Predicting escitalopram monotherapy response in depression: The role of anterior cingulate cortex

Abstract

Neuroimaging biomarkers of treatment efficacy can be used to guide personalized treatment in major depressive disorder (MDD). Escitalopram is recommended as first‐line therapy for MDD and severe depression. An interesting hypothesis suggests that the reconfiguration of dynamic brain networks might provide important insights into antidepressant mechanisms. The present study assesses whether the spatiotemporal modulation across functional brain networks could serve as a predictor of effective antidepressant treatment with escitalopram. A total of 106 first‐episode, drug‐naïve patients and 109 healthy controls from three different multicenters underwent resting‐state functional magnetic resonance imaging. Patients were considered as responders if they had a reduction of at least 50% in Hamilton Rating Scale for Depression scores at endpoint (>2 weeks). Multilayer modularity framework was applied on the whole brain to construct features in relation to network dynamic characters that were used for multivariate pattern analysis. Linear soft‐threshold support vector machine models were used to separate responders from nonresponders. The permutation tests demonstrated the robustness of discrimination performances. The discriminative regions formed a spatially distributed pattern with anterior cingulate cortex (ACC) as the hub in the default mode subnetwork. Interestingly, a significantly larger module allegiance of ACC was also found in treatment responders compared to nonresponders, suggesting high interactivities of ACC to other regions may be beneficial for the recovery after treatment. Consistent results across multicenters confirmed that ACC could serve as a predictor of escitalopram monotherapy treatment outcome, implying strong likelihood of replication in the future.

Resting State Functional Connectivity of Brain With Electroconvulsive Therapy in Depression: Meta-Analysis to Understand Its Mechanisms

“…There was a reduction in rsFC of the L. dACC after the course of ECT. Neuroimaging studies suggest increased activity in the ACC as an important biomarker for depression (Helm et al, 2018 ; Lai, 2019 ), which normalizes after treatment with antidepressants and serves as a predictor for the response (Arnone, 2019 ; Dunlop et al, 2019 ; Tian et al, 2020 ). However, sgACC/rACC are implicated here more often than dACC.…”

Measurement reliability for individual differences in multilayer network dynamics: Cautions and considerations

“…Specifically, these measures have been used to link network dynamics to inter-individual differences in a broad range of functional domains, including motor learning ( Bassett et al, 2011 , 2015 ; Wymbs et al, 2012 ; Telesford et al, 2016 ), working memory ( Braun et al, 2015 ; Finc et al, 2020 ), attention ( Shine et al, 2016 ), language ( Chai et al, 2016 ), mood ( Betzel et al, 2017 ), creativity ( Feng et al, 2019 ; He et al, 2019 ), and reinforcement learning ( Gerraty et al, 2018 ). Additionally, dynamic network reconfiguration has been suggested as a potential biomarker for diseases, such as schizophrenia ( Braun et al, 2016 ; Gifford et al, 2020 ), temporal lobe epilepsy ( He et al, 2018 ), and depression ( Wei et al, 2017 ; Zheng et al, 2018 ; Shao et al, 2019 ; Han et al, 2020 ), and has been used to predict antidepressant treatment outcome ( Tian et al, 2020 ).…”

CACNA1C Gene rs11832738 Polymorphism Influences Depression Severity by Modulating Spontaneous Activity in the Right Middle Frontal Gyrus in Patients With Major Depressive Disorder

“…In addition to MFG_R, we found that the right CPL and ACC were also affected by MDD. The ACC is responsible for regulating stress and processing emotion and has repeatedly been shown to play an important role in MDD (31)(32)(33). The cerebellum is traditionally thought to be involved only in motor function, but in recent years, it has been increasingly recognized that the cerebellum is important for cognition and emotion (34).…”