Long-term microstructure and cerebral blood flow changes in patients recovered from COVID-19 without neurological manifestations
Yuanyuan Qin, … , Jie Wang, Wenzhen Zhu
Yuanyuan Qin, … , Jie Wang, Wenzhen Zhu
Published February 25, 2021
Citation Information: J Clin Invest. 2021;131(8):e147329. https://doi.org/10.1172/JCI147329.
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Clinical Medicine COVID-19 Neuroscience

Long-term microstructure and cerebral blood flow changes in patients recovered from COVID-19 without neurological manifestations

Abstract

BACKGROUND The coronavirus disease 2019 (COVID-19) rapidly progressed to a global pandemic. Although some patients totally recover from COVID-19 pneumonia, the disease’s long-term effects on the brain still need to be explored.METHODS We recruited 51 patients with 2 subtypes of COVID-19 (19 mild and 32 severe) with no specific neurological manifestations at the acute stage and no obvious lesions on the conventional MRI 3 months after discharge. Changes in gray matter morphometry, cerebral blood flow (CBF), and white matter (WM) microstructure were investigated using MRI. The relationship between brain imaging measurements and inflammation markers was further analyzed.RESULTS Compared with healthy controls, the decrease in cortical thickness/CBF and the changes in WM microstructure were more severe in patients with severe disease than in those with mild disease, especially in the frontal and limbic systems. Furthermore, changes in brain microstructure, CBF, and tract parameters were significantly correlated (P < 0.05) with the inflammatory markers C-reactive protein, procalcitonin, and interleukin 6.CONCLUSION Indirect injury related to inflammatory storm may damage the brain, altering cerebral volume, CBF, and WM tracts. COVID-19–related hypoxemia and dysfunction of vascular endothelium may also contribute to neurological changes. The abnormalities in these brain areas need to be monitored during recovery, which could help clinicians understand the potential neurological sequelae of COVID-19.FUNDING Natural Science Foundation of China.

Authors

Yuanyuan Qin, Jinfeng Wu, Tao Chen, Jia Li, Guiling Zhang, Di Wu, Yiran Zhou, Ning Zheng, Aoling Cai, Qin Ning, Anne Manyande, Fuqiang Xu, Jie Wang, Wenzhen Zhu

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Figure 8

Illustration of the method of XTRACT automated tractography.

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Illustration of the method of XTRACT automated tractography. (A) Diagram...
(A) Diagram of the steps for the XTRACT automated tractography, with an example of the left IFO. Tractography protocol masks were defined in standard space (FSL_HCP1065 FA atlas, derived from the HCP data set) with seed (start point, blue), exclusion (rejection, black), target (destination, green), and stop (termination, red) masks. The native and standard tractography maps were extracted from a subject from the current sample. (B) Illustration of the projections of the HCP population tract atlases. Association fiber bundles: arcuate fascic-ulus (AF), FAT, ILF, IFO, middle longitudinal fasciculus (MdLF), SLF1, 2, and 3, uncinate fasciculus (UF), and vertical occipital fasciculus (VOF). Projection fiber bundles: AR, ATR, CST, OR, and superior thalamic radiation (STR). Limbic fiber bundles: cingulum bundle, perigenual (CBP), cingulum bundle, temporal (CBT), CBD, and FX. Commissural fiber bundles: anterior commissure (AC), forceps major (FMA), and FMI.