Cerebral microstructural alterations in Post-COVID-condition are related to cognitive impairment, olfactory dysfunction, and fatigue
Data files
Mar 15, 2024 version files 1.60 MB
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Dryad_final.xlsx
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README.md
Abstract
After contracting COVID-19, a substantial number of individuals develop a Post-COVID-Condition (PCC), marked by neurologic symptoms such as cognitive deficits, olfactory dysfunction, and fatigue, which can have detrimental socioeconomic consequences. Despite this, biomarkers and pathophysiological understandings of this condition remain limited. Employing magnetic resonance imaging, we conduct a comparative analysis of cerebral microstructure among patients with post-COVID condition, healthy controls, and individuals who contracted COVID-19 without long-term symptoms. This reveals widespread alterations in cerebral microstructure, attributed to a shift in volume from neuronal compartments to free fluid, associated with the severity of the initial infection. Correlating these alterations with cognition, olfaction, and fatigue unveils distinct affected networks, which are in a close anatomical-functional relationship with the respective symptoms. This plausibility of symptom-specific networks not only provides insights into the disease's pathophysiological foundations, which align well with an accelerated aging process but also underscores the significance of microstructure as an imaging biomarker.
README: Cerebral microstructural alterations in Post-COVID-condition are related to cognitive impairment, olfactory dysfunction, and fatigue
Description of the data and file structure
1. Excel-Sheet (Demographic and Clinical Data)
Demographic and clinical data of our cohorts. These data form the basis for Tables 1 and 2, as well as the "Demographic and clinical characteristics" section of the Results.
Tabular enumeration of the demographic and clinical data of PCC patients, UPC, and HNC controls. For Table 1 published in Hrynaszkiewicz et al., 2010 (PMID: 20113465), the only identifiable characteristics in our datasheet are sex and place of treatment (as we publish a monocentric cohort). We divided the patient's age into 5-year categories to complicate patient identification in this regard.
Not every group received the same assessments; for example, healthy controls showed no complaints. Therefore, in the absence of data, an empty cell was left.
- Questionnaires: Fatigue (WEIMuS, physical subscore = p; mental subscore = m), Depression (GDS-15)
- Grading: Severity 1-4; Disability 1-4
- Cognitive Test: MoCA
- Olfactory Testing: Sniffing-Stix 0-12
- Binary indication for the presence of the following symptoms (0 = no; 1 = yes): Attention, Memory, Multitasking, Word-finding difficulties, Fatigue, Olfactory symptoms acute (ini) and chronic (late)
- Comorbidities
- Delay between PCR-Test and Imaging (days)
2. Excel-Sheet (DMI-Data):
DMI-metrics of total gray and white matter. These data form the basis for Figure 1, as well as the "Group comparison of gross changes in DMI parameters in the gray matter" section of the Results.
- gm = gray matter; wm = white matter
- Compartments: V-extra; V-intra; V-CSF, values have no dimension
3. Excel-Sheet (Freesurfer Data):
Results of the morphometric Analysis using the FreeSurfer V6.0 toolbox (https://surfer.nmr.mgh.harvard.edu/fswiki). The following parameters were analyzed: Total gray matter volume; cortical volume; cortical thickness and cortical surface. Parameters were extracted based on an atlas (Desikan-Killany) and for the right (rh) and left hemisphere (lh) as given by FeeSurfer. These data form the basis the section "Evaluation of conventional MRI and cortical morphometric analysis" of the Results.
- Total gray matter volume: suffix ..._GrayVol
- Corical volume: suffix ..._Volume_mm3
- Cortical thickness: suffix ..._ThickAvg
- Cortical surface:: suffix ..._SurfArea
4. Excel-Sheet (Combined):
Combined dataset
Not every group received the same assessments; for example, healthy controls showed no complaints. Therefore, in the absence of data, an empty cell was left.
Sharing/Access information
Questions should be directed to the corresponding author (JAH, jonas.hosp@uniklinik-freiburg.de)
Code/Software
This file contains self-programmed code used in R (version 4.1.2). It was utilized for ANCOVA analysis of DMI- parameters, region-based gray matter volumetry, and evaluation of Freesurfer-generated data (cortical thickness, surface area, and gray matter volume). Patients' age and sex were employed as covariates.
Methods
In this prospective monocentric study, we analyzed clinical and (micro-)structural MRI data from a cohort of 89 patients who fulfilled the WHO diagnosis criteria for PCC (49, IQR [23] years; 55 females; 85% mild course of COVID-19 infection) and underwent MRI approximately nine months (254, IQR [209] days) after a positive PCR test result for SARS-CoV-2. Due to PCC symptoms (i.e. impaired attention, multitasking, and memory, word-finding difficulties, disturbed olfaction/gustation, and fatigue), 53% of patients could not return to their previous level of independence or employment. Cognitive performance, as measured by the MoCA test, was impaired in 41% of patients (26, IQR [4] points, cut-off value: < 26/30). Olfactory performance was impaired in 74% of patients (9, IQR [4] items identified, < 11/12 is the cut-off value). The WEIMuS questionnaire indicated the presence of fatigue in 78% of patients (43, IQR [17] points, > 33/68 is the cut-off value). PCC patients were compared to matched healthy controls (Healthy non-COVID, HNC; n = 46) as well as controls that had contracted COVID-19 without an ensuing PCC (Unimpaired Post-COVID, UPC; n = 38). Conventional MRI sequences yielded age-appropriate results without signs of gray matter atrophy or changes in cortical morphometry (FreeSurfer-based). Analysis of whole-brain DMI data revealed a volume shift from the membrane-enclosed compartment into the free-water fraction in the gray matter, which was positively associated with the severity of initial COVID-19 infection (P = 0.007). However, voxel-based inter-group comparisons of DMI parameters allowed an even more distinguishable view of the COVID-19-related effect: Whereas a marked decrease in the volume of the membrane-enclosed compartment occurred in neocortical gray matter and thalamus, an increase was observed within the corpus callosum, internal capsule, cerebellum, and brainstem. Moreover, PCC patients could be distinguished from UPC patients based on the different emphasis of this pattern. To further determine the microstructural correlates of PCC-associated symptoms after COVID-19, voxel-based associations of DMI parameters with clinical scores were performed. Here, symptom-specific networks emerged that were significantly correlated with impaired MoCA- or olfactory performance and fatigue. In summary, DMI revealed microstructural changes after COVID-19 infection, with different patterns in patients with or without PCC. Expression of PCC symptoms was associated with affection of specific cerebral networks, suggesting a pathophysiological basis for this syndrome.