4.6 Article

Abnormal static and dynamic functional network connectivity in stable chronic obstructive pulmonary disease

期刊

FRONTIERS IN AGING NEUROSCIENCE
卷 14, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fnagi.2022.1009232

关键词

chronic obstructive pulmonary disease; dynamic; functional connectivity; cognitive impairment; independent component analysis

资金

  1. National Natural Science Foundation of China
  2. Natural Science Foundation Project of Jiangxi Province, China
  3. Education Department Project of Jiangxi Province, China
  4. Department of Health Project of Jiangxi Province, China
  5. [81860307]
  6. [20202BABL216036]
  7. [20181ACB20023]
  8. [GJJ190133]
  9. [202210211]

向作者/读者索取更多资源

This study aimed to explore the changes in dynamic functional network attributes and their relationship with cognitive impairment in stable COPD patients. The results revealed significant differences in sFNC and dFNC between COPD patients and healthy controls, and these measures were significantly correlated with some clinical indicators. These findings provide a new perspective for understanding the cognitive neural mechanisms in COPD patients.
ObjectiveMany studies have explored the neural mechanisms of cognitive impairment in chronic obstructive pulmonary disease (COPD) patients using the functional MRI. However, the dynamic properties of brain functional networks are still unclear. The purpose of this study was to explore the changes in dynamic functional network attributes and their relationship with cognitive impairment in stable COPD patients. Materials and methodsThe resting-state functional MRI and cognitive assessments were performed on 19 stable COPD patients and 19 age-, sex-, and education-matched healthy controls (HC). We conducted the independent component analysis (ICA) method on the resting-state fMRI data, and obtained seven resting-state networks (RSNs). After that, the static and dynamic functional network connectivity (sFNC and dFNC) were respectively constructed, and the differences of functional connectivity (FC) were compared between the COPD patients and the HC groups. In addition, the correlation between the dynamic functional network attributes and cognitive assessments was analyzed in COPD patients. ResultsCompared to HC, there were significant differences in sFNC among COPD patients between and within networks. COPD patients showed significantly longer mean dwell time and higher fractional windows in weaker connected State I than that in HC. Besides, in comparison to HC, COPD patients had more extensive abnormal FC in weaker connected State I and State IV, and less abnormal FC in stronger connected State II and State III, which were mainly located in the default mode network, executive control network, and visual network. In addition, the dFNC properties including mean dwell time and fractional windows, were significantly correlated with some essential clinical indicators such as FEV1, FEV1/FVC, and c-reactive protein (CRP) in COPD patients. ConclusionThese findings emphasized the differences in sFNC and dFNC of COPD patients, which provided a new perspective for understanding the cognitive neural mechanisms, and these indexes may serve as neuroimaging biomarkers of cognitive performance in COPD patients.

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