Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning
- PDF / 1,077,592 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 95 Downloads / 153 Views
RESEARCH ARTICLE
Open Access
Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques Hao Li1,2†, Liqian Cui1,2*†, Liping Cao3*, Yizhi Zhang3, Yueheng Liu4,5, Wenhao Deng3 and Wenjin Zhou3
Abstract Background: Bipolar 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. Methods: In 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. Results: After 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). Conclusions: A 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. Keywords: Bipolar disorder, Multimodality magnetic resonance imaging, Support vector machine
Background 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/ * Correspondence: [email protected]; [email protected] † Hao Li and Liqian Cui contributed equally to this work. 1 Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China 3 Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, Guangdong, China Full list of author information is available at the end of the article
hypomanic episodes. Due to the early onset of the disease, high rates of self-inflicted injury and hospitalization, and the negative stigma of BPD, the disease causes significant social and economic burden [1, 2]. It was previously reported that the risk of suicide was 20-times higher in patients with BPD than the more general population [3–6]. In addition, the clinical symptoms of BPD overlap with those of many other mood disorders, including ma
Data Loading...
