Prediction of remission among patients with a major depressive disorder based on the resting-state functional connectivity of emotion regulation networks

Abstract

The prediction of antidepressant response is critical for psychiatrists to select the initial antidepressant drug for patients with major depressive disorders (MDD). The implicated brain networks supporting emotion regulation (ER) are critical in the pathophysiology of MDD and the prediction of antidepressant response. Therefore, the primary aim of the current study was to identify the neuroimaging biomarkers for the prediction of remission in patients with MDD based on the resting-state functional connectivity (rsFC) of the ER networks. A total of 81 unmedicated adult MDD patients were investigated and they underwent resting-state functional magnetic resonance imagining (fMRI) scans. The patients were treated with escitalopram for 12 weeks. The 17-item Hamilton depression rating scale was used for assessing remission. The 36 seed regions from predefined ER networks were selected and the rsFC matrix was caculated for each participant. The support vector machine algorithm was employed to construct prediction model, which separated the patients with remission from those with non-remission. And leave-one-out cross-validation and the area under the curve (AUC) of the receiver operating characteristic were used for evaluating the performance of the model. The accuracy of the prediction model was 82.08% (sensitivity = 71.43%, specificity = 89.74%, AUC = 0.86). The rsFC between the left medial superior frontal gyrus and the right inferior frontal gyrus as well as the precuneus were the features with the highest discrimination ability in predicting remission from escitalopram among the MDD patients. Results from our study demonstrated that rsFC of the ER brain networks are potential predictors for the response of antidepressant drugs. The trial name: appropriate technology study of MDD diagnosis and treatment based on objective indicators and measurement. URL: http://www.chictr.org.cn/showproj.aspx?proj=21377 . Registration number: ChiCTR-OOC-17012566.

Brain connectivity in major depressive disorder: a precision component of treatment modalities?

“… Functional (cont.) Wu et al [ 12 ] 81 MDD -Escitalopram (SSRI) -12 weeks -Seed-based: 36 seeds from emotion regulation networks -Baseline MRI Patients with remission and non-remission were separated with an accuracy of 82%. The FC between the left medial SFG, right IFG, and PCu were the features with the highest discrimination ability.…”

“…Ye et al (2022) reported increased baseline right angular gyrus FC within the DMN in the remitted group compared with the non-remitted group, which was positively correlated with the reduction of depressive scores [ 8 ]. In addition to the increased FC within the DMN that was associated with better treatment outcomes [ 9 – 11 ], increased FC between DMN and frontoparietal network (FPN) [ 9 , 10 , 12 ], and somatomotor network (SMN) [ 10 ] was also reported. Additionally, treatment response was associated with increased FC between the right FPN and the posterior DMN, SMN, and somatosensory association cortex (SAC) by Martens et al [ 13 ].…”

Functional connectivity of the default mode network subsystems in patients with major depressive episodes with mixed features

“…The subjects whose head motion was >2.5 mm maximum translation in any direction of x, y or z or 2.5° of maximum rotation or if their mean FD exceeded 3 SD of the mean value were excluded. 21 …”

“…To quantify microhead motions, the ‘bad’ time points were removed from the time series by employing a ‘scrubbing’ method with a frame-wise displacement (FD)20 threshold of 0.5 mm. The subjects whose head motion was >2.5 mm maximum translation in any direction of x, y or z or 2.5° of maximum rotation or if their mean FD exceeded 3 SD of the mean value were excluded 21…”

“…The subjects whose head motion was >2.5 mm maximum translation in any direction of x, y or z or 2.5° of maximum rotation or if their mean FD exceeded 3 SD of the mean value were excluded. 21 Definition of ROIs A total of 24 regions of interest (ROI) were extracted from the 17-network parcellation by Yeo et al 22 They were divided into three DMN subsystems: nine ROIs belong to the core subsystem, nine ROIs belong to the dMPFC subsystem and six ROIs belong to the MTL subsystem (figure 2). General Psychiatry subsystems.…”