao Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiong Jiao
- School of BIomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.,Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Zhou
- School of BIomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.,Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junfeng Sun
- School of BIomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chunbo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, China.,Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China.,Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, China.,Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China
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