ArticlePsychiatry investigation2015
Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance.
Article in Psychiatry investigation, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 4 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 4 syntheses or guidelines pooled it, 58 citations in OpenAlex.
- Pooled it
- Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis.Translational psychiatry · 2022Pooled it
- Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice.Schizophrenia bulletin · 2021Pooled it
- Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis.The American journal of psychiatry · 2019Pooled it
- Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: A resting state electroencephalography study.Journal of affective disorders · 2019Trial
- Predictors of Somatic Symptom Severity in Adult Psychiatric Outpatients: A Cross-sectional Heart Rate Variability and Quantitative Electroencephalography Study.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2026Article
- Novel TMS-derived metrics enable machine learning classification of major depressive disorder.NPP - digital psychiatry and neuroscience · 2026Article
- Article
- Predicting Depression Therapy Outcomes Using EEG-Derived Amplitude Polar Maps.Brain sciences · 2025Article
- How to Solve Clinical Challenges in Mood Disorders; Machine Learning Approaches Using Electrophysiological Markers.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2024Review
- Prolonged Disease Course Leads to Impaired Brain Function in Anxiety Disorder: A Resting State EEG Study.Neuropsychiatric disease and treatment · 2024Article
- A convolutional recurrent neural network with attention for response prediction to repetitive transcranial magnetic stimulation in major depressive disorder.Scientific reports · 2023Article
- Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment.Brain informatics · 2023Review
- Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review.Network neuroscience (Cambridge, Mass.) · 2022Article
- The promise of machine learning in predicting treatment outcomes in psychiatry.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2021Article
- Personalization of Repetitive Transcranial Magnetic Stimulation for the Treatment of Major Depressive Disorder According to the Existing Psychiatric Comorbidity.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2021Article
- The Changes of qEEG Approximate Entropy during Test of Variables of Attention as a Predictor of Major Depressive Disorder.Brain sciences · 2020Article
- Updates on Transcranial Magnetic Stimulation Therapy for Major Depressive Disorder.The Psychiatric clinics of North America · 2018Review
- Non-linear Entropy Analysis in EEG to Predict Treatment Response to Repetitive Transcranial Magnetic Stimulation in Depression.Frontiers in pharmacology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe combination of repetitive transcranial magnetic stimulation (rTMS), a non-pharmacological form of therapy for treating major depressive disorder (MDD), and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. This study aims to explore whether pre-treating frontal quantitative EEG (QEEG) cordance is associated with response to rTMS treatment among MDD patients by using an artificial intelligence approach, artificial neural network (ANN).
methodsThe artificial neural network using pre-treatment cordance of frontal QEEG classification was carried out to identify responder or non-responder to rTMS treatment among 55 MDD subjects. The classification performance was evaluated using k-fold cross-validation.
resultsThe ANN classification identified responders to rTMS treatment with a sensitivity of 93.33%, and its overall accuracy reached to 89.09%. Area under Receiver Operating Characteristic (ROC) curve (AUC) value for responder detection using 6, 8 and 10 fold cross validation were 0.917, 0.823 and 0.894 respectively.
conclusionPotential utility of ANN approach method can be used as a clinical tool in administering rTMS therapy to a targeted group of subjects suffering from MDD. This methodology is more potentially useful to the clinician as prediction is possible using EEG data collected before this treatment process is initiated. It is worth using feature selection algorithms to raise the sensitivity and accuracy values.
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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.