ArticleBrain sciences2023
Predictive Biomarkers of Treatment Response in Major Depressive Disorder.
Article in Brain sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 4 of them syntheses that pooled it.
What it found
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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.
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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
15 citing papers in PubMed, 4 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- Applications of machine learning in deep brain stimulation for major depressive disorder: a systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- A Systematic Review of Aperiodic Neural Activity in Clinical Investigations.The European journal of neuroscience · 2025Pooled it
- Neuroimaging Insights into the Public Health Burden of Neuropsychiatric Disorders: A Systematic Review of Electroencephalography-Based Cognitive Biomarkers.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Short-term and long-term efficacy of accelerated transcranial magnetic stimulation for depression: a systematic review and meta-analysis.BMC psychiatry · 2024Pooled it
- Comparison of the clinical efficacy, safety and EEG functional connectivity changes between 18-Hz rTMS and iTBS of accelerated dTMS treatment for major depressive disorder: a randomized controlled trial.Molecular psychiatry · 2026Trial
- Antidepressant response time across intermittent theta burst stimulation regimens and efficacy indicators in adolescents depression: a secondary analysis from a randomized controlled trial.BMC psychiatry · 2024Trial
- Stanford neuromodulation therapy for treatment-resistant depression: a randomized controlled trial confirming efficacy, and an EEG study providing insight into mechanism of action and a potentially predictive biomarker of efficacy.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026Article
- Remotely Supervised Home-Based tDCS in Treatment-Resistant Depression: Feasibility, Effectiveness and EEG Biomarkers of Response.Research square · 2026Article
- Age-Related Differences and Effects of Internalizing Symptoms on Aperiodic Neural Activity in Adolescents.Psychophysiology · 2026Article
- High alpha oscillations in portable prefrontal EEG indicate gender-sensitive biomarkers for emotional disorders.Scientific reports · 2025Article
- EEG Biomarkers for a Precision-Medicine Approach to Noninvasive Brain Stimulation for Major Depressive Disorder.Research square · 2025Article
- Alterations in aperiodic neural activity associated with major depressive disorder.Nature. Mental health · 2025Article
- Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data.Bioengineering (Basel, Switzerland) · 2025Article
- Deciphering the Role of BRAFV600E Immunohistochemistry in Breast Lesions: A Comprehensive Review.Cureus · 2024Review
- Immediate and long-term electrophysiological biomarkers of antidepressant-like behavioral effects after subanesthetic ketamine and medial prefrontal cortex deep brain stimulation treatment.Frontiers in neuroscience · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Major depressive disorder (MDD) is a highly prevalent, debilitating disorder with a high rate of treatment resistance. One strategy to improve treatment outcomes is to identify patient-specific, pre-intervention factors that can predict treatment success. Neurophysiological measures such as electroencephalography (EEG), which measures the brain's electrical activity from sensors on the scalp, offer one promising approach for predicting treatment response for psychiatric illnesses, including MDD. In this study, a secondary data analysis was conducted on the publicly available Two Decades Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) database. Logistic regression modeling was used to predict treatment response, defined as at least a 50% improvement on the Beck's Depression Inventory, in 119 MDD patients receiving repetitive transcranial magnetic stimulation (rTMS). The results show that both age and baseline symptom severity were significant predictors of rTMS treatment response, with older individuals and more severe depression scores associated with decreased odds of a positive treatment response. EEG measures contributed predictive power to these models; however, these improvements in outcome predictability only trended towards statistical significance. These findings provide confirmation of previous demographic and clinical predictors, while pointing to EEG metrics that may provide predictive information in future studies.
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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.