ArticleHuman brain mapping2020
Predicting escitalopram monotherapy response in depression: The role of anterior cingulate cortex.
Article in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- Machine learning in the prediction of treatment response for emotional disorders: A systematic review and meta-analysis.Clinical psychology review · 2025Pooled it
- Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis.Molecular psychiatry · 2025Pooled it
- Resting State Functional Connectivity of Brain With Electroconvulsive Therapy in Depression: Meta-Analysis to Understand Its Mechanisms.Frontiers in human neuroscience · 2020Pooled it
- Modulation of functional networks related to the serotonin neurotransmitter system by citalopram: Evidence from a multimodal neuroimaging study.Journal of psychopharmacology (Oxford, England) · 2023Trial
- Resting-state fMRI-based machine learning for predicting SSRI treatment response in major depressive disorder.BMC psychiatry · 2026Article
- Identifying neurobiological markers as predictors of antidepressant treatment using diffusion tensor imaging: A tract-based spatial statistical analysis of cingulate bundle.CNS spectrums · 2025Article
- Predicting antidepressant response via local-global graph neural network and neuroimaging biomarkers.NPJ digital medicine · 2025Article
- Resting-state fMRI graph theory analysis for predicting selective serotonin reuptake inhibitors treatment response in adolescent major depressive disorder.Frontiers in psychiatry · 2025Article
- Towards closed-loop precision psychiatry: Integrating MRI biomarkers for individualized care of major depressive disorder.Psychoradiology · 2025Review
- Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- AI-assisted prediction of differential response to antidepressant classes using electronic health records.NPJ digital medicine · 2023Article
- Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment.Brain informatics · 2023Review
- Topological properties and connectivity patterns in brain networks of patients with refractory epilepsy combined with intracranial electrical stimulation.Frontiers in neuroscience · 2023Article
- Prediction of remission among patients with a major depressive disorder based on the resting-state functional connectivity of emotion regulation networks.Translational psychiatry · 2022Article
- Personalized Diagnosis and Treatment for Neuroimaging in Depressive Disorders.Journal of personalized medicine · 2022Review
- Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review.Network neuroscience (Cambridge, Mass.) · 2022Article
- Anxiolytic effect of GABAergic neurons in the anterior cingulate cortex in a rat model of chronic inflammatory pain.Molecular brain · 2021Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Measurement reliability for individual differences in multilayer network dynamics: Cautions and considerations.NeuroImage · 2021Article
- Predicting escitalopram monotherapy response in depression: The role of anterior cingulate cortex.Human brain mapping · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.