Objective:

Bipolar disorder in the depressive phase (BDd) may be misdiagnosed as major depressive disorder (MDD), resulting in poor treatment outcomes. To identify biomarkers distinguishing BDd from MDD is of substantial clinical significance. This study aimed to characterize specific alterations in intrinsic functional connectivity (FC) patterns in BDd and MDD by combining whole-brain static and dynamic FC.

Methods:

A total of 40 MDD and 38 BDd patients, and 50 age-, sex-, education-, and handedness-matched healthy controls (HCs) were included in this study. Static and dynamic FC strengths (FCSs) were analyzed using complete time-series correlations and sliding window correlations, respectively. One-way analysis of variance was performed to test group effects. The combined static and dynamic FCSs were then used to distinguish BDd from MDD and to predict clinical symptom severity.

Results:

Compared with HCs, BDd patients showed lower static FCS in the medial orbitofrontal cortex and greater static FCS in the caudate, while MDD patients exhibited greater static FCS in the medial orbitofrontal cortex. BDd patients also demonstrated greater static and dynamic FCSs in the thalamus compared with both MDD patients and HCs, while MDD patients exhibited greater dynamic FCS in the precentral gyrus compared with both BDd patients and HCs. Combined static and dynamic FCSs yielded higher accuracy than either static or dynamic FCS analysis alone, and also predicted anhedonia severity in BDd patients and negative mood severity in MDD patients.

Conclusion:

Altered FC within frontal–striatal–thalamic circuits of BDd patients and within the default mode network/sensorimotor network of MDD patients accurately distinguishes between these disorders. These unique FC patterns may serve as biomarkers for differential diagnosis and provide clues to the pathogenesis of mood disorders.

中文翻译：

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静态和动态功能连接特征相结合，将双相抑郁症与重度抑郁症区分开来。

目的：

抑郁期（BDd）的双相情感障碍可能被误诊为重度抑郁症（MDD），导致治疗效果不佳。鉴定区分BDd和MDD的生物标志物具有重要的临床意义。这项研究旨在通过结合全脑静态和动态FC来表征BDd和MDD中固有功能连接（FC）模式的特定变化。

方法：

这项研究总共包括40名MDD和38名BDd患者，以及50名年龄，性别，教育程度和与手性相匹配的健康对照（HCs）。分别使用完整的时间序列相关性和滑动窗口相关性分析了静态和动态FC强度（FCS）。进行单向方差分析以测试组效果。然后将组合的静态和动态FCS用于区分BDd和MDD，并预测临床症状的严重程度。

结果：

与HCs相比，BDd患者在眼眶额叶内侧皮质中显示出较低的静态FCS，而在尾状中显示出较高的静态FCS，而MDD患者在眼眶额中部中显示出较高的静态FCS。与MDD患者和HCs相比，BDd患者在丘脑中还表现出更大的静态和动态FCS，而与BDd患者和HCs相比，MDD患者在中央前回中表现出更大的动态FCS。组合的静态和动态FCS比单独的静态或动态FCS分析产生更高的准确性，并且还预测了BDd患者的狂躁症严重程度和MDD患者的情绪低落程度。

结论：

BDd患者额-纹状体-丘脑回路内的FC改变以及MDD患者的默认模式网络/感觉运动网络内的FC准确区分了这些疾病。这些独特的FC模式可作为鉴别诊断的生物标志物，并为情绪障碍的发病机理提供线索。