Using machine-learning to predict sudden gains in treatment for major depressive disorder

BackgroundMajor depressive disorder (MDD) is a global health challenge that impacts the quality of patients’ lives severely. The disorder can manifest in many forms with different combinations of symptoms, which makes its clinical diagnosis difficult. Robust biomarkers are greatly needed to improve diagnosis and to understand the etiology of the disease. The main purpose of this study was to create a predictive model for MDD diagnosis based on peripheral blood transcriptomes.Materials and MethodsWe collected nine RNA expression datasets for MDD patients and healthy samples from the Gene Expression Omnibus database. After a series of quality control and heterogeneity tests, 302 samples from six studies were deemed suitable for the study. R package “MetaOmics” was applied for systematic meta-analysis of genome-wide expression data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic effectiveness of individual genes. To obtain a better diagnostic model, we also adopted the support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), and naive Bayesian (NB) tools for modeling, with the RF method being used for feature selection.ResultsOur analysis revealed six differentially expressed genes (AKR1C3, ARG1, KLRB1, MAFG, TPST1, and WWC3) with a false discovery rate (FDR) < 0.05 between MDD patients and control subjects. We then evaluated the diagnostic ability of these genes individually. With single gene prediction, we achieved a corresponding area under the curve (AUC) value of 0.63 ± 0.04, 0.67 ± 0.07, 0.70 ± 0.11, 0.64 ± 0.08, 0.68 ± 0.07, and 0.62 ± 0.09, respectively, for these genes. Next, we constructed the classifiers of SVM, RF, kNN, and NB with an AUC of 0.84 ± 0.09, 0.81 ± 0.10, 0.73 ± 0.11, and 0.83 ± 0.09, respectively, in validation datasets, suggesting that the SVM classifier might be superior for constructing an MDD diagnostic model. The final SVM classifier including 70 feature genes was capable of distinguishing MDD samples from healthy controls and yielded an AUC of 0.78 in an independent dataset.ConclusionThis study provides new insights into potential biomarkers through meta-analysis of GEO data. Constructing different machine learning models based on these biomarkers could be a valuable approach for diagnosing MDD in clinical practice.

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Single-Channel EEG-Based Machine Learning Method for Prescreening Major Depressive Disorder

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622019500342 ◽

2019 ◽

Vol 18 (05) ◽

pp. 1579-1603 ◽

Cited By ~ 2

Author(s):

Zhijiang Wan ◽

Hao Zhang ◽

Jiajin Huang ◽

Haiyan Zhou ◽

Jie Yang ◽

...

Keyword(s):

Machine Learning ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Single Channel ◽

Regression Tree ◽

Classification And Regression Tree ◽

Machine Learning Method ◽

Learning Method ◽

Eeg Analysis ◽

Major Depressive

Many studies developed the machine learning method for discriminating Major Depressive Disorder (MDD) and normal control based on multi-channel electroencephalogram (EEG) data, less concerned about using single channel EEG collected from forehead scalp to discriminate the MDD. The EEG dataset is collected by the Fp1 and Fp2 electrode of a 32-channel EEG system. The result demonstrates that the classification performance based on the EEG of Fp1 location exceeds the performance based on the EEG of Fp2 location, and shows that single-channel EEG analysis can provide discrimination of MDD at the level of multi-channel EEG analysis. Furthermore, a portable EEG device collecting the signal from Fp1 location is used to collect the second dataset. The Classification and Regression Tree combining genetic algorithm (GA) achieves the highest accuracy of 86.67% based on leave-one-participant-out cross validation, which shows that the single-channel EEG-based machine learning method is promising to support MDD prescreening application.

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GWAS-based machine learning approach to predict duloxetine response in major depressive disorder

Journal of Psychiatric Research ◽

10.1016/j.jpsychires.2017.12.009 ◽

2018 ◽

Vol 99 ◽

pp. 62-68 ◽

Cited By ~ 21

Author(s):

Malgorzata Maciukiewicz ◽

Victoria S. Marshe ◽

Anne-Christin Hauschild ◽

Jane A. Foster ◽

Susan Rotzinger ◽

...

Keyword(s):

Machine Learning ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Learning Approach ◽

Major Depressive ◽

Machine Learning Approach

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Detection of major depressive disorder using vocal acoustic analysis and machine learning—an exploratory study

Research on Biomedical Engineering ◽

10.1007/s42600-020-00100-9 ◽

2020 ◽

Author(s):

Caroline Wanderley Espinola ◽

Juliana Carneiro Gomes ◽

Jessiane Mônica Silva Pereira ◽

Wellington Pinheiro dos Santos

Keyword(s):

Machine Learning ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Exploratory Study ◽

Acoustic Analysis ◽

Major Depressive

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Changes in Functional Connectivity Predict Outcome of Repetitive Transcranial Magnetic Stimulation Treatment of Major Depressive Disorder

Cerebral Cortex ◽

10.1093/cercor/bhz035 ◽

2019 ◽

Vol 29 (12) ◽

pp. 4958-4967 ◽

Cited By ~ 8

Author(s):

Juliana Corlier ◽

Andrew Wilson ◽

Aimee M Hunter ◽

Nikita Vince-Cruz ◽

David Krantz ◽

...

Keyword(s):

Machine Learning ◽

Major Depressive Disorder ◽

Transcranial Magnetic Stimulation ◽

Functional Connectivity ◽

Clinical Outcome ◽

Depressive Disorder ◽

Clinical Improvement ◽

Magnetic Stimulation ◽

Repetitive Transcranial Magnetic Stimulation ◽

Major Depressive

AbstractRepetitive transcranial magnetic stimulation (rTMS) treatment of major depressive disorder (MDD) is associated with changes in brain functional connectivity (FC). These changes may be related to the mechanism of action of rTMS and explain the variability in clinical outcome. We examined changes in electroencephalographic FC during the first rTMS treatment in 109 subjects treated with 10 Hz stimulation to left dorsolateral prefrontal cortex. All subjects subsequently received 30 treatments and clinical response was defined as ≥40% improvement in the inventory of depressive symptomatology-30 SR score at treatment 30. Connectivity change was assessed with coherence, envelope correlation, and a novel measure, alpha spectral correlation (αSC). Machine learning was used to develop predictive models of outcome for each connectivity measure, which were compared with prediction based upon early clinical improvement. Significant connectivity changes were associated with clinical outcome (P < 0.001). Machine learning models based on αSC yielded the most accurate prediction (area under the curve, AUC = 0.83), and performance improved when combined with early clinical improvement measures (AUC = 0.91). The initial rTMS treatment session produced robust changes in FC, which were significant predictors of clinical outcome of a full course of treatment for MDD.

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Using Machine Learning to Predict Remission in Patients With Major Depressive Disorder Treated With Desvenlafaxine

The Canadian Journal of Psychiatry ◽

10.1177/07067437211037141 ◽

2021 ◽

pp. 070674372110371

Author(s):

James R.A. Benoit ◽

Serdar M. Dursun ◽

Russell Greiner ◽

Bo Cao ◽

Matthew R.G. Brown ◽

...

Keyword(s):

Machine Learning ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Rating Scale ◽

Machine Learning Algorithms ◽

Phase Iii ◽

Support Vector ◽

Individual Treatment ◽

Major Depressive ◽

Specific Patient

Background Major depressive disorder (MDD) is a common and burdensome condition that has low rates of treatment success for each individual treatment. This means that many patients require several medication switches to achieve remission; selecting an effective antidepressant is typically a sequential trial-and-error process. Machine learning techniques may be able to learn models that can predict whether a specific patient will respond to a given treatment, before it is administered. This study uses baseline clinical data to create a machine-learned model that accurately predicts remission status for a patient after desvenlafaxine (DVS) treatment. Methods We applied machine learning algorithms to data from 3,399 MDD patients (90% of the 3,776 subjects in 11 phase-III/IV clinical trials, each described using 92 features), to produce a model that uses 26 of these features to predict symptom remission, defined as an 8-week Hamilton Depression Rating Scale score of 7 or below. We evaluated that learned model on the remaining held-out 10% of the data ( n = 377). Results Our resulting classifier, a trained linear support vector machine, had a holdout set accuracy of 69.0%, significantly greater than the probability of classifying a patient correctly by chance. We demonstrate that this learning process is stable by repeatedly sampling part of the training dataset and running the learner on this sample, then evaluating the learned model on the held-out instances of the training set; these runs had an average accuracy of 67.0% ± 1.8%. Conclusions Our model, based on 26 clinical features, proved sufficient to predict DVS remission significantly better than chance. This may allow more accurate use of DVS without waiting 8 weeks to determine treatment outcome, and may serve as a first step toward changing psychiatric care by incorporating clinical assistive technologies using machine-learned models.

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