Artículo

Impact of long‐COVID on the local and global efficiency of brain networks

Travi, Fermín Icon; Hernández, Micaela Anahí ; Bianchi, Bruno Icon; Crivelli, Lucía Icon; Allegri, Ricardo Francisco Icon; Fernandez Slezak, Diego Icon; Calandri, Ismael L. ; Kamienkowski, Juan Esteban Icon

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Fecha

08/2024

Editorial

Wiley

Revista

Clinical Neuroimaging

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Institución organizadora

ISSN

2837-3219

eISSN

ISBN

Idioma

Inglés

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Resumen

Background and purpose: Subjective cognitive complaints post-COVID-19, known as long-COVID, have unclear effects on neural activity. This study explores the neural basis of these cognitive impairments by comparing resting-state functional networks of long-COVID individuals to a control group. Methods: Forty-two individuals with cognitive complaints persisting 24 weeks post COVID-19 infection and 43 age-, sex- and education-matched healthy controls without a history of infection were studied using resting-state functional MRI (rs-fMRI) and the Uniform Data Set (UDS-3) neurocognitive test battery (NCT). Neuropsychological scores were adjusted to the mean and grouped into seven cognitive composites. The rs-fMRI data were partitioned into seven distinct functional neural networks—Salience/Ventral Attention, Dorsal Attention, Default, Frontoparietal, Visual, Somatomotor, and Limbic—and their efficiency, largest connected component, and modularity (Q) were studied. Results: The NCT scores yielded statistically significant differences in long-COVID subjects compared to controls at attention, language, memory, executive, and global composites. We observed significant differences (p < .001) in the global and mean local efficiency of the Salience/Ventral Attention and Global networks, and to a lesser extent (p < .005 and p < .01) in the Default and Dorsal Attention networks. Conclusions: Our findings reveal significant group-level differences in executive, attentional, language, and memory outcomes, alongside less efficient and organized connections among Salience/Ventral Attention and Global networks.

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