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Background: Changes in both the vascular system and brain tissues can occur after a prior episode of coronavirus disease 2019 (COVID-19), detectable through modifications in diffusion parameters using magnetic resonance imaging (MRI) techniques. These changes in diffusion parameters may be particularly prominent in highly organized structures such as the corpus callosum (CC), including its major components, which have not been adequately studied following COVID-19 infection. Therefore, the study aimed to evaluate microstructural changes in whole-brain (WB) diffusion, with a specific focus on the CC., Methods: A total of 101 probands (age range from 18 to 69 years) participated in this retrospective study, consisting of 55 volunteers and 46 post-COVID-19 patients experiencing neurological symptoms. The participants were recruited from April 2022 to September 2023 at the Institute for Clinical and Experimental Medicine in Prague, Czech Republic. All participants underwent MRI examinations on a 3T MR scanner with a diffusion protocol, complemented by additional MRI techniques. Two volunteers and five patients were excluded from the study due to motion artefacts, severe hypoperfusion or the presence of lesions. Participants were selected by a neurologist based on clinical examination and a serological test for COVID-19 antibodies. They were then divided into three groups: a control group of healthy volunteers (n=28), an asymptomatic group (n=25) with a history of infection but no symptoms, and a symptomatic group (n=41) with a history of COVID-19 and neurological symptoms. Symptomatic patients did not exhibit neurological symptoms before contracting COVID-19. Diffusion data underwent eddy current and susceptibility distortion corrections, and fiber tracking was performed using default parameters in DSI studio. Subsequently, various diffusion metrics, were computed within the reconstructed tracts of the WB and CC. To assess the impact of COVID-19 and its associated symptoms on diffusion indices within the white matter of the WB and CC regions, while considering age, we employed a statistical analysis using a linear mixed-effects model within the R framework., Results: Statistical analysis revealed a significant difference in mean diffusivity (MD) between the symptomatic and control groups in the forceps minor (P=0.001) and CC body (P=0.003). In addition to changes in diffusion, alterations in brain perfusion were observed in two post-COVID-19 patients who experienced a severe course. Furthermore, hyperintense lesions were identified in subcortical and deep white matter areas in the vast majority of symptomatic patients., Conclusions: The main finding of our study was that post-COVID-19 patients exhibit increased MD in the forceps minor and body of the CC. This finding suggests a potential association between microstructural brain changes in post-COVID-19 patients and reported neurological symptoms, with significant implications for research and clinical applications., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-162/coif). The authors have no conflicts of interest to declare., (2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Objective: The accuracy of phase-contrast magnetic resonance imaging (PC-MRI) measurement is investigated using a computational fluid dynamics (CFD) model with the objective to determine the magnitude of the flow underestimation due to turbulence behind a narrowed valve in a phantom experiment., Materials and Methods: An acrylic stationary flow phantom is used with three insertable plates mimicking aortic valvular stenoses of varying degrees. Positive and negative horizontal fluxes are measured at equidistant slices using standard PC-MRI sequences by 1.5T and 3T systems. The CFD model is based on the 3D lattice Boltzmann method (LBM). The experimental and simulated data are compared using the Bland-Altman-derived limits of agreement. Based on the LBM results, the turbulence is quantified and confronted with the level of flow underestimation., Results: LBM gives comparable results to PC-MRI for valves up to moderate stenosis on both field strengths. The flow magnitude through a severely stenotic valve was underestimated due to signal void in the regions of turbulent flow behind the valve, consistently with the level of quantified turbulence intensity., Discussion: Flow measured by PC-MRI is affected by noise and turbulence. LBM can simulate turbulent flow efficiently and accurately, it has therefore the potential to improve clinical interpretation of PC-MRI.

To identify progressive supranuclear palsy (PSP), we combined voxel-based morphometry (VBM) and support vector machine (SVM) classification using disease-specific features in multicentric magnetic resonance imaging (MRI) data. Structural brain differences were investigated at four centers between 20 patients with PSP and 20 age-matched healthy controls with T1-weighted MRI at 3T. To pave the way for future application in personalized medicine, we applied SVM classification to identify PSP on an individual level besides group analyses based on VBM. We found a major decline in gray matter density in the brainstem, insula, and striatum, and also in frontomedian regions, which is in line with current literature. Moreover, SVM classification yielded high accuracy rates above 80% for disease identification in imaging data. Focusing analyses on disease-specific regions-of-interest (ROI) led to higher accuracy rates compared to a whole-brain approach. Using a polynomial kernel (instead of a linear kernel) led to an increased sensitivity and a higher specificity of disease detection. Our study supports the application of MRI for individual diagnosis of PSP, if combined with SVM approaches. We demonstrate that SVM classification provides high accuracy rates in multicentric data-a prerequisite for potential application in diagnostic routine.

Complex systems are commonly characterized by the properties of their graph representation. Dynamical complex systems are then typically represented by a graph of temporal dependencies between time series of state variables of their subunits. It has been shown recently that graphs constructed in this way tend to have relatively clustered structure, potentially leading to spurious detection of small-world properties even in the case of systems with no or randomly distributed true interactions. However, the strength of this bias depends heavily on a range of parameters and its relevance for real-world data has not yet been established. In this work, we assess the relevance of the bias using two examples of multivariate time series recorded in natural complex systems. The first is the time series of local brain activity as measured by functional magnetic resonance imaging in resting healthy human subjects, and the second is the time series of average monthly surface air temperature coming from a large reanalysis of climatological data over the period 1948-2012. In both cases, the clustering in the thresholded correlation graph is substantially higher compared with a realization of a density-matched random graph, while the shortest paths are relatively short, showing thus distinguishing features of small-world structure. However, comparable or even stronger small-world properties were reproduced in correlation graphs of model processes with randomly scrambled interconnections. This suggests that the small-world properties of the correlation matrices of these real-world systems indeed do not reflect genuinely the properties of the underlying interaction structure, but rather result from the inherent properties of correlation matrix.