Using Support Vector Machine to Identify Imaging Biomarkers of Major Depressive Disorder and Anxious Depression

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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Anxious depression as a clinically relevant subtype of pediatric major depressive disorder

Journal of Neural Transmission ◽

10.1007/s00702-019-02069-x ◽

2019 ◽

Vol 126 (9) ◽

pp. 1217-1230 ◽

Cited By ~ 1

Author(s):

Isabelle Häberling ◽

Noemi Baumgartner ◽

Sophie Emery ◽

Paola Keller ◽

Michael Strumberger ◽

...

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Major Depressive ◽

Anxious Depression

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Electroconvulsive Therapy Induces Cortical Morphological Alterations in Major Depressive Disorder Revealed with Surface-Based Morphometry Analysis

International Journal of Neural Systems ◽

10.1142/s0129065719500059 ◽

2019 ◽

Vol 29 (07) ◽

pp. 1950005 ◽

Cited By ~ 7

Author(s):

Jinping Xu ◽

Jiaojian Wang ◽

Tongjian Bai ◽

Xiaodong Zhang ◽

Tian Li ◽

...

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Surface Area ◽

Cortical Thickness ◽

Electroconvulsive Therapy ◽

Therapeutic Efficacy ◽

Support Vector ◽

Major Depressive ◽

Before And After ◽

Surface Based Morphometry

Although electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder (MDD), the mechanism underlying the therapeutic efficacy and side effects of ECT remains poorly understood. Here, we investigated alterations in the cortical morphological measurements including cortical thickness (CT), surface area (SA), and local gyrification index (LGI) in 23 MDD patients before and after ECT. Furthermore, multivariate pattern analysis using linear support vector machine (SVM) was applied to investigate whether the changed morphological measurements can be effective indicators for therapeutic efficacy of ECT. Surface-based morphometry (SBM) analysis found significantly increased vertex-wise and regional cortical thickness (CT) and surface area (SA) in widespread regions, mainly located in the left insula (INS) and left fusiform gyrus, as well as hypergyrification in the left middle temporal gyrus (MTG) in MDD patients after ECT. Partial correlational analyses identified associations between the morphological properties and depressive symptom scores and impaired memory scores. Moreover, SVM result showed that the changed morphological measurements were effective to classify the MDD patients before and after ECT. Our findings suggested that ECT may enhance cortical neuroplasticity to facilitate neurogenesis to remit depressive symptoms and to impair delayed memory. These findings indicated that the cortical morphometry is a good index for therapeutic efficacy of ECT.

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Abnormal Default-Mode Network Homogeneity in Melancholic and Nonmelancholic Major Depressive Disorder at Rest

Neural Plasticity ◽

10.1155/2021/6653309 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Meiqi Yan ◽

Xilong Cui ◽

Feng Liu ◽

Huabing Li ◽

Renzhi Huang ◽

...

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Middle Temporal Gyrus ◽

Support Vector ◽

Healthy Controls ◽

Major Depressive ◽

Middle Temporal ◽

Inferior Temporal Gyrus ◽

Medial Frontal Gyrus ◽

The Right

Background. Melancholic depression has been assumed as a severe type of major depressive disorder (MDD). We aimed to explore if there were some distinctive alterations in melancholic MDD and whether the alterations could be used to discriminate the melancholic MDD and nonmelancholic MDD. Methods. Thirty-one outpatients with melancholic MDD, thirty-three outpatients with nonmelancholic MDD, and thirty-two age- and gender-matched healthy controls were recruited. All participants were scanned by resting-state functional magnetic resonance imaging (fMRI). Imaging data were analyzed with the network homogeneity (NH) and support vector machine (SVM) methods. Results. Both patient groups exhibited increased NH in the right PCC/precuneus and right angular gyrus and decreased NH in the right middle temporal gyrus compared with healthy controls. Compared with nonmelancholic patients and healthy controls, melancholic patients exhibited significantly increased NH in the bilateral superior medial frontal gyrus and decreased NH in the left inferior temporal gyrus. But merely for melancholic patients, the NH of the right middle temporal gyrus was negatively correlated with TEPS total and contextual anticipatory scores. SVM analysis showed that a combination of NH values in the left superior medial frontal gyrus and left inferior temporal gyrus could distinguish melancholic patients from nonmelancholic patients with accuracy, sensitivity, and specificity of 79.66% (47/59), 70.97% (22/31), and 89.29%(25/28), respectively. Conclusion. Our findings showed distinctive network homogeneity alterations in melancholic MDD which may be potential imaging markers to distinguish melancholic MDD and nonmelancholic MDD.

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Reduced Venous Blood Basophil Count and Anxious Depression in Patients with Major Depressive Disorder

Psychiatry Investigation ◽

10.4306/pi.2016.13.3.321 ◽

2016 ◽

Vol 13 (3) ◽

pp. 321 ◽

Cited By ~ 9

Author(s):

Ji Hyun Baek ◽

Hee-Jin Kim ◽

Maurizio Fava ◽

David Mischoulon ◽

George I Papakostas ◽

...

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Venous Blood ◽

Major Depressive ◽

Anxious Depression ◽

Blood Basophil

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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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