Distinct patterns of functional brain network integration between treatment-resistant depression and non treatment-resistant depression: A resting-state functional magnetic resonance imaging study

Sun, Jifei; Ma, Yue; Guo, Chunlei; Du, Zhongming; Chen, Limei; Wang, Zhi; Li, Xiaojiao; Luo, Yi; Yang, Hong; Yu, Xue; Xiao, Xue; Fang, Jiliang; Lu, Jie

doi:10.1016/j.pnpbp.2022.110621

Cited by 12 publications

(7 citation statements)

References 78 publications

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“…In the present study, we found triple-network abnormalities during emotion recognition tasks in depressive patients. Moreover, the activation of the DMN and deactivation of SN and ECN during the explicit tasks in depression are consistent with the findings of resting-state FC ( 67 , 79 , 87 ). In addition, when performing an explicit emotion recognition task, depressive patients are asked to classify emotional stimuli.…”

Section: Discussionsupporting

confidence: 87%

“…SN takes part in the perception and integration of emotional information. The study showed that the resting-state FC alteration of SN decreased in treatment-resistant patients with depression ( 79 ). The insula is responsible for regulating attention, working memory, and some other higher cognitive functions and facilitating the processing of task-related information ( 80 – 82 ).…”

Section: Discussionmentioning

confidence: 99%

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The conscious processing of emotion in depression disorder: a meta-analysis of neuroimaging studies

Gou

Guo

et al. 2023

Front. Psychiatry

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BackgroundDepression is generally accompanied by a disturbed conscious processing of emotion, which manifests as a negative bias to facial/voice emotion information and a decreased accuracy in emotion recognition tasks. Several studies have proved that abnormal brain activation was responsible for the deficit function of conscious emotion recognition in depression. However, the altered brain activation related to the conscious processing of emotion in depression was incongruent among studies. Therefore, we conducted an activation likelihood estimation (ALE) analysis to better understand the underlying neurophysiological mechanism of conscious processing of emotion in depression.MethodElectronic databases were searched using the search terms “depression,” “emotion recognition,” and “neuroimaging” from inceptions to April 10th, 2023. We retrieved trials which explored the neuro-responses of depressive patients to explicit emotion recognition tasks. Two investigators independently performed literature selection, data extraction, and risk of bias assessment. The spatial consistency of brain activation in conscious facial expressions recognition was calculated using ALE. The robustness of the results was examined by Jackknife sensitivity analysis.ResultsWe retrieved 11,365 articles in total, 28 of which were included. In the overall analysis, we found increased activity in the middle temporal gyrus, superior temporal gyrus, parahippocampal gyrus, and cuneus, and decreased activity in the superior temporal gyrus, inferior parietal lobule, insula, and superior frontal gyrus. In response to positive stimuli, depressive patients showed hyperactivity in the medial frontal gyrus, middle temporal gyrus, and insula (uncorrected p < 0.001). When receiving negative stimuli, a higher activation was found in the precentral gyrus, middle frontal gyrus, precuneus, and superior temporal gyrus (uncorrected p < 0.001).ConclusionAmong depressive patients, a broad spectrum of brain areas was involved in a deficit of conscious emotion processing. The activation of brain regions was different in response to positive or negative stimuli. Due to potential clinical heterogeneity, the findings should be treated with caution.Systematic review registrationhttps://inplasy.com/inplasy-2022-11-0057/, identifier: 2022110057.

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Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

The conscious processing of emotion in depression disorder: a meta-analysis of neuroimaging studies

Gou

Guo

et al. 2023

Front. Psychiatry

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“…41 Compared with nontreatment-resistant depression, FC alterations in AN were more abnormal in the patients with treatment-resistant depression. 42 Chen et al 43 found that repetitive transcranial magnetic simulation treatment could enhance AN connectivity in MDD patients, which is closely linked to emotional improvement. These findings indicate that AN is involved in the pathophysiology of MDD and plays an important role in antidepressant efficacy.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Early Antidepressant Efficacy in Patients with Major Depressive Disorder Based on Multidimensional Features of rs-fMRI and P11 Gene DNA Methylation: Prédiction de l’efficacité précoce d’un antidépresseur chez des patients souffrant du trouble dépressif majeur d’après les caractéristiques multidimensionnelles de la méthylation de l’ADN du gène P11 et de la IRMf-rs

Wang,

Gao,

et al. 2023

Can J Psychiatry

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Objective This study established a machine learning model based on the multidimensional data of resting-state functional activity of the brain and P11 gene DNA methylation to predict the early efficacy of antidepressant treatment in patients with major depressive disorder (MDD). Methods A total of 98 Han Chinese MDD were analysed in this study. Patients were divided into 51 responders and 47 nonresponders according to whether the Hamilton Depression Rating Scale-17 items (HAMD-17) reduction rate was ≥50% after 2 weeks of antidepressant treatment. At baseline, the Illumina HiSeq Platform was used to detect the methylation of 74 CpG sites of the P11 gene in peripheral blood samples. Resting-state functional magnetic resonance imaging (rs-fMRI) scan detected the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) in 116 brain regions. The least absolute shrinkage and selection operator analysis method was used to perform feature reduction and feature selection. Four typical machine learning methods were used to establish support vector machine (SVM), random forest (RF), Naïve Bayes (NB), and logistic regression (LR) prediction models based on different combinations of functional activity of the brain, P11 gene DNA methylation and clinical/demographic features after screening. Results The SVM model based on ALFF, ReHo, FC, P11 methylation, and clinical/demographic features showed the best performance, with 95.92% predictive accuracy and 0.9967 area under the receiver operating characteristic curve, which was better than RF, NB, and LR models. Conclusion The multidimensional data features combining rs-fMRI, DNA methylation, and clinical/demographic features can predict the early antidepressant efficacy in MDD.

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“…The brain regions found to be associated with DBS outcome (the NAc, midcingulate cortex, and the caudate nucleus) have previously shown to be involved in reward processes and rewardbased decision making (23)(24)(25). In addition, these regions have previously been implicated in the neurobiology of TRD (26)(27)(28), and have consistently shown hypoactivity during reward feedback in MDD and relations to anhedonia (15,29,30). While we did not find a significant difference in brain activity in these regions between patients and controls at baseline, our findings imply that functional variation in the NAc, midcingulate cortex, and caudate nucleus within the patient group may still be relevant for the outcome of DBS.…”

Section: Discussionmentioning

confidence: 99%

Reward circuit function and treatment outcome following vALIC deep brain stimulation in treatment-resistant depression

Runia,

van de Mortel,

Smith

et al. 2023

Preprint

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Depression is associated with abnormal functioning of the reward circuitry. Several deep brain stimulation (DBS) targets for treatment-resistant depression (TRD) directly modulate white matter bundles of the reward circuitry. Here we investigated whether baseline reward processing in the brain is associated with ventral anterior limb of the internal capsule (vALIC) DBS outcome and whether vALIC DBS changes neural activity in the reward circuitry.We studied fifteen patients with TRD who performed a monetary reward task during functional magnetic resonance imaging (fMRI) before vALIC DBS surgery, after DBS parameter optimization, and during a sham-controlled crossover phase. Additionally, fifteen matched healthy controls were investigated twice to account for test-retest effects. We investigated brain responses to reward anticipation, loss anticipation, reward feedback and loss feedback.Results showed that lower baseline nucleus accumbens activation during loss anticipation and higher baseline caudate nucleus and midcingulate cortex activation during reward feedback processing were associated with worse DBS outcome. No significant changes in reward processing were observed following vALIC DBS in comparison to healthy controls or after active compared to sham stimulation. Instead, increased middle frontal gyrus responses following DBS to loss feedback was associated with better DBS outcome.These results suggest that DBS efficacy in TRD is related to individual differences in reward circuitry functioning at baseline and to changes in middle frontal gyrus responses following DBS.

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Distinct patterns of functional brain network integration between treatment-resistant depression and non treatment-resistant depression: A resting-state functional magnetic resonance imaging study

Cited by 12 publications

References 78 publications

The conscious processing of emotion in depression disorder: a meta-analysis of neuroimaging studies

The conscious processing of emotion in depression disorder: a meta-analysis of neuroimaging studies

Reward circuit function and treatment outcome following vALIC deep brain stimulation in treatment-resistant depression

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