DOI: 10.5061/dryad.kkwh70s9g
title: Cerebral microstructural alterations in Post-COVID-condition are related to cognitive impairment, olfactory dysfunction, and fatigue

description: 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.
description: 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: &lt;
 26/30). Olfactory performance was impaired in 74% of patients (9, IQR [4]
 items identified, &lt; 11/12 is the cut-off value). The WEIMuS
 questionnaire indicated the presence of fatigue in 78% of patients (43,
 IQR [17] points, &gt; 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.
description: # 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).
 ](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](mailto: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.