key: cord-0025661-1eh4vrey
authors: Huang, Sihong; Zhou, Zhiguo; Yang, Danhui; Zhao, Wei; Zeng, Mu; Xie, Xingzhi; Du, Yanyao; Jiang, Yingjia; Zhou, Xianglin; Yang, Wenhan; Guo, Hu; Sun, Hui; Liu, Ping; Liu, Jiyang; Luo, Hong; Liu, Jun
title: Persistent white matter changes in recovered COVID-19 patients at the 1-year follow-up
date: 2021-12-16
journal: Brain
DOI: 10.1093/brain/awab435
sha: f354efc57534071009c0447f83b6464d11b9000e
doc_id: 25661
cord_uid: 1eh4vrey

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 one-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 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 one 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 one-year follow-up.

The coronavirus disease 2019 (COVID-19) pandemic has posed great challenges 2 worldwide, including diagnosis, treatment, and postinfection care for survivors. Although 3 substantial progress has been made in addressing the acute effects of COVID-19, the long-term 4 health consequences of recovered patients remain unknown. As the population of recovered 5 COVID-19 patients continues to grow, increasing attention has been given to postinfection care. 6 It is well known that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) attacks the 7 lungs, subsequently causing viral pneumonia, but it also affects the CNS through direct and/or 8 indirect impacts. [1] [2] [3] Neurological manifestations, such as encephalitis, cerebral hemorrhage, and 9 impaired consciousness, 1 and neuroimaging findings, such as cerebrovascular disease, perfusion 10 abnormalities, and white matter (WM) changes, 4 have been detected in the acute and subacute 11 stages of the disease. However, patients without these manifestations have also demonstrated 12 persistent CNS abnormalities after recovery. 5 Therefore, detecting and evaluating these changes 13 is clinically vital, and a deeper investigation into the sequelae of COVID-19 can inform 14 individual-based medical care for recovered patients. Additionally, patients admitted to the 15 intensive care unit (ICU) have different imaging manifestations in the acute stage and worse 16 cognitive outcomes after discharge than patients who had never been admitted to the ICU. 6, 7 17 Therefore, we also conducted a comparison between patients who had or had not been admitted 18 to the ICU. 19 Diffusion tensor imaging (DTI), an imaging modality based on a simplistic model of brain diffusion, is considered to better reflect diffusion in biological tissues, especially in brain areas 1 with high tissue heterogeneity. However, the DTI and DKI models are both based on the "signal 2 representations" approach, which lacks specificity and can only provide an indirect 3 characterization of the microstructure. Neurite orientation dispersion and density imaging 4 (NODDI), based on the "tissue model", is a more advanced multicompartment diffusion 5 model. 9,10 NODDI can directly measure properties in three microstructural environments, 6 namely, intracellular, extracellular, and free water environments, which makes it possible to 7 estimate biologically relevant parameters. Several studies have reported WM changes in 8 recovered COVID-19 patients, 5, 11 indicating that these patients present with persistent WM 9 abnormalities. However, the status and changes in WM in recovered COVID-19 patients after 10 one year remain unknown, and WM changes evaluated by DKI and NODDI models have not yet 11 been reported. Tract-based spatial statistics (TBSS) 12 is a whole-brain analysis that combines the 12 strengths of voxel-based analyses and tractography-based analyses. It overcomes the alignment 13 and smooth kernel problems of voxel-based morphometry and improves the sensitivity, 14 objectivity and interpretability of the analysis of multisubject diffusion imaging studies. 15 Therefore, we used this tool to investigate changes in WM. 16 In this context, the purposes of this study were to assess the long-term change in WM by 17 using these three diffusion models, to assess cognitive function in recovered COVID-19 patients 18 and to investigate correlations with clinical characteristics in an attempt to explain the 19 mechanisms underlying the abnormalities observed at the one-year follow-up. In total, 237 recovered COVID-19 patients were recruited from the First Hospital of 6 Changsha. The inclusion criteria for the recovered COVID-19 group were as follows: (1) a 7 diagnosis of COVID-19 according to the guidelines of the National Health Commission 13 and a 8 discharge date between February and April 2020; (2) age greater than 18 years; and (3) 9 willingness and ability to undergo brain MRI scanning. The exclusion criterion was a structural 10 abnormality on traditional neuroimaging except for WM hyperintensity. Age-, sex-and 11 education-matched healthy controls (HCs) were recruited, and subjects with severe psychiatric 12 disease (e.g., schizophrenia or depression), severe somatic disease (e.g., diabetes, uncontrolled 13 hypertension, or heart disease), drug abuse, history of traumatic brain injury or surgery, or brain 14 structural abnormality (e.g., encephalomalacia foci, brain infections or neoplasms) on 15 neuroimaging were excluded, except for mild-moderate WM hyperintensity. Among 237 16 discharged patients, 23 volunteered to participate in our research, and one patient was excluded 17 because he did not undergo an MRI scan. Finally, 22 recovered COVID-19 patients and 21 HCs 18 were included. A flowchart of patient inclusion is shown in Figure 1 .

All subjects underwent psychiatric evaluations via face-to-face interviews conducted by 20 trained medical staff. Information on the following clinical characteristics was collected: age; 21 sex; education; history of sojourn; clinical type (National Health Commission guidelines: mild,

8 moderate or severe); hospitalization days; and the presence of fever, cough, or gastrointestinal 1 symptoms. Four inflammatory markers were also collected: erythrocyte sedimentation rate 2 (ESR); C-reactive protein (CRP); neutrophil/lymphocyte ratio (NLR) and systemic immune-3 inflammation index (SII) (SII = platelets * neutrophils/lymphocytes). 14 Baseline clinical 4 characteristics and inflammatory markers were used for further analysis in this study. The 5 demographic characteristics and neuropsychological tests of the recovered COVID-19 patients 6 and HCs are presented in Table 1 . The clinical features of the recovered COVID-19 patients are 7 presented in Table 2 . The demographic and clinical characteristics of the ICU and non-ICU 8 groups are presented in Table 3 . The median interval time from discharge to MRI scan was 9 351.5 days.

All MRI data were acquired on a 3-T MRI scanner (MAGNETOM Skyra, Siemens digit symbol substitution test (DSST), which has been frequently used to assess participants' 7 processing speed, sustained attention and working memory. 16, 17 The patients were shown 9 8 numbers and their corresponding symbols and then the were instructed to match the correct 9 symbols to the corresponding numbers in two minutes. The total score was the number of 10 correctly matched symbols, and a higher score indicated better performance in the assessment. which assess visual and visuospatial sequence representation, respectively). 20 In our study, the 19 DS task was presented as sequences of digits of increasing length, ranging from 2 to 9 numbers. 20 (5) The word fluency test (WFT): in one minute, the subjects were asked to name as many 21 animals as possible. The subjects completed these neuropsychological tests on the same day as 22 the MRI scan.

Image processing included initial preprocessing and diffusion metric computations. Prior to 2 preprocessing, each subject's diffusion images were visually inspected to verify that they were 3 free from major artifacts (e.g., head motion). Motion, eddy current artifacts, and geometric 4 distortions were corrected using the eddy command provided in the FMRIB Software Library 5 (FSL). 21 Using an in-house MATLAB script, the transformation matrices, output from the eddy 6 command, were used to rotate the corresponding diffusion-weighting directions to match the 7 rotation of the brain image during the motion correction process. Then, the b0 images were 8 extracted, and nonbrain voxels were masked out by applying the FSL bet command to the 9 subject's b0 image. Then, four DTI metrics (fractional anisotropy (FA), radial diffusivity (RD), 10 axial diffusivity (AD) and mean diffusivity (MD)) were calculated by the FSL dtifit command. 

The data that support the findings of this study are available from the corresponding author 2 upon reasonable request. Table 1 . There were no statistically significant differences between the patients and HCs with 8 regard to sex ratio, age, or education, justifying their use as the experimental group and control 9 group, respectively. Two patients had complications: one had sepsis and multiple organ 10 dysfunction syndrome (MODS), and the other had acute respiratory distress syndrome (ARDS).

The clinical features of the recovered COVID-19 group are presented in Table 2 The TBSS analyses revealed a lower V ic value in the patients than in the controls; further 5 details of the significant results are shown in Table 4 . Abnormal diffusion metrics were detected The TBSS analyses revealed a lower FA in the ICU group than in the non-ICU group. The 10 body of the CC (150 voxels) was significantly different between these two subgroups (Figure 2 11 C).

Neuropsychological test results and correlation analysis 13 The entire neuropsychological test datasets were lost for 2 recovered COVID-19 patients. Table 1 and Table 3) . 18 Within the COVID-19 group, V ic of cluster 1 was negatively correlated with length of In the present study, we comprehensively investigated WM changes in recovered COVID-5 19 patients at the one-year follow-up using conventional DTI metrics and DKI and NODDI 6 models. To the best of our knowledge, this is the first study to investigate WM changes at the 7 one-year follow-up. Our results showed that recovered COVID-19 patients had lower V ic values 8 than HCs one year after recovery. Additionally, patients who were admitted to the ICU had 9 slightly more white matter abnormalities. Compared with healthy controls, recovered COVID-19 10 patients showed no significant decline in cognitive function. Finally, white matter tended to 11 present with fewer abnormalities for shorter hospital stays and longer follow-up times.

V ic , a potential proxy for axonal density measurements, may be explained by edema and showing that the WM difference between the ICU group and non-ICU group was very subtle. 14 The CR, CC and SLF were the main areas with abnormal fibers presented in our results. 15 Although WM is not the key target of neurotropic viruses, these connecting fibers could act as 16 channels for intracranial viral transmission. 11 The CR consists of a large number of projection 17 fibers that connect the cortex to the brainstem and the thalamus in both an afferent and efferent 18 manner. 30 The CC connects the bilateral cerebral hemispheres and communicates between brain 19 regions with powerful parallel fibers. The CC is a vulnerable target, and damage to this region 20 has been found in the acute phase and during follow-up. 6,27 The SLF is a long association fiber 21 tract that travels in discrete fascicles, leading to distant cortical areas in the same hemisphere. 31

The CR and SLF are important components of the connecting fibers with the CC, which play a Additionally, abnormalities of these tracts have been found in previous relatively short follow-up 2 studies. 5,11,27 At the same time, the significant brain voxels were primarily anterior brain regions, 3 which may be related to the high density of ACE2 in the frontal cortex. 32,33 4 No significant decline in cognitive function was found in recovered COVID-19 patients in 5 our research. In accordance with previous studies, our patients may undergo a process of 6 cognitive decline and recovery. If baseline and short-term follow-up cognitive function can be 7 obtained, this conjecture can be better supported, but to date we have been unable to obtain 8 baseline or short-term neuropsychological test data. Several studies have shown cognitive 9 impairment in COVID-19 patients 14,34,35 ; however, these studies represent relatively short-term 10 research. Additionally, previous long-term studies have shown that complications such as 11 delirium and ARDS have an impact on patients' long-term cognitive function. 7,36,37 However, 12 only two patients in our study had COVID-19 complications, which may be the reason why we 13 obtained negative results. Furthermore, the cognitive function was relatively low in the HCs 14 compared to COVID-19 patients. Years and quality of education may be the most likely cause. 15 The white matter in the COVID-19 patients tended to present with fewer abnormalities for There were several limitations in the present study. First, our study had a small sample size.

To improve the reliability of the results, we included subjects who volunteered to participate and 4 did not make subjective choices through researchers. We used multiple diffusion models and 5 metrics to more comprehensively display WM changes using voxel-based methods. Strict 6 statistical analysis and correction were also performed. However, more patients and HCs should 7 be recruited in future studies to test and clarify the results of the present research. Second, the 8 patients in our study had no prior brain MRI scans because they had demonstrated no severe 9 neurological manifestations. Therefore, we could not obtain the patients' baseline imaging status 10 or assess dynamic changes during the follow-up period. However, we will conduct follow-up 11 observations on these patients to explore long-term dynamic changes in the future. Third, WM 12 hyperintensity is a common condition in elderly individuals, 38 but moderate-severe WM 13 hyperintensity could influence white matter integrity. 39 We counted the degree and number of 14 patients with WM hyperintensity according to the modified version of the Fazekas scale 40 to 15 compare the constituent ratios of the two groups before the analysis. There was no significant 16 difference in the constituent ratio between the two groups (p = 0.609). We will attempt to include 17 more subjects to overcome this limitation. Last, we used only diffusion imaging to explore WM 

The authors report no competing interests. 

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