The Changes of Functional Connectivity Strength in Electroconvulsive Therapy for Depression: A Longitudinal Study

Wei, Qiang; Bai, Tongjian; Chen, Yang; Ji, Gong‐Jun; Hu, Xiaopeng; Xie, Wen; Xiong, Zhi‐Gang; Zhu, Daomin; Lin, Wei; Hu, Panpan; Yu, Yongqiang; Wang, Kai; Tian, Yanghua

doi:10.3389/fnins.2018.00661

Cited by 39 publications

(30 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another study declared that the cognitive control network (CCN) and the default mode network (DMN) play a vital role as the most effective brain network in regulating brain connections after ECT (Menon, 2015 ). Moreover, ECT-mediated connectivity changes include increased intra-network connectivity in CCN (Wang H. et al, 2018 ; Wang J. et al, 2018 ), decreased dorsolateral prefrontal cortex global functional connectivity (DLPFC) as a part of CCN (Perrin et al, 2012 ), and connectivity changes in the DMN (Mulders et al, 2016 ; Wei et al, 2018 ; Bai et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder

Dini

Sendi

Sui

et al. 2021

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Background: Electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder. Recently, there has been increasing attention to evaluate the effect of ECT on resting-state functional magnetic resonance imaging (rs-fMRI). This study aims to compare rs-fMRI of depressive disorder (DEP) patients with healthy participants, investigate whether pre-ECT dynamic functional network connectivity network (dFNC) estimated from patients rs-fMRI is associated with an eventual ECT outcome, and explore the effect of ECT on brain network states.Method: Resting-state functional magnetic resonance imaging (fMRI) data were collected from 119 patients with depression or depressive disorder (DEP) (76 females), and 61 healthy (HC) participants (34 females), with an age mean of 52.25 (N = 180) years old. The pre-ECT and post-ECT Hamilton Depression Rating Scale (HDRS) were 25.59 ± 6.14 and 11.48 ± 9.07, respectively. Twenty-four independent components from default mode (DMN) and cognitive control network (CCN) were extracted, using group-independent component analysis from pre-ECT and post-ECT rs-fMRI. Then, the sliding window approach was used to estimate the pre-and post-ECT dFNC of each subject. Next, k-means clustering was separately applied to pre-ECT dFNC and post-ECT dFNC to assess three distinct states from each participant. We calculated the amount of time each subject spends in each state, which is called “occupancy rate” or OCR. Next, we compared OCR values between HC and DEP participants. We also calculated the partial correlation between pre-ECT OCRs and HDRS change while controlling for age, gender, and site. Finally, we evaluated the effectiveness of ECT by comparing pre- and post-ECT OCR of DEP and HC participants.Results: The main findings include (1) depressive disorder (DEP) patients had significantly lower OCR values than the HC group in state 2, where connectivity between cognitive control network (CCN) and default mode network (DMN) was relatively higher than other states (corrected p = 0.015), (2) Pre-ECT OCR of state, with more negative connectivity between CCN and DMN components, is linked with the HDRS changes (R = 0.23 corrected p = 0.03). This means that those DEP patients who spent less time in this state showed more HDRS change, and (3) The post-ECT OCR analysis suggested that ECT increased the amount of time DEP patients spent in state 2 (corrected p = 0.03).Conclusion: Our finding suggests that dynamic functional network connectivity (dFNC) features, estimated from CCN and DMN, show promise as a predictive biomarker of the ECT outcome of DEP patients. Also, this study identifies a possible underlying mechanism associated with the ECT effect on DEP patients.

show abstract

Section: Introductionmentioning

confidence: 99%

Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder

Dini

Sendi

Sui

et al. 2021

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Depressed patients are different from healthy subjects in the decoding of negative emotions. In a study, it was found that electroconvulsive therapy (ECT) could modulate the functional connectivity of the left angular gyrus in patients with depression (Wei et al, 2018 ). From the performance and treatment of depression, it can be concluded that there may be differences in brain structure between patients with depression and healthy subjects.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals

Duan

Duan²,

Qiao

et al. 2020

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Emotional decoding and automatic identification of major depressive disorder (MDD) are helpful for the timely diagnosis of the disease. Electroencephalography (EEG) is sensitive to changes in the functional state of the human brain, showing its potential to help doctors diagnose MDD. In this paper, an approach for identifying MDD by fusing interhemispheric asymmetry and cross-correlation with EEG signals is proposed and tested on 32 subjects [16 patients with MDD and 16 healthy controls (HCs)]. First, the structural features and connectivity features of the θ-, α-, and β-frequency bands are extracted on the preprocessed and segmented EEG signals. Second, the structural feature matrix of the θ-, α-, and β-frequency bands are added to and subtracted from the connectivity feature matrix to obtain mixed features. Finally, the structural features, connectivity features, and the mixed features are fed to three classifiers to select suitable features for the classification, and it is found that our mode achieves the best classification results using the mixed features. The results are also compared with those from some state-of-the-art methods, and we achieved an accuracy of 94.13%, a sensitivity of 95.74%, a specificity of 93.52%, and an F1-score (f1) of 95.62% on the data from Beijing Anding Hospital, Capital Medical University. The study could be generalized to develop a system that may be helpful in clinical purposes.

show abstract

“…A voxel-wise, whole-brain functional connectivity analysis was performed on the preprocessed rsfMRI data as in our previous study [31]. First, Pearson's correlations between the residual time series of all pairs of brain voxels were computed and a whole brain connectivity matrix for each participant was constructed.…”

Section: Whole Brain Fcs Analysismentioning

confidence: 99%

A common variant of the NOTCH4 gene modulates functional connectivity of the occipital cortex and its relationship with schizotypal traits

Xie

Zhang

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Schizotypal traits are considered as inheritable traits and the endophenotype for schizophrenia. A common variant in the NOTCH4 gene, rs204993, has been linked with schizophrenia, but the neural underpinnings are largely unknown. Methods: In present study, we compared the differences of brain functions between different genotypes of rs204993 and its relationship with schizotypal traits among 402 Chinese Han healthy volunteers. The brain function was evaluated with functional connectivity strength (FCS) using the resting-state functional magnetic resonance image（rs-fMRI）. The schizotypal traits were measured by the schizotypal personality questionnaire (SPQ). Results: Our results showed that carriers with the AA genotype showed reduced FCS in the left occipital cortex when compared with carriers with the AG and GG genotypes, and the carriers with the AG genotype showed reduced FCS in the left occipital cortex when compared with carriers with the GG genotype. The FCS values in the left occipital lobe were negatively associated with the SPQ scores and its subscale scores within the carriers with the GG genotype, but not within the carriers with AA or AG genotype.Conclusion: Our results suggested that the common variant in the NOTCH4 gene, rs204993, modulates the function of the occipital cortex, which may contribute to schizotypal traits. These findings provide insight for genetic effects on schizotypal traits and its potential neural substrate.

show abstract

The Changes of Functional Connectivity Strength in Electroconvulsive Therapy for Depression: A Longitudinal Study

Cited by 39 publications

References 30 publications

Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder

Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder

Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals

A common variant of the NOTCH4 gene modulates functional connectivity of the occipital cortex and its relationship with schizotypal traits

Contact Info

Product

Resources

About