Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data

Responders to rTMS for depression show increased fronto-midline theta and theta connectivity compared to non-responders

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Predicting antidepressant response by monitoring early improvement of individual symptoms of depression: individual patient data meta-analysis

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Pretreatment Rostral Anterior Cingulate Cortex Theta Activity in Relation to Symptom Improvement in Depression

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Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis

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Differentiating responders and non-responders to rTMS treatment for depression after one week using resting EEG connectivity measures

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Early improvement as a resilience signal predicting later remission to antidepressant treatment in patients with Major Depressive Disorder: Systematic review and meta-analysis

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A wavelet-based technique to predict treatment outcome for Major Depressive Disorder

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EEG alpha asymmetry as a gender-specific predictor of outcome to acute treatment with different antidepressant medications in the randomized iSPOT-D study

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Data mining EEG signals in depression for their diagnostic value

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Frontal and rostral anterior cingulate (rACC) theta EEG in depression: Implications for treatment outcome?

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Baseline brain perfusion and brain structure in patients with major depression: a multimodal magnetic resonance imaging study

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Potential Application of Machine Learning in Health Outcomes Research and Some Statistical Cautions

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Large-Scale Network Dysfunction in Major Depressive Disorder

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The effectiveness of prefrontal theta cordance and early reduction of depressive symptoms in the prediction of antidepressant treatment outcome in patients with resistant depression: analysis of naturalistic data

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Subgenual Cingulate Theta Activity Predicts Treatment Response of Repetitive Transcranial Magnetic Stimulation in Participants With Vascular Depression

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Examining relations between alpha power as well as anterior cingulate cortex-localized theta activity and response to single or dual antidepressant pharmacotherapies

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A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder

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DEVELOPING BIOMARKERS IN MOOD DISORDERS RESEARCH THROUGH THE USE OF RAPID-ACTING ANTIDEPRESSANTS

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Biomarkers Predicting Antidepressant Treatment Response: How Can We Advance the Field?

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Pretreatment anterior cingulate activity predicts antidepressant treatment response in major depressive episodes

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Combination antidepressant therapy for major depressive disorder: Speed and probability of remission

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Toward a Neuroimaging Treatment Selection Biomarker for Major Depressive Disorder

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Distinguishing between Unipolar Depression and Bipolar Depression: Current and Future Clinical and Neuroimaging Perspectives

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Neurophysiological predictors of non-response to rTMS in depression

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Use of Clinical Neurophysiology for the Selection of Medication in the Treatment of Major Depressive Disorder: The State of the Evidence

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EEG Hemispheric Asymmetries during Cognitive Tasks in Depressed Patients with High versus Low Trait Anxiety

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Novel biomarkers in major depression

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The neurobiology of the EEG biomarker as a predictor of treatment response in depression

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Factors Predicting Reduced Antidepressant Response: Experience with the SNRI Duloxetine in Patients with Major Depression

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Current Source Density Measures of Electroencephalographic Alpha Predict Antidepressant Treatment Response

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The Antidepressant Treatment Response Index and Treatment Outcomes in a Placebo-Controlled Trial of Fluoxetine

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Early and Delayed Onset of Response to Antidepressants in Individual Trajectories of Change During Treatment of Major Depression

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Discovering imaging endophenotypes for major depression

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Grand challenges in global mental health

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Towards the utilization of EEG as a brain imaging tool

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Functional Biomarkers of Depression: Diagnosis, Treatment and Pathophysiology

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Electroencephalography-Derived Biomarkers of Antidepressant Response

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Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders

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An investigation of EEG, genetic and cognitive markers of treatment response to antidepressant medication in patients with major depressive disorder: A pilot study

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The use of the EEG in measuring therapeutic drug action: focus on depression and antidepressants

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The Elements of Statistical Learning: Data Mining, Inference, and Prediction

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Introduction to machine learning for brain imaging

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Efficacy and Effectiveness of Antidepressants: Current Status of Research

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Anatomical and functional correlates in major depressive disorder: The contribution of neuroimaging studies

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Biomarkers to Predict Antidepressant Response

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Rostral anterior cingulate cortex theta current density and response to antidepressants and placebo in major depression

(2009) Alexander S. Korb et al.   CLINICAL NEUROPHYSIOLOGY

Frontal EEG predictors of treatment outcome in major depressive disorder

(2009) Dan V. Iosifescu et al.   EUROPEAN NEUROPSYCHOPHARMACOLOGY

Early Improvement in the First 2 Weeks as a Predictor of Treatment Outcome in Patients With Major Depressive Disorder

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Early reduction in prefrontal theta QEEG cordance value predicts response to venlafaxine treatment in patients with resistant depressive disorder

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Machine learning classifiers and fMRI: A tutorial overview

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Electroencephalographic Alpha Measures Predict Therapeutic Response to a Selective Serotonin Reuptake Inhibitor Antidepressant: Pre- and Post-Treatment Findings

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