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

Fuqiu Tang; Lan Li; Dechang Peng; Jingjing Yu; Huizhen Xin; Xuan Tang; Kunyao Li; Yaping Zeng; Wei Xie; Haijun Li

doi:10.3389/fnagi.2022.1009232

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

Front Aging Neurosci. 2022 Oct 17:14:1009232. doi: 10.3389/fnagi.2022.1009232. eCollection 2022.

Authors

Fuqiu Tang¹, Lan Li², Dechang Peng^{1

3}, Jingjing Yu⁴, Huizhen Xin¹, Xuan Tang¹, Kunyao Li¹, Yaping Zeng¹, Wei Xie¹, Haijun Li^{1

3}

Affiliations

¹ Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, China.
² Department of Infection Management, Jiangxi Provincial Maternal and Child Health Hospital, Nanchang, China.
³ PET Center, The First Affiliated Hospital of Nanchang University, Nanchang, China.
⁴ Department of Respiratory and Critical Care, The First Affiliated Hospital of Nanchang University, Nanchang, China.

Abstract

Objective: Many 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 methods: The 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.

Results: Compared 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.

Conclusion: These 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.

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