Intra and inter-network functional connectivity among long-Covid patients with ongoing disease duration
Manuel Leitner1,2, Stefan Ropele1, Maria Fellner3
1Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036, Graz, Austria.
Abstract:
Long-Covid can cause neurological and cognitive dysfunctions. The impact of disease duration on these neurobiological changes remains underexplored. This study aims to investigate how long-Covid disease duration influences intra and inter-network resting-state functional connectivity (rs-fMRI) and its association with clinical, demographic, cognitive, and mental health variables. We conducted a cross-sectional study using rs-fMRI to assess functional connectivity and stratified patients into short (n = 20, M = 8.88 months, SD = 3.88) and long (n = 19, M = 27.77 months, SD = 4.50) disease duration groups. We identified 17 resting-state networks using independent component analysis (ICA) and compared intra- and inter-network connectivity between groups. Additionally, clinical, demographic, cognitive, and mental health data were collected and analysed to explore their relationship with connectivity patterns. Our analysis revealed significant intra-network connectivity differences in six networks. Patients with longer disease duration exhibited increased functional connectivity in networks associated with cognitive functions (e.g. dorsal attention network). Inter-network connectivity analysis showed reduced connectivity between key networks, particularly involving the default mode network in patients with prolonged illness. Notably, there were no significant differences between the groups in demographic, clinical, cognitive, or mental health measures. The observed intra- and inter network connectivity changes might reflect adaptive compensatory mechanisms. Especially, the emerging imbalance of inter-network connectivity with prolonged disease duration, particularly involving the default mode network, may represent an adaptive process that enhances task-specific network efficiency.
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