Widespread White Matter Oedema in Subacute COVID-19 Patients With Neurological Symptoms

Alexander Rau; Nils Schroeter; Ganna Blazhenets; Andrea Dressing; Lea I. Walter; Elias Kellner; Tobias Bormann; Hansjörg Mast; Dirk Wagner; Horst Urbach; Cornelius Weiller; Philipp T. Meyer; Marco Reisert; Jonas A. Hosp

Disclosures

Brain. 2022;145(9):3203-3213. 

In This Article

Abstract and Introduction

Abstract

While neuropathological examinations in patients who died from COVID-19 revealed inflammatory changes in cerebral white matter, cerebral MRI frequently fails to detect abnormalities even in the presence of neurological symptoms. Application of multi-compartment diffusion microstructure imaging (DMI), that detects even small volume shifts between the compartments (intra-axonal, extra-axonal and free water/CSF) of a white matter model, is a promising approach to overcome this discrepancy.

In this monocentric prospective study, a cohort of 20 COVID-19 inpatients (57.3 ± 17.1 years) with neurological symptoms (e.g. delirium, cranial nerve palsies) and cognitive impairments measured by the Montreal Cognitive Assessment (MoCA test; 22.4 ± 4.9; 70% below the cut-off value <26/30 points) underwent DMI in the subacute stage of the disease (29.3 ± 14.8 days after positive PCR). A comparison of whole-brain white matter DMI parameters with a matched healthy control group (n = 35) revealed a volume shift from the intra- and extra-axonal space into the free water fraction (V-CSF). This widespread COVID-related V-CSF increase affected the entire supratentorial white matter with maxima in frontal and parietal regions. Streamline-wise comparisons between COVID-19 patients and controls further revealed a network of most affected white matter fibres connecting widespread cortical regions in all cerebral lobes. The magnitude of these white matter changes (V-CSF) was associated with cognitive impairment measured by the MoCA test (r = −0.64, P = 0.006) but not with olfactory performance (r = 0.29, P = 0.12). Furthermore, a non-significant trend for an association between V-CSF and interleukin-6 emerged (r = 0.48, P = 0.068), a prominent marker of the COVID-19 related inflammatory response. In 14/20 patients who also received cerebral 18F-FDG PET, V-CSF increase was associated with the expression of the previously defined COVID-19-related metabolic spatial covariance pattern (r = 0.57; P = 0.039). In addition, the frontoparietal-dominant pattern of neocortical glucose hypometabolism matched well to the frontal and parietal focus of V-CSF increase.

In summary, DMI in subacute COVID-19 patients revealed widespread volume shifts compatible with vasogenic oedema, affecting various supratentorial white matter tracts. These changes were associated with cognitive impairment and COVID-19 related changes in 18F-FDG PET imaging.

Introduction

Neurological complications[1] and long-term consequences[2] of an acute respiratory syndrome coronavirus 2 (SARS-CoV-2/COVID-19) infection are widely recognized. In a recent study, we described an impairment of frontoparietal cognitive functions in subacute COVID-19 inpatients accompanied by a frontoparietal-dominant neocortical glucose hypometabolism revealed by 18F-FDG PET.[3] Insights into the potential pathophysiological basis of this phenomenon came from recent neuropathological studies investigating microstructural changes within brains of patients who died from COVID-19.[4–6] Here, activation of microglia and astrocytes has been detected particularly in the brainstem and cerebellum. Interestingly, neuropathological changes involved the white more than the grey matter. Thus, we hypothesized that an inflammatory affection of white matter fibres could compromise the function of connected cortical areas—as witnessed by reduced neocortical glucose metabolism and cognitive impairments as measured by the Montreal Cognitive Assessment (MoCA).[3]

On structural MRI, overall findings in acute and subacute COVID-19 patients are heterogeneous and mainly comprise leukoencephalopathy and microhaemorrhages, neurovascular complications (thromboses and infarctions) or inflammatory syndromes (e.g. acute necrotizing encephalitis).[7,8] However, structural MRI frequently fails to detect seminal changes in COVID-19 patients with neurological symptoms.[9,10] Particularly, MRI-derived in vivo evidence of inflammatory white matter alterations as seen in post-mortem histopathological studies is widely lacking.[11] To bridge the gap between classic in vivo MRI and post-mortem histology, the application of multi-compartment diffusion microstructure imaging (DMI) could be a promising approach to capture COVID-19-related changes in white matter meso-/microstructure.[12] Technically, DMI is based on advanced multi-shell diffusion protocols that allow distinguishing different anatomical compartments by their diffusion properties. Whereas 'free' water molecules randomly move without restriction, water molecules in neuronal processes or the extracellular matrix are aligned by organelles and membranes. This led to the delineation of a standard white matter model[13–15] consisting of three components: (i) the free water/CSF fraction (V-CSF); (ii) the intra-axonal volume fraction (V-intra) with almost one-dimensional molecule diffusion due to tight membrane borders; and (iii) the extra-axonal volume fraction (V-extra) representing the extra-axonal cellular compartment and the extracellular matrix, characterized by restricted diffusion. In contrast to classical diffusion tensor imaging (DTI) indices, the DMI diffusivity parameters offer more specific and interpretable measures of tissue integrity. An increase of V-CSF has been linked to neurodegeneration in amyotrophic lateral sclerosis[16] or to vasogenic oedema in traumatic brain injury.[17] In turn, a decrease of V-intra is a sensitive indicator of reduced white matter fibre integrity caused by chronic inflammation[18] or mechanical stress.[19] To decouple the contribution of these components to DMI-signals and allow the estimation of compartment-specific volume fractions, we recently introduced a supervised machine learning approach in the form of a Bayesian estimator.[14] Here, we applied this approach to assess and analyse DMI signals to detect white matter alterations in a monocentric cohort of 20 subacute COVID-19 inpatients with neurological symptoms. Parts of this cohort have been previously described in the aforementioned study showing frontoparietal-dominant neocortical glucose hypometabolism and cognitive impairments related to COVID-19.[3] Moreover, we assessed whether DMI signals were related to the clinical parameters MoCA, olfactory performance as well as the inflammatory marker interleukin-6 (IL-6). As 14 of 20 patients also underwent 18F-FDG PET imaging, we further investigated the association between DMI-parameters and the expression of the COVID-19-related spatial covariance pattern of cerebral glucose metabolism.

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