In clinical practice, bipolar depression (BD) and unipolar depression (UD) appear to have similar symptoms, causing BD being frequently misdiagnosed as UD, leading to improper treatment decision and outcome. Therefore, it is in urgent need of distinguishing BD from UD based on clinical objective biomarkers as early as possible. Here, we aimed to integrate brain neuroimaging data and an advanced machine learning technique to predict different types of mood disorder patients at the individual level.

中文翻译：

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脑磁图静止状态频谱指纹图谱可区分双相抑郁和单相抑郁。

在临床实践中，双相抑郁症（BD）和单相抑郁症（UD）似乎具有相似的症状，导致BD经常被误诊为UD，导致治疗决策和结局不当。因此，迫切需要尽早基于临床客观生物标志物将BD与UD区分开。在这里，我们旨在整合大脑神经影像数据和先进的机器学习技术，以在个体水平上预测不同类型的情绪障碍患者。