Bipolar disorder (BD) is a complex psychiatric condition marked by significant mood fluctuations that deeply affect quality of life. Understanding the neural mechanisms underlying BD is critical for improving diagnostic accuracy and developing more effective treatments. This study utilized resting-state functional magnetic resonance imaging (rs-fMRI) to investigate functional connectivity within the ventral and dorsal attention networks in 52 patients with BD and 51 healthy controls. Independent Component Analysis (ICA) was employed to establish network templates, while Network Homogeneity (NH) analysis facilitated the comparison of NH values across various brain regions. We examined the association of NH values with clinical measures, including the Hamilton Depression Scale, Perceptual Deficit Questionnaire, and Young Mania Scale. Results indicated that BD patients exhibited lower NH values in the right inferior temporal gyrus of the dorsal attention network and the right middle temporal gyrus of the ventral attention network compared to controls. Notably, NH values in the right superior marginal gyrus of the ventral network were higher in the BD group. Although no significant correlations were found between NH values and clinical symptoms, Support Vector Machine (SVM) analysis demonstrated over 60% accuracy in differentiating BD patients based on NH values. These findings highlight the potential of NH measures as biomarkers for BD, underscore the importance of advanced neuroimaging in uncovering the disorder's complex neural dynamics, and point to the challenges and need for further research to improve predictive accuracy.
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