Temporal trajectory of brain tissue property changes induced by electroconvulsive therapy

Gyger, Lucien; Ramponi, Cristina; Mall, Jean-Frédéric; Swierkosz-Lenart, Kevin; Stoyanov, Drozdstoy; Lutti, Antoine; Gunten, Armin von; Kherif, Ferath; Draganski, Bogdan

doi:10.1016/j.neuroimage.2021.117895

Cited by 23 publications

(20 citation statements)

References 57 publications

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“…Whilst reduced mean diffusivity could theoretically reflect acute cytotoxic edema, a recent multi-contrast quantitative MRI study reported that GMV increase following ECT was not paralleled by an increase in proton density, a proxy for tissue water content. These findings suggest that neither vasogenic nor cytotoxic edema are likely to be the primary contributor to MTL volume increase following ECT [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, reversible peri-ictal MRI signal changes are frequently reported [ 30 – 32 ] particularly in the MTL [ 33 ] and are hypothesized to reflect the consequences of ictal hemodynamic and metabolic changes that are associated with vasogenic and cytotoxic edema. However, several studies report an absence of increased visible T2 signal or diffusivity changes following ECT, which argues against edema contributing to volumetric change [ 34 – 39 ]. Whilst epileptic seizures arise against a backdrop of neuropathology which limits the utility of epilepsy as a neurobiological model for ECT, there remains parallels between the two conditions which could provide useful insights into post-ictal structural brain changes following ECT.…”

Section: Introductionmentioning

confidence: 99%

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Biophysical mechanisms of electroconvulsive therapy-induced volume expansion in the medial temporal lobe: A longitudinal in vivo human imaging study

et al. 2021

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Background: Electroconvulsive therapy (ECT) applies electric currents to the brain to induce seizures for therapeutic purposes. ECT increases gray matter (GM) volume, predominantly in the medial temporal lobe (MTL). The contribution of induced seizures to this volume change remains unclear. Methods: T1-weighted structural MRI was acquired from thirty patients with late-life depression (mean age 72.5 ± 7.9 years, 19 female), before and one week after one course of right unilateral ECT. Whole brain voxel-/deformation-/surface-based morphometry analyses were conducted to identify tissue-specific (GM, white matter: WM), and cerebrospinal fluid (CSF) and cerebral morphometry changes following ECT. Whole-brain voxel-wise electric field (EF) strength was estimated to investigate the association of EF distribution and regional brain volume change. The association between percentage volume change in the right MTL and ECT-related parameters (seizure duration, EF, and number of ECT sessions) was investigated using multiple regression. Results: ECT induced widespread GM volume expansion with corresponding contraction in adjacent CSF compartments, and limited WM change. The regional EF was strongly correlated with the distance from the electrodes, but not with regional volume change. The largest volume expansion was identified in the right MTL, and this was correlated with the total seizure duration. Conclusions: Right unilateral ECT induces widespread, bilateral regional volume expansion and contraction, with the largest change in the right MTL. This dynamic volume change cannot be explained by the effect of electrical stimulation alone and is related to the cumulative effect of ECT-induced seizures.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Biophysical mechanisms of electroconvulsive therapy-induced volume expansion in the medial temporal lobe: A longitudinal in vivo human imaging study

et al. 2021

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“…Longitudinal structural neuroimaging studies have proven that ECT increases the volume of the hippocampus, amygdala, caudate nucleus, and temporal lobe. Some studies have found that ECT increases the volume of the hippocampus and amygdala in the temporal lobe system in patients with depression[ 62 - 64 ]. The strongest evidence of structural changes in the brain after ECT was an increase in the volume of the temporal lobe and subcortical structures, such as the hippocampal-amygdala complex, anterior cingulate cortex and striatum[ 65 ].…”

Section: Brain Structural Imaging Study For Depression With Ectmentioning

confidence: 99%

Current progress in neuroimaging research for the treatment of major depression with electroconvulsive therapy

Li¹,

Qiu²

2022

WJP

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Electroconvulsive therapy (ECT) uses a certain amount of electric current to pass through the head of the patient, causing convulsions throughout the body, to relieve the symptoms of the disease and achieve the purpose of treatment. ECT can effectively improve the clinical symptoms of patients with major depression, but its therapeutic mechanism is still unclear. With the rapid development of neuroimaging technology, it is necessary to explore the neurobiological mechanism of major depression from the aspects of brain structure, brain function and brain metabolism, and to find that ECT can improve the brain function, metabolism and even brain structure of patients to a certain extent. Currently, an increasing number of neuroimaging studies adopt various neuroimaging techniques including functional magnetic resonance imaging (MRI), positron emission tomography, magnetic resonance spectroscopy, structural MRI, and diffusion tensor imaging to reveal the neural effects of ECT. This article reviews the recent progress in neuroimaging research on ECT for major depression. The results suggest that the neurobiological mechanism of ECT may be to modulate the functional activity and connectivity or neural structural plasticity in specific brain regions to the normal level, to achieve the therapeutic effect.

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“…To test the significance of the difference between the diagnostic categories an analysis of the eigenvalues of the variance matrices was used to derive the canonical vector that corresponded to the linear combination that best explains the variance of the data, together with an F-test of the Wilks lambda (see 30 ). Detailed data on the analysis code and results are available at https://zenodo.org/record/5865628.…”

Section: Statistical Analysis and Model Testingmentioning

confidence: 99%

Application of Mass Multivariate Analysis on Neuroimaging Data Sets in Psychiatric Diagnostics

Paunova¹,

Kandilarova²,

Todeva-Radneva³

et al. 2022

Preprint

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We have used Mass Multivariate Method on structural, resting state and task related fMRI data from two groups of patients with schizophrenia and depression, respectively, in order to define several regions of significant relevance to the differential diagnosis between those conditions. The regions included the left Planum polare, Left opercular part of the inferior frontal gyrus (OpIFG), Medial orbital gyrus (MOrG), Posterior Insula (PIns), and Parahippocampal gyrus (PHG). This study delivers evidence that multimodal neuroimaging approach can potentially enhance the validity of psychiatric diagnosis. Either structural, or resting state or task related functional MRI modality cannot provide independent biomarkers. Further studies need to consider and implement a model of incremental validity to combine clinical measures with different neuroimaging modalities to discriminate depressive disorders from schizophrenia. Biological signatures of disease on the level of neuroimaging are more likely to underpin broader nosological entities in psychiatry.

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Temporal trajectory of brain tissue property changes induced by electroconvulsive therapy

Cited by 23 publications

References 57 publications

Biophysical mechanisms of electroconvulsive therapy-induced volume expansion in the medial temporal lobe: A longitudinal in vivo human imaging study

Biophysical mechanisms of electroconvulsive therapy-induced volume expansion in the medial temporal lobe: A longitudinal in vivo human imaging study

Current progress in neuroimaging research for the treatment of major depression with electroconvulsive therapy

Application of Mass Multivariate Analysis on Neuroimaging Data Sets in Psychiatric Diagnostics

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