Long COVID: Lung Pathophysiology and its Relationship with Cognitive Dysfunction

Long COVID: Lung Pathophysiology and its Relationship with Cognitive Dysfunction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Long COVID: Lung Pathophysiology and its Relationship with Cognitive Dysfunction Keegan R Staab, Marrissa J McIntosh, Abhilash S Kizhakke Puliyakote, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7464124/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Post-acute sequelae of COVID-19 (Long COVID) includes physical and cognitive symptoms that can last long after acute infection. Links between lung pathophysiology and cognitive dysfunction in Long COVID remain largely unexplored. Long COVID participants were recruited from a post-COVID-19 clinic. Participants completed Patient-Reported Outcomes Measurement Information System (PROMIS) symptom questionnaires for Sleep Disturbance, Anxiety, Depression, and Cognitive Function, the National Institute of Health Toolbox Cognition Battery (NIHTB-CB), pulmonary function tests (spirometry, diffusion capacity of the lung), structural and functional brain magnetic resonance imaging (MRI), and 129 Xe MRI for ventilation and regional pulmonary gas exchange evaluation, at the same study visit. Bivariate relationships between lung and cognitive function in Long COVID were assessed using Spearman partial correlations, adjusted for age. Twelve participants (age=54±11 yrs.; 10 females) that were 32±5 months from infection were evaluated. PROMIS symptom scores indicated reduced perceived cognitive function in everyday life along with increased fatigue, anxiety, depressive symptoms, and sleep disturbance. However, objective cognitive function performance on NIHTB-CB were broadly within normal limits. Lower 129 Xe MRI gas exchange was correlated with more severe symptoms of sleep disturbance, reduced executive functioning performance, and elevated cerebral perfusion via brain MRI. These results are suggestive of a link between lung pathophysiology and cognitive dysfunction in this Long COVID population with enduring respiratory and cognitive symptoms more than two years after infection. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION A substantial portion of individuals recovering from acute COVID-19 continue to experience persistent COVID-related symptoms well beyond the resolution of the initial infection. These symptoms are highly heterogeneous and affect multiple organ systems 1 , 2 . The most common symptoms include dyspnea, fatigue, exercise intolerance and brain fog 1 , 3 . The persistence of symptoms for more than three months is broadly called Long COVID and, as recent evidence suggests, can last for years 1 . In the United States, Long COVID is estimated to affect approximately 14% of those initially infected with SARS-CoV-2 4 , though some estimates suggest the prevalence to be as high as 60%, impacting millions of people 5 . Dyspnea is one of the most prevalent symptoms of Long COVID, but unfortunately, traditional pulmonary function testing does not detect significant pulmonary dysfunction in most of these patients complicating both diagnosis and management. Hyperpolarized xenon-129 ( 129 Xe) magnetic resonance imaging (MRI) is a technique which allows for the sensitive, regional measurement of lung function, including gas exchange. This is possible because 129 Xe can freely diffuse into the lung parenchyma and blood plasma, and red blood cells (RBC) with distinct chemical shift frequencies on MRI. 129 Xe MRI has been used in several Long COVID studies to characterize ventilation and gas exchange 6 – 13 , revealing that 129 Xe has sufficient sensitivity to detect lung function abnormalities associated with Long COVID. Longitudinal studies further indicate that while some improvement in 129 Xe MRI measures occurs over time, abnormalities may persist for up to a year post infection 8 , 13 . In addition to respiratory symptoms, approximately 40% of Long COVID patients report brain fog such as memory complaints and trouble concentrating 14 . Brain MRI is used as a tool to study these cognitive and central nervous system manifestations. Results have varied between studies, however a review article of over 1200 Long COVID participants from 25 studies that used brain MRI highlighted gray matter (GM) atrophy, changes in diffusion weighted imaging, and a reduction in perfusion to be the most consistent findings 15 . Despite the clear respiratory involvement in acute COVID-19 and the persistence of both respiratory and cognitive symptoms in Long COVID, the relationship between pulmonary and neurological abnormalities remains poorly understood. To date, no prospective studies have systematically investigated the links between lung and brain dysfunction in this population. We have three primary hypotheses: 1) This Long COVID sample will have lower measures of gas exchange compared to the control population, 2) There is a link between pulmonary and cognitive function such that metrics of 129 Xe pulmonary gas exchange will correlate with objective cognitive function in people with Long COVID, and 3) 129 Xe MRI measures of lung function will correlate with brain MRI measures of volume, perfusion, and white matter (WM) integrity. RESULTS Study Sample Twelve participants (10 females; age=54±11 years, range=30-64 years) and ten healthy controls (8 females; age=47±12, range=28-65) were evaluated. Long COVID participants were 32±5 months (range=24-43 months) from acute COVID-19 infection. One participant was omitted from National Institute of Health Toolbox V3 Cognition Battery (NIHTB-CB) analysis due to non-credible performance validity identified using the EVI criteria described in the methods. Due to time constraints during study visits, not all neuroimaging scans were acquired for every participant. Diffusion tensor imaging (DTI) was obtained for 9 out of 12 participants and arterial spin labeling (ASL) was acquired for 10 out of 12 participants. However, structural brain scans were obtained for all participants. Demographics, Medical History and Lung Measures Cohort demographics and pulmonary function are shown in Table 1 . Long COVID participants and healthy controls were well-matched for age (p=0.17), sex (p=0.85), and BMI (p=0.36). A summary of pre-existing comorbidities and Long COVID symptoms are reported in Table 2 . Of the participants with a complete medical record, reported pre-existing comorbidities include hypertension (3/11), asthma (3/11), fibromyalgia (2/11), depression (2/11), neuropathy (2/11), diabetes mellitus (1/11), and sleep apnea (1/11). Each participant had a minimum of two symptoms that can be attributed to Long COVID with the most reported symptom being fatigue (8/11), followed by dyspnea (6/11), and headache (5/11). All participants had normal PFTs except one Long COVID participant who had a normal FVC (>80 percent predicted) but abnormal FEV1, FEV1/FVC, FEF25-75, and DLCO (all ≤70 percent predicted). There were no significant differences between Long COVID participants and healthy controls on PFT measures or 129 Xe pulmonary MRI (all p>0.35). Symptom Questionnaires and Objective Cognitive Functioning Box and whisker plots of t-scores for Patient-Reported Outcomes Measurement Information System (PROMIS) symptom questionnaires and NIHTB-CB are shown in Figure 1 . Mean PROMIS scores ( Table S1 ) for participants with Long COVID indicated significantly reduced perceived cognitive function in everyday life (µ t-score =33.9, p<0.001), and elevated symptoms of fatigue (µ t-score =62.7, p<0.001), anxiety (µ t-score =58.2, p=0.005), depression (µ t-score =56.8, p=0.001), and sleep disturbance (µ t-score =59.9, p<0.001), but average dyspnea severity (µ t-score =47.2, p=0.33), and pain interference (µ t-score =53.9, p=0.28) relative to normative expectations for healthy individuals. In contrast to the subjective report of significant cognitive difficulties in everyday life, the participants’ performance on objective NIHTB-CB tasks showed that ten of eleven participants had total cognitive composite scores that were within normal limits (i.e., within one standard deviation of the mean, or better). The single participant with total cognitive composite more than one standard deviation below the mean performed low on executive function (EF), processing speed (PS), and language domains. Mean t-scores for the sample were within normal limits for total cognitive composite (µ t-score =55.5, p=0.08), EF (µ t-score =49.8, p=0.95), PS (µ t-score =52.5, p=0.22), language (µ t-score =52.8, p=0.13), and memory (µ t-score =57.0, p=0.01). Association between Lung Function and Brain Function Figure 2 shows representative lung gas exchange and ASL perfusion images from two example participants. Participant A has a higher 129 Xe MRI measure of pulmonary gas exchange than Participant B (RBC:mem=0.49 vs 0.27), but a lower cerebral perfusion (CP; 26.8 ml/min/100g vs 46.9 ml/min/100g). Participant A also had higher objective cognitive function (t-score=65.0 vs 45.0) and lower sleep disturbances assessed via PROMIS (t-score=59.3 vs 52.8). Shown quantitatively in Figure 3 , RBC:mem and RBC:gas were negatively associated with total CP measured via ASL ( ρ =-0.70, p=0.04 for both measures). Each of which remained statistically significant when using anxiety and depression PROMIS questionnaire scores as covariates. Also in Figure 3 , RBC:mem ( ρ =-0.54, p=0.09) and RBC:gas ( ρ =-0.91, p<0.001) were inversely correlated with severity of sleep disturbance on questionnaires. Interestingly, CP also showed a strong positive relationship with sleep disturbance ( ρ =0.85, p=0.004). 129 Xe MRI measures were also associated with objective cognitive testing ( Figure 4 ). Specifically, RBC:mem was positively correlated with EF ( ρ =0.67, p=0.03) and showed a trend with total cognitive composite score ( ρ =0.55, p=0.10). The associations remained statistically significant when using anxiety and depression PROMIS questionnaire scores as covariates. In contrast, 129 Xe pulmonary MRI measures were not associated with subjective report of cognitive difficulties in everyday life (i.e., PROMIS Cognitive Function). An initial analysis of lobar brain volumes yielded significant relationships with 129 Xe pulmonary MRI that were no longer significant after accounting for age ( Table S2 ). Similarly, when evaluating WM hyperintensity volume and brain diffusion metrics (FA, MD, RD), relationships were not significant when controlling for age. DISCUSSION In this investigation of 12 patients with Long COVID more than two years post-acute infection, we observed significant associations between lung function on 129 Xe MRI, self-reported symptom surveys, quantitative cognitive performance and brain perfusion. In this study, we initially proposed three hypotheses. The most prominent findings were that reduced 129 Xe MRI gas exchange correlated with greater sleep disturbance, increased CP via ASL, and lower performance on executive function tests, which align with the second and third hypotheses. However, contrary to the first hypothesis, these participants showed no significant differences when compared to controls. Notably, while participants reported cognitive symptoms and sleep disturbance on subjective questionnaires, their objective cognitive test scores remained within normal limits. The discrepancy between self-reported symptoms of cognitive difficulties in everyday life and objective cognitive test performance is interesting. Some previous studies utilizing large research cohorts have documented reduced performance on objective measures of cognition including reasoning and executive function among patients with Long COVID 16 – 18 . But, our findings align with a body of literature reporting significant subjective cognitive difficulties in everyday life on questionnaire measures but average objective cognitive testing scores in Long COVID 19 , 20 . Ryan et al. 19 suggests that cognitive effects could be subclinical and compensated for in short bursts, potentially leading to a decline in cognitive function over the course of a day that is captured by patient reported outcomes but not by brief objective tests. Whiteside et al. 20 observed a significant relationship between cognitive complaints and psychological distress, thus concluding that mood and anxiety may be a significant contributor to perceived cognitive deficits. Our study adds a new perspective by highlighting the potential contribution of pulmonary function and respiratory disease processes to these symptoms. Increased work of breathing may exacerbate psychological distress and the perception of poor health. Sleep disturbance is also common in people with Long COVID 19 , 21 and can contribute to cognitive symptoms and mental health concerns. Poorer 129 Xe gas exchange was correlated with elevated sleep disturbance and increased brain perfusion. Breathing difficulties can lead to poorer sleep, but we found no statistical difference between Long COVID and healthy controls with respect to PFTs or 129 Xe MRI. Possibly pulmonary function recovery by the time of our studies well after acute infection had returned lung function back to normal, while autonomic control remained disrupted, impacting sleep or vice versa. The multifactorial nature of sleep disturbance, including stress and psychological distress, complicates efforts to establish direct causal links with cross sectional data. Brain perfusion studies previously observed decreased cerebral perfusion in Long COVID compared to healthy controls 22 , 23 , similar to findings in patients with COPD where cerebral blood flow was similarly lower 24 . Our finding of a negative association between gas exchange on 129 Xe MRI, i.e. poorer gas exchange, and brain perfusion deserves further mention and suggests the importance of brain perfusion in Long COVID. We emphasize that all brain MRI studies occurred prior to Xe MRI and so any residual subclinical anesthetic effects are not a factor. The negative association of lung function and brain perfusion could be a normal compensatory effect to offset reduced gas exchange efficiency by increasing oxygen delivery through increased blood flow to the brain. Further support of the importance of cerebral perfusion was its positive correlation with sleep disturbance. The correlation of pulmonary gas exchange and cerebral perfusion with each other and with sleep disturbance score suggest a possible common underlying mechanism such as systemic vascular injury or autonomic dysfunction contributing to both effects. A mechanism known as “fetal brain sparing” 25 has been discovered where resting hypoxemia will lead to vasodilation in the cerebrovascular bed of a fetus and a vasoconstriction in the periphery. However, these participants did not show any signs of resting hypoxia based on finger plethysmography during Xe MRI. These complex findings underscore the need for further research in larger, well-characterized cohorts, potentially incorporating exercise or stress testing to unmask physiological deficits. Moreover, they also suggest that interventions targeting vascular regulation and sleep quality may hold promise for managing persistent Long COVID symptoms. There are several important limitations of our study. This is a small sample, meaning it is potentially underpowered. Also, the absence of brain MRI and cognitive performance studies in our control sample for comparison with the Long COVID sample prevents evaluation of group differences. While we can show that pulmonary function is similar between the groups, it is impossible to confirm that brain perfusion differs from a healthy control sample. Therefore, we cannot determine whether these relationships are unique to the pathophysiology of Long COVID or are instead a normal compensatory mechanism of sleep disturbance or reduced pulmonary gas exchange. Moreover, it has been well established that pulmonary function and brain function change with age. Some variables in this study have reference standards (PFT, NIHTB-CB), but not the imaging metrics necessitating future comparisons to an age-matched control group. There has been recent work attempting to determine how aging and other demographic factors impact 129 Xe gas exchange, but this work is still ongoing 26 – 28 . The small sample size further limits our ability to generate a multivariate model, so in this study we account for age through partial correlations. In summary, we observed that participants with Long COVID report respiratory and cognitive symptoms, but appear normal when undergoing PFT, 129 Xe pulmonary MRI, and objective cognitive testing greater than two years after acute infection. 129 Xe pulmonary MRI measures of reduced gas exchange were correlated with sleep disturbance, EF, and cerebral perfusion, demonstrating a potential link between lung function and cognition that may be related to persistent symptoms from COVID-19 infection. METHODS Study Participants and Design Participants aged 18 years or older with persistent dyspnea and/or fatigue were recruited from a post-COVID-19 clinic at an academic medical center. Participants provided written informed consent to a protocol approved by the University of Iowa ethics board (IRB-01-202108151), which included longitudinal study visits. All experiments were performed in accordance with published guidelines, regulations and the Declaration of Helsinki. Four participants had been hospitalized during their acute infection, while the remainder were ambulatory. Inclusion criteria required a current negative nasopharyngeal swab COVID-19 PCR test and at least one of following: hospitalization during the acute infection, evidence of abnormal pulmonary function testing, or abnormalities on chest CT. Participants were excluded if there was a history of other cardiopulmonary disease or if they had any active respiratory infection. During a single study visit (Fig. 5 ), participants completed: symptom questionnaires, objective cognitive function testing, pulmonary function testing (PFT, including spirometry and diffusion capacity), brain MRI and 129 Xe pulmonary MRI. Healthy controls from another study, who only underwent 129 Xe pulmonary MRI and PFTs, were retrospectively included for comparisons. Controls underwent screening to verify no prior history of heart or lung disease and no respiratory or Long COVID symptoms. They were selected to have a similar age and sex distribution to the Long COVID sample. Symptoms Measures, Cognitive Function, and Pulmonary Function Testing Participants’ medical records were reviewed to identify Long COVID symptoms from their post-COVID clinic note, as well as identify any documented comorbidities. PROMIS symptom questionnaires were administered under supervision of study personnel to assess both physical and mental health 29 . Surveys included cognitive function (v2.0, form 10a), fatigue (v1.0, form 4a), anxiety (v1.0, form 4a), depression (v1.0, form 4a), sleep disturbance (v1.0, form 4a), dyspnea severity (v1.0, form 10a), and pain interference (v1.0, form 4a). Objective cognitive functioning was assessed using the performance-based NIHTB-CB 30 , 31 . Both the PROMIS questionnaires and the NIHTB-CB have normative samples available to calculate a t-score relative to expectations for healthy demographically matched individuals for each questionnaire or test. The t-distribution is centered at 50 and has a standard deviation of ten, meaning that a score of 60 would indicate a performance of one standard deviation above the mean. Age- and education-adjusted t-scores from all individual tests on NIHTB-CB were averaged to create a total cognition composite score. Domain t-score averages were also calculated including the following cognitive domains identified a priori: 1) EF ( Flanker Inhibitory Control and Attention, Dimensional Change Card Sort ), 2) PS ( Pattern Comparison Processing Speed, Oral Symbol Digit ), 3) language ( Oral Reading Recognition, Picture Vocabulary) , and 4) memory ( Picture Sequence Memory ). Evaluation of performance validity is often conducted during cognitive assessment to identify non-credible performance due to potential insufficient effort or engagement to ensure scores are reflective of true participant ability 32 . We examined embedded validity indicators (EVI) based on NIHTB-CB scores, using a method outlined in Abeare et al. 33 . This approach was applied to all participants’ NIHTB-CB profiles using liberal cutoffs for traditional EVIs. PFTs were performed by a certified respiratory therapist included forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FEV1/FVC ratio, forced mid-expiratory flow (FEF25-75), and diffusion capacity of the lungs for carbon monoxide (DLCO) according to American Thoracic Society and European Respiratory Society guidelines 34 , 35 . Brain MRI Acquisition and Analysis Brain MRI was acquired on a 3T Signa Premier (GE Healthcare, Waukesha, WI) using a 48-channel head coil with the participant supine and head-first orientation. The protocol included two structural images; a magnetization-prepared rapid gradient echo (MPRAGE) and a T2 weighted fluid-attenuated inversion recovery (FLAIR). The MPRAGE images were processed using BRAINSAutoWorkup to quantify cerebral GM and WM volumes 36 . Preprocessing was performed using Advanced Normalization Tools (ANTs) package and includes Rician denoising and an N4 bias field correction 37 . The pipeline also incorporates an iterative framework for brain parcellation adapted from Desikan-Killiany atlas 38 . FLAIR images were processed using FreeSurfer’s SAMSEG Tool to identify WM hyperintensity volume 39 . Two additional sequences were acquired including a DTI and T1 weighted pseudo-continuous ASL scan. DTI and ASL data were processed in FMRIB Software Library 40 . For DTI, Top-up and eddy tools were used to correct for phase encoding distortion and eddy current artifacts. DTIfit was then used to reconstruct diffusion tensor models for each voxel. These tensors were used to calculate fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD). The ASL acquisition included both a control and labeled image. These two images are subtracted to estimate CP. Anatomical and diffusion images were normalized to ICBM 2009b space. 129 Xe MRI Acquisition and Analysis Isotopically enriched 129 Xe was hyperpolarized using a commercial hyperpolarizer (Model 9820, Polarean, NC). Following hyperpolarization, where the xenon was cryogenically stored, the xenon was sublimated and dispensed into a 1L Tedlar dose delivery bag. The total volume per dose was calculated using 20% of the participants predicted FVC, based on age, sex, height and ethnicity 41 . 129 Xe MRI was acquired using the same MRI system previously mentioned for brain acquisition. 129 Xe images were analyzed using an in-house pipeline and ANTs. Images were bias field corrected using the N4 algorithm. The participant was positioned supine, feet-first and fitted with a commercial, single channel chest coil (Polarean Xenoview, Raleigh-Durham, NC). Three 129 Xe scans were obtained (A calibration, ventilation, and gas exchange acquisition); each under a 10 second breath hold 42 . The calibration scan was acquired to calculate the center frequency, transmit voltage, and echo time (TE) where alveolar-capillary membrane (mem) and red blood cell (RBC) compartments are 90 \(\:^\circ\:\) out of phase (TE90). The ventilation acquisition is a 2D multi-slice fast gradient echo. Using an adaptive K-means clustering algorithm 43 , a ventilation defect percentage (VDP) was calculated for each participant. Both the calibration and ventilation scans used high purity nitrogen as a buffer, yielding a dose equivalent volume of 91.6 ± 9.8 mL of 129 Xe 42 . Gas exchange images were obtained using a 1-point Dixon technique 42 , 44 . These scans used an interleaved 3D radial acquisition with 1000 radial projections of each gas and dissolved phase signal were acquired with 0.5 \(\:^\circ\:\) and 20 \(\:^\circ\:\) flip angles using the previously described TE90. Gas exchange was then quantified by taking the ratio of each compartment, generating RBC:mem, RBC:gas, and Mem:gas maps and measurements. These acquisitions used unbuffered, pure xenon with a dose equivalent volume of 195.5 ± 28.6 mL of 129 Xe. A table of all brain and pulmonary MRI metrics with their physiologic meaning is shown in Table 3 . Statistical Analysis R Statistical Software (V4.3.1, R Core Team, 2023) was used for statistical analysis. PFT and 129 Xe MRI metrics were compared between Long COVID and healthy controls using Welch’s t-test. PROMIS and NIHTB-CB scores were compared to the internal reference distribution using a one sample t-test. Bivariate relationships between lung function measures and brain outcomes were assessed using Spearman partial correlations with age as a covariate. PROMIS surveys of mental health (depression and anxiety) were also included as covariates, specifically for assessment of gas exchange to executive function and gas exchange to cerebral perfusion to confirm these relationships. Specifically, correlations were performed between measures of gas exchange on 129 Xe MRI with PROMIS symptom questionnaires (cognitive function and sleep disturbances), NIHTB-CB (total cognition composite and four cognitive domains) and brain MRI metrics (whole brain volume, WM hyperintensity volume, FA, MD, RD, and CP). Declarations Competing Interests SBF reports relationships with Siemens Healthcare, GE Healthcare, Polarean, Inc., and Regeneron Pharmaceuticals Inc. that include consulting, funded grants, and travel reimbursement. ADH reports relationships with Polarean, Inc. that includes consulting fees. EAH is a founder and shareholder of VIDA Diagnostics, a company commercializing lung image analysis software developed, in part, at the University of Iowa, & an unremunerated member of the Siemens Healthineers’ photon counting CT advisory board. APC reports relationships with VIDA Diagnostics that includes unremunerated consulting. KRS, MJM, ASK, NA, JLP, TL, JT, CL, CJW, EB, JCS and KH have no disclosures. FUNDING This study was funded by NIH NCATS S10OD026960, NIH CTSA program grant UM1TR004403, and the American Lung Association COVID-19 and Emerging Respiratory Viruses Research Award, NIH NHLBI R01HL169765, NIH NHLBI R01HL126771. Research funding for development of pulmonary imaging from GE Healthcare, Polarean PLC, and Siemens Healthcare. MJM is supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowship award. Author Contribution KRS was responsible for data analysis, statistical analysis, interpretation of the results and for preparing the first draft of the manuscript. MJM was responsible for data analysis, interpretation of the results and editing the manuscript. ASK was responsible for interpretation of the results. ADH was responsible for data analysis and interpretation of the results. NA was responsible for data collection and interpretation of the results. JLP was responsible for interpretation of the results. TL, JT and CL were responsible for data collection, data analysis and interpretation of the results. CJW and EB were responsible for data collection. JCS and EAH were responsible for developing the study design and interpretation of the results. APC was responsible for developing the study design, recruiting study participants and interpretation of the results. KH was responsible for developing the study design and interpretation of the results. SBF was responsible for developing the study design, interpretation of the results, and direction of all aspects of the study, and writing and editing the manuscript. All authors had an opportunity to review and revise the manuscript and approved its final submitted version. Acknowledgement The authors would like to acknowledge the support from Eric Axelson and Josh Cochran for processing brain MR images, Dr. Sinan Akay for performing clinical brain MRI overreads, and MRI technologists at the Magnetic Resonance Research Facility for operating the MRI scanner. Data Availability Data measures and analysis results can be made available by the corresponding author by request. References Fernandez-de-Las-Penas, C. Long COVID: current definition. Infection 50 , 285-286 (2022). https://doi.org:10.1007/s15010-021-01696-5 Crook, H., Raza, S., Nowell, J., Young, M. & Edison, P. Long covid-mechanisms, risk factors, and management. 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J. et al. Protocols for multi-site trials using hyperpolarized (129) Xe MRI for imaging of ventilation, alveolar-airspace size, and gas exchange: A position paper from the (129) Xe MRI clinical trials consortium. Magn Reson Med 86 , 2966-2986 (2021). https://doi.org:10.1002/mrm.28985 Zha, W. et al. Semiautomated Ventilation Defect Quantification in Exercise-induced Bronchoconstriction Using Hyperpolarized Helium-3 Magnetic Resonance Imaging: A Repeatability Study. Acad Radiol 23 , 1104-1114 (2016). https://doi.org:10.1016/j.acra.2016.04.005 Kaushik, S. S. et al. Single-breath clinical imaging of hyperpolarized (129)Xe in the airspaces, barrier, and red blood cells using an interleaved 3D radial 1-point Dixon acquisition. Magn Reson Med 75 , 1434-1443 (2016). https://doi.org:10.1002/mrm.25675 Tables Table 1. Demographics and Pulmonary Function Characteristic Long COVID (n = 12) Controls (n = 10) P value Demographics Age (years) 54 ± 11 47 ± 12 0.17 No. females, n (%) 10 (83) 8 (80) 0.85 BMI (kg/m 2 ) 30.2 ± 7.8 27.3 ± 7.0 0.36 Months since infection 32 ± 5 - - No. hospitalized, n (%) 4 (33) - - Education (years) 15 ± 2.7 - - Pulmonary Function Testing FEV 1 (% pred ) 102 ± 17 99 ± 12 0.62 FVC (% pred ) 107 ± 15 102 ± 14 0.45 FEV 1 /FVC (%) 76 ± 7 78 ± 4 0.38 FEF 25-75% (% pred ) 91 ± 28 90 ± 19 0.89 DL CO (% pred ) 96 ± 12 93 ± 6 0.57 129 Xe MRI VDP (%) 4.0 ± 6.7 2.1 ± 2.8 0.41 RBC:Mem 0.36 ± 0.12 0.38 ± 0.08 0.57 RBC:Gas (%) 0.37 ± 0.13 0.36 ± 0.07 0.82 Mem:Gas (%) 0.98 ± 0.20 0.94 ± 0.21 0.63 Data are presented as mean ± standard deviation unless indicated otherwise. P-value from Welch’s two sample t-test. PFT only available for 8/10 healthy controls. Abbreviations: BMI=body mass index; FEV 1 =forced expiratory volume in first second; %pred=percent of predicted value; FVC=forced vital capacity; FEF 25-75% =forced mid-expiratory flow; DL CO =diffusing capacity of the lung for carbon monoxide; MRI=magnetic resonance imaging; VDP=ventilation defect percent; RBC=red blood cell; Mem=membrane Table 2. Comorbidities and Long COVID Symptoms Characteristic Long COVID (n = 11) Pre-Existing Comorbidities Hypertension/ hypercholesterolemia 3 (27) Asthma 3 (27) Fibromyalgia 2 (18) Depression 2 (18) Neuropathy 2 (18) Diabetes Mellitus 1 (9) Sleep apnea 1 (9) Long COVID Symptoms Fatigue 8 (73) Dyspnoea 6 (55) Headache 5 (54) Cognitive impairment 4 (36) Joint pain 4 (36) Neuropathy 4 (36) Anosmia/ageusia 3 (27) Anxiety 3 (27) Depression 3 (27) Sleep impairment 3 (27) Vision problems 3 (27) Data are presented as n (%). Information was obtained from participants’ medical record. Pre-existing comorbidities were included if they were charted prior to COVID-19 infection. Long COVID symptoms were included from appointments relating to their Long COVID symptoms/diagnosis. Information was only available for 11/12 participants. Table 3. Imaging metrics and the physiologic meaning Metric Physiologic Meaning Brain Imaging MPRAGE (GM & WM Volume) Reflects the overall brain volume. Decreased volume occurs with aging but can indicate neurodegenerative processes. FLAIR (WM Hyperintensity Volume) Bright spots in the WM seen in FLAIR imaging, often associated with small vessel disease and aging. DTI (FA, MD, RD) Measures the structure and integrity of WM tracts through the diffusion of water molecules. Can offer insight into the organization of the WM and assess myelin damage ASL (CP) A non-invasive imaging technique that measures brain perfusion, typically reflective of the brain's metabolic activity. 129 Xe Pulmonary MRI VDP The percentage of the lung that is poorly/not ventilated. High VDP can be due to obstructive or restrictive lung diseases. RBC:mem Measures the ratio of xenon uptake by RBCs relative to the alveolar-capillary membrane. Reflects gas transfer efficiency RBC:gas Measures the ratio of xenon uptake by RBCs to the xenon in the alveolar airspace. Mem:gas Measures the ratio of xenon uptake in the alveolar-capillary membrane relative to the xenon in the alveolar airspace. Surrogate for membrane thickness. Abbreviations: GM=gray matter; WM=white matter; FLAIR=fluid attenuating inversion recovery; DTI=diffusion tensor imaging; FA=fractional anisotropy; MD=mean diffusivity; RD=radial diffusivity; ASL=arterial spin labeling; CP=cerebral perfusion; VDP=ventilation defect percent; RBC=red blood cell; Mem=membrane Additional Declarations Competing interest reported. SBF reports relationships with Siemens Healthcare, GE Healthcare, Polarean, Inc., and Regeneron Pharmaceuticals Inc. that include consulting, funded grants, and travel reimbursement. ADH reports relationships with Polarean, Inc. that includes consulting fees. EAH is a founder and shareholder of VIDA Diagnostics, a company commercializing lung image analysis software developed, in part, at the University of Iowa, & an unremunerated member of the Siemens Healthineers’ photon counting CT advisory board. APC reports relationships with VIDA Diagnostics that includes unremunerated consulting. 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07:21:46","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73948,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7464124/v1/04f6db47235d490ea5d32215.png"},{"id":93012296,"identity":"8ed5c537-648e-4a40-be91-93e144668d6b","added_by":"auto","created_at":"2025-10-08 07:21:47","extension":"xml","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":146900,"visible":true,"origin":"","legend":"","description":"","filename":"93721df6552c46eba0f8f96d624f3be11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7464124/v1/0cf3df8f1aa1f94f81424e1f.xml"},{"id":93012297,"identity":"4454cffe-a7db-4296-a6b3-3b6a308214a6","added_by":"auto","created_at":"2025-10-08 07:21:47","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":162895,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7464124/v1/dffcf551bcb66bb2341d100f.html"},{"id":93012269,"identity":"cf7d0037-e1ef-4aa2-9ba8-e69a69151475","added_by":"auto","created_at":"2025-10-08 07:21:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":135193,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBox and whisker plots for objective and subjective cognitive assessments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTop: PROMIS t-scores indicating decreased cognitive function (33.9[29.6-38.7]) as well as elevated fatigue (62.7[56.6-66.8]), anxiety (58.2[55.2-63.3]), depression (56.8[52.0-60.9]), and sleep disturbance (59.9[53.4-64.1]).\u003c/p\u003e\n\u003cp\u003eBottom: NIHTB-CB age and education adjusted t-scores indicating slightly decreased executive function (49.8[44.3-57.3]), and slightly increased processing speed (52.5[50.0-56.8]), language (52.8[51.3-55.8]), memory (57.0[52.3-62.0]), and total cognition composite scores (55.5[51.0-61.0]). All values reported as (median [1\u003csup\u003est\u003c/sup\u003e quartile-3\u003csup\u003erd\u003c/sup\u003e quartile]).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: PROMIS=Patient Report Outcomes Measurement Information System; NIHTB-CB=National Institute of Health Toolbox Cognition Battery\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7464124/v1/13cd3be5f06b71ebf29dac12.jpg"},{"id":93014906,"identity":"f477b47d-88a5-4466-a194-969129d99854","added_by":"auto","created_at":"2025-10-08 07:45:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":871551,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGas exchange and ASL perfusion images\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRepresentative images of \u003csup\u003e129\u003c/sup\u003eXe gas exchange [RBC:mem, RBC:gas, mem:gas] and brain ASL perfusion overlayed onto an anatomical T1 image from a 36 year old female (Participant A) and a 58 year old female (Participant B). Functional \u003csup\u003e129\u003c/sup\u003eXe maps are colored based on standard deviation from a healthy reference distribution mean (green being normal healthy reference).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: RBC=red blood cell; mem=membrane; ASL=arterial spin labeling\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7464124/v1/1a37d9193ae41e8c3020be52.jpg"},{"id":93012272,"identity":"37b1973e-e1cf-43b5-a82e-3d98b2f44827","added_by":"auto","created_at":"2025-10-08 07:21:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":161115,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationships between pulmonary gas exchange, sleep disturbance, and brain perfusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTop row:\u003c/em\u003e PROMIS sleep disturbance is inversely correlated with RBC:Mem (\u003cem\u003eρ\u003c/em\u003e= -0.54, p = 0.09) and RBC:Gas (\u003cem\u003eρ\u003c/em\u003e = -0.91, p \u0026lt; 0.001). High PROMIS sleep disturbance scores indicate worse sleep quality.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBottom row:\u003c/em\u003e CP is inversely correlated with RBC:Mem (\u003cem\u003eρ\u003c/em\u003e=-0.699, p=0.036) and RBC:Gas (\u003cem\u003eρ\u003c/em\u003e=-0.701, p=0.035). Correlations are spearman partial correlations controlling for age.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: RBC=red blood cell; Mem=membrane; CP=cerebral perfusion\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7464124/v1/1376aca70a4bafa076b783a4.jpg"},{"id":93014196,"identity":"61de5e7b-2991-4cf0-90f2-58de80d2e2c1","added_by":"auto","created_at":"2025-10-08 07:37:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":153885,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationships between pulmonary gas exchange and cognition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTop row\u003c/em\u003e: Total cognition composite t-scores trend towards a relationship with RBC:Mem (\u003cem\u003eρ\u003c/em\u003e= 0.55, p = 0.10) but not with RBC:Gas (\u003cem\u003eρ\u003c/em\u003e = 0.23, p = 0.52)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBottom row:\u003c/em\u003e Executive function cognitive construct t-scores are correlated with RBC:Mem (\u003cem\u003eρ\u003c/em\u003e = 0.67, p = 0.03) but not with RBC:Gas (\u003cem\u003eρ\u003c/em\u003e = 0.50, p = 0.14) Correlations are spearman partial correlations controlling for age.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: RBC=red blood cell; Mem=membrane\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7464124/v1/adad043ace192d24e9e22cb4.jpg"},{"id":93012287,"identity":"93d4149f-b0e5-4544-aa42-3b9e70cbb7d3","added_by":"auto","created_at":"2025-10-08 07:21:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":113134,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy visit timeline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChronological order of a participant study visit. Participants began with PFT, PROMIS, and NIHTB-CB. Then brain imaging was performed, followed by \u003csup\u003e129\u003c/sup\u003eXe MRI.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: PFT=pulmonary function testing; PROMIS=Patient-Reported Outcomes Measurement Information System; NIHTB-CB=National Institute of Health Toolbox Cognition Battery; MPRAGE=magnetization prepared rapid gradient echo; ASL=arterial spin labeling; FLAIR=fluid attenuating inversion recovery; DTI=diffusion tensor imaging; MRI=magnetic resonance imaging\u003cbr\u003e\n\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7464124/v1/78421b2b55e4154cb12dfd25.jpg"},{"id":97178525,"identity":"258fe3b9-281a-4e3a-8d94-0254808244f3","added_by":"auto","created_at":"2025-12-01 16:10:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2591372,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7464124/v1/cfda6782-423f-4901-a1cf-e77bcbb5b4f4.pdf"},{"id":93012270,"identity":"c991df09-5d39-4778-9f20-fc4928809c23","added_by":"auto","created_at":"2025-10-08 07:21:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21571,"visible":true,"origin":"","legend":"","description":"","filename":"LongCOVIDLungBrainSupplementalInformationFinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-7464124/v1/9547901e62460ea40359b9db.docx"}],"financialInterests":"Competing interest reported. SBF reports relationships with Siemens Healthcare, GE Healthcare, Polarean, Inc., and Regeneron Pharmaceuticals Inc. that include consulting, funded grants, and travel reimbursement. ADH reports relationships with Polarean, Inc. that includes consulting fees. EAH is a founder and shareholder of VIDA Diagnostics, a company commercializing lung image analysis software developed, in part, at the University of Iowa, \u0026 an unremunerated member of the Siemens Healthineers’ photon counting CT advisory board. APC reports relationships with VIDA Diagnostics that includes unremunerated consulting. KRS, MJM, ASK, NA, JLP, TL, JT, CL, CJW, EB, JCS and KH have no disclosures.","formattedTitle":"Long COVID: Lung Pathophysiology and its Relationship with Cognitive Dysfunction","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eA substantial portion of individuals recovering from acute COVID-19 continue to experience persistent COVID-related symptoms well beyond the resolution of the initial infection. These symptoms are highly heterogeneous and affect multiple organ systems\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The most common symptoms include dyspnea, fatigue, exercise intolerance and brain fog\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The persistence of symptoms for more than three months is broadly called Long COVID and, as recent evidence suggests, can last for years\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In the United States, Long COVID is estimated to affect approximately 14% of those initially infected with SARS-CoV-2\u003csup\u003e4\u003c/sup\u003e, though some estimates suggest the prevalence to be as high as 60%, impacting millions of people\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDyspnea is one of the most prevalent symptoms of Long COVID, but unfortunately, traditional pulmonary function testing does not detect significant pulmonary dysfunction in most of these patients complicating both diagnosis and management. Hyperpolarized xenon-129 (\u003csup\u003e129\u003c/sup\u003eXe) magnetic resonance imaging (MRI) is a technique which allows for the sensitive, regional measurement of lung function, including gas exchange. This is possible because \u003csup\u003e129\u003c/sup\u003eXe can freely diffuse into the lung parenchyma and blood plasma, and red blood cells (RBC) with distinct chemical shift frequencies on MRI. \u003csup\u003e129\u003c/sup\u003eXe MRI has been used in several Long COVID studies to characterize ventilation and gas exchange\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11 CR12\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, revealing that \u003csup\u003e129\u003c/sup\u003eXe has sufficient sensitivity to detect lung function abnormalities associated with Long COVID. Longitudinal studies further indicate that while some improvement in \u003csup\u003e129\u003c/sup\u003eXe MRI measures occurs over time, abnormalities may persist for up to a year post infection\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn addition to respiratory symptoms, approximately 40% of Long COVID patients report brain fog such as memory complaints and trouble concentrating\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Brain MRI is used as a tool to study these cognitive and central nervous system manifestations. Results have varied between studies, however a review article of over 1200 Long COVID participants from 25 studies that used brain MRI highlighted gray matter (GM) atrophy, changes in diffusion weighted imaging, and a reduction in perfusion to be the most consistent findings\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite the clear respiratory involvement in acute COVID-19 and the persistence of both respiratory and cognitive symptoms in Long COVID, the relationship between pulmonary and neurological abnormalities remains poorly understood. To date, no prospective studies have systematically investigated the links between lung and brain dysfunction in this population. We have three primary hypotheses: 1) This Long COVID sample will have lower measures of gas exchange compared to the control population, 2) There is a link between pulmonary and cognitive function such that metrics of \u003csup\u003e129\u003c/sup\u003eXe pulmonary gas exchange will correlate with objective cognitive function in people with Long COVID, and 3) \u003csup\u003e129\u003c/sup\u003eXe MRI measures of lung function will correlate with brain MRI measures of volume, perfusion, and white matter (WM) integrity.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy Sample\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwelve participants (10 females; age=54\u0026plusmn;11 years, range=30-64 years) and ten healthy controls (8 females; age=47\u0026plusmn;12, range=28-65) were evaluated. Long COVID participants were 32\u0026plusmn;5 months (range=24-43 months) from acute COVID-19 infection. One participant was omitted from\u0026nbsp;National Institute of Health Toolbox V3 Cognition Battery\u0026nbsp;(NIHTB-CB) analysis due to non-credible performance validity identified using the EVI criteria described in the methods. Due to time constraints during study visits, not all neuroimaging scans were acquired for every participant.\u0026nbsp;Diffusion tensor imaging\u0026nbsp;(DTI) was obtained for 9 out of 12 participants and\u0026nbsp;arterial spin labeling\u0026nbsp;(ASL) was acquired for 10 out of 12 participants. However, structural brain scans were obtained for all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDemographics, Medical History and Lung Measures\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCohort demographics and pulmonary function are shown in \u003cstrong\u003eTable 1\u003c/strong\u003e. Long COVID participants and healthy controls were well-matched for age (p=0.17), sex (p=0.85), and BMI (p=0.36). A summary of pre-existing comorbidities and Long COVID symptoms are reported in \u003cstrong\u003eTable 2\u003c/strong\u003e. Of the participants with a complete medical record, reported pre-existing comorbidities include hypertension (3/11), asthma (3/11), fibromyalgia (2/11), depression (2/11), neuropathy (2/11), diabetes mellitus (1/11), and sleep apnea (1/11). Each participant had a minimum of two symptoms that can be attributed to Long COVID with the most reported symptom being fatigue (8/11), followed by dyspnea (6/11), and headache (5/11). All participants had normal PFTs except one Long COVID participant who had a normal FVC (\u0026gt;80 percent predicted) but abnormal FEV1, FEV1/FVC, FEF25-75, and DLCO (all \u0026le;70 percent predicted). There were no significant differences between Long COVID participants and healthy controls on PFT measures or \u003csup\u003e129\u003c/sup\u003eXe pulmonary MRI (all p\u0026gt;0.35).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSymptom Questionnaires and Objective Cognitive Functioning\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBox and whisker plots of t-scores for\u0026nbsp;Patient-Reported Outcomes Measurement Information System (PROMIS) symptom questionnaires and NIHTB-CB are shown in \u003cstrong\u003eFigure 1\u003c/strong\u003e. Mean PROMIS scores (\u003cstrong\u003eTable S1\u003c/strong\u003e) for participants with Long COVID indicated significantly reduced perceived cognitive function in everyday life (\u0026micro;\u003csub\u003et-score\u003c/sub\u003e=33.9, p\u0026lt;0.001), and elevated symptoms of fatigue (\u0026micro;\u003csub\u003et-score\u003c/sub\u003e=62.7, p\u0026lt;0.001), anxiety (\u0026micro;\u003csub\u003et-score\u003c/sub\u003e=58.2, p=0.005), depression (\u0026micro;\u003csub\u003et-score\u003c/sub\u003e=56.8, p=0.001), and sleep disturbance (\u0026micro;\u003csub\u003et-score\u003c/sub\u003e=59.9, p\u0026lt;0.001), but average dyspnea severity (\u0026micro;\u003csub\u003et-score\u003c/sub\u003e=47.2, p=0.33), and pain interference (\u0026micro;\u003csub\u003et-score\u003c/sub\u003e=53.9, p=0.28) relative to normative expectations for healthy individuals. In contrast to the subjective report of significant cognitive difficulties in everyday life, the participants\u0026rsquo; performance on objective NIHTB-CB tasks showed that ten of eleven participants had total cognitive composite scores that were within normal limits (i.e., within one standard deviation of the mean, or better). The single participant with total cognitive composite more than one standard deviation below the mean performed low on executive function (EF), processing speed (PS), and language domains. Mean t-scores for the sample were within normal limits for total cognitive composite (\u0026micro;\u003csub\u003et-score\u003c/sub\u003e=55.5, p=0.08), EF (\u0026micro;\u003csub\u003et-score\u003c/sub\u003e=49.8, p=0.95), PS (\u0026micro;\u003csub\u003et-score\u003c/sub\u003e=52.5, p=0.22), language (\u0026micro;\u003csub\u003et-score\u003c/sub\u003e=52.8, p=0.13), and memory (\u0026micro;\u003csub\u003et-score\u003c/sub\u003e=57.0, p=0.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssociation between Lung Function and Brain Function\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2\u003c/strong\u003e shows representative lung gas exchange and ASL perfusion images from two example participants. Participant A has a higher \u003csup\u003e129\u003c/sup\u003eXe MRI measure of pulmonary gas exchange than Participant B (RBC:mem=0.49 vs 0.27), but a lower cerebral perfusion (CP; 26.8 ml/min/100g vs 46.9 ml/min/100g). Participant A also had higher objective cognitive function (t-score=65.0 vs 45.0) and lower sleep disturbances assessed via PROMIS (t-score=59.3 vs 52.8). Shown quantitatively in \u003cstrong\u003eFigure 3\u003c/strong\u003e, RBC:mem and RBC:gas were negatively associated with total CP measured via ASL (\u003cem\u003e\u0026rho;\u003c/em\u003e=-0.70, p=0.04 for both measures). Each of which remained statistically significant when using anxiety and depression PROMIS questionnaire scores as covariates. Also in \u003cstrong\u003eFigure 3\u003c/strong\u003e, RBC:mem (\u003cem\u003e\u0026rho;\u003c/em\u003e=-0.54, p=0.09) and RBC:gas (\u003cem\u003e\u0026rho;\u003c/em\u003e=-0.91, p\u0026lt;0.001) were inversely correlated with severity of sleep disturbance on questionnaires. Interestingly, CP also showed a strong positive relationship with sleep disturbance (\u003cem\u003e\u0026rho;\u003c/em\u003e=0.85, p=0.004).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e129\u003c/sup\u003eXe MRI measures were also associated with objective cognitive testing (\u003cstrong\u003eFigure 4\u003c/strong\u003e). Specifically, RBC:mem was positively correlated with EF (\u003cem\u003e\u0026rho;\u003c/em\u003e=0.67, p=0.03) and showed a trend with total cognitive composite score (\u003cem\u003e\u0026rho;\u003c/em\u003e=0.55, p=0.10). The associations remained statistically significant when using anxiety and depression PROMIS questionnaire scores as covariates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast, \u003csup\u003e129\u003c/sup\u003eXe pulmonary MRI measures were not associated with subjective report of cognitive difficulties in everyday life (i.e., PROMIS Cognitive Function). \u0026nbsp;An initial analysis of lobar brain volumes yielded significant relationships with \u003csup\u003e129\u003c/sup\u003eXe pulmonary MRI that were no longer significant after accounting for age (\u003cstrong\u003eTable S2\u003c/strong\u003e). Similarly, when evaluating WM hyperintensity volume and brain diffusion metrics (FA, MD, RD), relationships were not significant when controlling for age.\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this investigation of 12 patients with Long COVID more than two years post-acute infection, we observed significant associations between lung function on \u003csup\u003e129\u003c/sup\u003eXe MRI, self-reported symptom surveys, quantitative cognitive performance and brain perfusion. In this study, we initially proposed three hypotheses. The most prominent findings were that reduced \u003csup\u003e129\u003c/sup\u003eXe MRI gas exchange correlated with greater sleep disturbance, increased CP via ASL, and lower performance on executive function tests, which align with the second and third hypotheses. However, contrary to the first hypothesis, these participants showed no significant differences when compared to controls. Notably, while participants reported cognitive symptoms and sleep disturbance on subjective questionnaires, their objective cognitive test scores remained within normal limits.\u003c/p\u003e\n\u003cp\u003eThe discrepancy between self-reported symptoms of cognitive difficulties in everyday life and objective cognitive test performance is interesting. Some previous studies utilizing large research cohorts have documented reduced performance on objective measures of cognition including reasoning and executive function among patients with Long COVID\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. But, our findings align with a body of literature reporting significant \u003cem\u003esubjective\u003c/em\u003e cognitive difficulties in everyday life on questionnaire measures but average \u003cem\u003eobjective\u003c/em\u003e cognitive testing scores in Long COVID\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Ryan et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e suggests that cognitive effects could be subclinical and compensated for in short bursts, potentially leading to a decline in cognitive function over the course of a day that is captured by patient reported outcomes but not by brief objective tests. Whiteside et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e observed a significant relationship between cognitive complaints and psychological distress, thus concluding that mood and anxiety may be a significant contributor to perceived cognitive deficits.\u003c/p\u003e\n\u003cp\u003eOur study adds a new perspective by highlighting the potential contribution of pulmonary function and respiratory disease processes to these symptoms. Increased work of breathing may exacerbate psychological distress and the perception of poor health. Sleep disturbance is also common in people with Long COVID\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and can contribute to cognitive symptoms and mental health concerns. Poorer \u003csup\u003e129\u003c/sup\u003eXe gas exchange was correlated with elevated sleep disturbance and increased brain perfusion. Breathing difficulties can lead to poorer sleep, but we found no statistical difference between Long COVID and healthy controls with respect to PFTs or \u003csup\u003e129\u003c/sup\u003eXe MRI. Possibly pulmonary function recovery by the time of our studies well after acute infection had returned lung function back to normal, while autonomic control remained disrupted, impacting sleep or vice versa. The multifactorial nature of sleep disturbance, including stress and psychological distress, complicates efforts to establish direct causal links with cross sectional data.\u003c/p\u003e\n\u003cp\u003eBrain perfusion studies previously observed decreased cerebral perfusion in Long COVID compared to healthy controls\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, similar to findings in patients with COPD where cerebral blood flow was similarly lower\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Our finding of a negative association between gas exchange on \u003csup\u003e129\u003c/sup\u003eXe MRI, i.e. poorer gas exchange, and brain perfusion deserves further mention and suggests the importance of brain perfusion in Long COVID. We emphasize that all brain MRI studies occurred prior to Xe MRI and so any residual subclinical anesthetic effects are not a factor. The negative association of lung function and brain perfusion could be a normal compensatory effect to offset reduced gas exchange efficiency by increasing oxygen delivery through increased blood flow to the brain. Further support of the importance of cerebral perfusion was its positive correlation with sleep disturbance. The correlation of pulmonary gas exchange and cerebral perfusion with each other and with sleep disturbance score suggest a possible common underlying mechanism such as systemic vascular injury or autonomic dysfunction contributing to both effects. A mechanism known as \u0026ldquo;fetal brain sparing\u0026rdquo;\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e has been discovered where resting hypoxemia will lead to vasodilation in the cerebrovascular bed of a fetus and a vasoconstriction in the periphery. However, these participants did not show any signs of resting hypoxia based on finger plethysmography during Xe MRI. These complex findings underscore the need for further research in larger, well-characterized cohorts, potentially incorporating exercise or stress testing to unmask physiological deficits. Moreover, they also suggest that interventions targeting vascular regulation and sleep quality may hold promise for managing persistent Long COVID symptoms.\u003c/p\u003e\n\u003cp\u003eThere are several important limitations of our study. This is a small sample, meaning it is potentially underpowered. Also, the absence of brain MRI and cognitive performance studies in our control sample for comparison with the Long COVID sample prevents evaluation of group differences. While we can show that pulmonary function is similar between the groups, it is impossible to confirm that brain perfusion differs from a healthy control sample. Therefore, we cannot determine whether these relationships are unique to the pathophysiology of Long COVID or are instead a normal compensatory mechanism of sleep disturbance or reduced pulmonary gas exchange. Moreover, it has been well established that pulmonary function and brain function change with age. Some variables in this study have reference standards (PFT, NIHTB-CB), but not the imaging metrics necessitating future comparisons to an age-matched control group. There has been recent work attempting to determine how aging and other demographic factors impact \u003csup\u003e129\u003c/sup\u003eXe gas exchange, but this work is still ongoing\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The small sample size further limits our ability to generate a multivariate model, so in this study we account for age through partial correlations.\u003c/p\u003e\n\u003cp\u003eIn summary, we observed that participants with Long COVID report respiratory and cognitive symptoms, but appear normal when undergoing PFT, \u003csup\u003e129\u003c/sup\u003eXe pulmonary MRI, and objective cognitive testing greater than two years after acute infection. \u003csup\u003e129\u003c/sup\u003eXe pulmonary MRI measures of reduced gas exchange were correlated with sleep disturbance, EF, and cerebral perfusion, demonstrating a potential link between lung function and cognition that may be related to persistent symptoms from COVID-19 infection.\u003c/p\u003e"},{"header":"METHODS","content":"\u003ch2\u003eStudy Participants and Design\u003c/h2\u003e\n\u003cp\u003eParticipants aged 18 years or older with persistent dyspnea and/or fatigue were recruited from a post-COVID-19 clinic at an academic medical center. Participants provided written informed consent to a protocol approved by the University of Iowa ethics board (IRB-01-202108151), which included longitudinal study visits. All experiments were performed in accordance with published guidelines, regulations and the Declaration of Helsinki. Four participants had been hospitalized during their acute infection, while the remainder were ambulatory. Inclusion criteria required a current negative nasopharyngeal swab COVID-19 PCR test and at least one of following: hospitalization during the acute infection, evidence of abnormal pulmonary function testing, or abnormalities on chest CT. Participants were excluded if there was a history of other cardiopulmonary disease or if they had any active respiratory infection. During a single study visit (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), participants completed: symptom questionnaires, objective cognitive function testing, pulmonary function testing (PFT, including spirometry and diffusion capacity), brain MRI and \u003csup\u003e129\u003c/sup\u003eXe pulmonary MRI. Healthy controls from another study, who only underwent \u003csup\u003e129\u003c/sup\u003eXe pulmonary MRI and PFTs, were retrospectively included for comparisons. Controls underwent screening to verify no prior history of heart or lung disease and no respiratory or Long COVID symptoms. They were selected to have a similar age and sex distribution to the Long COVID sample.\u003c/p\u003e\n\u003ch3\u003eSymptoms Measures, Cognitive Function, and Pulmonary Function Testing\u003c/h3\u003e\n\u003cp\u003eParticipants\u0026rsquo; medical records were reviewed to identify Long COVID symptoms from their post-COVID clinic note, as well as identify any documented comorbidities. PROMIS symptom questionnaires were administered under supervision of study personnel to assess both physical and mental health\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Surveys included cognitive function (v2.0, form 10a), fatigue (v1.0, form 4a), anxiety (v1.0, form 4a), depression (v1.0, form 4a), sleep disturbance (v1.0, form 4a), dyspnea severity (v1.0, form 10a), and pain interference (v1.0, form 4a). Objective cognitive functioning was assessed using the performance-based NIHTB-CB\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Both the PROMIS questionnaires and the NIHTB-CB have normative samples available to calculate a t-score relative to expectations for healthy demographically matched individuals for each questionnaire or test. The t-distribution is centered at 50 and has a standard deviation of ten, meaning that a score of 60 would indicate a performance of one standard deviation above the mean.\u003c/p\u003e\n\u003cp\u003eAge- and education-adjusted t-scores from all individual tests on NIHTB-CB were averaged to create a total cognition composite score. Domain t-score averages were also calculated including the following cognitive domains identified a priori: 1) EF (\u003cem\u003eFlanker Inhibitory Control and Attention, Dimensional Change Card Sort\u003c/em\u003e), 2) PS (\u003cem\u003ePattern Comparison Processing Speed, Oral Symbol Digit\u003c/em\u003e), 3) language (\u003cem\u003eOral Reading Recognition, Picture Vocabulary)\u003c/em\u003e, and 4) memory (\u003cem\u003ePicture Sequence Memory\u003c/em\u003e). Evaluation of performance validity is often conducted during cognitive assessment to identify non-credible performance due to potential insufficient effort or engagement to ensure scores are reflective of true participant ability\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. We examined embedded validity indicators (EVI) based on NIHTB-CB scores, using a method outlined in Abeare et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This approach was applied to all participants\u0026rsquo; NIHTB-CB profiles using liberal cutoffs for traditional EVIs.\u003c/p\u003e\n\u003cp\u003ePFTs were performed by a certified respiratory therapist included forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FEV1/FVC ratio, forced mid-expiratory flow (FEF25-75), and diffusion capacity of the lungs for carbon monoxide (DLCO) according to American Thoracic Society and European Respiratory Society guidelines\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eBrain MRI Acquisition and Analysis\u003c/h2\u003e\n \u003cp\u003eBrain MRI was acquired on a 3T Signa Premier (GE Healthcare, Waukesha, WI) using a 48-channel head coil with the participant supine and head-first orientation. The protocol included two structural images; a magnetization-prepared rapid gradient echo (MPRAGE) and a T2 weighted fluid-attenuated inversion recovery (FLAIR). The MPRAGE images were processed using BRAINSAutoWorkup to quantify cerebral GM and WM volumes \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Preprocessing was performed using Advanced Normalization Tools (ANTs) package and includes Rician denoising and an N4 bias field correction \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The pipeline also incorporates an iterative framework for brain parcellation adapted from Desikan-Killiany atlas\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. FLAIR images were processed using FreeSurfer\u0026rsquo;s SAMSEG Tool to identify WM hyperintensity volume\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eTwo additional sequences were acquired including a DTI and T1 weighted pseudo-continuous ASL scan. DTI and ASL data were processed in FMRIB Software Library\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. For DTI, Top-up and eddy tools were used to correct for phase encoding distortion and eddy current artifacts. DTIfit was then used to reconstruct diffusion tensor models for each voxel. These tensors were used to calculate fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD). The ASL acquisition included both a control and labeled image. These two images are subtracted to estimate CP. Anatomical and diffusion images were normalized to ICBM 2009b space.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u003cstrong\u003e129\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eXe MRI Acquisition and Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIsotopically enriched \u003csup\u003e129\u003c/sup\u003eXe was hyperpolarized using a commercial hyperpolarizer (Model 9820, Polarean, NC). Following hyperpolarization, where the xenon was cryogenically stored, the xenon was sublimated and dispensed into a 1L Tedlar dose delivery bag. The total volume per dose was calculated using 20% of the participants predicted FVC, based on age, sex, height and ethnicity\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. \u003csup\u003e129\u003c/sup\u003eXe MRI was acquired using the same MRI system previously mentioned for brain acquisition. \u003csup\u003e129\u003c/sup\u003eXe images were analyzed using an in-house pipeline and ANTs. Images were bias field corrected using the N4 algorithm.\u003c/p\u003e\n \u003cp\u003eThe participant was positioned supine, feet-first and fitted with a commercial, single channel chest coil (Polarean Xenoview, Raleigh-Durham, NC). Three \u003csup\u003e129\u003c/sup\u003eXe scans were obtained (A calibration, ventilation, and gas exchange acquisition); each under a 10 second breath hold\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The calibration scan was acquired to calculate the center frequency, transmit voltage, and echo time (TE) where alveolar-capillary membrane (mem) and red blood cell (RBC) compartments are 90\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:^\\circ\\:\\)\u003c/span\u003e\u003c/span\u003e out of phase (TE90). The ventilation acquisition is a 2D multi-slice fast gradient echo. Using an adaptive K-means clustering algorithm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, a ventilation defect percentage (VDP) was calculated for each participant. Both the calibration and ventilation scans used high purity nitrogen as a buffer, yielding a dose equivalent volume of 91.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8 mL of \u003csup\u003e129\u003c/sup\u003eXe\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eGas exchange images were obtained using a 1-point Dixon technique\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. These scans used an interleaved 3D radial acquisition with 1000 radial projections of each gas and dissolved phase signal were acquired with 0.5\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:^\\circ\\:\\)\u003c/span\u003e\u003c/span\u003e and 20\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:^\\circ\\:\\)\u003c/span\u003e\u003c/span\u003e flip angles using the previously described TE90. Gas exchange was then quantified by taking the ratio of each compartment, generating RBC:mem, RBC:gas, and Mem:gas maps and measurements. These acquisitions used unbuffered, pure xenon with a dose equivalent volume of 195.5\u0026thinsp;\u0026plusmn;\u0026thinsp;28.6 mL of \u003csup\u003e129\u003c/sup\u003eXe. A table of all brain and pulmonary MRI metrics with their physiologic meaning is shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eR Statistical Software (V4.3.1, R Core Team, 2023) was used for statistical analysis. PFT and \u003csup\u003e129\u003c/sup\u003eXe MRI metrics were compared between Long COVID and healthy controls using Welch\u0026rsquo;s t-test. PROMIS and NIHTB-CB scores were compared to the internal reference distribution using a one sample t-test. Bivariate relationships between lung function measures and brain outcomes were assessed using Spearman partial correlations with age as a covariate. PROMIS surveys of mental health (depression and anxiety) were also included as covariates, specifically for assessment of gas exchange to executive function and gas exchange to cerebral perfusion to confirm these relationships. Specifically, correlations were performed between measures of gas exchange on \u003csup\u003e129\u003c/sup\u003eXe MRI with PROMIS symptom questionnaires (cognitive function and sleep disturbances), NIHTB-CB (total cognition composite and four cognitive domains) and brain MRI metrics (whole brain volume, WM hyperintensity volume, FA, MD, RD, and CP).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eSBF reports relationships with Siemens Healthcare, GE Healthcare, Polarean, Inc., and Regeneron Pharmaceuticals Inc. that include consulting, funded grants, and travel reimbursement. ADH reports relationships with Polarean, Inc. that includes consulting fees. EAH is a founder and shareholder of VIDA Diagnostics, a company commercializing lung image analysis software developed, in part, at the University of Iowa, \u0026amp; an unremunerated member of the Siemens Healthineers\u0026rsquo; photon counting CT advisory board. APC reports relationships with VIDA Diagnostics that includes unremunerated consulting. KRS, MJM, ASK, NA, JLP, TL, JT, CL, CJW, EB, JCS and KH have no disclosures.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e\u003cp\u003eThis study was funded by NIH NCATS S10OD026960, NIH CTSA program grant UM1TR004403, and the American Lung Association COVID-19 and Emerging Respiratory Viruses Research Award, NIH NHLBI R01HL169765, NIH NHLBI R01HL126771. Research funding for development of pulmonary imaging from GE Healthcare, Polarean PLC, and Siemens Healthcare. MJM is supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowship award.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKRS was responsible for data analysis, statistical analysis, interpretation of the results and for preparing the first draft of the manuscript. MJM was responsible for data analysis, interpretation of the results and editing the manuscript. ASK was responsible for interpretation of the results. ADH was responsible for data analysis and interpretation of the results. NA was responsible for data collection and interpretation of the results. JLP was responsible for interpretation of the results. TL, JT and CL were responsible for data collection, data analysis and interpretation of the results. CJW and EB were responsible for data collection. JCS and EAH were responsible for developing the study design and interpretation of the results. APC was responsible for developing the study design, recruiting study participants and interpretation of the results. KH was responsible for developing the study design and interpretation of the results. SBF was responsible for developing the study design, interpretation of the results, and direction of all aspects of the study, and writing and editing the manuscript. All authors had an opportunity to review and revise the manuscript and approved its final submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to acknowledge the support from Eric Axelson and Josh Cochran for processing brain MR images, Dr. Sinan Akay for performing clinical brain MRI overreads, and MRI technologists at the Magnetic Resonance Research Facility for operating the MRI scanner.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData measures and analysis results can be made available by the corresponding author by request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFernandez-de-Las-Penas, C. Long COVID: current definition. \u003cem\u003eInfection\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 285-286 (2022). https://doi.org:10.1007/s15010-021-01696-5\u003c/li\u003e\n\u003cli\u003eCrook, H., Raza, S., Nowell, J., Young, M. \u0026amp; Edison, P. 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Spirometric reference values from a sample of the general U.S. population. \u003cem\u003eAm J Respir Crit Care Med\u003c/em\u003e \u003cstrong\u003e159\u003c/strong\u003e, 179-187 (1999). https://doi.org:10.1164/ajrccm.159.1.9712108\u003c/li\u003e\n\u003cli\u003eNiedbalski, P. 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S.\u003cem\u003e et al.\u003c/em\u003e Single-breath clinical imaging of hyperpolarized (129)Xe in the airspaces, barrier, and red blood cells using an interleaved 3D radial 1-point Dixon acquisition. \u003cem\u003eMagn Reson Med\u003c/em\u003e\u003cstrong\u003e75\u003c/strong\u003e, 1434-1443 (2016). https://doi.org:10.1002/mrm.25675\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Demographics and Pulmonary Function\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong COVID\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 12)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cem\u003eDemographics\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u0026nbsp; Age (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e54 \u0026plusmn; 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e47 \u0026plusmn; 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u0026nbsp; No. females, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e10 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e8 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u0026nbsp; BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e30.2 \u0026plusmn; 7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e27.3 \u0026plusmn; 7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u0026nbsp; Months since infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e32 \u0026plusmn; 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u0026nbsp; No. hospitalized, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e4 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u0026nbsp; Education (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e15 \u0026plusmn; 2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cem\u003ePulmonary Function Testing\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;FEV\u003csub\u003e1\u003c/sub\u003e (%\u003csub\u003epred\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e102 \u0026plusmn; 17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e99 \u0026plusmn; 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;FVC (%\u003csub\u003epred\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e107 \u0026plusmn; 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e102 \u0026plusmn; 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;FEV\u003csub\u003e1\u003c/sub\u003e/FVC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e76 \u0026plusmn; 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e78 \u0026plusmn; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;FEF\u003csub\u003e25-75%\u003c/sub\u003e (%\u003csub\u003epred\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e91 \u0026plusmn; 28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e90 \u0026plusmn; 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;DL\u003csub\u003eCO\u003c/sub\u003e (%\u003csub\u003epred\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e96 \u0026plusmn; 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e93 \u0026plusmn; 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003csup\u003e129\u003c/sup\u003e\u003c/em\u003e\u003cem\u003eXe MRI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;VDP (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e4.0 \u0026plusmn; 6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.1 \u0026plusmn; 2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;RBC:Mem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.36 \u0026plusmn; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.38 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;RBC:Gas (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.37 \u0026plusmn; 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.36 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Mem:Gas (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.98 \u0026plusmn; 0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.94 \u0026plusmn; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; standard deviation unless indicated otherwise. P-value from Welch\u0026rsquo;s two sample t-test. PFT only available for 8/10 healthy controls. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: BMI=body mass index; FEV\u003csub\u003e1\u003c/sub\u003e=forced expiratory volume in first second; %pred=percent of predicted value; FVC=forced vital capacity; FEF\u003csub\u003e25-75%\u003c/sub\u003e=forced mid-expiratory flow; DL\u003csub\u003eCO\u003c/sub\u003e=diffusing capacity of the lung for carbon monoxide; MRI=magnetic resonance imaging; VDP=ventilation defect percent; RBC=red blood cell; Mem=membrane\u003c/em\u003e\u003cem\u003e\u003cbr\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Comorbidities and Long COVID Symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"551\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong COVID\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 11)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u003cem\u003ePre-Existing Comorbidities\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Hypertension/ hypercholesterolemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e3 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Asthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e3 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Fibromyalgia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e2 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e2 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Neuropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e2 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Diabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e1 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Sleep apnea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e1 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u003cem\u003eLong COVID Symptoms\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e8 (73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Dyspnoea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e6 (55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Headache\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e5 (54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Cognitive impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e4 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Joint pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e4 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Neuropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e4 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Anosmia/ageusia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e3 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e3 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e3 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Sleep impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e3 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Vision problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e3 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as n (%). Information was obtained from participants\u0026rsquo; medical record. Pre-existing comorbidities were included if they were charted prior to COVID-19 infection. Long COVID symptoms were included from appointments relating to their Long COVID symptoms/diagnosis. Information was only available for 11/12 participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Imaging metrics and the physiologic meaning\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"551\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetric\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 359px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysiologic Meaning\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cem\u003eBrain Imaging\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 359px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; MPRAGE\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; (GM \u0026amp; WM Volume)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eReflects the overall brain volume. Decreased volume occurs with aging but can indicate neurodegenerative processes.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; FLAIR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; (WM Hyperintensity\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Volume)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eBright spots in the WM seen in FLAIR imaging, often associated with small vessel disease and aging.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; DTI\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; (FA, MD, RD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eMeasures the structure and integrity of WM tracts through the diffusion of water molecules. Can offer insight into the organization of the WM and assess myelin damage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; ASL\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; (CP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eA non-invasive imaging technique that measures brain perfusion, typically reflective of the brain\u0026apos;s metabolic activity.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003csup\u003e129\u003c/sup\u003e\u003c/em\u003e\u003cem\u003eXe Pulmonary MRI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 359px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; VDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eThe percentage of the lung that is poorly/not ventilated. High VDP can be due to obstructive or restrictive lung diseases.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; RBC:mem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eMeasures the ratio of xenon uptake by RBCs relative to the alveolar-capillary membrane. Reflects gas transfer efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; RBC:gas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eMeasures the ratio of xenon uptake by RBCs to the xenon in the alveolar airspace.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Mem:gas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eMeasures the ratio of xenon uptake in the alveolar-capillary membrane relative to the xenon in the alveolar airspace. Surrogate for membrane thickness.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: GM=gray matter; WM=white matter; FLAIR=fluid attenuating inversion recovery; DTI=diffusion tensor imaging; FA=fractional anisotropy; MD=mean diffusivity; RD=radial diffusivity; ASL=arterial spin labeling; CP=cerebral perfusion; VDP=ventilation defect percent; RBC=red blood cell; Mem=membrane\u003c/em\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7464124/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7464124/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Post-acute sequelae of COVID-19 (Long COVID) includes physical and cognitive symptoms that can last long after acute infection. Links between lung pathophysiology and cognitive dysfunction in Long COVID remain largely unexplored. Long COVID participants were recruited from a post-COVID-19 clinic. Participants completed Patient-Reported Outcomes Measurement Information System (PROMIS) symptom questionnaires for Sleep Disturbance, Anxiety, Depression, and Cognitive Function, the National Institute of Health Toolbox Cognition Battery (NIHTB-CB), pulmonary function tests (spirometry, diffusion capacity of the lung), structural and functional brain magnetic resonance imaging (MRI), and 129 Xe MRI for ventilation and regional pulmonary gas exchange evaluation, at the same study visit. Bivariate relationships between lung and cognitive function in Long COVID were assessed using Spearman partial correlations, adjusted for age. Twelve participants (age=54±11 yrs.; 10 females) that were 32±5 months from infection were evaluated. PROMIS symptom scores indicated reduced perceived cognitive function in everyday life along with increased fatigue, anxiety, depressive symptoms, and sleep disturbance. However, objective cognitive function performance on NIHTB-CB were broadly within normal limits. Lower 129 Xe MRI gas exchange was correlated with more severe symptoms of sleep disturbance, reduced executive functioning performance, and elevated cerebral perfusion via brain MRI. These results are suggestive of a link between lung pathophysiology and cognitive dysfunction in this Long COVID population with enduring respiratory and cognitive symptoms more than two years after infection.","manuscriptTitle":"Long COVID: Lung Pathophysiology and its Relationship with Cognitive Dysfunction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 07:21:41","doi":"10.21203/rs.3.rs-7464124/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-14T06:50:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-11T08:23:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275617280086921075426008545337116848799","date":"2025-10-07T05:29:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12952329858749796068558645674870732319","date":"2025-10-07T01:58:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-01T20:23:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214185223255921751237993276143346579243","date":"2025-09-24T15:31:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-24T15:15:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-24T15:11:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-04T09:55:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-02T23:13:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-02T23:10:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2698d174-cca1-4306-8c31-a83e80240a02","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55838496,"name":"Health sciences/Diseases"},{"id":55838497,"name":"Health sciences/Health care"},{"id":55838498,"name":"Health sciences/Medical research"},{"id":55838499,"name":"Health sciences/Neurology"},{"id":55838500,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2025-12-01T16:03:11+00:00","versionOfRecord":{"articleIdentity":"rs-7464124","link":"https://doi.org/10.1038/s41598-025-26568-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-24 15:57:13","publishedOnDateReadable":"November 24th, 2025"},"versionCreatedAt":"2025-10-08 07:21:41","video":"","vorDoi":"10.1038/s41598-025-26568-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-26568-y","workflowStages":[]},"version":"v1","identity":"rs-7464124","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7464124","identity":"rs-7464124","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}