Dynamical regional activity in putamen distinguishes bipolar type I depression and unipolar depression

Intrinsic human brain activity is time-varying and dynamic. However, there is still a lack of knowledge about the dynamic regional activity differences between unipolar depression (UD) and bipolar type I depression (BD-I), and whether their differential pattern can help to distinguish these two patient groups who are prone to misdiagnosis in clinical practice.

In this study, we used the dynamical fractional amplitude of low-frequency fluctuations (dfALFF) to examine the resting-state dynamical regional activity in 40 BD-I, 42 UD, and 44 healthy controls (HCs). Analysis of covariance was applied to explore the shared and distinct dfALFF pattern among three groups, and machine-learning methods were conducted to classify BD-I from UD by using the detected distinct dfALFF pattern.

Compared with HCs, both BD-I and UD exhibited decreased dfALFF temporal variability in the left inferior temporal gyrus. The BD-I showed significantly decreased dfALFF temporal variability in the left putamen compared to UD. By using the dfALFF variability pattern of the left putamen as features, we achieved the 75.61% accuracy and 0.756 area under curve in classifying BD-I from UD.

The small sample size of the current study may limit the generalizability of the findings.

The current study demonstrated that the dfALFF temporal variability pattern in the putamen may show a promise as future diagnostic aids for BD-I and UD.