Frontiers in neurology · 2025
Abnormal neural network connectivity in heart failure with reduced and mild-ranged ejection fraction patients: an independent component and dynamic functional network connectivity analysis.
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Gao Qian, Wen Junyan, Lu Yi, Li Jun, Hua Baotong, Mo Yin, Mao Yanfei, Xu Yunyun, Xia Ping, Xie Kaipeng, Zeng Yizhen, Wen Ge
Аннотация
INTRODUCTION: Heart failure (HF) is frequently accompanied by cognitive and affective impairments, yet the neural mechanisms underlying these comorbidities remain insufficiently understood. This study aimed to investigate alterations in static and dynamic functional connectivity (FC) within large-scale brain networks in patients with reduced (HFrEF) and mid-range (HFmrEF) ejection fraction.
METHODS: Independent component analysis (ICA) was used to identify resting-state networks (RSNs) and FC disparities between HF patients and healthy controls (HCs) within the RSNs. The ICA, sliding window approach, and k-means clustering analysis were used to compute dynamic functional network connectivity (dFNC) matrices and estimate different dynamic connection states. The temporal characteristics of the two groups were analyzed in each state. The correlations among significantly diverse temporal aspects and clinical measures were finally determined.
RESULTS: Compared to HCs, HF patients showed reduced FC in the right inferior parietal lobule (IPL) within the dorsal attention and frontoparietal networks, alongside increased FC in the salience network. dFNC analysis revealed five recurrent connectivity states. Notably, HF patients exhibited shorter dwell time in a sensory-cognitive segregation state (State 5), and dwell time in this state correlated positively with both left ventricular ejection fraction (LVEF) and Mini-Mental State Examination (MMSE) scores.
CONCLUSION: The disrupted static and dynamic connectivity in HF patients-marked by alterations in frontoparietal, attention, and salience networks and reduced stability of a sensory-cognitive segregation state-may underlie cognitive and affective vulnerability, providing potential imaging markers for early risk monitoring and management in HF.
METHODS: Independent component analysis (ICA) was used to identify resting-state networks (RSNs) and FC disparities between HF patients and healthy controls (HCs) within the RSNs. The ICA, sliding window approach, and k-means clustering analysis were used to compute dynamic functional network connectivity (dFNC) matrices and estimate different dynamic connection states. The temporal characteristics of the two groups were analyzed in each state. The correlations among significantly diverse temporal aspects and clinical measures were finally determined.
RESULTS: Compared to HCs, HF patients showed reduced FC in the right inferior parietal lobule (IPL) within the dorsal attention and frontoparietal networks, alongside increased FC in the salience network. dFNC analysis revealed five recurrent connectivity states. Notably, HF patients exhibited shorter dwell time in a sensory-cognitive segregation state (State 5), and dwell time in this state correlated positively with both left ventricular ejection fraction (LVEF) and Mini-Mental State Examination (MMSE) scores.
CONCLUSION: The disrupted static and dynamic connectivity in HF patients-marked by alterations in frontoparietal, attention, and salience networks and reduced stability of a sensory-cognitive segregation state-may underlie cognitive and affective vulnerability, providing potential imaging markers for early risk monitoring and management in HF.