Electroconvulsive therapy regulates brain connectome dynamics in major depressive disorder
Poster No:
41
Submission Type:
Abstract Submission
Authors:
Yuanyuan Guo1, Mingrui Xia2, Rong Ye3, Tongjian Bai1, Yue Wu4, Yang Ji1, Yue Yu1, Gongjun Ji3, Kai Wang1, Yong He2, Yanghua Tian4
Institutions:
1The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 2Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 3School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui, 4The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui
First Author:
Co-Author(s):
Mingrui Xia
Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University
Beijing
Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University
Beijing
Gongjun Ji
School of Mental Health and Psychological Sciences, Anhui Medical University
Hefei, Anhui
School of Mental Health and Psychological Sciences, Anhui Medical University
Hefei, Anhui
Introduction:
Major depressive disorder (MDD) is a common and severe affective disorder characterized by negative mood and high risk of suicide. Electroconvulsive therapy (ECT) is an effective treatment for MDD patients, but its underlying neural mechanisms remain largely unknown. The aim of this study was to identify changes in brain connectome dynamics after ECT in MDD and to explore their associations with treatment outcome.
Methods:
We collected longitudinal resting-state fMRI data from 80 MDD patients (50 with suicidal ideation and 30 without; SI and NSI, respectively) before and after ECT and 37 age- and sex-matched healthy controls. A multilayer network model was used to assess modular switching over time in functional connectomes. Repeated measures analysis of variance was applied to assess differences in dependent variables (network dynamics) with group (MDD vs. healthy controls and MDD-SI vs. MDD-NSI) and time (before vs. after ECT) served as the independent variables. Post-hoc analyses were also performed using a two-sample t-test between groups and paired t-test between times. Support vector regression was used to assess whether pre-ECT network dynamics could predict treatment response in terms of symptom severity.
Results:
Analysis of global modularity (F = 7.25, p = 0.008, ηp2 = 0.06) and modular variability (F = 8.80, p = 0.004, ηp2 = 0.07) both revealed significant group-by-time interaction effects. Post-hoc analysis showed that MDD patients had lower global modularity (t = -4.33, p < 0.001, Cohen's d = -0.86) and higher modular variability (t =1.99, p = 0.049, Cohen's d = 0.45) in functional connectomes compared to controls at baseline. ECT enhanced global modularity (t = 2.94, p = 0.004, Cohen's d = 0.47) and reduced variability (t = -4.18, p < 0.001, Cohen's d = -0.61) in MDD patients. Analysis of regional modular variability revealed a significant group-by-time interaction effect predominantly located in the default mode and somatomotor networks (all F > 7.76, p < 0.006, FDR corrected). Post-hoc analysis showed that modular variability was significantly lower after ECT in those regions in MDD patients (all t < -2.60, p < 0.011, FDR corrected). Support vector regression analysis showed pre-ECT modular variability could accurately predict symptom improvement in MDD patients (r = 0.315, p = 0.004, 1,000 permutation tests). In suicidal ideation subgroup analysis, ECT was associated with decreased modular variability in the left dorsal anterior cingulate cortex of MDD-SI (t = -4.33, p < 0.001, Cohen's d = -0.38), but not MDD-NSI (t = 0.975, p =0.338, Cohen's d = 0.21) patients, and pre-ECT modular variability could accurately predict symptom improvement in the MDD-SI group (r = 0.295, p = 0.039, 1,000 permutation tests), but not in the MDD-NSI group.
·Figure 1. ECT effects on clinical symptom and brain dynamics in MDD patients.
·Figure 2. Suicidal ideation subgroup analysis.
Conclusions:
We highlight ECT-induced changes in MDD brain network dynamics and their predictive value for treatment outcome, particularly in patients with suicidal ideation. This study advances our understanding of the neural mechanisms of ECT from a dynamic brain network perspective and suggests potential prognostic biomarkers for predicting ECT efficacy in patients with MDD.
Brain Stimulation:
Non-invasive Electrical/tDCS/tACS/tRNS 1
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Affective Disorders
FUNCTIONAL MRI
Treatment
Other - electroconvulsive therapy; depression; suicidal ideation; connectomics; modularity; network dynamics
1|2Indicates the priority used for review
Provide references using author date format
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