期刊
JOURNAL OF AFFECTIVE DISORDERS
卷 150, 期 2, 页码 601-609出版社
ELSEVIER
DOI: 10.1016/j.jad.2013.01.051
关键词
Schizophrenia; Bipolar; Functional connectivity; Networks; Resting state
资金
- NIMH
- NARSAD
- NIA
- Allon
- Novartis
- McDonnell Center for Systems Neuroscience
- NIH [P50 MH071616, R01 MH56584, K08 MH085948]
Background: Bipolar disorder (BPD) and schizophrenia (SCZ) share clinical characteristics and genetic contributions. Functional dysconnectivity across various brain networks has been reported to contribute to the pathophysiology of both SCZ and BPD. However, research examining resting-state neural network dysfunction across multiple networks to understand the relationship between these two disorders is lacking. Methods: We conducted a resting-state functional connectivity fMRI study of 35 BPD and 25 SCZ patients, and 33 controls. Using previously cleaned regions-of-interest, we computed the mean connectivity within and between five neural networks: default mode (DM), fronto-parietal (FP), cingulo-opercular (CO), cerebellar (CER), and salience (SAL). Repeated measures ANOVAs were used to compare groups, adjusting false discovery rate to control for multiple comparisons. The relationship of connectivity with the SANS/SAPS, vocabulary and matrix reasoning was investigated using hierarchical linear regression analyses. Results.: Decreased within-network connectivity was only found for the CO network in BPD. Across groups, connectivity was decreased between CO-CER (p < 0.001), to a larger degree in SCZ than in BPD. In SCZ, there was also decreased connectivity in CO SAL, FP CO, and FP-CER, while BPD showed decreased CER-SAL connectivity. Disorganization symptoms were predicted by connectivity between CO-CER and CER-SAL. Discussion: Our findings indicate dysfunction in the connections between networks involved in cognitive and emotional processing in the pathophysiology of BPD and SCZ. Both similarities and differences in connectivity were observed across disorders. Further studies are required to investigate relationships of neural networks to more diverse clinical and cognitive domains underlying psychiatric disorders. (C) 2013 Elsevier B.V. All rights reserved.
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