Microstructural alterations in hypoxia-related BRAIN centers after COVID-19 by using DTI: A preliminary study.

PURPOSE: To investigate whether the diffusion tensor imaging (DTI) parameters alterations in the in hypoxia-related neuroanatomical localizations in patients after COVID-19. Additionally, the relationship between DTI findings and the clinical severity of the disease is evaluated. METHODS: The patients with COVID-19 were classified into group 1 (total patients, n = 74), group 2 (outpatient, n = 46), and group 3 (inpatient, n = 28) and control (n = 52). Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were calculated from the bulbus, pons, thalamus, caudate nucleus, globus pallidum, putamen, and hippocampus. DTI parameters were compared between groups. Oxygen saturation, D dimer and lactate dehydrogenase (LDH) values associated with hypoxia were analyzed in inpatient group. Laboratory findings were correlated with ADC and FA values. RESULTS: Increased ADC values in the thalamus, bulbus and pons were found in group 1 compared to control. Increased FA values in the thalamus, bulbus, globus pallidum and putamen were detected in group 1 compared to control. The FA and ADC values obtained from putamen were higher in group 3 compared to group 2. There was a negative correlation between basal ganglia and hippocampus FA values and plasma LDH values. The ADC values obtained from caudate nucleus were positively correlated with plasma D Dimer values. CONCLUSION: ADC and FA changes may reveal hypoxia-related microstructural damage after COVID-19 infection. We speculated that the brainstem and basal ganglia can affected during the subacute period.

Cognitive, Motor, & Negative Valence Systems in Neurologic Pasc: A Preliminary Study

Background: Nervous system post-acute sequelae of COVID-19 (NS-PASC) include cognitive and mental health symptoms. To further define these, we applied a Research Domain Criteria (RDoC) approach to examine motor, positive valence (PV) and negative valence (NV) systems, and social processing data in The COVID Mind Study of NS-PASC. Method(s): NS-PASC participants (>3 months after COVID-19) referred from a NeuroCOVID Clinic and non-COVID controls from New Haven, CT and Baltimore, MD completed an RDoC test battery for cognition (language, declarative and working memory, cognitive control, perception), motor, PV, NV, and social processes. To date, 3T MRI with diffusion tensor imaging was performed in 11 NS-PASC to assess white matter integrity (global white matter fractional anisotropy [FA]) as a contributor to alterations identified on the RDoC tests. Analysis of Covariance examined group differences after adjusting for sex, race, ethnicity, age, and years of education. Result(s): 25 NS-PASC participants (age 43.4+/-11.3 yrs, 76% female, 402 days after COVID-19 symptom onset) and 29 controls (age 46.2.6+/-13.1 yrs, 66% female) completed the battery. Controls were more racially diverse and less educated than NS-PASC (43% vs. 12% Black, p=0.005;14.5 vs. 16.1 yrs of education, p< 0.05). Means and statistics for RDoC between NS-PASC and controls are shown in Table. NS-PASC performed worse in language, verbal working and declarative memory, and perception and reported greater cognitive control difficulties (e.g., behavioral inhibition, set shifting) without issues on performance-based metrics (Stroop, Trail Making Test-Part B), and had slower motor function. NS-PASC reported more NV issues including greater symptoms of depression, rumination in response to depressive mood, apathy, childhood trauma, anxiety, and perceived stress. There were no differences in PV and social processing. In a subset of NS-PASC participants who underwent MRI, there was a dynamic range of FA values with a mean of 0.509 (IQR 0.481 - 0.536). Conclusion(s): Our findings extend previous PASC studies characterizing cognitive and mental health alterations, indicating that additional RDoC assessments warrant focus, including alterations in motor and the negative valence system. In future analyses, we will examine white matter integrity as a pathophysiologic contributor to these RDoC systems.

Post-Covid Cognitive Impairment Mri Brain Scan Abnormalities: A Potential Biomarker

Background: Post-acute sequelae of SARS-COV-2 infection (PASC) is associated with cognitive impairment (CI) with unclear pathogenesis though blood brain barrier (BBB) impairment and excitotoxic injury appear significant. Post-acute sequelae of SARS-COV-2 infection (PASC) is associated with cognitive impairment (CI) with unclear pathogenesis though blood brain barrier (BBB) impairment and excitotoxic injury appear significant. We hypothesized that PASC CI patients would have brain inflammation and BBB disruption using advanced MR imaging. Method(s): In this prospective longitudinal study, 14 patients with PASC CI (mild and non-hospitalised) were enrolled (mean age of 45;10 F and 4 M) and 10 sex and age matched healthy controls. 13 had a follow up MR at 9-12 months (mean 10 months). All participants underwent DCE perfusion (an index of BBB integrity with Ktrans as the measurement), Diffusion Tensor Imaging (DTI) and single voxel MR spectroscopy (MRS) of the frontal cortex/white matter and the brainstem in addition to brain anatomical MRI. Between group analyses were used to determine which MRI outcomes were significantly different from controls in patients with PASC CI. Result(s): The PASCI CI group showed significantly increased (ie BBB impairment) Ktrans, and increased region (Frontal white matter and Brain Stem)-specific areas in the brain (p=< 0.005), reduction in NAA (ie neuronal injury) and mild reduction of Glx (ie excitotoxicity) in the frontal white matter and brain stem (p=0.004), and reduction in white matter integrity (increased diffusivity -greater radial and mean diffusivity). Increased Ktrans was correlated with increased both radial and mean diffusivity (r=0.9) in all tested brain regions. Ktrans significantly improved in the follow up MR (p= 002596 Z=-2.794872) with no difference between subjects and controls indicating BBB normalisation (p= 0.442418, z= -0.144841). White matter integrity also improved especially in the fractional anisotropy values in the executive networks (p=< 0.00045). MRS showed significant improvement in the NAA in the frontal white matter but Glx remain high as compared to the controls (p=0.0006). Conclusion(s): PASC CI was characterised by reversible diffuse BBB impairment, neuronal/axonal and excitotoxic injury. BBB impairment was associated with white matter disruption. These are suggestive biomarkers for the presence, severity and prognosis of PASC CI. Such biomarkers could underpin appropriate trial design and timing of intervention.

Feasibility of diffusion-tensor and correlated diffusion imaging for studying white-matter microstructural abnormalities: Application in COVID-19.

There has been growing attention on the effect of COVID-19 on white-matter microstructure, especially among those that self-isolated after being infected. There is also immense scientific interest and potential clinical utility to evaluate the sensitivity of single-shell diffusion magnetic resonance imaging (MRI) methods for detecting such effects. In this work, the performances of three single-shell-compatible diffusion MRI modeling methods are compared for detecting the effect of COVID-19, including diffusion-tensor imaging, diffusion-tensor decomposition of orthogonal moments and correlated diffusion imaging. Imaging was performed on self-isolated patients at the study initiation and 3-month follow-up, along with age- and sex-matched controls. We demonstrate through simulations and experimental data that correlated diffusion imaging is associated with far greater sensitivity, being the only one of the three single-shell methods to demonstrate COVID-19-related brain effects. Results suggest less restricted diffusion in the frontal lobe in COVID-19 patients, but also more restricted diffusion in the cerebellar white matter, in agreement with several existing studies highlighting the vulnerability of the cerebellum to COVID-19 infection. These results, taken together with the simulation results, suggest that a significant proportion of COVID-19 related white-matter microstructural pathology manifests as a change in tissue diffusivity. Interestingly, different b-values also confer different sensitivities to the effects. No significant difference was observed in patients at the 3-month follow-up, likely due to the limited size of the follow-up cohort. To summarize, correlated diffusion imaging is shown to be a viable single-shell diffusion analysis approach that allows us to uncover opposing patterns of diffusion changes in the frontal and cerebellar regions of COVID-19 patients, suggesting the two regions react differently to viral infection.

Multi-Layered Projected Entangled Pair States for Image Classification

Tensor networks have been recognized as a powerful numerical tool;they are applied in various fields, including physics, computer science, and more. The idea of a tensor network originates from quantum physics as an efficient representation of quantum many-body states and their operations. Matrix product states (MPS) form one of the simplest tensor networks and have been applied to machine learning for image classification. However, MPS has certain limitations when processing two-dimensional images, meaning that it is preferable for an projected entangled pair states (PEPS) tensor network with a similar structure to the image to be introduced into machine learning. PEPS tensor networks are significantly superior to other tensor networks on the image classification task. Based on a PEPS tensor network, this paper constructs a multi-layered PEPS (MLPEPS) tensor network model for image classification. PEPS is used to extract features layer by layer from the image mapped to the Hilbert space, which fully utilizes the correlation between pixels while retaining the global structural information of the image. When performing classification tasks on the Fashion-MNIST dataset, MLPEPS achieves a classification accuracy of 90.44%, exceeding tensor network models such as the original PEPS. On the COVID-19 radiography dataset, MLPEPS has a test set accuracy of 91.63%, which is very close to the results of GoogLeNet. Under the same experimental conditions, the learning ability of MLPEPS is already close to that of existing neural networks while having fewer parameters. MLPEPS can be used to build different network models by modifying the structure, and as such it has great potential in machine learning.

Comparative Analysis of Various Models for Potato Leaf Disease Classification using Deep Learning

Potato is one among the most extensively consumed staple foods, ranking fourth on the global food pyramid. Moreover, because of the global coronavirus outbreak, global potato consumption is expanding dramatically. Potato diseases, on the other hand, are the primary cause of crop quality and quantity decline. Plant conditions will be dramatically worsened by incorrect disease classification and late identification. Fortunately, leaf conditions can help identify various illnesses in potato plants. Potato (Solanum tuberosum L) is one of the majorly farmed vegetable food crops in worldwide. The output of potato crops in both quality and quantity is affected majorly due to fungal blight infections, which causes a severe impact on the global food yield. The most severe foliar diseases for potato crops are early blight and late blight. The causes of these diseases are Alternaria solani and Phytophthora infestants respectively. Farmers suspect such problems by focusing on the color change or transformation in potato leaves, which is effortless due to subjectivity and lengthy time commitment. In such circumstances, it is critical to develop computer models that can diagnose those diseases quickly and accurately, even in their early stages. © 2023 IEEE.

Abnormal brain diffusivity in participants with persistent neuropsychiatric symptoms after COVID-19.

Objectives: We aimed to compare brain white matter integrity in participants with post-COVID-19 conditions (PCC) and healthy controls. Methods: We compared cognitive performance (NIH Toolbox®), psychiatric symptoms and diffusion tensor imaging (DTI) metrics between 23 PCC participants and 24 controls. Fractional anisotropy (FA), axial (AD), radial (RD), and mean (MD) diffusivities were measured in 9 white matter tracts and 6 subcortical regions using MRICloud. Results: Compared to controls, PCC had similar cognitive performance, but greater psychiatric symptoms and perceived stress, as well as higher FA and lower diffusivities in multiple white matter tracts (ANCOVA-p-values≤0.001-0.048). Amongst women, PCC had higher left amygdala-MD than controls (sex-by-PCC p=0.006). Regardless of COVID-19 history, higher sagittal strata-FA predicted greater fatigue (r=0.48-0.52, p<0.001) in all participants, and higher left amygdala-MD predicted greater fatigue (r=0.61, p<0.001) and anxiety (r=0.69, p<0.001) in women, and higher perceived stress (r=0.45, p=0.002) for all participants. Conclusions: Microstructural abnormalities are evident in PCC participants averaged six months after COVID-19. The restricted diffusivity (with reduced MD) and higher FA suggest enhanced myelination or increased magnetic susceptibility from iron deposition, as seen in stress conditions. The higher amygdala-MD in female PCC suggests persistent neuroinflammation, which might contribute to their fatigue, anxiety, and perceived stress.

Tensor-based insights into systems immunity and infectious disease.

Profiling immune responses across several dimensions, including time, patients, molecular features, and tissue sites, can deepen our understanding of immunity as an integrated system. These studies require new analytical approaches to realize their full potential. We highlight recent applications of tensor methods and discuss several future opportunities.

College students' mental health evaluation model based on tensor fusion network with multimodal data during the COVID-19 pandemic.

The COVID-19 pandemic has caused a series of effects on the mental health of college students, especially long-term home isolation or online learning, which has caused college students to have both academic pressure and employment pressure. How to accurately and effectively assess the mental health status of college students has become a research hotspot. Traditional methods based on questionnaires such as Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) are difficult to collect data and have poor evaluation accuracy. This paper analyzes the psychological state through text-images of multi-modal data with tensor fusion networks and constructs a mental health assessment model for college students. First, the validity of the model is verified through the MVSA (Multi-View Sentiment Analysis) dataset. Second, the psychological state of college students under the epidemic is analyzed using the collected text-images dataset. The results show that the TFN-MDA (Tensor Fusion Network-Multimodal Data Analysis) based mental health assessment model constructed in this paper can effectively assess the mental health status of college students, with an average accuracy of more than 70%.

Explainability of the COVID-19 epidemiological model with nonnegative tensor factorization.

The world is witnessing the devastating effects of the COVID-19 pandemic. Each country responded to contain the spread of the virus in the early stages through diverse response measures. Interpreting these responses and their patterns globally is essential to inform future responses to COVID-19 variants and future pandemics. A stochastic epidemiological model (SEM) is a well-established mathematical tool that helps to analyse the spread of infectious diseases through communities and the effects of various response measures. However, interpreting the outcome of these models is complex and often requires manual effort. In this paper, we propose a novel method to provide the explainability of an epidemiological model. We represent the output of SEM as a tensor model. We then apply nonnegative tensor factorization (NTF) to identify patterns of global response behaviours of countries and cluster the countries based on these patterns. We interpret the patterns and clusters to understand the global response behaviour of countries in the early stages of the pandemic. Our experimental results demonstrate the advantage of clustering using NTF and provide useful insights into the characteristics of country clusters.

A Dynamic Interaction Semiparametric Function-on-Scalar Model

Motivated by recent work studying massive functional data, such as the COVID-19 data, we propose a new dynamic interaction semiparametric function-on-scalar (DISeF) model. The proposed model is useful to explore the dynamic interaction among a set of covariates and their effects on the functional response. The proposed model includes many important models investigated recently as special cases. By tensor product B-spline approximating the unknown bivariate coefficient functions, a three-step efficient estimation procedure is developed to iteratively estimate bivariate varying-coefficient functions, the vector of index parameters, and the covariance functions of random effects. We also establish the asymptotic properties of the estimators including the convergence rate and their asymptotic distributions. In addition, we develop a test statistic to check whether the dynamic interaction varies with time/spatial locations, and we prove the asymptotic normality of the test statistic. The finite sample performance of our proposed method and of the test statistic are investigated with several simulation studies. Our proposed DISeF model is also used to analyze the COVID-19 data and the ADNI data. In both applications, hypothesis testing shows that the bivariate varying-coefficient functions significantly vary with the index and the time/spatial locations. For instance, we find that the interaction effect of the population aging and the socio-economic covariates, such as the number of hospital beds, physicians, nurses per 1000 people and GDP per capita, on the COVID-19 mortality rate varies in different periods of the COVID-19 pandemic. The healthcare infrastructure index related to the COVID-19 mortality rate is also obtained for 141 countries estimated based on the proposed DISeF model.

Using DTI tractography to guide tDCS interventions in a complex acquired brain Injury neurorehabilitation protocol - a case report

51-year-old man (C.P.) had a diffuse-axonal-injury after falling from a 5-meter height, followed by a 22-minute anoxia due to a cardiac arrest. In the ICU, he tested positive to COVID-19, and needed intubation. After coronavirus infection, C.P. presented Guillain-Barre syndrome. 2months after discharge, he was admitted to rehabilitation. DTI tractography for evaluation of the structural integrity of white matter tracts revealed: i) Lesions in the basal ganglia;ii) Sequelary lesions in the right frontal, cortical, subcortical, temporal, parieto-occipital and cerebellar hemispheres;iii) Asymmetry of the corticospinal tracts - less fibers on the left;iv) Poor definition of the fibers of the right arcuate fasciculus;v)Asymmetrical thinning of the cortico-ponto-cerebellar tracts, worse on the left, and more discreetly in the spinocerebellar tracts. Based on this, C.P. underwent 4 different 30-session tDCS protocols consisting of twice-daily 20min 2mA sessions (10min interval), 5days/week (120sessions total), combined with physiotherapy, cognitive, swallowing and speech therapy. Montages: Pr1 (anode: Cz - 5x10cm;cathode: 10th Thoracic Vertebra - 5x7cm);Pr2 (1 - anode:C3;cathode:Fp2 / 2 - anode: Cerebellum;cathode:Fp2);Pr3 (anode:F3;cathode:Fp2) and Pr4 (anode:Cp5;cathode:Fp2). Except for Pr1, electrode size for all protocols were 5x7cm. We used the Coma Recovery Scale (CRS-R) and Rancho Los Amigos Scale (RLAS) for clinical assessments at the baseline and after every 10 sessions until the end of the intervention. At the baseline, C.P. presented a minimal responsive state of consciousness (CRS-R: 3;RLAS: Level 1) and tolerated well the tDCS interventions. CRS-R scores gradually improved in various domains during the treatment. At the end, RLAS score was level 5 and CRS-R, 19. Our preliminary results suggest DTI tractography may be a potential biomarker to guide more personalized tDCS interventions for complex cases of patients with acquired brain injuries. A second DTI tractography will be made in the future for comparison purposes. Research Category and Technology and Methods Clinical Research: 9. Transcranial Direct Current Stimulation (tDCS) Keywords: Acquired Brain Injury, Traumatic Brain Injury, COVID-19, Guillain Barre SyndromeCopyright © 2023

Deep Network Analysis and Prediction of Ophthalmic Disorders

Everybody, around the globe, is aware that their kids, relatives, and family are suffering from the pandemic COVID-19. S everal people are still facing post-COVID-19 issues. During COVID-19's second wave, mucormycosis, sometimes known as "black fungus, " plagued people, especially those who had previously been infected with the virus. The clinical manifestations of mucormycosis are quite varied, the disease affects the skin, subcutaneous fatty tissue, and visceral organs such as the eyes and brain. This paper surveys the Mucormycosis-affected eye diseases due to post-COVID-19 complications and leverages the Machine learning model to differentiate it from other eye diseases. COVID-19-associated Mucormycosis carries a very high mortality rate and timely detection that can assist people in starting therapy at an early stage of the disease, increasing their chances of recovery. Though it was evaluated for a specific disease (COVID-19-associated mucormycosis) we ended up developing a framework that can detect other eye diseases. Thus, the goal of this research is to distinguish Mucormycosis from other eye diseases such as Bulging Eyes, Cataracts, Crossed Eyes, Glaucoma, and Uveitis. This study implies Deep learning techniques with a Convolutional Neural Network based on the TensorFlow and Keras model to detect and make use of computer vision to accurately classify eye diseases. We achieved a precision of 70% in this study by developing a webpage using the trained model for an eye diseases evaluation. © 2022 IEEE

TTRISK: Tensor train decomposition algorithm for risk averse optimization

This article develops a new algorithm named TTRISK to solve high‐dimensional risk‐averse optimization problems governed by differential equations (ODEs and/or partial differential equations [PDEs]) under uncertainty. As an example, we focus on the so‐called Conditional Value at Risk (CVaR), but the approach is equally applicable to other coherent risk measures. Both the full and reduced space formulations are considered. The algorithm is based on low rank tensor approximations of random fields discretized using stochastic collocation. To avoid nonsmoothness of the objective function underpinning the CVaR, we propose an adaptive strategy to select the width parameter of the smoothed CVaR to balance the smoothing and tensor approximation errors. Moreover, unbiased Monte Carlo CVaR estimate can be computed by using the smoothed CVaR as a control variate. To accelerate the computations, we introduce an efficient preconditioner for the Karush–Kuhn–Tucker (KKT) system in the full space formulation.The numerical experiments demonstrate that the proposed method enables accurate CVaR optimization constrained by large‐scale discretized systems. In particular, the first example consists of an elliptic PDE with random coefficients as constraints. The second example is motivated by a realistic application to devise a lockdown plan for United Kingdom under COVID‐19. The results indicate that the risk‐averse framework is feasible with the tensor approximations under tens of random variables.

Multidimensional Signal Processing and Applications—New Approaches

This chapter is a short introduction in the contemporary approaches aimed at the multidimensional processing and analysis of various kinds of signals, investigated in related research works, which were presented at the Third International Workshop "New Approaches for Multidimensional Signal Processing”, (NAMSP), held at the Technical University of Sofia, Bulgaria in July 2022. Some of the works cover various topics, as: moving objects tracking in video sequences, automatic audio classification, representation of color video чpeз 2-level tensor spectrum pyramid, etc., and also introduce multiple applications of the kind: analysis of electromyography signals, diagnostics of COVID based on ECG, etc. Short descriptions are given for the main themes covered by the book, which comprises the following three sections: multidimensional signal processing;applications of multidimensional signal processing, and applications of blockchain and network technologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Brain correlates of subjective cognitive complaints in COVID-19 survivors: A multimodal magnetic resonance imaging study.

Cognitive impairment represents a leading residual symptom of COVID-19 infection, which lasts for months after the virus clearance. Up-to-date scientific reports documented a wide spectrum of brain changes in COVID-19 survivors following the illness's resolution, mainly related to neurological and neuropsychiatric consequences. Preliminary insights suggest abnormal brain metabolism, microstructure, and functionality as neural under-layer of post-acute cognitive dysfunction. While previous works focused on brain correlates of impaired cognition as objectively assessed, herein we investigated long-term neural correlates of subjective cognitive decline in a sample of 58 COVID-19 survivors with a multimodal imaging approach. Diffusion Tensor Imaging (DTI) analyses revealed widespread white matter disruption in the sub-group of cognitive complainers compared to the non-complainer one, as indexed by increased axial, radial, and mean diffusivity in several commissural, projection and associative fibres. Likewise, the Multivoxel Pattern Connectivity analysis (MVPA) revealed highly discriminant patterns of functional connectivity in resting-state among the two groups in the right frontal pole and in the middle temporal gyrus, suggestive of inefficient dynamic modulation of frontal brain activity and possible metacognitive dysfunction at rest. Beyond COVID-19 actual pathophysiological brain processes, our findings point toward brain connectome disruption conceivably translating into clinical post-COVID cognitive symptomatology. Our results could pave the way for a potential brain signature of cognitive complaints experienced by COVID-19 survivors, possibly leading to identify early therapeutic targets and thus mitigating its detrimental long-term impact on quality of life in the post-COVID-19 stages.

Structural brain changes in patients with persistent headache after COVID-19 resolution.

Headache is among the most frequently reported symptoms after resolution of COVID-19. We assessed structural brain changes using T1- and diffusion-weighted MRI processed data from 167 subjects: 40 patients who recovered from COVID-19 but suffered from persistent headache without prior history of headache (COV), 41 healthy controls, 43 patients with episodic migraine and 43 patients with chronic migraine. To evaluate gray matter and white matter changes, morphometry parameters and diffusion tensor imaging-based measures were employed, respectively. COV patients showed significant lower cortical gray matter volume and cortical thickness than healthy subjects (p < 0.05, false discovery rate corrected) in the inferior frontal and the fusiform cortex. Lower fractional anisotropy and higher radial diffusivity (p < 0.05, family-wise error corrected) were observed in COV patients compared to controls, mainly in the corpus callosum and left hemisphere. COV patients showed higher cortical volume and thickness than migraine patients in the cingulate and frontal gyri, paracentral lobule and superior temporal sulcus, lower volume in subcortical regions and lower curvature in the precuneus and cuneus. Lower diffusion metric values in COV patients compared to migraine were identified prominently in the right hemisphere. COV patients present diverse changes in the white matter and gray matter structure. White matter changes seem to be associated with impairment of fiber bundles. Besides, the gray matter changes and other white matter modifications such as axonal integrity loss seemed subtle and less pronounced than those detected in migraine, showing that persistent headache after COVID-19 resolution could be an intermediate state between normality and migraine.

Brain FDG PET imaging of patients with persistent post- COVID-19 fatigue

Aim/Introduction: A recent report prepared by the Centers for Disease Control and Prevention indicates that 71% of patients experience persistent fatigue even after recovery from the acute phase of COVID-19 infection. We investigated if post-COVID-19 fatigue is associated with alterations in brain metabolism and microstructure to better understand the underlying neurobiological mechanism. Material(s) and Method(s): Brain F-18 FDG PET and diffusion tensor magnetic resonance imaging (DTIMR) were performed in 12 patients experiencing persistent post- COVID-19 fatigue that lasted more than six weeks post-discharge from hospitalization or discontinued home isolation after acute SARS-CoV-2 infection (fatigue group, Male:Female = 6:6, mean > SD age 35.7 > 13.8 years, Chalder fatigue scale score 8.3 > 2.2, time since COVID-19 diagnosis 7.9 > 5.5 months) and 9 recovered patients without such fatigue (non-fatigue group, M:F = 3:6, age 25.6 > 9.2, fatigue score 1.6 > 1.5, time since COVID-19 diagnosis 8.0 > 6.0 months). A commercially available normative brain FDG PET database (MIMneuro, v7.0.5, MIM Software, Inc.) was used to derive z scores for regional cerebral glucose metabolism. Fractional anisotropy (FA) values were extracted from DTI-MR datasets. Twotailed t-tests were performed for group comparison and P < 0.05 was considered statistically significant. Result(s): The fatigue group demonstrated significantly higher regional cerebral glucose metabolism in the left inferior and middle cerebellar peduncle (P = 0.001 and 0.043, respectively), left middle temporal gyrus (P = 0.002), left parahippocampal gyrus (P = 0.029), primary visual cortex (P = 0.031), supplementary motor area (P = 0.036), supramarginal gyrus (P = 0.044), and lower metabolism in the left precentral gyrus (P = 0.001) when compared to the non-fatigue group. The fatigue group also demonstrated significantly higher FA values in the left and right middle frontal gyrus (P = 0.014 and 0.038, respectively), left precentral gyrus (P = 0.024), right superior frontal gyrus (P =0.032), right postcentral gyrus (P = 0.047), left superior parietal gyrus (P = 0.048), and corpus callosum (P = 0.016) when compared to the nonfatigue group. Conclusion(s): Patients experiencing persistent fatigue after recovering from acute SARS-CoV-2 infection demonstrated significant changes in regional cerebral glucose metabolism and microstructure, when compared to those individuals without on-going fatigue symptoms. The altered cerebral metabolic and microstructural profile may help to better understand the neurobiological mechanism for management of patients suffering from lingering post-COVID-19 fatigue.

Brain FDG PET imaging of patients with persistent post- COVID-19 fatigue

Aim/Introduction: A recent report prepared by the Centers for Disease Control and Prevention indicates that 71% of patients experience persistent fatigue even after recovery from the acute phase of COVID-19 infection. We investigated if post-COVID-19 fatigue is associated with alterations in brain metabolism and microstructure to better understand the underlying neurobiological mechanism. Material(s) and Method(s): Brain F-18 FDG PET and diffusion tensor magnetic resonance imaging (DTIMR) were performed in 12 patients experiencing persistent post- COVID-19 fatigue that lasted more than six weeks post-discharge from hospitalization or discontinued home isolation after acute SARS-CoV-2 infection (fatigue group, Male:Female = 6:6, mean > SD age 35.7 > 13.8 years, Chalder fatigue scale score 8.3 > 2.2, time since COVID-19 diagnosis 7.9 > 5.5 months) and 9 recovered patients without such fatigue (non-fatigue group, M:F = 3:6, age 25.6 > 9.2, fatigue score 1.6 > 1.5, time since COVID-19 diagnosis 8.0 > 6.0 months). A commercially available normative brain FDG PET database (MIMneuro, v7.0.5, MIM Software, Inc.) was used to derive z scores for regional cerebral glucose metabolism. Fractional anisotropy (FA) values were extracted from DTI-MR datasets. Twotailed t-tests were performed for group comparison and P < 0.05 was considered statistically significant. Result(s): The fatigue group demonstrated significantly higher regional cerebral glucose metabolism in the left inferior and middle cerebellar peduncle (P = 0.001 and 0.043, respectively), left middle temporal gyrus (P = 0.002), left parahippocampal gyrus (P = 0.029), primary visual cortex (P = 0.031), supplementary motor area (P = 0.036), supramarginal gyrus (P = 0.044), and lower metabolism in the left precentral gyrus (P = 0.001) when compared to the non-fatigue group. The fatigue group also demonstrated significantly higher FA values in the left and right middle frontal gyrus (P = 0.014 and 0.038, respectively), left precentral gyrus (P = 0.024), right superior frontal gyrus (P =0.032), right postcentral gyrus (P = 0.047), left superior parietal gyrus (P = 0.048), and corpus callosum (P = 0.016) when compared to the nonfatigue group. Conclusion(s): Patients experiencing persistent fatigue after recovering from acute SARS-CoV-2 infection demonstrated significant changes in regional cerebral glucose metabolism and microstructure, when compared to those individuals without on-going fatigue symptoms. The altered cerebral metabolic and microstructural profile may help to better understand the neurobiological mechanism for management of patients suffering from lingering post-COVID-19 fatigue.