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1.
Article En | MEDLINE | ID: mdl-38558145

Previous studies about anhedonia symptoms in bipolar depression (BD) ignored the unique role of gender on brain function. This study aims to explore the regional brain neuroimaging features of BD with anhedonia and the sex differences in these patients. The resting-fMRI by applying fractional amplitude of low-frequency fluctuation (fALFF) method was estimated in 263 patients with BD (174 high anhedonia [HA], 89 low anhedonia [LA]) and 213 healthy controls. The effects of two different factors in patients with BD were analyzed using a 3 (group: HA, LA, HC) × 2 (sex: male, female) ANOVA. The fALFF values were higher in the HA group than in the LA group in the right medial cingulate gyrus and supplementary motor area. For the sex-by-group interaction, the fALFF values of the right hippocampus, left medial occipital gyrus, right insula, and bilateral medial cingulate gyrus were significantly higher in HA males than in LA males but not females. These results suggested that the pattern of high activation could be a marker of anhedonia symptoms in BD males, and the sex differences should be considered in future studies of BD with anhedonia symptoms.

2.
J Affect Disord ; 351: 414-424, 2024 Apr 15.
Article En | MEDLINE | ID: mdl-38272369

BACKGROUND: Response inhibition is a key neurocognitive factor contributing to impulsivity in mood disorders. Here, we explored the common and differential alterations of neural circuits associated with response inhibition in bipolar disorder (BD) and unipolar disorder (UD) and whether the oscillatory signatures can be used as early biomarkers in BD. METHODS: 39 patients with BD, 36 patients with UD, 29 patients initially diagnosed with UD who later underwent diagnostic conversion to BD, and 36 healthy controls performed a Go/No-Go task during MEG scanning. We carried out time-frequency and connectivity analysis on MEG data. Further, we performed machine learning using oscillatory features as input to identify bipolar from unipolar depression at the early clinical stage. RESULTS: Compared to healthy controls, patients had reduced rIFG-to-pre-SMA connectivity and delayed activity of rIFG. Among patients, lower beta power and higher peak frequency were observed in BD patients than in UD patients. These changes enabled accurate classification between BD and UD with an accuracy of approximately 80 %. CONCLUSIONS: The inefficiency of the prefrontal control network is a shared mechanism in mood disorders, while the abnormal activity of rIFG is more specific to BD. Neuronal responses during response inhibition could serve as a diagnostic biomarker for BD in early stage.


Bipolar Disorder , Depressive Disorder , Humans , Bipolar Disorder/diagnosis , Bipolar Disorder/psychology , Depressive Disorder/diagnosis , Risk Assessment , Biomarkers , Machine Learning
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