Article

https://doi.org/10.1038/s41467-023-42320-4

Para-infectious brain injury in COVID-19
persists at follow-up despite attenuated
cytokine and autoantibody responses
Received: 4 May 2023

A list of authors and their afﬁliations appears at the end of the paper

Accepted: 6 October 2023

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Published online: 22 December 2023
Check for updates

To understand neurological complications of COVID-19 better both acutely
and for recovery, we measured markers of brain injury, inﬂammatory mediators, and autoantibodies in 203 hospitalised participants; 111 with acute sera
(1–11 days post-admission) and 92 convalescent sera (56 with COVID-19associated neurological diagnoses). Here we show that compared to 60
uninfected controls, tTau, GFAP, NfL, and UCH-L1 are increased with COVID-19
infection at acute timepoints and NfL and GFAP are signiﬁcantly higher in
participants with neurological complications. Inﬂammatory mediators (IL-6,
IL-12p40, HGF, M-CSF, CCL2, and IL-1RA) are associated with both altered
consciousness and markers of brain injury. Autoantibodies are more common
in COVID-19 than controls and some (including against MYL7, UCH-L1, and
GRIN3B) are more frequent with altered consciousness. Additionally, convalescent participants with neurological complications show elevated GFAP
and NfL, unrelated to attenuated systemic inﬂammatory mediators and to
autoantibody responses. Overall, neurological complications of COVID-19 are
associated with evidence of neuroglial injury in both acute and late disease and
these correlate with dysregulated innate and adaptive immune responses
acutely.

At the beginning of the COVID-19 pandemic, neurological complications occurred in a signiﬁcant proportion of hospitalised patients1 and
even in those with mild COVID-19 infection2. While these neurological
‘complications’ were often mild (headache and myalgia), it became
clear that more signiﬁcant neurological sequelae were observed,
including encephalitis/encephalopathies, Guillain Barre Syndrome,
seizure, and stroke3–6.
Although in vitro studies show that SARS-CoV-2 can infect neurons and astrocytes7,8, autopsy studies indicate that direct viral invasion is unlikely to be a cause of neurological dysfunction in vivo9. Postmortem studies failed to detect viral infection of the brain by immunohistochemistry in the majority of cases, and viral qPCR levels were
often low and may simply have reﬂected viraemia10–12. In addition, virus
and/or anti-viral antibodies were rarely found in cerebrospinal ﬂuid
(CSF)13. Thus, it seems more likely that the virus affects the brain
indirectly. This could be through peripherally generated inﬂammatory

mediators, immune cells, autoantibodies and/or blood brain barrier
changes associated with endothelial damage14,15. Immune inﬁltrates
have been found in autopsy studies, including neutrophils and T cells,
although agonal effects could not be excluded16. On the other hand,
elevated IL-6 levels in sera and CSF have been associated with neurological complications, including meningitis, thrombosis, stroke, cognitive and memory deﬁcits, regardless of respiratory disease
severity17–20. One study found that the brain injury markers NfL and
GFAP, and inﬂammatory cytokines were elevated in COVID-19 and
scaled with severity21–25; another study showed that baseline CSF NfL
levels correlated with neurological outcomes at follow-up26 but overall,
the relationships between these immune mediators and markers of
brain injury and neuropathology remains to be fully explored. Finally,
speciﬁc neuronal autoantibodies have been reported in some neurological patients raising the possibility of para- or post-infectious
autoimmunity14,27.

e-mail: benmic@liverpool.ac.uk

Nature Communications | (2023)14:8487

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Article
To assess the relationship between host immune response and
markers of brain injury with neurological injury, we studied two large,
multisite cohorts which, in combination, provided acute, early and late
convalescent sera from COVID-19-positive (COVID+ve) participants.
We measured brain injury markers, a range of cytokines and associated
inﬂammatory mediators, and autoantibodies in these samples, and
related them to reduced levels of consciousness (deﬁned as a Glasgow
Coma Scale Score [GCS] GCS ≤ 14) in the acute phase, or the history of
a neurological complication of COVID-19 in convalescent participants.
We tested the hypothesis that immune mediators would correlate with
brain injury markers and reveal a signature of neurological complications associated with COVID-19.

Results
COVID-19 results in acute elevation of serum markers of brain
injury, more so in participants with abnormal Glasgow coma
scale (GCS) score
We used sera from the International Severe Acute Respiratory and
emerging Infection Consortium Clinical Characterisation Protocol
United Kingdom (ISARIC CCP-UK) study, obtained 1–11 days post
admission, that included 111 participants with COVID-19 of varying
severity and 60 uninfected healthy controls (labelled Control). Participants were stratiﬁed by normal (n = 76) or abnormal (n = 35) Glasgow
Coma Scale scores (labelled GCS = 15 or GCS ≤ 14, respectively) to
provide a proxy for neurological dysfunction (Fig. 1a). GFAP (glial
ﬁbrillary acidic protein, marker of astrocyte injury), UCH-L1 (a marker
of neuronal cell body injury), and NfL (neuroﬁlament light) and Tau
(both markers of axonal and dendritic injury) were measured. Overall,
serum levels of NfL, GFAP, and total-Tau (tTau) were signiﬁcantly
higher in COVID-19 participants compared to the uninfected healthy
controls but, as shown in Fig. 1b–e, those participants with abnormal
GCS scores had higher levels of NfL and UCH-L1 than those with normal
GCS scores. Thus, all four markers of brain injury were raised in COVID19 participants (both GCS = 15 and GCS ≤ 14) but, in addition, axonal
and neuronal body injury biomarkers discriminated between participants with and without reduced GCS.

Markers of brain injury remain elevated in the early and late
convalescent phases in participants who have had a CNS
complication of COVID-19
To ask whether these ﬁndings persisted in participants recovering
from COVID-19-related neurological complications, ninety-two
COVID-19 participants were recruited to the COVID-Clinical Neuroscience Study (COVID-CNS), 56 who had had a new neurological
diagnosis that developed as an acute complication of COVID-19
(group labelled “neuro-COVID”), and 36 with no such neurological
complication (group labelled “COVID”, Fig. 1f, Table 1, Supplementary Tables 1 and 2). When compared to the same healthy controls
(n = 60), across all timepoints, both COVID-19 subgroups (COVID and
neuro-COVID) showed increased levels of NfL, GFAP, and tTau (but
not UCH-L1 (Fig. 1g–j, Supplementary Table 1)). Furthermore, participants recovering from neuro-COVID had signiﬁcantly higher levels
of NfL, and a trend towards higher levels of tTau, than the COVID
participants (Fig. 1g, j). Highest NfL serum levels were present in
participants with cerebrovascular conditions, whereas tTau was elevated in participants with cerebrovascular, CNS inﬂammation and
peripheral nerve complications (Fig. 1k, l). NfL remained signiﬁcantly
elevated in a multiple regression model adjusted for age (Supplementary Fig. 1a, b). We then separately compared the two cohorts at
early and late convalescent follow-up periods (less than and over six
weeks after admission respectively). NfL and GFAP levels remained
elevated in all COVID-19 participants in the convalescent period, but
only remained elevated beyond 6 weeks in participants who had
suffered an acute neurological complication (neuro-COVID,
Fig. 1m–p; Supplementary Fig. 1c). The presence of elevated brain

Nature Communications | (2023)14:8487

https://doi.org/10.1038/s41467-023-42320-4

injury markers in the acute phase of COVID-19 conﬁrms previous
ﬁndings14, but the elevated levels of NfL and GFAP in those who are
convalescent from acute neurological complications suggest ongoing neuroglial injury.

Clinical and brain injury markers evidence of neurological insult
levels are associated with levels of innate inﬂammatory
mediators in the acute phase of COVID-19
To explore whether the acute and persistent elevation of markers of
brain injury observed in participants with COVID-19 was associated
with an acute inﬂammatory response, we measured a panel of 48
inﬂammatory mediators in serum at the same timepoints. In the ISARIC
samples, six mediators were signiﬁcantly higher in participants with an
abnormal GCS than in those with a normal GCS (interleukin [IL]-6,
hepatocyte growth factor [HGF], IL-12p40, IL-1RA, CCL2 and macrophage colony stimulating factor [M-CSF]), indicating increased innate
inﬂammation (Fig. 2a, Supplementary Fig. 2a). Pearson’s correlation
tests identiﬁed correlations between these signiﬁcant immune mediators in an interrelated pro-inﬂammatory network (Fig. 2b, c), and
unsupervised Euclidean hierarchical cluster analysis revealed clusters
of pro-inﬂammatory mediators elevated together (Fig. 2d). The ﬁrst
cluster incorporated the IL-1 family (including IL-1RA), interferons and
M-CSF, and the second cluster included IL-6, CCL2, CXCL9, HGF, and
IL-12p40 (boxes in Fig. 2d). Brain injury biomarkers correlated with
elevations in these inﬂammatory mediators: GFAP and UCL-H1 correlated with a number of mediators in the ﬁrst cluster, whereas tTau and
NfL correlated strongly with HGF and IL-12p40 in the second cluster
(Supplementary Table 3).
A more stringent analysis of median-centred cytokine data (which
corrected for between participant skewing of mediator levels) conﬁrmed that HGF and IL-12p40 were higher in the abnormal GCS COVID19 participants, and correlated with cognate NfL levels (Supplementary
Table 4). Taken together these data suggest that activation of the
innate immune system was related to both clinical and blood marker
evidence of CNS insult.

Inﬂammatory mediators are not elevated across the participant
cohort at late timepoints after COVID-19; but late tTau elevations correlate with levels of several inﬂammatory mediators
In contrast to the acute data, the levels of cytokines and associated
mediators were lower when measured during the convalescent periods
even in those who had suffered neurological complications of COVID19 (group labelled “neuro-COVID”. Supplementary Fig. 2b). The correlations between cytokines and associated mediators no longer displayed the same tight clusters (Fig. 2e, f). GFAP remained elevated
during the convalescent phase of neurological complications (Fig. 1p)
but did not show correlations with the inﬂammatory mediators.
Similarly NfL was higher overall in those with neurological complications (Fig. 1n) but there were no signiﬁcant correlations with inﬂammatory mediators (Fig. 2f). However, tTau remained elevated overall in
those with neurological complications ((1.7 (1.3, 2.2) pg/mL versus 1.3
(1.1, 1.9) pg/mL)) and levels correlated with eight immune mediators
including CCL2, IL-1RA, IL-2Rα and M-CSF along with CCL7, stem cell
factor (SCF), IL-16 and IL-18 (Fig. 2f, Supplementary Table 5, Supplementary Fig. 2c). This last association was speciﬁc to the late phase of
the illness and was not found in acute COVID-19.

Cytokine networks are signiﬁcantly altered in participants with
neurological complications of COVID-19: both acute encephalopathy, and those recovering from a neurological complication
We used graph theoretical approaches to compare these cytokine
networks between participants with: acute COVID-19 and normal GCS;
acute COVID-19 with altered consciousness (GCS ≤ 14), and convalescent participants recovering from a neurological complication of
COVID-19 (neuro-COVID). Participants with both neurological
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COVID+ve with
neurological disease,
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n=56
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n=13
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NfL pg/mL

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GFAP pg/mL

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Nature Communications | (2023)14:8487

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Fig. 1 | Brain injury markers are elevated acutely in COVID-19 participants with
an abnormal Glasgow coma scale score (GCS) and in participants who experienced a neurological complication associated with COVID-19. a The acute
ISARIC cohort included Day 1–11 hospital admission timepoints. b–e Acute serum
brain injury markers were assessed by Simoa: b NfL, c UCH-L1, d GFAP, and e tTau.
All four were elevated in COVID-19 cases with normal Glasgow coma scale scores
(GCS) relative to controls overall. Dotted lines show lower limit of quantiﬁcation
(LLOQ). f–j The Simoa analyses were performed for the sera from the COVID-CNS
COVID and neuro-COVID groups at early and late convalescent timepoints (g, j)
showing persistence of NfL, GFAP, and tTau in COVID participants, with NfL
higher in neuro-COVID than COVID participants. k Within the combined early and

<0.0001

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No neurological
disease
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n=36
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n=28
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a

late convalescent COVID-19 neurological cases, the highest levels of NfL were
observed in participants who had suffered a cerebrovascular event at the time of
SARS-CoV-2 infection. l tTau levels were raised in the cerebrovascular, CNS
inﬂammatory and seizure conditions. m, n Serum NfL remained elevated in both
the early (<6 weeks from positive SARS-CoV-2 test) and late convalescent phases
(>6 weeks) in neuro-COVID compared to COVID non-neurological cases.
o, p GFAP was elevated in neurological cases in the early and late convalescent
phase. Box and whisker plots show all data points with median as centre line with
25th and 75th percentiles. Sample sizes shown in (a) and (f). Group comparisons
are by Kruskal–Wallis test with Dunn’s post-hoc multiple comparison test, no
statistical comparison made for panel (h) as medians were at LLOQ.

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Table 1 | Clinical characteristics of healthy controls and COVID-CNS participants
Control (n = 60)

COVID (n = 36)

Neuro-COVID (n = 56)a

Mean (SD)

48 (18)

51 (17)

58 (13)

Median (Q1,Q3)

50 (32,62)

52 (34,65)

61 (48,67)

Gender

Male n (%)

21 (35%)

21 (58.3%)

36 (64.3%)

Sampling time (days)b,c

Median (Q1,Q3)

8 (4,14)

148 (52,272)

COVID severityc,d

Median (Q1,Q3)

5 (5,5)

7 (5,8)

Clinical characteristics
Age

a

Neuro-COVID group comprises cases of: cerebrovascular conditions (21%), CNS inﬂammation (16%), movement disorders (5.4%), seizures and other CNS conditions (30%), and peripheral nervous
system conditions (27%).
b
Sampling time in days between the ﬁrst COVID+ve test and serum sample.
c
Two Neuro-COVID participants with approximate COVID-infection timing and nine with indeterminate severity.
d
As per COVID-19 WHO severity score from 0 to 10.

consequences of COVID-19 (GCS ≤ 14) and Neuro-COVID both showed
cytokine networks that were different from COVID-19 participants with
no neurological problems (Fig. 2b, c, e; p < 0.001, Steiger test), suggesting a speciﬁc dysregulated innate immune response that is associated with neurological complications of COVID-19. Further pathway
analyses using the KEGG enrichment scores on the signiﬁcantly different cytokines, revealed many commonalities with other inﬂammatory syndromes (Supplementary Fig. 3a, b). Interestingly, cytokine
proﬁles of the neurological complications groups from both the ISARIC and COVID-CNS cohort led to JAK-STAT signalling being a signiﬁcant involved pathway which would be amenable to
immunomodulation, for example, by tofacitinib, which has been
shown to reduce mortality in COVID-1928.

COVID-19 is associated with an acute adaptive immune response
overall, which includes antibodies to viral antigen and CNS
autoantigens in those with abnormal GCS scores
Given past reports of autoantibody responses following COVID-1914,27,
we sought evidence of similar dysregulated adaptive immune
responses in our participant cohorts. We used a bespoke protein
microarray of 153 viral and tissue proteins to measure IgM (Fig. 3a–d)
and IgG (Fig. 4a–d) reactivity in the acute phase ISARIC sera. The
median ﬂuorescence intensities for each putative antigen were normalized for each participant and the Z-scores were compared to
healthy control data, to determine positive reactivity to the different
antigens (with a threshold for detection set at three standard
deviations above controls for each antigen; see Supplementary
Table 6, Supplementary Fig. 4a). IgM and IgG responses in COVID-19
participants showed greater reactivity overall (both GCS = 15 and
GCS ≤ 14), compared to the controls, with no difference in normalised ﬂuorescence Z scores or the number of participants with IgG
‘hits’ (a Z-score >3) between those with normal or abnormal GCS
score (Fig. 3a, b, Fig. 4a, b). However, several IgM and IgG autoantibodies, including those against the CNS antigens UCH-L1, GRIN3B
and DRD2, along with the cardiac antigen, myosin light chain (MYL)7, were present in a greater proportion of participants with an
abnormal GCS score, as were antibodies to spike protein (Figs. 3c,
4c). None of the antibodies correlated signiﬁcantly with levels of
brain injury markers (Supplementary Figs. 4b, c, 5b, c), but they did
show correlations with each other (Figs. 3d, 4d, h), suggesting a nonspeciﬁc antibody response in some individuals during the
acute phase.
Normalized ﬂuorescence Z scores of serum IgM and IgG autoantibodies in the early and late convalescent samples were similar to
those in the acute samples (Figs. 3e, 4e), and the IgM and IgG ‘hits’
were more frequent than in controls (highest in the neuro-COVID
group, Figs. 3f, 4f, Supplementary Fig. 5a). However, speciﬁc autoantibody responses to MYL7, gonadotrophin releasing hormone
receptor (GNRHR) and several HLA antigens were common in the
neuro-COVID participants (Figs. 3g, 4g, Supplementary Fig. 5a).
When the IgM and IgG hits were stratiﬁed by condition,

Nature Communications | (2023)14:8487

cerebrovascular and inﬂammatory conditions showed the highest
number (Supplementary Fig. 5d, e). As in the acute phase, autoantibody responses did not show signiﬁcant associations with brain
injury markers, but did tend to correlate with each other (Fig. 4h,
Supplementary Fig. 5b, c).
Finally, to explore binding to native neuronal antigens, sera from
acute COVID-19 participants with CNS antigen reactivity were incubated with sections of rat brain, neurons and antigen-expressing cells.
Binding to rat brain sections identiﬁed 42/185 (23%) of participants with
strongly positive immunohistochemical staining (e.g. Fig. 4i) and
overall, sera from the COVID+ve ISARIC participants showed more
frequent binding to brainstem regions than control sera, but this did
not relate to the GCS or neurological disease of the participants (Fig. 4j,
Supplementary Fig. 6). In addition, from 34 selected samples tested via
cell-based assays to examine for the presence of speciﬁc autoantibodies
(LGI1, CASPR2, NMDAR, GABAB receptor), only one bound to the
extracellular domain of the GABAB receptor (from the ISARIC cohort,
Supplementary Fig. 7a, b), as expected of a pathogenic autoantibody.

Discussion
We used several approaches to study neurological complications of
COVID-19 infection. These included assessment of immune mediators
and markers of brain injury in participants with and without neurological complications, both in the acute and convalescent phases after
COVID-19 infection. We demonstrated increased levels of brain injury
markers following COVID-19, which showed speciﬁc patterns with
disease phase (acute or convalescent), and varied with the presence or
absence of neurological injury or dysfunction. In the acute phase, all
four brain injury markers (GFAP, NfL, tTau and UCH-L1) were elevated
in participants when compared to controls, and speciﬁc markers of
dendritic and axonal injury (tTau and NfL) were signiﬁcantly higher in
participants who showed a reduced level of consciousness (GCS ≤ 14).
In the early convalescent phase (<6 weeks post-infection), GFAP, NfL,
and tTau were elevated in participants recovering from COVID-19, with
no differences between those who had or had not sustained a neurological complication of disease. However, at late timepoints (>6 weeks)
elevations of NfL and GFAP were only seen in participants who had
sustained a neurological complication of COVID-19 in the acute phase
of their illness. These data suggest that clinical neurological dysfunction in COVID-19 is reﬂected by increases in markers of neuroglial
injury, both in the acute phase and at follow-up, which are related to a
dysregulated immune response, more robustly in the acute phase of
illness.
In the acute phase, when compared to controls, we also observed
increases in a range of inﬂammatory mediators (IL-6, HGF, IL-12p40, IL1RA, CCL2, and M-CSF) in the overall cohort of COVID-19 participants,
with HGF and IL-12p40 showing robust differentiation between participants with and without alterations in consciousness. By contrast,
participants at the late phase after COVID-19 showed no group level
elevation of inﬂammatory mediators. However, late elevations in tTau
correlated with levels of CCL2, CCL7, IL-1RA, IL-2Rα, M-CSF, SCF, IL-16,
4

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a

d

** ** ** **
* *
** ***
**

Protein abundance

b

**

GCS=15***

* *
*

f
c

**

*
*

GCS≤14***

*

*

NeuroCOVID COVID
*
**
*

e

Neuro-COVID***

*
*
*
**
*

Fig. 2 | Immune mediators are elevated acutely and correlate with different
brain injury markers at different timepoints. Serum mediators from the ISARIC
and COVID-CNS cohorts were assessed by Luminex. a A volcano plot was generated
to identify mediators which were elevated in participants with an abnormal GCS
(GCS ≤ 14) compared to normal GCS (GCS = 15) and b, c a network analysis identiﬁed the highest correlations between the mediators (***signiﬁcantly different from
ISARIC GCS = 15 by Steiger test p < 0.001). d Unbiased Euclidean hierarchical cluster
and correlation analyses identiﬁed two clusters of up-regulation of several proinﬂammatory mediators in concert. The ﬁrst group included interleukin (IL)-6, IL12p40, CCL2, CXCL9 and hepatocyte growth factor (HGF) and the second group

Nature Communications | (2023)14:8487

included the IL-1 family, interferons, and macrophage colony stimulating factor (MCSF); to the right is shown the correlations between each cytokine with the four
brain injury biomarkers (signiﬁcance indicated by asterisks). e Network analysis and
heatmaps of correlations between mediators did not demonstrate the tight interconnectedness that had been identiﬁed in acute samples and there were differences between neuro-COVID (e) and ISARIC GCS = 15 and GCS ≤ 14 by Steiger test
(***p < 0.001). f At this later stage several mediators correlated with tTau. Volcano
plot used multiple two-tailed Mann–Whitney U tests with a false discovery rate set
to 5%. Correlations are Pearson’s coefﬁcients (*p < 0.05, **p < 0.01, ***p < 0.001,
****p < 0.0001).

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10000
1000
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GCS ≤14 (n=34); GCS = 15 (n=72)

Spike

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Normalized Z-scores

a

CNS antigens

b
IgM hits (Z-score above 3)

BBB

ACE

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Nucleocapsid
SFTPC
HLA-B
GLRA1
DPYSL5
DLAT
ACE

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GCS≤14

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Control

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Fig. 3 | There is an IgM antibody response in participants with COVID-19
directed at SARS-CoV-2 spike protein and against several self-antigens. a Acute
samples were tested for IgM antibodies by protein microarray with normalized
ﬂuorescence Z-scores shown. b COVID-19 participants showed considerably more
binding ‘hits’ than healthy controls (ﬂuorescence with a Z-score of 3 or above
compared to controls), although overall there was no difference in the acute
samples between participants with normal (GCS = 15) or abnormal GCS (GCS ≤ 14)).
Nevertheless, c COVID-19 participants with abnormal GCS (GCS ≤ 14) more frequently had raised IgM antibodies than COVID-19 participants with a normal GCS
(GCS = 15), including those directed at SARS-CoV-2 spike protein (Fisher’s exact
tests *p < 0.05). d A chord diagram shows the associations between antibodies,

including those against Spike. e IgM antibodies were also analysed in the convalescent participants. f A largr proportion of COVID and Neuro-COVID participants
had positive antibody ‘hits’ for IgM (deﬁned by Z-score 3 and above compared to
controls). g Of those antibodies against self-antigens identiﬁed, they were only two
with different frequencies between the groups (Fisher’s exact tests *p < 0.05). At
this timepoint there was no signiﬁcant difference in the proportion of individuals
with IgM against SARS-CoV-2 spike or nucleocapsid epitopes. Violin plots show all
data points with median at centre line and 25th and 75th quartile lines. Group
comparisons are by Kruskal–Wallis test with post-hoc Dunn’s multiple comparison
test, pairwise comparisons by two-tailed Mann–Whitney U test, and correlations are
Pearson’s coefﬁcients.

and IL-18, suggesting that these markers of the late innate host
response were associated with persisting markers of dendritic/axonal
injury markers. A network analysis showed that the repertoire of
cytokine responses was different in participants both with acute
reductions in GCS, or those recovering from a neurological complication of COVID-19 when compared to the GCS = 15 group.
Participants with acute COVID-19 also developed IgG autoantibody responses to a larger number of both neural and non-neural
antigens, than seen in controls. These increased IgG responses

persisted into the late phase but to different antigens. While the
diversity of autoantibody response did not differ between participants
with and without neurological dysfunction, autoantibody responses to
speciﬁc antigens, including the neural antigens UCH-L1, GRIN3B, and
DRD2, were more common in participants with abnormal GCS at presentation. In the late phase, participants who had or had not experienced a neurological complication of COVID-19 were distinguished by
the presence of autoantibodies to HLA antigens rather than neural
antigens.

Nature Communications | (2023)14:8487

6

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https://doi.org/10.1038/s41467-023-42320-4

10000
1000
100
10

GCS = 15 (n=72); GCS ≤14 (n=34)
Nucleocapsid

Spike

1
0.1
0.01

.

Normalized Z-scores

a

CNS antigens
<0.0001

IgG hits (Z-score above 3)

b

50

c

<0.0001

40
30
20
10

Spike
Nucleocapsid
GLRA1
MYL7
FGB
F2
UCH-L1
GRIN3B
DRD2

0
C

on

l
tro
G

C

15
S=

Normalized Z-scores

e

G

C

Non-CNS antigens

*
*
*
*

*
*
*

UCH-L1

d

F2

GRIN3B

FGB

GCS=15
GCS≤14

GLRA1

*

MYL7

0 5 10 15 20 50
100
% of participants with antibody

14
S≤

10000
1000
100
10

BBB

DRD2
Spike
Nucleocapsid
Spike
SFTPA1

COVID (n=36); Neuro-COVID (n=56)

1
0.1
0.01

.
CNS antigens

g
IgG hits (Z-score above 3)

f
60

0.0271

BBB

Non-CNS antigens

h

Spike

GNRHR

HLA-DQA1

Nucleocapsid
MYL7

40

HLA-G
HLA-F
HLA-DQA1

20

GNRHR
0
COVID Neuro-COVID

*
*
*
*

*

Spike

0
50
100
% of participants with antibody

MYL7

j

Binding to rat brainstem
Acute

80

Convalescent

<0.05

Negative

<0.05

60

Positive
40
20
0

Fig. 4 | There is an IgG antibody response in participants with COVID-19
directed at SARS-CoV-2 nucleocapsid and spike proteins and against several
self-antigens. a Acute samples were tested for IgG antibodies by protein microarray with normalized ﬂuorescence Z-scores shown. b COVID-19 participants
showed considerably more binding ‘hits’ than healthy controls (ﬂuorescence with a
Z-score of 3 or above compared to controls) but, overall, there was no difference in
the acute samples between participants with normal (GCS = 15) or abnormal GCS
(GCS ≤ 14). c COVID-19 participants with abnormal GCS more frequently had several
raised IgG antibodies than COVID-19 participants with a normal GCS, including
those directed at SARS-CoV-2 spike protein and several CNS proteins ((DRD2,
GRIN3B, and UCH-L1) Fisher’s exact tests * p < 0.05)). d A chord diagram shows the
association of antibodies with differences in frequency, including those against
Spike. e IgG antibodies in early and late convalescent sera were also analysed. f A
larger proportion of Neuro-COVID participants had positive antibody ‘hits’ for IgG

Nature Communications | (2023)14:8487

HLA-G

COVID
neuro-COVID

Number of sera tested

i

HLA-F

Control

GCS=15

GCS≤14

COVID Neuro-COVID

(deﬁned by Z-score 3 and above compared to controls). g Of those antibodies
against self-antigens identiﬁed, only ﬁve showed a difference in frequency between
groups (Fisher’s exact tests *p < 0.05). At this timepoint there was no signiﬁcant
difference in the proportion of individuals with IgG against SARS-CoV-2 spike or
nucleocapsid epitopes. h A chord diagram shows the association of antibodies with
differences in frequency plus anti-Spike antibody. i Representative images of rat
brains incubated with participants’ sera and screened for IHC binding of antihuman IgG to detect CNS reactivity. Scale bars = 1 mm. j Percentage of participant
serum IgG reactivity to rat brainstems, detected by Fisher’s exact test with Benjamini and Hochberg correction. Violin plots show all data points with median at
centre line and 25th and 75th quartile lines. Group comparisons are by
Kruskal–Wallis test with post-hoc Dunn’s multiple comparison test, pairwise comparisons by two-tailed Mann–Whitney U test, and correlations are Pearson’s
coefﬁcients.

7

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These data from clinical disease provide important insights
regarding the pathophysiology and pathogenesis of neurological
injury, dysfunction, and disease in COVID-19. The clinical characteristics of our participant cohorts, and the elevation in brain injury
markers, provide evidence of both acute and ongoing neurological
injury29. Furthermore, the literature data on the rarity of direct CNS
infection by the virus, suggest that the innate and adaptive host
responses that we document should be explored as pathogenic
mechanisms. The incidence of neurological cases has decreased since
the ﬁrst wave of the pandemic, possibly due to the use of immunosuppressants, such as dexamethasone, although this may also reﬂect
vaccines attenuating disease and changes in the prevalence of different strains of SARS-CoV-230.
The inﬂammatory mediators that we found to be elevated in the
acute phase are broadly concordant with many other publications that
have examined innate immune responses in COVID-1921,22 but there are
limited data addressing associations between such responses and the
development of neurological complications. It is possible that some of
the risk of developing such complications is simply related to the
severity of systemic infection and the host response, and it would be
surprising if these were not strong contributors. However, our data
suggest that acute neurological dysfunction in COVID-19 is also associated with a different repertoire of cytokine responses, with HGF and
IL-12p40 showing the statistically most robust discrimination between
participants with and without an abnormal GCS. HGF has important
roles in brain development and synaptic biology31 and its elevation may
represent a protective/reparative response in participants with neurological injury. IL-12p40 has a core role in orchestrating Th1 responses, and has been reported to be central in the development of central
and peripheral neuroinﬂammation, with p40 monomer subunits perhaps acting as inhibitors of the process32–34. Interestingly, the cytokine
network that was activated in the late convalescent phase was different, potentially indicating differential drivers of neurological injury
throughout the disease course. Though group level comparisons with
controls showed some commonalities in inﬂammatory mediator
increase, most notably in IL-1RA, CCL2, and M-CSF, there were many
differences. The late tTau elevation that we demonstrated was signiﬁcantly associated with elevations in these three mediators, but also
CCL7, IL-2Rα, SCF, IL-16, and IL-18. These are all important proinﬂammatory mediators, and their association with tTau levels may
reﬂect the persistence of a systemic inﬂammatory response that can
enhance neuroinﬂammation32,34,35.
We found a general increase in antibody production following
COVID-19 infection and only a few autoantibody frequencies were
different when compared by GCS or COVID versus neuro-COVID cases.
Of note, absolute levels of autoantibodies were low in comparison to
anti-viral antibodies that developed over the course of the acute illness, with the exception of SFTPA1. Antibodies to SFTPA1, a lung surfactant protein, have been found to correlate with COVID-19 severity14,
but these antibodies were present in only a few acute cases. HLA
antibodies, on the other hand, were more frequent in Neuro-COVID
than COVID participants and this requires further investigation. The
autoantibodies detected in COVID-19, as in other infections, could be
through molecular mimicry or bystander effects36–39, but the lack of
association of autoantibody levels with markers of brain injury is evidence against a causal role for these adaptive immune responses.
Further analysis by screening the antibodies against brain antigens
ex vivo revealed sporadic reactivity in both cases and controls with
only the brainstem showing increased reactivity in acute COVID+ve
participants; the frequencies were lower in COVID and neuro-COVID
cases with no difference between them.
Our studies have several limitations including: limited clinical
information on the acute participants and lack of longitudinal blood
samples; in addition, the low GCS could indicate sedation for intubation, rather than CNS disease, in the acute cohort. Although we did not

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https://doi.org/10.1038/s41467-023-42320-4

have COVID-19 severity scores, we did know whether participants had
required oxygen or not; when data were analysed within the cohorts
comparing participants who had or had not required oxygen, 5 out of 6
cytokines remained signiﬁcantly elevated in the abnormal GCS group.
In the COVID-CNS study where we did have in-depth clinical information, we were limited by not having acute blood samples. Nevertheless,
several cytokines showed signiﬁcant positive correlations with the
brain injury marker tTau, and interestingly, three of them were cytokines that were signiﬁcantly associated with abnormal GCS in the acute
cohort (IL-1RA, CCL2, and M-CSF) highlighting a network of coupregulated immune mediators associated with neurological complications. The commonalities in innate immune response in participants
who suffered neurological dysfunction/complications, both in the
acute phase and at convalescence, is underlined by the results of
network analysis. Pro-inﬂammatory cytokines are expected to be
increased in the anti-viral response, but we found that they not only
correlate with COVID-19 severity, but with GCS, as well. Strengths of
our study include the large cohort of participants studied with wellcharacterized neurological syndromes and a known range of timings
since COVID-19 infection. We studied aspects of the innate and adaptive immune response as well as brain injury markers in order to discover useful markers of neurological complications over time.
Several hypotheses for how SARS-CoV-2 causes neuropathology
have been tested. A prospective study of hospitalised patients showing
IL-6 and D-dimer as risk factors for neurological complications implicates the innate immune response and coagulation pathways19. The
complement pathway and microthrombosis have been associated with
brain endothelial damage from the infection, and this phenotype persists months after COVID-1940,41. Animal models have provided key
insights into COVID-19 neuropathology that warrant discussion. There
have been at least two reports of viral encephalitis and neuron degeneration and apoptosis observed in non-human primates42,43. It is
important to note that in these studies the virus was present at low
amounts in the brain and predominantly in the vasculature as visualized
by co-localization with Von Willebrand Factor43. Similar to the clinical
scenario, there was no correlation of neuropathology with respiratory
disease severity43. A recent mouse study is particularly relevant to our
work and involved assessment of a mouse model that lacked direct viral
neural invasion by infecting mice that were intratracheally transfected
with human ACE2. This study reported increased CXCL11 (eotaxin) in
mouse serum and CSF that correlated with demyelination and was
recapitulated by giving CXCL11 intraperitoneally44; this was linked to
clinical studies that showed elevated CXCL11 in patients with brain fog44.
A combined analysis of hamster and clinical studies showed that COVID19 led to IL-1β and IL-6 expression within the hippocampus and medulla
oblongata and decreased neurogenesis in the hippocampal dentate
gyrus which may relate to learning and memory deﬁcits45. This was also
borne out during in vitro studies that showed that serum from COVID
patients with delirium lead to decreased proliferation and increased
apoptosis of a human hippocampal progenitor cell line mediated by
elevated IL-646.
In conclusion, we show evidence of quantiﬁable neuroglial injury
markers in blood from COVID-19 infection, which is more prominent in
patients with neurological dysfunction in the acute phase of the illness,
and persists in the convalescent phase in patients who suffered deﬁned
acute neurological complications. These brain injury markers are
associated with dysregulated systemic innate and adaptive immune
responses in the acute phase of the disease, and suggest that these may
represent targets for therapy.

Methods
Human participant studies/healthy controls and ethics
information
The ISARIC WHO Clinical Characterization Protocol for Severe Emerging Infections in the UK (CCP-UK) was a prospective cohort study of
8

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hospitalised patients with COVID-19, which recruited across England,
Wales, and Scotland (National Institute for Health Research Clinical
Research Network Central Portfolio Management System ID: 14152).
Participants were recruited prospectively during their hospitalisation
with COVID-19 between February 2020 and May 2021. The protocol,
revision history, case report form, patient information leaﬂets, consent
forms and details of the Independent Data and Material Access Committee are available online47. Ethical approval for CCP-UK was given by
the South Central - Oxford C Research Ethics Committee in England
(Ref 13/SC/0149) and the Scotland A Research Ethics Committee (Ref
20/SS/0028). We examined 111 participants with anonymized clinical
data including Glasgow coma scale score and consented serum sample. ISARIC samples were collected during the acute phase (1–11 days
from hospital admission). Healthy control participants between the
ages of 20–79 years old were recruited through the Cambridge Biomedical Research Centre (prior to the COVID-19 pandemic) and were
non-hospitalised, without SARS-CoV-2 infection, and had no neurological diagnoses. All participants provided written consent. Sex was not
considered in the study design and the sex of participants was selfreported.
Participants were recruited into the COVID-Clinical Neuroscience
Study (COVID-CNS) between October 2020 and October 2022 and
either the participant or their next of kin consented in accordance with
the ethically-approved NIHR Bioresource (East of England—Cambridge
Central Research Ethics Committee (Ref 17/EE/0025; 22/EE/0230). The
purpose of the study was to investigate patients who had been hospitalised with COVID-19 with or without neurological complications.
These were deﬁned by the following criteria: neurological disease
onset within 6 weeks of acute SARS-CoV-2 infection and no evidence of
other commonly associated causes, and diagnostic criteria previously
described48. Participants were recruited both as in-patients and retrospectively after discharge. The diagnosis was reviewed and ﬁnalized by
a multi-disciplinary Clinical Case Evaluation panel. In this study, there
were COVID patients without neurological complications (COVIDcontrols) and COVID patients with neurological complications (NeuroCOVID cases) and these cases were stratiﬁed by diagnostic deﬁnitions
of each type of neurological complication, very few had overlapping
syndromes in this relatively small cohort and the Evaluation Panel were
able to provide a primary diagnosis for all”4. Co-morbidities and known
treatments are shown in Supplementary Table 7. Serum samples were
collected at either the early (<6 weeks from COVID-19 positive test) or
late convalescent (>6 weeks) phases. The samples were aliquoted,
labelled with anonymised identiﬁers, and frozen immediately
at −70 °C.

Human brain injury markers measurements
Brain injury markers were measured in thawed sera using a Quanterix
Simoa kit run on an automated HD-X Analyser according to the manufacturer’s protocol (Quanterix, Billerica, MA, USA, Neurology 4-Plex B
Advantage Kit, cat#103345). We assessed neuroﬁlament light chain
(NfL), Ubiquitin C-Terminal Hydrolase L1 (UCH-L1), total-Tau (tTau),
and glial ﬁbrillary acidic protein (GFAP) in sera diluted 1:4 and used the
manufacturer’s calibrators to calculate concentrations.

Human serum cytokine measurements
Analytes in thawed sera were quantiﬁed using the BioRad human
cytokine screening 48-plex kit (Cat# 12007283) following manufacturer’s instructions on a Bioplex 200 using Manager software 6.2.
This involved incubation of 1:4 diluted sera with antibody-coated
magnetic beads, automated magnetic plate washing, incubating the
beads with secondary detection antibodies, and adding streptavidinPE. Standard curves of known protein concentrations were used to
quantify analytes. Samples that were under the limit of detection
were valued at the lowest detectable value adjusted for 1:4 dilution
factor.

Nature Communications | (2023)14:8487

https://doi.org/10.1038/s41467-023-42320-4

Median-centred normalization of human serum cytokine
measurements
To minimise any potential impact of any possible variation in sample
storage and transport, concentrations were median-centred and normalised for each participant, using established methodology49–51. The
pg/mL of cytokines were log-transformed and the median per participant across all cytokines was calculated. The log-transformed median was subtracted from each log-transformed value to generate a
normalized set.

Protein microarray autoantibody proﬁling
Autoantibodies were measured from thawed sera as previously
described in Needham et al.14. Brieﬂy, a protein array of antigens (based
on the HuProt™ (version 4.0) platform) was used to measure bound
IgM and IgG from sera, using secondary antibodies with different
ﬂuorescent labels detected by a Tecan LS400 scanner and GenePix Pro
v4 software. As developed in previous studies14,52, antibody positivity
was determined by measuring the median ﬂuorescence intensity (MFI)
of the four quadruplicate spots of each antigen. The MFI was then
normalized to the MFI of all antigens for that patient’s sample by
dividing each value by the median MFI. Z-scores were obtained from
these normalized values based on the distribution derived for each
antigen from the healthy control cohort. A positive autoantibody ‘hit’
was deﬁned as an antigen where Z ≥ 3.

Detection of antibodies by immunohistochemistry
Immunohistochemistry was performed on sagittal sections of female
Wistar rat brains. Brains were removed, ﬁxed in 4% paraformaldehyde
(PFA) at 4 °C for 1 h, cryoprotected in 40% sucrose for 48 h, embedded
in freezing medium and snap-frozen in isopentane chilled on dry ice.
10-µm-thick sections were cut and mounted on slides in a cryostat. A
standard avidin-biotin peroxidase method was used, as reported
previously53,54, where thawed sera were diluted 1:200 in 5% normal goat
serum and incubated at 4 °C overnight, and secondary biotinylated
goat anti-human IgG Fc was diluted (1:500) and incubated at room
temperature for 1 h. Finally, slides were counter-stained using cresyl
violet.

Detection of autoantibodies with cell-based assays
HEK293T cells were seeded on 96 well plates in DMEM + 10% FCS at
37 °C and 5% CO2, transiently transfected with polyethylenimine with
the relevant antigen-encoding plasmids GABAB-R1 and GABAB-R2 of
the GABAB receptor, membrane tethered LGI1, CASPR2 and the
NR1 subunit of the NMDA receptor, as described previously55–57.
Thawed serum samples were incubated at 1:100 dilution for CASPR2
and GABAB receptor assays, and at 1:20 for LGI1 and NMDAR. After
washing, cells were ﬁxed with 4% PFA, washed again and incubated
with unconjugated goat anti-human IgG Fc antibody, and donkey antigoat IgG heavy and light chain Alexa Fluor 568 antibody. Cells were costained with DAPI.

Statistical analyses
Prism software (version 9.4.1, GraphPad Software Inc.) was used for
graph generation and statistical analysis. The Shapiro-Wilk normality
test used to check the normality of the distribution. Individual data
points, median lines, and ﬁrst and third quartiles are shown on box
and whisker plots and violin plots with minimum and maximum
points as error bars. Heatmaps, volcano plots and Chord diagrams
were made using R studio (version 4.1.1 RStudio, PBC). The 2D
cytokine network analyses were created using the qgraph package in
R software and matrices differences were assessed by Steiger test58.
Univariate analyses were conducted to test for differences between
two groups. Differences between two normally distributed groups
were tested using the paired or unpaired Student’s t test as appropriate. The difference between two non-normally distributed groups
9

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was tested using Mann–Whitney U test. Volcano plots used multiple
Mann–Whitney U tests with a false discovery rate set to 5%, and
heatmaps show Pearson’s correlations adjusted for a false discovery
rate of 5%. Group comparisons were by Kruskal–Wallis test. Frequency differences of antibodies were measured by Fisher’s exact
tests. Proteins which were statistically signiﬁcantly different in the
COVID-positive controls (GCS = 15 or COVID groups, respectively)
versus the GCS less than or equal to 14 or neurological cases by
Mann-Whitney test (p ≤ 0.05) were analysed with the KEGG (Kyoto
Encyclopedia of Genes and Genomes) database. Pathway classiﬁcations from the KEGG map search results were ranked by highest
number of mapped candidates and exported in the KGML format
using R package clusterProﬁler. p ≤ 0.05 was considered statistically
signiﬁcant.

Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.

Data availability
The individual-level data from these studies is not publicly available to
main conﬁdentiality. Data generated by the ISARIC4C consortium is
available for collaborative analysis projects through an independent
data and materials access committee at isaric4c.net/sample_access.
Data and samples from the COVID-Clinical Neuroscience Study are
available through collaborative research by application through the
NIHR bioresource at https://bioresource.nihr.ac.uk/using-ourbioresource/apply-for-bioresource-data-access/. Brain injury marker
and immune mediator data are present in the paper and in the source
data ﬁle. Source data are provided with this paper.

References
1.

Drake, T. M. et al. Characterisation of in-hospital complications
associated with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol UK: a prospective, multicentre cohort study.
Lancet 398, 223–237 (2021).
2. Xu, E., Xie, Y. & Al-Aly, Z. Long-term neurologic outcomes of COVID19. Nat. Med. 1–10 https://doi.org/10.1038/s41591-022-02001-z (2022).
3. Varatharaj, A. et al. Neurological and neuropsychiatric complications of COVID-19 in 153 patients: a UK-wide surveillance study.
Lancet Psychiatry 7, 875–882 (2020).
4. Ross Russell, A. L. et al. Spectrum, risk factors and outcomes of
neurological and psychiatric complications of COVID-19: a UK-wide
cross-sectional surveillance study. Brain Commun. 3,
fcab168 (2021).
5. Paterson, R. W. et al. The emerging spectrum of COVID-19 neurology: clinical, radiological and laboratory ﬁndings. Brain 143,
3104–3120 (2020).
6. Mao, L. et al. Neurologic manifestations of hospitalized patients
with coronavirus disease 2019 in Wuhan, China. JAMA Neurol. 77,
683–690 (2020).
7. Song, E. et al. Neuroinvasion of SARS-CoV-2 in human and mouse
brain. J. Exp. Med. 218, e20202135 (2021).
8. Crunﬂi, F. et al. Morphological, cellular, and molecular basis of
brain infection in COVID-19 patients. Proc. Natl Acad. Sci. USA 119,
e2200960119 (2022).
9. Normandin, E. et al. Neuropathological features of SARS-CoV-2
delta and omicron variants. J. Neuropathol. Exp. Neurol. 82,
283–295 (2023).
10. Meinhardt, J. et al. Olfactory transmucosal SARS-CoV-2 invasion as
a port of central nervous system entry in individuals with COVID-19.
Nat. Neurosci. 24, 168–175 (2021).
11. Thakur, K. T. et al. COVID-19 neuropathology at Columbia University
Irving Medical Center/New York Presbyterian Hospital. Brain 144,
2696–2708 (2021).

Nature Communications | (2023)14:8487

https://doi.org/10.1038/s41467-023-42320-4
12. Khan, M. et al. Visualizing in deceased COVID-19 patients how
SARS-CoV-2 attacks the respiratory and olfactory mucosae but
spares the olfactory bulb. Cell 184, 5932–5949.e15 (2021).
13. Domingues, R. B., Leite, F. B. V. de M. & Senne, C. Cerebrospinal
ﬂuid analysis in patients with COVID-19-associated central nervous
system manifestations: a systematic review. Arq. Neuropsiquiatr.
https://doi.org/10.1590/0004-282X-ANP-2021-0117 (2022).
14. Needham, E. J. et al. Brain injury in COVID-19 is associated with
dysregulated innate and adaptive immune responses. Brain
awac321, https://doi.org/10.1093/brain/awac321 (2022).
15. Dunai, C., Collie, C. & Michael, B. D. Immune-mediated mechanisms
of COVID-19 neuropathology. Front. Neurol. 13, 782 (2022).
16. Maiese, A. et al. SARS-CoV-2 and the brain: a review of the current
knowledge on neuropathology in COVID-19. Brain Pathol. 31,
e13013 (2021).
17. Reinhold, D. et al. The brain reacting to COVID-19: analysis of the
cerebrospinal ﬂuid proteome, RNA and inﬂammation. J. Neuroinﬂammation 20, 30 (2023).
18. Fara, M. G. et al. Macrothrombosis and stroke in patients with
mild COVID-19 infection. J. Thromb. Haemost. 18, 2031–2033
(2020).
19. Frontera, J. A. et al. A prospective study of neurologic disorders in
hospitalized patients with COVID-19 in New York City. Neurology
96, e575–e586 (2021).
20. Hampshire, A. et al. Multivariate proﬁle and acute-phase correlates
of cognitive deﬁcits in a COVID-19 hospitalised cohort. eClinicalMedicine 47, 101417 (2022).
21. Lucas, C. et al. Longitudinal analyses reveal immunological misﬁring in severe COVID-19. Nature 584, 463–469 (2020).
22. Thwaites, R. S. et al. Inﬂammatory proﬁles across the spectrum of
disease reveal a distinct role for GM-CSF in severe COVID-19. Sci.
Immunol. 6, eabg9873 (2021).
23. Perreau, M. et al. The cytokines HGF and CXCL13 predict the
severity and the mortality in COVID-19 patients. Nat. Commun. 12,
4888 (2021).
24. Li, L. et al. Interleukin-8 as a biomarker for disease prognosis of coronavirus disease-2019 patients. Front. Immunol. 11, 602395 (2021).
25. Kanberg, N. et al. Neurochemical signs of astrocytic and neuronal
injury in acute COVID-19 normalizes during long-term follow-up.
EBioMedicine 70, 103512 (2021).
26. Guasp, M. et al. CSF biomarkers in COVID-19 associated encephalopathy and encephalitis predict long-term outcome. Front.
Immunol. 13, 1600 (2022).
27. Bertin, D. et al. Anticardiolipin IgG autoantibody level is an independent risk factor for COVID‐19 severity. Arthritis Rheumatol.
Hoboken Nj 72, 1953–1955 (2020).
28. Guimarães, P. O. et al. Tofacitinib in patients hospitalized with
COVID-19 pneumonia. N. Engl. J. Med. 385, 406–415 (2021).
29. Thelin, E. P. et al. Serial sampling of serum protein biomarkers for
monitoring human traumatic brain injury dynamics: a systematic
review. Front. Neurol. 8, 300 (2017).
30. Grundmann, A. et al. Impact of dexamethasone and remdesivir on
neurological complications during COVID-19. https://doi.org/10.
2139/ssrn.4065552 (2022).
31. Desole, C. et al. HGF and MET: from brain development to neurological disorders. Front. Cell Dev. Biol. 9, 683609 (2021).
32. Bao, L. et al. The critical role of IL-12p40 in initiating, enhancing, and
perpetuating pathogenic events in murine experimental autoimmune neuritis. Brain Pathol. 12, 420–429 (2002).
33. Mondal, S. et al. IL-12 p40 monomer is different from other IL-12
family members to selectively inhibit IL-12Rβ1 internalization
and suppress EAE. Proc. Natl Acad. Sci. USA 117, 21557–21567
(2020).
34. Kroenke, M. A., Carlson, T. J., Andjelkovic, A. V. & Segal, B. M. IL-12and IL-23-modulated T cells induce distinct types of EAE based on

10

Article
histology, CNS chemokine proﬁle, and response to cytokine inhibition. J. Exp. Med. 205, 1535–1541 (2008).
35. Yu, S. et al. Neutralizing antibodies to IL-18 ameliorate experimental
autoimmune neuritis by counter-regulation of autoreactive Th1
responses to peripheral myelin antigen. J. Neuropathol. Exp. Neurol.
61, 614–622 (2002).
36. Rivera-Correa, J. & Rodriguez, A. Autoantibodies during infectious
diseases: Lessons from malaria applied to COVID-19 and other
infections. Front. Immunol. 13, 938011 (2022).
37. Mohkhedkar, M., Venigalla, S. S. K. & Janakiraman, V. Untangling
COVID-19 and autoimmunity: Identiﬁcation of plausible targets
suggests multi organ involvement. Mol. Immunol. 137,
105–113 (2021).
38. Moody, R., Wilson, K., Flanagan, K. L., Jaworowski, A. & Plebanski, M.
Adaptive immunity and the risk of autoreactivity in COVID-19. Int. J.
Mol. Sci. 22, 8965 (2021).
39. Johnson, D. & Jiang, W. Infectious diseases, autoantibodies, and
autoimmunity. J. Autoimmun. 137, 102962 (2023).
40. Lee, M. H. et al. Neurovascular injury with complement activation
and inﬂammation in COVID-19. Brain J. Neurol. 145,
2555–2568 (2022).
41. Pretorius, E. et al. Persistent clotting protein pathology in Long
COVID/Post-Acute Sequelae of COVID-19 (PASC) is accompanied
by increased levels of antiplasmin. Cardiovasc. Diabetol. 20,
172 (2021).
42. Choudhary, S. et al. Modeling SARS-CoV-2: comparative pathology
in rhesus macaque and Golden Syrian hamster models. Toxicol.
Pathol. https://doi.org/10.1177/01926233211072767 (2022).
43. Rutkai, I. et al. Neuropathology and virus in brain of SARS-CoV-2
infected non-human primates. Nat. Commun. 13, 1745 (2022).
44. Fernández-Castañeda, A. et al. Mild respiratory COVID can cause
multi-lineage neural cell and myelin dysregulation. Cell 185,
2452–2468.e16 (2022).
45. Soung, A. L. et al. COVID-19 induces CNS cytokine expression and
loss of hippocampal neurogenesis. Brain awac270 https://doi.org/
10.1093/brain/awac270 (2022).
46. Borsini, A. et al. Neurogenesis is disrupted in human hippocampal progenitor cells upon exposure to serum samples from
hospitalized COVID-19 patients with neurological symptoms.
Mol. Psychiatry 1–13, https://doi.org/10.1038/s41380-02201741-1 (2022).
47. ISARIC4C Comprehensive Clinical Characterisation Collaboration
Website. https://isaric4c.net.
48. Ellul, M. A. et al. Neurological associations of COVID-19. Lancet
Neurol. 19, 767–783 (2020).
49. Michael, B. D. et al. Characteristic cytokine and chemokine proﬁles
in encephalitis of infectious, immune-mediated, and unknown
aetiology. PLoS ONE 11, e0146288 (2016).
50. Grifﬁths, M. J. et al. In enterovirus 71 encephalitis with cardiorespiratory compromise, elevated interleukin 1β, interleukin 1
receptor antagonist, and granulocyte colony-stimulating factor
levels are markers of poor prognosis. J. Infect. Dis. 206,
881–892 (2012).
51. Michael, B. D. et al. Post-acute serum eosinophil and neutrophilassociated cytokine/chemokine proﬁle can distinguish between
patients with neuromyelitis optica and multiple sclerosis; and
identiﬁes potential pathophysiological mechanisms—a pilot study.
Cytokine 64, 90–96 (2013).
52. Needham, E. J. et al. Complex autoantibody responses occur following moderate to severe traumatic brain injury. J. Immunol. 207,
90–100 (2021).
53. Ances, B. M. et al. Treatment-responsive limbic encephalitis identiﬁed by neuropil antibodies: MRI and PET correlates. Brain 128,
1764–1777 (2005).

Nature Communications | (2023)14:8487

https://doi.org/10.1038/s41467-023-42320-4
54. Lai, M. et al. Investigation of LGI1 as the antigen in limbic encephalitis previously attributed to potassium channels: a case series.
Lancet Neurol. 9, 776–785 (2010).
55. Irani, S. R. et al. Antibodies to Kv1 potassium channel-complex
proteins leucine-rich, glioma inactivated 1 protein and contactinassociated protein-2 in limbic encephalitis, Morvan’s syndrome and
acquired neuromyotonia. Brain 133, 2734–2748 (2010).
56. Uy, C. E. et al. Detection and signiﬁcance of neuronal autoantibodies in patients with meningoencephalitis in Vientiane, Lao
PDR. Trans. R. Soc. Trop. Med. Hyg. 116, 959–965 (2022).
57. Irani, S. R. et al. N-methyl-d-aspartate antibody encephalitis: temporal progression of clinical and paraclinical observations in a
predominantly non-paraneoplastic disorder of both sexes. Brain
133, 1655–1667 (2010).
58. Masi, A. et al. Cytokine levels and associations with symptom
severity in male and female children with autism spectrum disorder.
Mol. Autism 8, 63 (2017).

Acknowledgements
We thank the patients and their loved ones who volunteered to contribute to these studies at one of the most difﬁcult times in their lives,
and the research staff in every hospital who recruited patients at personal risk under challenging conditions. This research was funded by the
National Institute for Health and Care Research (NIHR) (CO-CIN-01) and
jointly by NIHR and UK Research and Innovation (CV220-169,
MC_PC_19059). B.D.M. is supported by the UKRI/MRC (MR/V03605X/1),
the MRC/UKRI (MR/V007181/1), MRC (MR/T028750/1) and Wellcome
(ISSF201902/3). C.D. is supported by MRC (MC_PC_19044). We would
like to thank the University of Liverpool GCP laboratory facility team for
Luminex assistance and the Liverpool University Biobank team for all
their help, especially Dr. Victoria Shaw, Lara Lavelle-Langham, and Sue
Holden. We would like to acknowledge the Liverpool Experimental
Cancer Medicine Centre for providing infrastructure support for this
research (Grant Reference: C18616/A25153). We acknowledge the
Liverpool Centre for Cell Imaging (CCI) for provision of imaging equipment (Dragonﬂy confocal microscope) and excellent technical assistance (BBSRC grant number BB/R01390X/1). Tom Solomon is supported
by The Pandemic Institute and the NIHR Health Protection Research Unit
(HPRU) in Emerging and Zoonotic Infections at University of Liverpool.
D.K.M. and E.N. are supported by the NIHR Cambridge Biomedical
Centre and by NIHR funding to the NIHR BioResource (RG94028 and
RG85445), and by funding from Brain Research UK 201819-20. We thank
NIHR BioResource volunteers for their participation, and gratefully
acknowledge NIHR BioResource centres, NHS Trusts and staff for their
contribution. We thank the National Institute for Health and Care
Research, NHS Blood and Transplant, and Health Data Research UK as
part of the Digital Innovation Hub Programme. Support for title page
creation and format was provided by AuthorArranger, a tool developed
at the National Cancer Institute. The authors would like to acknowledge
the eDRIS team (Public Health Scotland) for their support in obtaining
approvals, the provisioning and linking of data and facilitating access to
the National Safe Haven. The views expressed are those of the author(s)
and not necessarily those of the UKRI, NHS, the NIHR or the Department
of Health and Social Care.

Author contributions
B.D.M., C.D., E.J.N., K.T., R.W., Y.H., G.K.W., C.C., J. Cavanagh, S.R.I.,
A.V., L.S.T. and D.K.M. designed, analysed data, interpreted experiments, and wrote the manuscript. T.S., J.P.S., G.B., M.G., J.-C.S., A.J.C.,
M.A.E. and A.P. provided scientiﬁc orientation and critically reviewed
the manuscript. K. Stirrups, N.K., J.R.B., and P.F.C. provided oversight of
the COVID-Clinical Neuroscience Study. J.K.B., P.J.M.O., and M.G.S. led
the ISARIC4C study and provided oversight of the manuscript. G.S.,
A.G. and A.-C.C. analysed data. S.A.B., J. Clark, P.S., K. Subramaniam,

11

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M.H., C.H. and F.N.E. performed preliminary feasibility experiments.
C.D., R.D., A.R., E.N., M.L., S.E., A.F. and H.F. performed experiments
and analysed the data. E.T. managed the collection of the patients’
samples. ISARIC4C and COVID-CNS consortia recruited the patients for
the study.

Correspondence and requests for materials should be addressed to
Benedict D. Michael.

Competing interests

Reprints and permissions information is available at
http://www.nature.com/reprints

T.S. is the Director of The Pandemic Institute which has received funding
from Innova and CSL Seqirus and Aviva and DAM Health. T.S. was an
advisor to the GSK Ebola Vaccine programme and the Siemens Diagnostic Programme. T.S. Chaired the Siemens Healthineers Clinical
Advisory Board. T.S. Co-Chaired the WHO Neuro-COVID task force and
sat on the UK Government Advisory Committee on Dangerous Pathogens, and the Medicines and Healthcare Products Regulatory Agency
(MHRA) Expert Working Group on Covid-19 vaccines. T.S. Advised to the
UK COVID-19 Therapeutics Advisory Panel (UK-TAP). T.S. was a Member
of COVID-19 Vaccines Beneﬁt Risk Expert Working Group for the Commission on Human Medicines (CHM) committee of the Medicines and
Healthcare products Regulatory Agency (MHRA). T.S. has been a
member of the Encephalitis Society since 1998 and President of the
Encephalitis Society since 2019.

Additional information
Supplementary information The online version contains supplementary
material available at
https://doi.org/10.1038/s41467-023-42320-4.

Peer review information Nature Communications thanks Avindra Nath,
Michael Pizzi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review ﬁle is available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afﬁliations.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
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long as you give appropriate credit to the original author(s) and the
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© The Author(s) 2023, corrected publication 2024

Benedict D. Michael 1,2,3,82 , Cordelia Dunai 1,2,82, Edward J. Needham 4,5, Kukatharmini Tharmaratnam 6,
Robyn Williams 7,8, Yun Huang 1, Sarah A. Boardman 1, Jordan J. Clark9,10,11, Parul Sharma12,
Krishanthi Subramaniam12, Greta K. Wood1, Ceryce Collie 1, Richard Digby 5, Alexander Ren5, Emma Norton5,
Maya Leibowitz5, Soraya Ebrahimi5, Andrew Fower7, Hannah Fox7, Esteban Tato 13,14, Mark A. Ellul1,3,
Geraint Sunderland 1, Marie Held 15, Claire Hetherington 1, Franklyn N. Egbe 1, Alish Palmos 13,14,
Kathy Stirrups 16,17, Alexander Grundmann18,19, Anne-Cecile Chiollaz20, Jean-Charles Sanchez20, James P. Stewart 12,
Michael Grifﬁths1, Tom Solomon 1,2,3,21, Gerome Breen 13,14, Alasdair J. Coles4, Nathalie Kingston16,22,
John R. Bradley16,23, Patrick F. Chinnery 4,16, Jonathan Cavanagh 24, Sarosh R. Irani 7,8, Angela Vincent 25,
J. Kenneth Baillie 26,27, Peter J. Openshaw 28,29, Malcolm G. Semple 1,2,30, ISARIC4C Investigators*, COVID-CNS
Consortium*, Leonie S. Taams 31,83 & David K. Menon 5,83
1

Clinical Infection, Microbiology, and Immunology, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool L69 7BE, UK.
NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool, Liverpool L69 7BE, UK. 3The Walton Centre NHS
Foundation Trust, Liverpool L9 7BB, UK. 4Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK. 5Division of Anaesthesia,
Department of Medicine, University of Cambridge, Cambridge CB2 0QQ, UK. 6Health Data Science, Institute of Population Health, University of Liverpool,
Liverpool L69 3GF, UK. 7Oxford Autoimmune Neurology Group, Nufﬁeld Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK.
8
Departments of Neurology and Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA. 9University of Liverpool, Liverpool L69 7BE, UK. 10Department of
Microbiology, Icahn School of Medicine, Mount Sinai, NY 10029, USA. 11Center for Vaccine Research and Pandemic Preparedness (C-VARPP), Icahn School of
Medicine, Mount Sinai, NY 10029, USA. 12Infection Biology & Microbiomes, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool,
Liverpool L3 5RF, UK. 13Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London,
London SE5 8AF, UK. 14NIHR Maudsley Biomedical Research Centre, King’s College London, London SE5 8AF, UK. 15Centre for Cell Imaging, Liverpool Shared
Research Facilities, Faculty of Health and Life Sciences, University of Liverpool, Liverpool L69 7ZB, UK. 16NIHR BioResource, Cambridge University Hospitals
NHS Foundation, Cambridge CB2 0QQ, UK. 17Department of Haematology, University of Cambridge, Cambridge CB2 0QQ, UK. 18Clinical Neurosciences,
Clinical and Experimental Science, Faculty of Medicine, University of Southampton, Southampton SO17 1BF, UK. 19Department of Neurology, Wessex
Neurological Centre, University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK. 20Département de médecine interne des
spécialités (DEMED), University of Geneva, Geneva CH-1211, Switzerland. 21The Pandemic Institute, Liverpool L7 3FA, UK. 22University of Cambridge, Cambridge CB2 0QQ, UK. 23Department of Medicine, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0QQ, UK. 24Centre for Immunology,
School of Infection & Immunity, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow G12 8TA, UK. 25Nufﬁeld Department of Clinical
2

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Neurosciences, University of Oxford, Oxford OX3 9DU, UK. 26Roslin Institute, University of Edinburgh, Edinburgh EH25 9RG, UK. 27Intensive Care Unit, Royal
Inﬁrmary of Edinburgh, Edinburgh EH10 5HF, UK. 28National Heart and Lung Institute, Imperial College London, London SW7 2BX, UK. 29Imperial College
Healthcare NHS Trust, London W2 1NY, UK. 30Respiratory Unit, Alder Hey Children’s Hospital NHS Foundation Trust, Liverpool L14 5AB, UK. 31Centre for
Inﬂammation Biology and Cancer Immunology, King’s College London, London SE1 9RT, UK. 82These authors contributed equally: Benedict D. Michael,
Cordelia Dunai. 83These authors jointly supervised this work: Leonie S. Taams, David K. Menon. *Lists of authors and their afﬁliations appear at the end of the
e-mail: benmic@liverpool.ac.uk
paper.

ISARIC4C Investigators
J. Kenneth Baillie32, Peter J. Openshaw33, Malcolm G. Semple34, Beatrice Alex32, Petros Andrikopoulos33, Benjamin Bach32,
Wendy S. Barclay33, Debby Bogaert32, Meera Chand35, Kanta Chechi33, Graham S. Cooke33, Ana da Silva36, Thushan de
Silva37, Annemarie B. Docherty32, Gonçalo dos Santos33, Marc-Emmanuel Dumas33, Jake Dunning35, Tom Fletcher38,
Christoper A. Green39, William Greenhalf34, Julian L. Grifﬁn33, Rishi K. Gupta40, Ewen M. Harrison32, Antonia Y. Wai36,
Karl Holden34, Peter W. Horby41, Samreen Ijaz35, Saye Khoo34, Paul Klenerman41, Andrew Law32, Matthew R. Lewis33,
Sonia Liggi33, Wei S. Lim42, Lynn Maslen33, Alexander J. Mentzer43, Laura Merson41, Alison M. Meynert32, Shona C. Moore34,
Mahdad Noursadeghi40, Michael Olanipekun33, Anthonia Osagie33, Massimo Palmarini36, Carlo Palmieri34,
William A. Paxton34, Georgios Pollakis34, Nicholas Price44, Andrew Rambaut32, David L. Robertson36, Clark D. Russell32,
Vanessa Sancho-Shimizu33, Caroline J. Sands33, Janet T. Scott36, Louise Sigfrid41, Tom Solomon34, Shiranee Sriskandan33,
David Stuart41, Charlotte Summers45, Olivia V. Swann32, Zoltan Takats33, Panteleimon Takis33, Richard S. Tedder35,
A. A. R. Thompson37, Emma C. Thomson36, Ryan S. Thwaites33, Lance C. Turtle34, Maria Zambon35, Thomas M. Drake32,
Cameron J. Fairﬁeld32, Stephen R. Knight32, Kenneth A. Mclean32, Derek Murphy32, Lisa Norman32, Riinu Pius32,
Catherine A. Shaw32, Marie Connor34, Jo Dalton34, Carrol Gamble34, Michelle Girvan34, Sophie Halpin34, Janet Harrison34,
Clare Jackson34, James Lee41, Laura Marsh34, Daniel Plotkin41, Stephanie Roberts34, Egle Saviciute34, Sara Clohisey32,
Ross Hendry32, Susan Knight46, Eva Lahnsteiner47, Gary Leeming48, Lucy Norris32, James Scott-Brown32, Sarah Tait46,
Murray Wham32, Richard Clark47, Audrey Coutts47, Lorna Donnelly47, Angie Fawkes47, Tammy Gilchrist47,
Katarzyna Hafezi47, Louise MacGillivray47, Alan Maclean47, Sarah McCafferty47, Kirstie Morrice47, Lee Murphy47,
Nicola Wrobel47, Gail Carson41, Kayode Adeniji47, Daniel Agranoff47, Ken Agwuh47, Dhiraj Ail47, Erin L. Aldera47,
Ana Alegria47, Sam Allen47, Brian Angus47, Abdul Ashish47, Dougal Atkinson47, Shahedal Bari47, Gavin Barlow47,
Stella Barnass47, Nicholas Barrett47, Christopher Bassford47, Sneha Basude47, David Baxter47, Michael Beadsworth47,
Jolanta Bernatoniene47, John Berridge47, Colin Berry47, Nicola Best47, Pieter Bothma47, Robin Brittain-Long47,
Naomi Bulteel47, Tom Burden47, Andrew Burtenshaw47, Vikki Caruth47, David Chadwick47, Duncan Chambler47,
Nigel Chee47, Jenny Child47, Srikanth Chukkambotla47, Tom Clark47, Paul Collini47, Catherine Cosgrove47, Jason Cupitt47,
Maria-Teresa Cutino-Moguel47, Paul Dark47, Chris Dawson47, Samir Dervisevic47, Phil Donnison47, Sam Douthwaite47,
Andrew Drummond47, Ingrid DuRand47, Ahilanadan Dushianthan47, Tristan Dyer47, Cariad Evans47, Chi Eziefula47,
Chrisopher Fegan47, Adam Finn47, Duncan Fullerton47, Sanjeev Garg47, Atul Garg47, Effrossyni Gkrania-Klotsas47,
Jo Godden47, Arthur Goldsmith47, Clive Graham47, Tassos Grammatikopoulos49, Elaine Hardy47, Stuart Hartshorn47,
Daniel Harvey47, Peter Havalda47, Daniel B. Hawcutt47, Maria Hobrok47, Luke Hodgson47, Anil Hormis47, Joanne Howard47,
Michael Jacobs47, Susan Jain47, Paul Jennings47, Agilan Kaliappan47, Vidya Kasipandian47, Stephen Kegg47,
Michael Kelsey47, Jason Kendall47, Caroline Kerrison47, Ian Kerslake47, Oliver Koch47, Gouri Koduri47, George Koshy47,
Shondipon Laha47, Steven Laird47, Susan Larkin47, Tamas Leiner47, Patrick Lillie47, James Limb47, Vanessa Linnett47,
Jeff Little47, Mark Lyttle47, Michael MacMahon47, Emily MacNaughton47, Ravish Mankregod47, Huw Masson47,
Elijah Matovu47, Katherine McCullough47, Ruth McEwen47, Manjula Meda47, Gary Mills47, Jane Minton47,
Kavya Mohandas47, Quen Mok47, James Moon47, Elinoor Moore47, Patrick Morgan47, Craig Morris47, Katherine Mortimore47,
Samuel Moses47, Mbiye Mpenge47, Rohinton Mulla47, Michael Murphy47, Thapas Nagarajan47, Megan Nagel47,
Mark Nelson47, Lillian Norris47, Matthew K. O’Shea47, Marlies Ostermann44, Igor Otahal47, Mark Pais47,
Selva Panchatsharam47, Danai Papakonstantinou47, Padmasayee Papineni47, Hassan Paraiso47, Brij Patel47,
Natalie Pattison47, Justin Pepperell47, Mark Peters47, Mandeep Phull47, Stefania Pintus47, Tim Planche47, Frank Post47,
David Price47, Rachel Prout47, Nikolas Rae47, Henrik Reschreiter47, Tim Reynolds47, Neil Richardson47, Mark Roberts47,
Devender Roberts47, Alistair Rose47, Guy Rousseau47, Bobby Ruge47, Brendan Ryan47, Taranprit Saluja47,
Matthias L. Schmid47, Aarti Shah47, Manu Shankar-Hari47, Prad Shanmuga47, Anil Sharma47, Anna Shawcross47,
Jagtur S. Pooni47, Jeremy Sizer47, Richard Smith47, Catherine Snelson47, Nick Spittle47, Nikki Staines47, Tom Stambach47,
Richard Stewart47, Pradeep Subudhi47, Tamas Szakmany47, Kate Tatham47, Jo Thomas47, Chris Thompson47,
Robert Thompson47, Ascanio Tridente47, Darell Tupper-Carey47, Mary Twagira47, Nick Vallotton47, Rama Vancheeswaran47,
Rachel Vincent47, Lisa Vincent-Smith47, Shico Visuvanathan47, Alan Vuylsteke47, Sam Waddy47, Rachel Wake47,

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Andrew Walden47, Ingeborg Welters47, Tony Whitehouse47, Paul Whittaker47, Ashley Whittington47, Meme Wijesinghe47,
Martin Williams47, Lawrence Wilson47, Stephen Winchester47, Martin Wiselka47, Adam Wolverson47, Daniel G. Wootton47,
Andrew Workman47, Bryan Yates47, Peter Young47, Sarah E. McDonald36, Victoria Shaw34, Katie A. Ahmed47,
Jane A. Armstrong47, Milton Ashworth47, Innocent G. Asiimwe47, Siddharth Bakshi47, Samantha L. Barlow47, Laura Booth47,
Benjamin Brennan47, Katie Bullock47, Nicola Carlucci47, Emily Cass47, Benjamin W. Catterall47, Jordan J. Clark47,
Emily A. Clarke47, Sarah Cole47, Louise Cooper47, Helen Cox47, Christopher Davis47, Oslem Dincarslan47,
Alejandra D. Carracedo47, Chris Dunn47, Philip Dyer47, Angela Elliott47, Anthony Evans47, Lorna Finch47, Lewis W. Fisher47,
Lisa Flaherty47, Terry Foster47, Isabel Garcia-Dorival47, Philip Gunning47, Catherine Hartley47, Anthony Holmes47,
Rebecca L. Jensen47, Christopher B. Jones47, Trevor R. Jones47, Shadia Khandaker47, Katharine King47, Robyn T. Kiy47,
Chrysa Koukorava47, Annette Lake47, Suzannah Lant47, Diane Latawiec47, Lara Lavelle-Langham47, Daniella Lefteri47,
Lauren Lett47, Lucia A. Livoti47, Maria Mancini47, Hannah Massey47, Nicole Maziere47, Sarah McDonald47,
Laurence McEvoy47, John McLauchlan47, Soeren Metelmann47, Nahida S. Miah47, Joanna Middleton47, Joyce Mitchell47,
Ellen G. Murphy47, Rebekah Penrice-Randal47, Jack Pilgrim47, Tessa Prince47, Will Reynolds47, P. M. Ridley47,
Debby Sales47, Victoria E. Shaw47, Rebecca K. Shears47, Benjamin Small47, Krishanthi S. Subramaniam47,
Agnieska Szemiel47, Aislynn Taggart47, Jolanta Tanianis-Hughes47, Jordan Thomas47, Erwan Trochu47, Libby v. Tonder47,
Eve Wilcock47, J. E. Zhang47, Seán Keating50, Cara Donegan34, Rebecca G. Spencer34, Chloe Donohue34, Fiona Grifﬁths51,
Hayley Hardwick34 & Wilna Oosthuyzen32
32

University of Edinburgh, Edinburgh, UK. 33Imperial College London, London, UK. 34University of Liverpool, Liverpool, UK. 35Public Health England,
London, UK. 36MRC-University of Glasgow Centre for Virus Research, Glasgow, UK. 37University of Shefﬁeld, Shefﬁeld, UK. 38Liverpool School of Tropical
Medicine, Liverpool, UK. 39University of Birmingham, Birmingham, UK. 40University College London, London, UK. 41University of Oxford, Oxford, UK. 42Nottingham University Hospitals NHS Trust, Nottingham, UK. 43John Radcliffe Hospital, Oxford, UK. 44King’s College London, London, UK. 45University of
Cambridge, Cambridge, UK. 46Public Health Scotland, Edinburgh, UK. 47ISARIC4C Investigators, Liverpool, UK. 48University of Manchester, Manchester, UK.
49
King’s College Hospital, London, UK. 50Royal Inﬁrmary Edinburgh, Edinburgh, UK. 51Roslin Institute, Edinburgh, UK.

COVID-CNS Consortium
Adam Hampshire33, Adam Sieradzki52, Adam W. Seed53, Afagh Garjani54, Akshay Nair44, Alaisdair Coles45, Alan Carson55,
Alastair Darby34, Alex Berry40, Alex Dregan44, Alexander Grundmann56, Alish Palmos44, Ammar Al-Chalabi44,
Andrew M. McIntosh32, Angela E. Holland57, Angela Roberts45, Angela Vincent41, Annalena Venneri37, Anthony S. David40,
Arina Tamborska34, Arvind Patel58, Ava Easton34, Benedict D. Michael34, Bethan Blackledge59, Bethany Facer34,
Bhagteshwar Singh34, Brendan Sargent34, Ceryce Collie34, Charles Leek34, Cherie Armour60, Christopher M. Morris61,
Christopher M. Allen54, Ciaran Mulholland60, Claire L. MacIver60, Cordelia Dunai34, Craig J. Smith48, Daniel J. van44,
Daniel Madarshahian63, David Christmas64, David Cousins61, David K. Menon45, David M. Christmas45, David P. Breen32,
Dina Monssen44, Edward Bullmore45, Edward Needham45, Emily McGlinchey60, Emma Thomson58, Eugene Duff41,
Eva M. Hodel34, Ewan Harrison45, Fernando Zelaya44, Gabriella Lewis65, Gavin McDonnell66, Gerome Breen44,
Greta K. Wood34, Guy B. Williams45, C. Hannah34, Henry C. Rogers44, Ian Galea56, Jacqueline Smith67, Jade D. Harris59,
James B. Lilleker68, Jay Amin56, John P. Aggleton62, John R. Bradley45, John-Paul Taylor61, Jonathan Cavanagh58,
Jonathan R. Coleman44, Jonathan Underwood62, Judith Breuer40, Julian Hiscox34, Karla Miller41, Katherine C. Dodd48,
Kiran Glen44, Laura Benjamin40, Leonie Taams44, Lily George44, Marc Hardwick56, Mark R. Baker61, Marlies Ostermann43,
Masud Husain41, Matthew Butler44, Matthew Hotopf44, Matthew R. Broome39, Merna Samuel34, Michael Grifﬁths34,
Michael P. Lunn40, Michael S. Zandi40, Monika Hartmann69, Nadine Cossette70, Naomi Martin44, Nathalie Nicholas71,
Neil A. Harrison62, Neil Basu58, Neil Harrison62, Nicholas Davies33, Nicholas Wood40, Nikos Evangelou54,
Obioma Orazulume40, Pamela J. Shaw37, Parisa Mansoori72, Paul J. Harrison41, Peter Jezzard41, Peter M. Fernandes32,
Rachel Upthegrove73, Rahul Batra44, Rebecca Gregory74, Rhys H. Thomas75, Richard Bethlehem45, Richard Francis76,
Ronan O’Malley77, Rustam A. Salman78, Ryan McIlwaine60, Sandar Kyaw79, Sarosh Irani41, Savini Gunatilake80,
Scott Semple32, Shahd H. Hamid34, Sharon Peacock45, Silvia Rota44, Simon Keller34, Sophie Pendered34, Suzanne Barrett81,
Stella Hughes66, Stella-Maria Paddick61, Stephen J. Sawcer45, Stephen Smith41, Steven Williams44, Sui H. Wong44,
Sylviane Defres38, Thomas Jackson73, Thomas M. Jenkins37, Thomas Pollak44, Timothy Nicholson44, Tom Solomon33,
Tonny Veenith39, Victoria Grimbly34 & Virginia Newcombe45
52

COVID-CNS Consortium, York, UK. 53Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK. 54University of Nottingham, Nottingham, UK.
Edinburgh University, Edinburgh, UK. 56University of Southampton, Southampton, UK. 57Nottingham University Hospital, Nottingham, UK. 58University of
Glasgow, Glasgow, UK. 59Salford Royal Foundation Trust, Manchester, UK. 60Queens University Belfast, Belfast, UK. 61Newcastle University, Newcastle, UK.
62
Cardiff University, Cardiff, UK. 63Shefﬁeld Institute for Translational Neuroscience, Shefﬁeld, UK. 64Dundee University, Dundee, UK. 65South London and
Maudsley NHS Foundation Trust, London, UK. 66Belfast Health and Social Care Trust, Belfast, UK. 67COVID-CNS Consortium, Edinburgh, UK. 68The University
55

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of Manchester, Manchester, UK. 69Kings College London, London, UK. 70Royal Inﬁrmary of Edinburgh, Edinburgh, UK. 71Aintree University Hospital,
Liverpool, UK. 72National Institute for Health Research (NIHR) Bioresource, London, UK. 73Birmingham University, Birmingham, UK. 74Shefﬁeld Teaching
Hospitals NHS Foundation Trust, Shefﬁeld, UK. 75Translational and Clinical Research, Newcastle, UK. 76The Stroke Association, London, UK. 77The University of
Shefﬁeld, Shefﬁeld, UK. 78The University of Edinburgh, Edinburgh, UK. 79Institute of Mental health, Nottingham, UK. 80Royal Stoke University Hospital, Stoke
on Trent, UK. 81Northern Health and Social Care Trust, Belfast, UK.

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