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COVID-19 has been associated with many neurological complications including stroke, delirium and encephalitis. Furthermore, many individuals experience a protracted post-viral syndrome which is dominated by neuropsychiatric symptoms, and is seemingly unrelated to COVID-19 severity. The true frequency and underlying mechanisms of neurological injury are unknown, but exaggerated host inflammatory responses appear to be a key driver of severe COVID-19 more broadly. We sought to investigate the dynamics of, and relationship between, serum markers of brain injury (neurofilament light [NfL], Glial Fibrillary Acidic Protein [GFAP] and total Tau) and markers of dysregulated host response including measures of autoinflammation (proinflammatory cytokines) and autoimmunity. Brain injury biomarkers were measured using the Quanterix Simoa HDx platform, cytokine profiling by Luminex (R&D) and autoantibodies by a custom protein microarray. During hospitalisation, patients with COVID-19 demonstrated elevations of NfL and GFAP in a severity-dependant manner, and there was evidence of ongoing active brain injury at follow-up 4 months later. Raised NfL and GFAP were associated with both elevations of pro-inflammatory cytokines and the presence of autoantibodies; autoantibodies were commonly seen against lung surfactant proteins as well as brain proteins such as myelin associated glycoprotein, but reactivity was seen to a large number of different antigens. Furthermore, a distinct process characterised by elevation of serum total Tau was seen in patients at follow-up, which appeared to be independent of initial disease severity and was not associated with dysregulated immune responses in the same manner as NfL and GFAP.
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BackgroundMagnetic resonance imaging (MRI) of the brain could be a key diagnostic and research tool for understanding the neuropsychiatric complications of COVID-19. For maximum impact, multi-modal MRI protocols will be needed to measure the effects of SARS-CoV2 infection on the brain by diverse potentially pathogenic mechanisms, and with high reliability across multiple sites and scanner manufacturers. MethodsA multi-modal brain MRI protocol comprising sequences for T1-weighted MRI, T2-FLAIR, diffusion MRI (dMRI), resting-state functional MRI (fMRI), susceptibility-weighted imaging (swMRI) and arterial spin labelling (ASL) was defined in close approximation to prior UK Biobank (UKB) and C-MORE protocols for Siemens 3T systems. We iteratively defined a comparable set of sequences for General Electric (GE) 3T systems. To assess multi-site feasibility and between-site variability of this protocol, N=8 healthy participants were each scanned at 4 UK sites: 3 using Siemens PRISMA scanners (Cambridge, Liverpool, Oxford) and 1 using a GE scanner (Kings College London). Over 2,000 Imaging Derived Phenotypes (IDPs) measuring both data quality and regional image properties of interest were automatically estimated by customised UKB image processing pipelines. Components of variance and intra-class correlations were estimated for each IDP by linear mixed effects models and benchmarked by comparison to repeated measurements of the same IDPs from UKB participants. ResultsIntra-class correlations for many IDPs indicated good-to-excellent between-site reliability. First considering only data from the Siemens sites, between-site reliability generally matched the high levels of test-retest reliability of the same IDPs estimated in repeated, within-site, within-subject scans from UK Biobank. Inclusion of the GE site resulted in good-to-excellent reliability for many IDPs, but there were significant between-site differences in mean and scaling, and reduced ICCs, for some classes of IDP, especially T1 contrast and some dMRI-derived measures. We also identified high reliability of quantitative susceptibility mapping (QSM) IDPs derived from swMRI images, multi-network ICA-based IDPs from resting-state fMRI, and olfactory bulb structure IDPs from T1, T2-FLAIR and dMRI data. ConclusionThese results give confidence that large, multi-site MRI datasets can be collected reliably at different sites across the diverse range of MRI modalities and IDPs that could be mechanistically informative in COVID brain research. We discuss limitations of the study and strategies for further harmonization of data collected from sites using scanners supplied by different manufacturers. These protocols have already been adopted for MRI assessments of post-COVID patients in the UK as part of the COVID-CNS consortium.
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BackgroundThe nature and extent of persistent neuropsychiatric symptoms after COVID-19 are not established. To help inform mental health service planning in the pandemic recovery phase, we systematically determined the prevalence of neuropsychiatric symptoms in survivors of COVID-19. MethodsFor this pre-registered systematic review and meta-analysis (PROSPERO ID CRD42021239750) we searched PubMed, EMBASE, CINAHL and PsycINFO to 20th February 2021, plus our own curated database. We included peer-reviewed studies reporting neuropsychiatric symptoms at post-acute or later time-points after COVID-19 infection, and in control groups where available. For each study a minimum of two authors extracted summary data. For each symptom we calculated a primary pooled prevalence using generalised linear mixed models. Heterogeneity was measured with I2. Subgroup analyses were conducted for COVID-19 hospitalisation, severity, and duration of follow-up. FindingsFrom 2,844 unique titles we included 51 studies (n=18,917 patients). The mean duration of follow-up after COVID-19 was 77 days (range 14-182 days). Study quality was generally moderate. The most frequent neuropsychiatric symptom was sleep disturbance (pooled prevalence=27{middle dot}4% [95%CI 21{middle dot}4- 34{middle dot}4%]), followed by fatigue (24{middle dot}4% [17{middle dot}5-32{middle dot}9%]), objective cognitive impairment (20{middle dot}2% [10{middle dot}3-35{middle dot}7%]), anxiety (19{middle dot}1%[13{middle dot}3-26{middle dot}8%]), and post-traumatic stress (15{middle dot}7% [9{middle dot}9-24{middle dot}1%]). Only two studies reported symptoms in control groups, both reporting higher frequencies in Covid-19 survivors versus controls. Between-study heterogeneity was high (I2=79{middle dot}6%-98{middle dot}6%). There was little or no evidence of differential symptom prevalence based on hospitalisation status, severity, or follow-up duration. InterpretationNeuropsychiatric symptoms are common and persistent after recovery from COVID-19. The literature on longer-term consequences is still maturing, but indicates a particularly high frequency of insomnia, fatigue, cognitive impairment, and anxiety disorders in the first six months after infection. FundingJPR is supported by the Wellcome Trust (102186/B/13/Z). IK is funded through the NIHR (Oxford Health Biomedical Research Facility, Development and Skills Enhancement Award) and the Medical Research Council (Dementias Platform UK and Deep and Frequent Phenotyping study project grants). HH is funded by the German Research Foundation (DFG, Grant: HO 1286/16-1). The funders played no role in the design, analysis or decision to publish. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSNeuropsychiatric symptoms like cognitive impairment, fatigue, insomnia, depression and anxiety can be highly disabling. Recently there has been increasing awareness of persistent neuropsychiatric symptoms after COVID-19 infection, but a systematic synthesis of these symptoms is not available. In this review we searched five databases up to 20th February 2021, to establish the pooled prevalence of individual neuropsychiatric symptoms up to six months after COVID-19. Added value of this studyThis study establishes which of a range of neuropsychiatric symptoms are the most common after COVID-19. We found high rates in general, with little convincing evidence that these symptoms lessen in frequency during the follow-up periods studied. ImplicationsPersistent neuropsychiatric symptoms are common and appear to be limited neither to the post-acute phase, nor to recovery only from severe COVID-19. Our results imply that health services should plan for high rates of requirement for multidisciplinary services (including neurological, neuropsychiatric and psychological management) as populations recover from the COVID-19 pandemic.
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Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) not only affects the respiratory tract but also causes neurological symptoms such as loss of smell and taste, headache, fatigue or severe cerebrovascular complications. Using transgenic mice expressing human angiotensin-converting enzyme 2 (hACE2) we investigated the spatiotemporal distribution and pathomorphological features in the CNS following intranasal infection with SARS-CoV-2 variants, also after prior influenza A virus infection. Apart from Omicron, we found all variants to frequently spread to and within the CNS. Infection was restricted to neurons and appeared to spread from the olfactory bulb mainly in basally orientated regions in the brain and into the spinal cord, independent of ACE2 expression and without evidence of neuronal cell death, axonal damage or demyelination. However, microglial activation, microgliosis and a mild macrophage and T cell dominated inflammatory response was consistently observed, accompanied by apoptotic death of endothelial, microglial and immune cells, without their apparent infection. Microgliosis and immune cell apoptosis indicate a potential role of microglia for pathogenesis and viral effect in COVID-19 and possible impairment of neurological functions, especially in long COVID. These data may also be informative for the selection of therapeutic candidates, and broadly support investigation of agents with adequate penetration into relevant regions of the CNS.
