Distinguish bipolar and major depressive disorder in adolescents based on multimodal neuroimaging: Results from the Adolescent Brain Cognitive Development study®

Yujun Liu; Kai Chen; Yangyang Luo; Jiqiu Wu; Qu Xiang; Li Peng; Jian Zhang; Weiling Zhao; Mingliang Li; Xiaobo Zhou

doi:10.1177/20552076221123705

Distinguish bipolar and major depressive disorder in adolescents based on multimodal neuroimaging: Results from the Adolescent Brain Cognitive Development study^®

Digit Health. 2022 Sep 5:8:20552076221123705. doi: 10.1177/20552076221123705. eCollection 2022 Jan-Dec.

Authors

Yujun Liu^{1

2}, Kai Chen³, Yangyang Luo^{1

2}, Jiqiu Wu^{1

4}, Qu Xiang^{1

2}, Li Peng^{1

2}, Jian Zhang^{1

2}, Weiling Zhao⁵, Mingliang Li^{1

2}, Xiaobo Zhou⁵

Affiliations

¹ West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
² Med-X Center for Informatics, Sichuan University, Chengdu, China.
³ School of Public Health, University of Texas Health Science Center at Houston, Houston, USA.
⁴ Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
⁵ Center for Computational Systems Medicine, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, USA.

Abstract

Background: Major depressive disorder and bipolar disorder in adolescents are prevalent and are associated with cognitive impairment, executive dysfunction, and increased mortality. Early intervention in the initial stages of major depressive disorder and bipolar disorder can significantly improve personal health.

Methods: We collected 309 samples from the Adolescent Brain Cognitive Development study, including 116 adolescents with bipolar disorder, 64 adolescents with major depressive disorder, and 129 healthy adolescents, and employed a support vector machine to develop classification models for identification. We developed a multimodal model, which combined functional connectivity of resting-state functional magnetic resonance imaging and four anatomical measures of structural magnetic resonance imaging (cortical thickness, area, volume, and sulcal depth). We measured the performances of both multimodal and single modality classifiers.

Results: The multimodal classifiers showed outstanding performance compared with all five single modalities, and they are 100% for major depressive disorder versus healthy controls, 100% for bipolar disorder versus healthy control, 98.5% (95% CI: 95.4-100%) for major depressive disorder versus bipolar disorder, 100% for major depressive disorder versus depressed bipolar disorder and the leave-one-site-out analysis results are 77.4%, 63.3%, 79.4%, and 81.7%, separately.

Conclusions: The study shows that multimodal classifiers show high classification performances. Moreover, cuneus may be a potential biomarker to differentiate major depressive disorder, bipolar disorder, and healthy adolescents. Overall, this study can form multimodal diagnostic prediction workflows for clinically feasible to make more precise diagnose at the early stage and potentially reduce loss of personal pain and public society.

Keywords: Bipolar disorder; cuneus; major depressive disorder; multimodal; support vector machine.