Electroconvulsive Therapy Modulates the Structural and Functional Architecture of Frontal Pole in Major Depressive Disorder

Xu, Jinping; Wang, Qiang; Xu, Ziyun; Hu, Qingmao; Tian, Yanghua; Wang, Kai; Wang, Jiaojian

doi:10.4172/neuropsychiatry.1000342

Cited by 3 publications

(3 citation statements)

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“…(Bludau et al, 2014;Gilbert et al, 2006). Previous studies have reported that depression is associated to reduced medial frontal pole volume (Bludau et al, 2016), and that decreased frontal pole volume and FC following ECT was related to therapeutic efficacy (Xu et al, 2018). The medial temporal lobes have been consistently implicated in ECT neuroimaging research and include the hippocampus and amygdala, which have shown to undergo structural changes in volume, functional connectivity, and perfusion following ECT (Leaver et al, 2016;Mulders et al, 2020;Ousdal et al, 2020;Redlich et al, 2016;Takamiya et al, 2018;Wilkinson, Sanacora, & Bloch, 2017).…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis

et al. 2023

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Background Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, monocenter studies indicate that both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. The objective of this study was to develop and validate neuroimaging biomarkers for ECT outcome in a multicenter setting. Methods Multimodal data (i.e. clinical, sMRI and resting-state fMRI) were collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluate which data modalities or combinations thereof could provide the best predictions for treatment remission (HAM-D score ⩽7) using a support vector machine classifier. Results Remission classification using a combination of gray matter volume and functional connectivity led to good performing models with average 0.82–0.83 area under the curve (AUC) when trained and tested on samples coming from the three largest centers (N = 109), and remained acceptable when validated using leave-one-site-out cross-validation (0.70–0.73 AUC). Conclusions These results show that multimodal neuroimaging data can be used to predict remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. Future development of a clinical decision support tool applying these biomarkers may be feasible.

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Section: Discussionmentioning

confidence: 99%

Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis

et al. 2023

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“…We propose that frontal and prefrontal circuits and especially their connectivity to depression-associated regions may affect ECT outcomes not solely dependent on electrode placement. Interestingly, structural [ 63 , 64 ] and functional [ 65 , 66 ] changes following ECT have been observed within the amygdala and within the FP [ 67 ]. On a functional level, the FP may subserve an integratory role for higher-order social, emotional, and cognitive processes [ 68 , 69 ] and contribute to the development of typical symptoms associated with depression e.g.…”

Section: Discussionmentioning

confidence: 99%

Functional connectivity changes between amygdala and prefrontal cortex after ECT are associated with improvement in distinct depressive symptoms

Domke

Hempel

Hartling

et al. 2023

Eur Arch Psychiatry Clin Neurosci

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Electroconvulsive therapy (ECT) is one of the most effective treatments for treatment-resistant depression. However, the underlying mechanisms of action are not yet fully understood. The investigation of depression-specific networks using resting-state fMRI and the relation to differential symptom improvement might be an innovative approach providing new insights into the underlying processes. In this naturalistic study, we investigated the relationship between changes in resting-state functional connectivity (rsFC) and symptom improvement after ECT in 21 patients with treatment-resistant depression. We investigated rsFC before and after ECT and focused our analyses on FC changes directly related to symptom reduction and on FC at baseline to identify neural targets that might predict individual clinical responses to ECT. Additional analyses were performed to identify the direct relationship between rsFC change and symptom dimensions such as sadness, negative thoughts, detachment, and neurovegetative symptoms. An increase in rsFC between the left amygdala and left dorsolateral prefrontal cortex (DLPFC) after ECT was related to overall symptom reduction (Bonferroni-corrected p = 0.033) as well as to a reduction in specific symptoms such as sadness (r = 0.524, uncorrected p = 0.014), negative thoughts (r = 0.700, Bonferroni-corrected p = 0.002) and detachment (r = 0.663, p = 0.004), but not in neurovegetative symptoms. Furthermore, high baseline rsFC between the left amygdala and the right frontal pole (FP) predicted treatment outcome (uncorrected p = 0.039). We conclude that changes in FC in regions of the limbic-prefrontal network are associated with symptom improvement, particularly in affective and cognitive dimensions. Frontal-limbic connectivity has the potential to predict symptom improvement after ECT. Further research combining functional imaging biomarkers and a symptom-based approach might be promising.

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“…(29,30). Previous studies have reported that depression is associated to reduced medial frontal pole volume (31) and that decreased frontal pole volume and FC following ECT was related to therapeutic efficacy (32). The medial temporal lobes have been consistently implicated in ECT neuroimaging research and include the hippocampus and amygdala, which have shown to undergo structural changes in volume, functional connectivity and perfusion following ECT treatment (5,24,26,(33)(34)(35).…”

Section: < Insert Figure 4 > Discussionmentioning

confidence: 99%

Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis

Bruin

Oltedal

Bartsch

et al. 2021

Preprint

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Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, mono-center studies indicate that both structural magnetic resonance imaging (MRI) and functional MRI biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. Here, we used MRI data of 189 depressed patients from seven participating centers of the Global ECT-MRI Research Collaboration (GEMRIC) to develop and validate neuroimaging biomarkers for ECT outcome in a multi-center setting. We used multimodal data (i.e., clinical, structural MRI and resting-state functional MRI) and evaluated which data modalities or combinations thereof could provide the best predictions for treatment response (≥50% symptom reduction) or remission (minimal symptoms after treatment) using a support vector machine (SVM) classifier. Remission classification using a combination of gray matter volume with functional connectivity led to good performing models with 0.82-0.84 area under the curve (AUC) when trained and tested on samples coming from all centers, and remained acceptable when validated on other centers with 0.71-0.73 AUC. These results show that multimodal neuroimaging data is able to provide good prediction of remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. This suggests that these biomarkers are robust, indicating that future development of a clinical decision support tool applying these biomarkers may be feasible.

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Electroconvulsive Therapy Modulates the Structural and Functional Architecture of Frontal Pole in Major Depressive Disorder

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Cited by 3 publications

References 46 publications

Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis

Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis

Functional connectivity changes between amygdala and prefrontal cortex after ECT are associated with improvement in distinct depressive symptoms

Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis

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