Abnormal dynamic reconfiguration of the large-scale functional network in schizophrenia during the episodic memory task.
Bing WangTingting PanMin GuoZhifeng LiXuexue YuDandan LiYan NiuXiaohong CuiJie XiangPublished in: Cerebral cortex (New York, N.Y. : 1991) (2022)
Episodic memory deficits are the core feature in schizophrenia (SCZ). Numerous studies have revealed abnormal brain activity associated with this disorder during episodic memory, however previous work has only relied on static analysis methods that treat the brain as a static monolithic structure, ignoring the dynamic features at different time scales. Here, we applied dynamic functional connectivity analysis to functional magnetic resonance imaging data during episodic memory and quantify integration and recruitment metrics to reveal abnormal dynamic reconfiguration of brain networks in SCZ. In the specific frequency band of 0.06-0.125 Hz, SCZ showed significantly higher integration during encoding and retrieval, and the abnormalities were mainly in the default mode, frontoparietal, and cingulo-opercular modules. Recruitment of SCZ was significantly higher during retrieval, mainly in the visual module. Interestingly, interactions between groups and task status in recruitment were found in the dorsal attention, visual modules. Finally, we observed that integration was significantly associated with memory performance in frontoparietal regions. Our findings revealed the time-varying evolution of brain networks in SCZ, while improving our understanding of cognitive decline and other pathophysiologies in brain diseases.
Keyphrases
- resting state
- functional connectivity
- working memory
- cognitive decline
- magnetic resonance imaging
- white matter
- bipolar disorder
- single cell
- mild cognitive impairment
- spinal cord
- cerebral ischemia
- traumatic brain injury
- machine learning
- computed tomography
- neuropathic pain
- magnetic resonance
- genome wide
- spinal cord injury
- mass spectrometry
- ionic liquid
- blood brain barrier
- dna methylation
- big data