Abnormalcerebral-limbic functional connectivity between bipolar mania and bipolar depression under resting state

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Abstract
Objective: Previous research has identified aberrant functional connectivity (FC) in the neural circuits of patients with bipolar mania (BD-M) and bipolar depression (BD-D), yet the specificity of these FC patterns to each mood state remains unelucidated. This study was designed to compare the cerebral-limbic FC characteristics among BD-M, BD-D patients and healthy control (HC) subjects. Method: Resting-state functional magnetic resonance imaging (fMRI) was performed on 30 BD-M patients, 31 BD-D patients and 30 HC subjects. Interregional cerebral FC values were calculated for group-wise comparisons, and the correlation between abnormal FC and depressive symptom severity was further explored. Results: No significant group differences in cerebral-limbic functional connectivity survived false discovery rate (FDR) correction for multiple comparisons (p<0.05); thus, all reported abnormal FC patterns were identified at a stringent uncorrected statistical threshold of p<0.001 and should be strictly interpreted as exploratory neurofunctional trends without statistical validation at the individual connection level. Abnormal cerebral-limbic FC in the default mode network (DMN), attention network and limbic areas was observed in both BD-M and BD-D groups. Specifically, BD-D patients showed elevated FC mainly in the DMN [posterior cingulate gyrus (PCG), precuneus (PCUN)], attention network [superior parietal gyrus (SPG), inferior parietal gyrus (IPG)] and limbic regions [hippocampus (HIP), parahippocampus (PHG)], while BD-M patients displayed reduced cerebral-limbic FC in the DMN and limbic areas. Conclusions: BD-M and BD-D show distinct and divergent cerebral-limbic FC patterns: reduced DMN FC in BD-M and increased DMN FC in BD-D. These exploratory patterns suggest potential neurofunctional correlates of mood states in bipolar disorder, offering preliminary clues to state-related differences that require validation in larger independent cohorts.

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