Liu Yujun, Chen Kai, Luo Yangyang, Wu Jiqiu, Xiang Qu, Peng Li, Zhang Jian, Zhao Weiling, Li Mingliang, Zhou Xiaobo
West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Med-X Center for Informatics, Sichuan University, Chengdu, China.
Digit Health. 2022 Sep 5;8:20552076221123705. doi: 10.1177/20552076221123705. eCollection 2022 Jan-Dec.
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.
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.
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.
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.
青少年重度抑郁症和双相情感障碍很常见，且与认知障碍、执行功能障碍及死亡率增加有关。在重度抑郁症和双相情感障碍的初始阶段进行早期干预可显著改善个人健康状况。
我们从青少年大脑认知发展研究中收集了309个样本，包括116名双相情感障碍青少年、64名重度抑郁症青少年和129名健康青少年，并使用支持向量机开发用于识别的分类模型。我们开发了一种多模态模型，该模型结合了静息态功能磁共振成像的功能连接性和结构磁共振成像的四种解剖学测量指标（皮质厚度、面积、体积和脑沟深度）。我们测量了多模态和单模态分类器的性能。
与所有五种单模态相比，多模态分类器表现出卓越的性能，在重度抑郁症与健康对照的分类中准确率为100%，双相情感障碍与健康对照的分类中为100%，重度抑郁症与双相情感障碍的分类中为98.5%（95%置信区间：95.4 - 100%），重度抑郁症与抑郁发作的双相情感障碍的分类中为100%，留一站点分析结果分别为77.4%、63.3%、79.4%和81.7%。
该研究表明多模态分类器具有较高的分类性能。此外，楔叶可能是区分重度抑郁症、双相情感障碍和健康青少年的潜在生物标志物。总体而言，本研究可形成临床可行的多模态诊断预测工作流程，以便在早期进行更精确的诊断，并可能减少个人痛苦和社会损失。
