Abstract

BackgroundBipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD.MethodsIn total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed.ResultsAfter using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5–95.3%), sensitivity of 86.4% (95%CI: 64.0–96.4%), and specificity of 88.9% (95%CI: 63.9–98.0%) in the test data (p = 0.0022).ConclusionsA combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

Highlights

  • Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed

  • No significant differences were found in age or gender between the BPD and healthy controls (HC) groups (p > 0.05), while the length of education was shorter in the BPD group as compared with the HC group

  • Significant differences in the Global Assessment Function (GAF), Hamilton Anxiety Table (HAMA), Hamilton Depression Rating Scale (HAMD), and Positive and Negative Syndrome Scale (PANSS) scores were found between the BPD group and HC group (p < 0.05)

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Summary

Introduction

Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. In this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD. Bipolar disorder (BPD) is a chronic and disabling mood disorder found in up to 2.5% of the population. It is characterized by extreme fluctuations in mood, functionality, and energy, in addition to recurrent depressive and manic/. It may take up to 10 years after initially seeking treatment to be correctly diagnosed with BPD [8]. Researchers are seeking new potential biomarkers to assist the diagnosis and therapeutic monitoring of BPD.

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