Abstract and Introduction
There is growing evidence that severe acute respiratory syndrome coronavirus 2 can affect the CNS. However, data on white matter and cognitive sequelae at the 1-year follow-up are lacking. Therefore, we explored these characteristics in this study.
We investigated 22 recovered coronavirus disease 2019 (COVID-19) patients and 21 matched healthy controls. Diffusion tensor imaging, diffusion kurtosis imaging and neurite orientation dispersion and density imaging were performed to identify white matter changes, and the subscales of the Wechsler Intelligence scale were used to assess cognitive function. Correlations between diffusion metrics, cognitive function and other clinical characteristics were then examined. We also conducted subgroup analysis based on patient admission to the intensive care unit.
The corona radiata, corpus callosum and superior longitudinal fasciculus had a lower volume fraction of intracellular water in the recovered COVID-19 group than in the healthy control group. Patients who had been admitted to the intensive care unit had lower fractional anisotropy in the body of the corpus callosum than those who had not. Compared with the healthy controls, the recovered COVID-19 patients demonstrated no significant decline in cognitive function. White matter tended to present with fewer abnormalities for shorter hospital stays and longer follow-up times.
Lower axonal density was detected in clinically recovered COVID-19 patients after 1 year. Patients who had been admitted to the intensive care unit had slightly more white matter abnormalities. No significant decline in cognitive function was found in recovered COVID-19 patients. The duration of hospital stay may be a predictor for white matter changes at the 1-year follow-up.
The coronavirus disease 2019 (COVID-19) pandemic has posed great challenges worldwide, including diagnosis, treatment and post-infection care for survivors. Although substantial progress has been made in addressing the acute effects of COVID-19, the long-term health consequences of recovered patients remain unknown. As the population of recovered COVID-19 patients continues to grow, increasing attention has been given to post-infection care. It is well known that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) attacks the lungs, subsequently causing viral pneumonia, but it also affects the CNS through direct and/or indirect impacts.[1–3] Neurological manifestations, such as encephalitis, cerebral haemorrhage and impaired consciousness, and neuroimaging findings, such as cerebrovascular disease, perfusion abnormalities and white matter changes, have been detected in the acute and subacute stages of the disease. However, patients without these manifestations have also demonstrated persistent CNS abnormalities after recovery. Therefore, detecting and evaluating these changes is clinically vital, and a deeper investigation into the sequelae of COVID-19 can inform individual-based medical care for recovered patients. Additionally, patients admitted to the intensive care unit (ICU) have different imaging manifestations in the acute stage and worse cognitive outcomes after discharge than patients who had never been admitted to the ICU.[6,7] Therefore, we also conducted a comparison between patients who had or had not been admitted to the ICU.
Diffusion tensor imaging (DTI), an imaging modality based on a simplistic model of brain microstructure, is the most common diffusion model used to evaluate white matter integrity. The DTI model assumes simple Gaussian diffusion through the brain microstructure. Diffusion kurtosis imaging (DKI), an advanced diffusion MRI technique based on the theory of non-Gaussian diffusion, is considered to better reflect diffusion in biological tissues, especially in brain areas with high tissue heterogeneity. However, the DTI and DKI models are both based on the 'signal representations' approach, which lacks specificity and can only provide an indirect characterization of the microstructure. Neurite orientation dispersion and density imaging (NODDI), based on the 'tissue model', is a more advanced multicompartment diffusion model.[9,10] NODDI can directly measure properties in three microstructural environments, namely intracellular, extracellular and free water environments, which makes it possible to estimate biologically relevant parameters. Several studies have reported white matter changes in recovered COVID-19 patients,[5,11] indicating that these patients present with persistent white matter abnormalities. However, the status and changes in white matter in recovered COVID-19 patients after 1 year remain unknown, and white matter changes evaluated by DKI and NODDI models have not yet been reported. Tract-based spatial statistics (TBSS) is a whole-brain analysis that combines the strengths of voxel-based analyses and tractography-based analyses. It overcomes the alignment and smooth kernel problems of voxel-based morphometry and improves the sensitivity, objectivity and interpretability of the analysis of multisubject diffusion imaging studies. Therefore, we used this tool to investigate changes in white matter.
In this context, the purposes of this study were to assess the long-term change in white matter by using these three diffusion models, to assess cognitive function in recovered COVID-19 patients and to investigate correlations with clinical characteristics in an attempt to explain the mechanisms underlying the abnormalities observed at the 1-year follow-up.
Brain. 2022;145(5):1830-1838. © 2022 Oxford University Press