Search

Background: Functional brain alterations in post-Covid-19 condition have been minimally explored to date. Here, we investigate differences in resting-state thalamic functional connectivity among post-Covid patients with and without fatigue, alongside structural brain changes and cognition., Methods: Thirty-nine post-Covid patients (n = 15 fatigued, n = 24 non-fatigued) participated in our study, undergoing comprehensive cognitive assessments, as well as functional and structural neuroimaging. We conducted a seed-based functional connectivity analysis using the thalamus as a seed region, exploring its connectivity with the entire brain. To further elucidate our findings, correlation analyses were performed using the functional coupling between the thalamus and regions showing different connectivity between the two patient groups., Results: Our results reveal that patients experiencing fatigue exhibit anti-correlated functional coupling between the thalamus and motor-associated regions, including the motor cortex (M1), supplementary motor area (SMA), and anterior cingulate cortex (ACC), compared to non-fatigued patients, who are showing positive functional coupling. Furthermore, this observed coupling was found to correlate with both the fatigue scores obtained from a fatigue questionnaire and performance on the Trail Making Test, Part A, which represents a measure of processing speed., Conclusions: Our study highlights significant differences in resting-state functional connectivity between post-Covid patients with and without fatigue, particularly within motor-associated brain regions. These findings suggest a potential neural mechanism underlying post-Covid fatigue and underscore the importance of considering both functional and structural brain changes in understanding the symptomatic sequelae of post-Covid-19 condition. Further research is warranted to provide insight into the longitudinal trajectories of these neural alterations., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Marisa Koini reports financial support was provided by Austrian Research Promotion Agency. The authors declare that there are no conflicts of interest. The co-authors affiliated with Probando GmbH and digitAAL life GmbH were not involved in the conception, design, execution, data analysis, or interpretation of the study. Their role was limited to patient recruitment (Probando GmbH) and designing a cognitive training app (not part of this manuscript; digitAAL life GmbH). All other authors contributed to the study in compliance with the journal's ethical and research guidelines. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Extracellular free water (FW) increases are suggested to better provide pathophysiological information in brain aging than conventional biomarkers such as fractional anisotropy. The aim of the present study was to determine the relationship between conventional biomarkers, FW in white matter hyperintensities (WMH), FW in normal appearing white matter (NAWM) and in white matter tracts and executive functions (EF) with a speed component in elderly persons. We examined 226 healthy elderly participants (median age 69.83 years, IQR: 56.99-74.42) who underwent brain MRI and neuropsychological examination. FW in WMH and in NAWM as well as FW corrected diffusion metrics and measures derived from conventional MRI (white matter hyperintensities, brain volume, lacunes) were used in partial correlation (adjusted for age) to assess their correlation with EF with a speed component. Random forest analysis was used to assess the relative importance of these variables as determinants. Lastly, linear regression analyses of FW in white matter tracts corrected for risk factors of cognitive and white matter deterioration, were used to examine the role of specific tracts on EF with a speed component, which were then ranked with random forest regression. Partial correlation analyses revealed that almost all imaging metrics showed a significant association with EF with a speed component (r = -0.213 - 0.266). Random forest regression highlighted FW in WMH and in NAWM as most important among all diffusion and structural MRI metrics. The fornix (R 2 =0.421, p = 0.018) and the corpus callosum (genu (R 2  = 0.418, p = 0.021), prefrontal (R 2  = 0.416, p = 0.026), premotor (R 2  = 0.418, p = 0.021)) were associated with EF with a speed component in tract based regression analyses and had highest variables importance. In a normal aging population FW in WMH and NAWM is more closely related to EF with a speed component than standard DTI and brain structural measures. Higher amounts of FW in the fornix and the frontal part of the corpus callosum leads to deteriorating EF with a speed component., Competing Interests: Declarations of Competing Interest None., (Copyright © 2022. Published by Elsevier Inc.)

Alzheimer's disease (AD) patients show altered patterns of functional connectivity (FC) on resting state functional magnetic resonance imaging (RSfMRI) scans. It is yet unclear which RSfMRI measures are most informative for the individual classification of AD patients. We investigated this using RSfMRI scans from 77 AD patients (MMSE = 20.4 ± 4.5) and 173 controls (MMSE = 27.5 ± 1.8). We calculated i) FC matrices between resting state components as obtained with independent component analysis (ICA), ii) the dynamics of these FC matrices using a sliding window approach, iii) the graph properties (e.g., connection degree, and clustering coefficient) of the FC matrices, and iv) we distinguished five FC states and administered how long each subject resided in each of these five states. Furthermore, for each voxel we calculated v) FC with 10 resting state networks using dual regression, vi) FC with the hippocampus, vii) eigenvector centrality, and viii) the amplitude of low frequency fluctuations (ALFF). These eight measures were used separately as predictors in an elastic net logistic regression, and combined in a group lasso logistic regression model. We calculated the area under the receiver operating characteristic curve plots (AUC) to determine classification performance. The AUC values ranged between 0.51 and 0.84 and the highest were found for the FC matrices (0.82), FC dynamics (0.84) and ALFF (0.82). The combination of all measures resulted in an AUC of 0.85. We show that it is possible to obtain moderate to good AD classification using RSfMRI scans. FC matrices, FC dynamics and ALFF are most discriminative and the combination of all the resting state measures improves classification accuracy slightly., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Diffusion magnetic resonance imaging (MRI) is a powerful non-invasive method to study white matter integrity, and is sensitive to detect differences in Alzheimer's disease (AD) patients. Diffusion MRI may be able to contribute towards reliable diagnosis of AD. We used diffusion MRI to classify AD patients (N=77), and controls (N=173). We use different methods to extract information from the diffusion MRI data. First, we use the voxel-wise diffusion tensor measures that have been skeletonised using tract based spatial statistics. Second, we clustered the voxel-wise diffusion measures with independent component analysis (ICA), and extracted the mixing weights. Third, we determined structural connectivity between Harvard Oxford atlas regions with probabilistic tractography, as well as graph measures based on these structural connectivity graphs. Classification performance for voxel-wise measures ranged between an AUC of 0.888, and 0.902. The ICA-clustered measures ranged between an AUC of 0.893, and 0.920. The AUC for the structural connectivity graph was 0.900, while graph measures based upon this graph ranged between an AUC of 0.531, and 0.840. All measures combined with a sparse group lasso resulted in an AUC of 0.896. Overall, fractional anisotropy clustered into ICA components was the best performing measure. These findings may be useful for future incorporation of diffusion MRI into protocols for AD classification, or as a starting point for early detection of AD using diffusion MRI., (Copyright © 2017 Elsevier Inc. All rights reserved.)