Redistribution activity is more important than reversibility when differentiating viable from nonviable territories using rest-redistribution Tl-201. In Tc-99m sestamibi SPECT, nitrate-induced activity changes are particularly useful in identifying myocardial viability. Baseline-nitrate Tc-99m sestamibi SPECT appears no less effective than rest-redistribution Tl-201 in predicting postrevascularization recovery.

Purpose We evaluated brain metabolic dysfunctions and associations with neurological and biological parameters in acute, subacute and chronic COVID-19 phases to provide deeper insights into the pathophysiology of the disease. Methods Twenty-six patients with neurological symptoms (neuro-COVID-19) and [ 18 F]FDG-PET were included. Seven patients were acute (< 1 month (m) after onset), 12 subacute (4 ≥ 1-m, 4 ≥ 2-m and 4 ≥ 3-m) and 7 with neuro-post-COVID-19 (3 ≥ 5-m and 4 ≥ 7-9-m). One patient was evaluated longitudinally (acute and 5-m). Brain hypo-and hypermetabolism were analysed at single-subject and group levels. Correlations between severity/extent of brain hypo-and hypermetabolism and biological (oxygen saturation and C-reactive protein) and clinical variables (global cognition and Body Mass Index) were assessed. ResultsThe "fronto-insular cortex" emerged as the hypometabolic hallmark of neuro-COVID-19. Acute patients showed the most severe hypometabolism affecting several cortical regions. Three-m and 5-m patients showed a progressive reduction of hypometabolism, with limited frontal clusters. After 7-9 months, no brain hypometabolism was detected. The patient evaluated longitudinally showed a diffuse brain hypometabolism in the acute phase, almost recovered after 5 months. Brain hypometabolism correlated with cognitive dysfunction, low blood saturation and high inflammatory status. Hypermetabolism in the brainstem, cerebellum, hippocampus and amygdala persisted over time and correlated with inflammation status. Conclusion Synergistic effects of systemic virus-mediated inflammation and transient hypoxia yield a dysfunction of the fronto-insular cortex, a signature of CNS involvement in neuro-COVID-19. This brain dysfunction is likely to be transient and almost reversible. The long-lasting brain hypermetabolism seems to reflect persistent inflammation processes. KeywordsNeuro-COVID • [ 18 F]FDG • Hypometabolism • Hypermetabolism • Recovery This article is part of the Topical Collection on Neurology.

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Purpose: An appropriate healthy control dataset is mandatory to achieve good performance in voxel-wise analyses. We aimed at evaluating [18F]FDG PET brain datasets of healthy controls (HC), based on publicly available data, for the extraction of voxel-based brain metabolism maps at the single-subject level. Methods: Selection of HC images was based on visual rating, after Cook's distance and Jack-knife analyses, to exclude artefacts and/or outliers. The performance of these HC datasets (ADNI-HC and AIMN-HC) to extract hypometabolism patterns in single patients was tested in comparison with the standard reference HC dataset (HSR-HC) by means of Dice score analysis. We evaluated the performance and comparability of the different HC datasets in the assessment of single-subject SPM-based hypometabolism in three independent cohorts of patients, namely ADD, bvFTD and DLB. Results: Two-steps Cook's distance analysis and the subsequent Jack-knife analysis resulted in the selection of n=125 subjects from the AIMN-HC dataset and n=75 subjects from the ADNI-HC dataset. The average concordance between SPM hypometabolism t-maps in the three patient cohorts, as obtained with the new datasets and compared to the HSR-HC standard reference dataset, was 0.87 for the AIMN-HC Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation dataset and 0.83 for the ADNI-HC dataset. Pattern expression analysis revealed high overall accuracy (>80%) of the SPM t-map classification according to different statistical thresholds and sample sizes. Conclusions: The applied procedures ensure validity of these HC datasets for the single-subject estimation of brain metabolism using voxel-wise comparisons. These well-selected HC datasets are ready-to-use in research and clinical settings. Response to Reviewers:Details about the raised technical aspects were provided in the manuscript and highlighted.A careful English editing was also performed, in accordance with the Referee's suggestion.

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