Aberrant temporal-spatial complexity of intrinsic fluctuations in major depression.
Kaizhong ZhengBaojuan LiHongbing LuHuaning WangJin LiuBaoyu YanKarl J FristonYuxia WuJian LiuXi ZhangMengwan LiuLiang LiJian QinBadong ChenDewen HuLingjiang LiPublished in: European archives of psychiatry and clinical neuroscience (2022)
Accumulating evidence suggests that the brain is highly dynamic; thus, investigation of brain dynamics especially in brain connectivity would provide crucial information that stationary functional connectivity could miss. This study investigated temporal expressions of spatial modes within the default mode network (DMN), salience network (SN) and cognitive control network (CCN) using a reliable data-driven co-activation pattern (CAP) analysis in two independent data sets. We found enhanced CAP-to-CAP transitions of the SN in patients with MDD. Results suggested enhanced flexibility of this network in the patients. By contrast, we also found reduced spatial consistency and persistence of the DMN in the patients, indicating reduced variability and stability in individuals with MDD. In addition, the patients were characterized by prominent activation of mPFC. Moreover, further correlation analysis revealed that persistence and transitions of RCCN were associated with the severity of depression. Our findings suggest that functional connectivity in the patients may not be simply attenuated or potentiated, but just alternating faster or slower among more complex patterns. The aberrant temporal-spatial complexity of intrinsic fluctuations reflects functional diaschisis of resting-state networks as characteristic of patients with MDD.
Keyphrases
- resting state
- functional connectivity
- end stage renal disease
- newly diagnosed
- ejection fraction
- prognostic factors
- peritoneal dialysis
- major depressive disorder
- magnetic resonance imaging
- healthcare
- bipolar disorder
- depressive symptoms
- social media
- artificial intelligence
- big data
- sleep quality
- liquid chromatography
- subarachnoid hemorrhage
- high speed
- data analysis