Microglial dysfunction and glutamatergic dysregulation are implicated in the neurobiology of schizophrenia. Microglia regulate brain glutamate levels through mechanisms including the cysteine-glutamate antiporter, raising the possibility that microglial dysfunction could underlie glutamatergic dysregulation in schizophrenia. We tested this using a combined cross-sectional/longitudinal study of individuals with first episode psychosis and healthy controls. We investigated two hypotheses: (1) increase in a positron emission tomography (PET) imaging marker of microglia is associated with increased anterior cingulate (ACC) glutamate levels; and (2) reducing immune trafficking into the CNS using the monoclonal antibody natalizumab would reduce ACC glutamate levels in people with first-episode psychosis. A total of 108 participants (68 patients and 40 healthy controls) underwent simultaneous proton magnetic resonance spectroscopy to quantify ACC glutamate and glutamate/glutamine (glx) levels and PET imaging with [18 F]DPA-714 to quantify translocator protein (TSPO) levels, a protein highly expressed by microglia in neuroinflammatory conditions. In the longitudinal arm, 50 first-episode psychosis patients were included and either randomised to receive natalizumab or placebo double-blind, or received open-label natalizumab. At baseline, there was no significant association between ACC glutamate concentration and ACC TSPO binding in the entire sample (β = -0.003, SE = 0.006, p = 0.55). In the longitudinal arm, natalizumab did not significantly alter ACC glutamate levels (mean difference = 0.227 i.u., t = 1.52, p = 0.139). These findings do not support the hypothesis that microglial dysfunction drives ACC glutamatergic dysregulation in early psychosis. Microglial dysfunction and glutamatergic dysregulation may therefore represent distinct mechanisms within the neurobiology of schizophrenia.

Prostate-specific membrane antigen (PSMA) is upregulated in high-grade glioma (HGG). This study aimed, firstly, to establish dosimetry for the radionuclide [68Ga]-PSMA-11 in HGG patients; secondly, to determine theoretical tumour doses for [177Lu]-PSMA, a potential therapeutic radionuclide; thirdly, to assess PSMA immunohistochemistry in targeted intra-operative HGG biopsies. Three HGG patients underwent PET-MRI after injection of 185 MBq [68Ga]-PSMA-11. Targeted intra-operative HGG biopsies were immunostained for PSMA and endothelium (CD34). There was durable, heterogeneous uptake of [ 68Ga]-PSMA-11 with moderate SUVmax (4.2-5.0) and high TBR (60-183). [68Ga]-PSMA-11 delivered a tumour dose of 0.01-0.03 mGy/MBq corresponding to 0.38-1.10 mGy/MBq for [177Lu]-PSMA. PSMA staining was predominantly seen in necrotic/proliferating CD34-positive cells. HGGs exhibited moderate and heterogeneous [68Ga]-PSMA-11 uptake, corresponding to a theoretical [177Lu] tumour dose lower than conventional external beam radiotherapy but within the range used for theranostic treatment in prostate cancer. PSMA staining was most prevalent in regions of tumour with necrotic/proliferating endothelium.

"Total Body" PET (TBP) represents a step change for all of PET, and brain imaging is no exception. Multiple benefits are already being seen. Lower injected doses benefit patients and staff and facilitate multitracer research. Shortened acquisition times for suitable radiopharmaceuticals enable better patient comfort and reduce the need for general anesthesia in children (albeit then partially negating the dose benefit). Later scanning enabled by the higher sensitivity palliates restrictions in tracer availability and allows better modeling. The large axial field-of-view enables better quantification via assessment of intravascular radioactivity concentrations. Early short-frame dynamic scanning has already enabled obtaining quantitative brain perfusion measurements for several radiopharmaceuticals. The large field-of-view allows assessment of brain-body interactions and multiorgan repercussions of disease. Despite some technical and methodological challenges remaining, TBP already offers added value in brain imaging, with exciting times ahead.

Recent work has shown improved lesion detectability and flexibility to reconstruction hyperparameters (e.g. scanner geometry or dose level) when PET images are reconstructed by leveraging pre-trained diffusion models. Such methods train a diffusion model (without sinogram data) on high-quality, but still noisy, PET images. In this work, we propose a simple method for generating subject-specific PET images from a dataset of multi-subject PET-MR scans, synthesizing "pseudo-PET" images by transforming between different patients' anatomy using image registration. The images we synthesize retain information from the subject's MR scan, leading to higher resolution and the retention of anatomical features compared to the original set of PET images. With simulated and real [^18F]FDG datasets, we show that pre-training a personalized diffusion model with subject-specific "pseudo-PET" images improves reconstruction accuracy with low-count data. In particular, the method shows promise in combining information from a guidance MR scan without overly imposing anatomical features, demonstrating an improved trade-off between reconstructing PET-unique image features versus features present in both PET and MR. We believe this approach for generating and utilizing synthetic data has further applications to medical imaging tasks, particularly because patient-specific PET images can be generated without resorting to generative deep learning or large training datasets.

Accurate segmentation of both the pituitary gland and adenomas from magnetic resonance imaging (MRI) is essential for diagnosis and treatment of pituitary adenomas. This systematic review evaluates automatic segmentation methods for improving the accuracy and efficiency of MRI-based segmentation of pituitary adenomas and the gland itself. We analysed 34 studies that employed automatic and semi-automatic segmentation methods out of 353 reviewed studies. We extracted and synthesized data on segmentation techniques and performance metrics (such as Dice overlap scores). The majority of reviewed studies utilized deep learning approaches, with U-Net-based models being the most prevalent. Automatic methods yielded Dice scores of 0%–89% for pituitary gland and 4%–96% for adenoma segmentation. Semi-automatic methods reported 80%–92% for pituitary gland and 75%–88% for adenoma segmentation. Most studies did not report important metrics such as MR field strength, age and adenoma size (macro/micro/giant) or even adenoma type and human subject numbers. Automated segmentation techniques such as U-Net-based models show promise, especially for adenoma segmentation, but further improvements are needed to achieve consistently good performance in small structures like the normal pituitary gland. Future progress will require methodological innovation and larger, more diverse datasets to enhance clinical applicability.Systematic Review Registration:https://www.crd.york.ac.uk/PROSPERO/view/CRD42023407127, PROSPERO CRD42023407127.

Positron emission tomography combined with magnetic resonance imaging (PET/MR) has not yet achieved the level of adoption of PET/CT. This study aimed to harmonise PET imaging protocols across a national PET/MR network and to quantitatively assess whether PET/MR can achieve reliability comparable to PET/CT. While previous PET test-retest studies have demonstrated good repeatability, they have typically been limited to small cohorts or restricted site configurations. We conducted a multi-site harmonisation and rigorous test-retest study across the network of eight PET/MR scanners. Thirty-seven healthy older participants (65-90 years) underwent harmonised one-hour amyloid PET/MR scans using either [ ^18 F]flutemetamol or [ ^18 F]florbetaben on two occasions. Retest scans were performed under conditions of same-site repeatability or multi-site reproducibility. Harmonised acquisition and reconstruction protocols were applied, and amyloid burden was quantified on the Centiloid (CL) scale. CL values across 74 scans showed excellent test-retest agreement (ICC = 0.968), improving to 0.987 after exclusion of one attenuation correction related outlier. Mean test-retest variability was 2.58

Accurate and reliable brain tumour segmentation from MRI remains a clinical challenge, particularly in low-resource settings such as Sub-Saharan Africa (SSA). We present BRAIN-CATS, a segmentation framework that combines the Attention U-Net architecture with calibration-aware training to improve both accuracy and model reliability. Our approach is specifically optimized for low-resolution, multi-modal MRI data typical in under-resourced environments. We trained the model using 5-fold cross-validation on n = 60 patients from the BraTS-Africa 2023 dataset, employing advanced preprocessing techniques, data augmentation, and a composite loss function that includes Dice, Binary Cross-Entropy, Focal Loss, and the marginal L1 Average Calibration Error (mL1-ACE). The calibration-aware component penalizes miscalibrated predictions, improving confidence estimates across tumour boundaries. The model achieved an average Dice score of 90.38

Deep learning models trained on datasets with spurious correlations can achieve high average accuracy whilst relying on shortcut features that do not generalise out of distribution. Whilst out-of-distribution testing highlights subgroup performance disparities arising from shortcut learning, it does not localise the regions within images that are associated with it. Existing research mostly uses attribution maps from interpretability methods to understand the spatial nature of spurious correlations. For example, conditional alignment methods separate task-relevant evidence from evidence tied to spurious correlations by comparing attribution maps from a task model, a sensitive attribute model, and a bias-reduced reference model. This yields shortcut-aligned and task-aligned contribution maps for each image. However, existing methods aggregate these maps across the dataset, potentially masking recurring spatial shortcut patterns that occur only in subsets of images. We address this limitation by grouping per-image shortcut and task contribution maps into recurring spatial patterns using K-means and non-negative matrix factorisation, and visualising the resulting shortcut groups through contribution maps and representative examples. Across CelebA, CheXpert, Waterbirds, Camelyon17, and ISIC2019, and across ResNet and ViT models, the discovered shortcut groups reveal both shared and distinct spatial patterns of shortcut and task contribution, with varying subgroup composition and error rates, enabling targeted inspection of image subsets with higher error rates. We perform input occlusion and internal test-time interventions to show that masking or suppressing task contribution regions substantially degrades the model classification performance and propose a combined shortcut suppression and task amplification feature intervention approach which generally reduces performance disparities.

Immune dysfunction is implicated in the pathophysiology of schizophrenia. The 18 kDa translocator protein (TSPO), expressed by various cell types, including microglia and astrocytes, is widely used as a marker for neuroinflammation and can be quantified in vivo using PET. However, findings from TSPO PET studies in recent-onset psychosis have been inconsistent, and it remains unknown whether TSPO levels can be modified in schizophrenia. We addressed these questions with a baseline case-control comparison of patients with a first-episode psychotic disorder who were symptomatic despite antipsychotic treatment and healthy volunteers, and a longitudinal study testing the effects of natalizumab (a monoclonal antibody previously shown to reduce TSPO levels in neuroinflammatory conditions) on TSPO levels and symptoms in patients. Baseline and 3-month follow-up brain imaging was carried out using 18F-DPA-714 TSPO PET, quantified as the distribution volume ratio (DVR) in total, frontal lobe and temporal lobe grey matter. A total of 103 volunteers (62 patients and 41 healthy controls) received baseline brain imaging, and 47 patients completed follow-up imaging after receiving natalizumab (n = 31) or placebo (n = 16) infusions. Natalizumab was well tolerated, with no serious treatment-related adverse events. The patient group also received clinical assessments with the Positive and Negative Syndrome Scale at baseline and follow-up. At baseline, DVR was significantly higher in patients relative to controls in total (eta(2) = 0.04) and temporal lobe (eta(2) = 0.06) grey matter. However, there was no significant change in DVR across these regions following natalizumab or placebo treatment. Mean +/- standard deviation (SD) CSF levels of natalizumab after treatment were 10.7 +/- 27.2 ng/ml, indicating that the monoclonal antibody crossed the blood-brain barrier. Patients receiving natalizumab showed a modest but statistically significant improvement in Positive and Negative Syndrome Scale total scores (mean +/- SD change: -3.7 +/- 9.1, Cohen's d = 0.40, P = 0.017), although there was no relationship between change in DVR and change in symptom severity (P > 0.05). These findings are consistent with elevated grey matter TSPO levels in first-episode psychosis relative to healthy controls. Although natalizumab treatment was associated with a modest reduction in symptoms, the absence of corresponding changes in DVR suggests that higher grey matter TSPO might reflect expression by non-microglial cells. The lack of significant changes in the placebo group indicates that it is a stable trait biomarker. Further work is needed to clarify the functional relevance and cellular specificity of TSPO alterations in psychosis.

Neurotransmitter receptors guide the propagation of signals between brain regions. Mapping receptor distributions in the brain is therefore necessary for understanding how neurotransmitter systems mediate the link between brain structure and function. Normative receptor density can be estimated using group averages from Positron Emission Tomography (PET) imaging. However, the generalizability and reliability of group-average receptor maps depends on the inter-individual variability of receptor density, which is currently unknown. Here we collect group standard deviation brain maps of PET-estimated protein abundance for 12 different neurotransmitter receptors and transporters across 7 neurotransmitter systems, including dopamine, serotonin, acetylcholine, glutamate, GABA, cannabinoid, and opioid. We illustrate how cortical and subcortical inter-individual variability of receptor and transporter density varies across brain regions and across neurotransmitter systems. We complement inter-individual variability with inter-regional variability, and show that receptors that vary more across brain regions than across individuals also demonstrate greater out-of-sample spatial consistency. Altogether, this work quantifies how receptor systems vary in healthy individuals, and provides a means of assessing the generalizability of PET-derived receptor density quantification.

Abstract Regional volumetric assessment of perinatal brain development is currently limited by the lack of consistent high quality multi-regional segmentation methods applicable to both fetal and neonatal MRI. We present Multi-BOUNTI, a deep learning pipeline for automated multi-lobe segmentation of fetal and neonatal T2w brain MRI. The method is based on a dedicated 43-label parcellation protocol and a 3D Attention U-Net trained on brain MRI datasets of subjects spanning 21–44 weeks gestational/postmenstrual age. The pipeline integrates preprocessing, segmentation and volumetric analysis, and was evaluated on independent datasets, demonstrating fast (< 10 min/case) and accurate performance with high agreement to manually refined labels. We demonstrate the application of the framework with 267 fetal and 593 neonatal MRI datasets from the developing Human Connectome Project without reported clinically significant brain anomalies to derive normative volumetric growth models across 21–44 weeks GA/PMA. These models were used to characterise developmental trajectories, assess differences between fetal and preterm neonatal cohorts, and analyse longitudinal changes. The resulting normative models were integrated into an automated reporting framework enabling subject-specific volumetric assessment via centiles and z-scores. Multi-BOUNTI provides a unified and scalable approach for perinatal brain segmentation and volumetry, supporting large-scale studies and facilitating future clinical translation. The full pipeline is publicly available at https://github.com/SVRTK/perinatal-brain-mri-analysis .

The robustness of machine learning models can be compromised by spurious correlations between non-causal features in the input data and target labels. A common way to test for such correlations is to train on data where the label is strongly tied to some non-causal cue, then evaluate on examples where that tie no longer holds. This idea is well established for classification tasks, but for semantic segmentation the specific failure modes are not well understood. We show that a model may achieve reasonable overlap while assigning the wrong semantic label, swapping one plausible foreground class for another, even when object boundaries are largely correct. We focus on this semantic label-flip behaviour and quantify it with a simple diagnostic (Flip) that counts how often ground truth foreground pixels are assigned the wrong foreground identity while remaining predicted as foreground. In a setting where category and scene are correlated during training, increasing the correlation consistently widens the gap between common and rare test conditions and increases these within-object label swaps on counterfactual groups. Overall, our results motivate assessing segmentation robustness under distribution shift beyond overlap by decomposing foreground errors into correct pixels, flipped-identity pixels, and missed-to-background pixels. We also propose an entropy-based, ground truth label-free `flip-risk' score, which is computed from foreground identity uncertainty, and show that it can flag flip-prone cases at inference time. Code is available at https://github.com/acharaakshit/label-flips.

Cortical folding of the brain is widely regarded as an interplay between genetic programming and biomechanical forces, closely linked to cytoarchitectonic regionalisation. Abnormal folding patterns are frequently observed in neurodevelopmental conditions and psychiatric disorders. However, significant inter-individual variability of secondary and tertiary folds obscures the detection of shape biomarkers and confounds the investigation of folding-functional relationships. Here, we investigate cortical folding heterogeneity at a fine scale, using Multimodal Surface Matching with Hierarchical Templates (MSM-HT), a hierarchical surface registration, to parse cortical folding patterns into a representative family of distinct anatomical templates. By applying this technique both to young adults from the Human Connectome Project (HCP) and neonates in the Developing HCP and Brain Imaging in Babies (BIBS) cohorts, we identify and characterise common lobe-wise folding patterns: observing consistency across both age groups, with neonatal samples showing less variation. Crucially, we highlight significant hemispheric asymmetry within the temporal lobe in adults, with a consistent trend in neonates. This study provides a critical step towards understanding brain asymmetry and complex relationships between folding and function, offering a robust framework to generalise the uncovered cortical folding motifs across datasets and developmental stages. Guo and colleagues parse cortical folding variability into a family of distinct anatomical motifs that generalise across adults and neonates. These motifs shed light on structural asymmetry, heritability, and folding-function links.

Harmonisation is widely used to mitigate site- and scanner-related batch variability in multisite neuroimaging studies and is particularly critical in longitudinal clinical trials, where detection of subtle biological or treatment-related changes depends on reliable measurement across scanners and timepoints. However, the effectiveness of harmonisation in small, heterogeneous clinical datasets remains insufficiently understood, particularly in relation to subject-level variability and consistency across acquisition settings, and its impact on both removal of technical variability and preservation of biological variation in pooled multisite analyses. We systematically evaluated a range of image-based and statistical harmonisation methods using a clinically realistic multisite, multiscanner structural T1-weighted (T1w) MRI test-retest dataset comprising three controlled acquisition scenarios: repeatability, intra-scanner reproducibility and inter-scanner reproducibility. Methods were applied under different batch specifications (site, scanner, or both) and performance was assessed within each scenario and in pooled data using a multi-metric framework capturing both technical and biological variability in volumetric imaging-derived phenotypes (IDPs) relevant to aging and dementia research. Across IDPs, before harmonisation variability was lowest in the repeatability scenario (median variability=0.6 to 2.7%, rank consistency ρ ≥0.9), with modest increases under intra-scanner reproducibility (0.5 to 3.2%, ρ=0.5 to 1.0) and substantially greater variability under inter-scanner reproducibility conditions (1.7 to 19.2%, ρ =−0.1 to 0.9). These results offer important information to consider for multisite study design, including sample size calculation in clinical trials. Harmonisation performance was strongly context dependent, with clearer benefits emerged in inter-scanner scenarios where both variability reduction and improvements in subject-level consistency were observed. In pooled data, approaches that explicitly modelled site as batch and accounted for repeated-measure structure showed greater consistency across IDPs in batch effect mitigation and more accurately reflected underlying biological variation. Our evaluation metrics enabled disentangling the removal of global batch effect while highlighting residual variability at the phenotype-specific or multivariate levels. These findings demonstrate that harmonisation cannot be treated as a one-size-fits-all solution and must be interpreted relative to the acquisition context, dataset structure, and downstream analytic goals. Multi-metric evaluation under realistic clinical constraints is essential to support reliable and translatable neuroimaging inference by ensuring appropriate correction of batch effects while preserving longitudinal biological signals and sensitivity to clinically meaningful change in multisite studies. ### Competing Interest Statement Author FB - Steering committee or Data Safety Monitoring Board member for Biogen, Merck, Eisai, Prothena and Idorsia. Advisory board member for Combinostics, Scottish Brain Sciences, IXICO and Alzheimer Europe. Consultant for Roche, Celltrion, Merck, Bracco. Research agreements with ADDI, Merck, Biogen, GE Healthcare, Icometrix, Roche. Co-founder and shareholder of Queen Square Analytics LTD. ### Funding Statement This study was supported by UK Medical Research Council Dementias Platform UK (MR/T033371/1), Alzheimers Association Grant (AARF-21-846366), and NIHR Oxford Health Biomedical Research Centre (NIHR203316). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The Centre for Integrative Neuroimaging was supported by core funding from the Wellcome Trust (203139/Z/16/Z and 203139/A/16/Z). Author J-P.T is supported by the NIHR Newcastle Biomedical Research Centre (BRC). Author FB is supported by the NIHR Biomedical Research Centre at UCLH. DT is supported by the UCLH NIHR Biomedical Research Centre. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of the NHS Health Research Authority (NRA) and the UK Administration of Radioactive Substances Advisory Committee (ARSAC) gave ethical approval for this work; The study was approved by an NHS Health Research Authority (NRA) ethics committee (Ref: 18/NW/0102; IRAS 223411) and the UK Administration of Radioactive Substances Advisory Committee (ARSAC). The University of Manchester acted as the sponsor for the study. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The DPUK PET/MR Network will make PET/MR imaging data and associated datasets available to the research community via controlled-access data-sharing procedures, in accordance with applicable data protection regulations.

Positron emission tomography (PET)-based connectivity analysis provides a molecular perspective that complements fMRI-derived functional connectivity. However, lack of standardized terminology and diverse methodologies in PET connectivity studies has resulted in inconsistencies, complicating the interpretation and comparison of results across studies. A standardized nomenclature is thus needed to reduce ambiguity, enhance reproducibility, and facilitate interpretability across radiotracers, imaging modalities and studies. Here, we define and differentiate the terms "molecular connectivity" and "molecular covariance". Drawing parallels from other imaging modalities, we propose "molecular connectivity" as an umbrella term to characterize statistical dependencies between the measured PET signal across brain regions at a within-subject level. Like fMRI resting-state functional connectivity, "molecular connectivity" leverages spatio-temporal associations in the PET signal to derive brain network associations. Conversely, "molecular covariance" denotes group-level computations of covariance matrices between-subjects. Further specification of the terminology can be achieved by including the target of the employed radioligand, such as "metabolic connectivity/covariance" for [18F]FDG or "amyloid covariance" for [18F]flutemetamol and "tau covariance" for [18F]flortaucipir. While this approach to standardization aims to clarify terminology, open questions remain about the neurobiological underpinnings of these connectivity metrics. Future research should focus on elucidating these mechanisms and developing advanced computational methodologies that evaluate diverse feature relationships and improve the robustness of PET-based connectivity metrics.

Magnetic resonance imaging (MRI) is the gold standard for brain imaging. Deep learning (DL) algorithms have been proposed to aid in the diagnosis of diseases such as Alzheimer's disease (AD) from MRI scans. However, DL algorithms can suffer from shortcut learning, in which spurious features, not directly related to the output label, are used for prediction. When these features are related to protected attributes, they can lead to performance bias against underrepresented protected groups, such as those defined by race and sex. In this work, we explore the potential for shortcut learning and demographic bias in DL based AD diagnosis from MRI. We first investigate if DL algorithms can identify race or sex from 3D brain MRI scans to establish the presence or otherwise of race and sex based distributional shifts. Next, we investigate whether training set imbalance by race or sex can cause a drop in model performance, indicating shortcut learning and bias. Finally, we conduct a quantitative and qualitative analysis of feature attributions in different brain regions for both the protected attribute and AD classification tasks. Through these experiments, and using multiple datasets and DL models (ResNet and SwinTransformer), we demonstrate the existence of both race and sex based shortcut learning and bias in DL based AD classification. Our work lays the foundation for fairer DL diagnostic tools in brain MRI. The code is provided at https://github.com/acharaakshit/ShortMR

Background:The COVID-19 pandemic disturbed sleep globally in both infected and uninfected individuals. Prolonged symptoms (particularly fatigue) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (post-COVID 2019 syndrome (PCS)) remain a health issue. Whether there is a relationship between PCS and sleep disturbance is largely unknown, with most studies lacking uninfected controls. We assessed sleep behaviours in a large UK cohort, analysing sleep disruption, fatigue, SARS-CoV-2 infection and symptom duration. Methods:UK adults previously recruited from the King's College London ZOE COVID Symptom Study to the COVID Symptom Study Biobank, with prospective symptom logging and SARS-CoV-2 testing, were invited to complete online validated questionnaires for sleep (Pittsburgh Sleep Quality Index, Sleep Condition Indicator, the STOP-Bang Questionnaire and Epworth Sleepiness Scale), fatigue (Chalder Fatigue Scale) and mental health (Generalised Anxiety Disorder 2 scale and Patient Health Questionnaire 2). Data were analysed considering SARS-CoV-2 infection, symptom duration and co-morbidities, including mental health. Results:Questionnaires were completed by 3833 of 8355 participants (2089 infected, 1721 uninfected, 23 unknown). Individuals with longer (versus shorter) symptom duration had poorer sleep scores for multiple questionnaires, but SARS-CoV-2 infection had no independent effect on sleep. However, previously infected (versus uninfected) individuals had greater fatigue, over a year since infection. Longer symptom duration, poorer sleep scores and greater fatigue were also associated with higher contemporaneous levels of anxiety and depression; however, an independent effect of prior SARS-CoV-2 infection on fatigue remained after adjustment. Higher body mass index, greater age and prior co-morbidities also independently worsened sleep scores. Conclusions:Sleep disturbance contributes to prolonged symptom reporting, irrespective of SARS-CoV-2 infection. Proven sleep interventions may help individuals with post-pandemic fatigue, including PCS.

BACKGROUND:COVID-19 symptoms may persist beyond acute SARS-CoV-2 infection, as ongoing symptomatic COVID-19 [OSC] (symptom duration 4-12 weeks) and post-COVID syndrome [PCS] (symptom duration ≥12 weeks). Vaccination against SARS-CoV-2 decreases OSC/PCS in individuals subsequently infected with SARS-CoV-2 post-vaccination. Whether vaccination against SARS-CoV-2, or any other vaccinations (such as against influenza) affects symptoms in individuals already experiencing OSC/PCS, more than natural symptom evolution, is unknown. METHOD:Using data from the ZOE COVID Symptom Study app, two comparative analyses were carried out, both in prospectively-reporting individuals with OSC/PCS: A) symptoms in individuals receiving first vaccination against SARS-CoV-2, compared with unvaccinated individuals, matched for age, sex, BMI and week of test (n=1679 in each group); B) symptoms in individuals receiving vaccination against influenza, compared with unvaccinated individuals, matched for age, sex, BMI, week of test and number of SARS-CoV-2 vaccinations (n=692 in each group). In both analyses, vaccination date (or equivalent time from start of symptoms in the unvaccinated group) was considered as the index time, and symptom evolution was measured by comparing symptoms during the second week before and second week after vaccination. Symptoms were considered by prevalence and burden over the considered periods; all results were adjusted for multiple comparisons. RESULTS:After first vaccination against SARS-CoV-2, many symptoms in individuals with OSC/PCS improved more rapidly than natural history resolution, including the commonly reported symptoms of fatigue (p<0.0001, β=--0.9 [95% CI: -1.86; -0.67]) and myalgia (p<0.001, β=-0.3 [95% CI: -0.50; -0.12]). No symptom worsened after vaccination. In contrast, there was no improvement in OSC/PCS symptoms beyond natural history resolution after vaccination against influenza. CONCLUSION:In individuals with OSC/PCS, symptom resolution improved after vaccination against SARS-CoV-2 ; this was not observed, however, after other vaccinations.