Iliopsoas haematoma during extracorporeal membrane oxygenation: A registry report from the COVID-19 critical care consortium across 30 countries.

INTRODUCTION: Iliopsoas haematoma (IPH) during extracorporeal membrane oxygenation (ECMO) is a rare bleeding complication that can be fatal due to its progression to abdominal compartment syndrome, but its incidence and risk factors are not well known. We have previously reported an IPH incidence rate of 16% in Japan. Among possible reasons for this high incidence, ethnicity has been hypothesised to play a role. Therefore, we used an international multi-centre cohort registry to test this hypothesis by determining the incidence rate of IPH. METHODS: This study was performed using the COVID-19 Critical Care Consortium database, conducted in 30 countries across five continents between 3 January 2020, and 20 June 2022. RESULTS: Overall, 1102 patients received ECMO for COVID-19-related acute respiratory distress syndrome. Of them, only seven were reported to have IPH, indicating an incidence rate of 0.64%, with comparable rates between the countries. The IPH group tended to have a higher mortality rate (71.4%) than the non-IPH group (51%). CONCLUSIONS: Overall incidence of IPH in the studied COVID-19 ECMO cohort was 0.64%. Most cases were reported from Japan, Belgium, and Italy. In our study, this rare complication did not appear to be confined to Asian patients. Due to the high fatality rate, awareness about the occurrence of IPH should be recognised.

Systemic perturbations of the kynurenine pathway precede progression to dementia independently of amyloid-ß.

Increasing evidence suggests that kynurenine pathway (KP) dyshomeostasis may promote disease progression in dementia. Studies in Alzheimer's disease (AD) patients confirm KP dyshomeostasis in plasma and cerebrospinal fluid (CSF) which correlates with amyloid-ß and tau pathology. Herein, we performed the first comprehensive study assessing baseline levels of KP metabolites in participants enrolling in the Australian Imaging Biomarkers Flagship Study of Aging. Our purpose was to test the hypothesis that changes in KP metabolites may be biomarkers of dementia processes that are largely silent. We used a cross-sectional analytical approach to assess non-progressors (N = 73); cognitively normal (CN) or mild cognitive impairment (MCI) participants at baseline and throughout the study, and progressors (N = 166); CN or MCI at baseline but progressing to either MCI or AD during the study. Significant KP changes in progressors included increased 3-hydroxyanthranilic acid (3-HAA) and 3-hydroxyanthranilic acid/anthranilic acid (3-HAA/AA) ratio, the latter having the largest effect on the odds of an individual being a progressor (OR 35.3; 95% CI between 14 and 104). 3-HAA levels were hence surprisingly bi-phasic, high in progressors but low in non-progressors or participants who had already transitioned to MCI or dementia. This is a new, unexpected and interesting result, as most studies of the KP in neurodegenerative disease show reduced 3-HAA/AA ratio after diagnosis. The neuroprotective metabolite picolinic acid was also significantly decreased while the neurotoxic metabolite 3-hydroxykynurenine increased in progressors. These results were significant even after adjustment for confounders. Considering the magnitude of the OR to predict change in cognition, it is important that these findings are replicated in other populations. Independent validation of our findings may confirm the utility of 3-HAA/AA ratio to predict change in cognition leading to dementia in clinical settings.

Rest-activity rhythms and tract specific white matter lesions in older adults at risk for cognitive decline.

White matter lesions (WMLs) are common in older adults and represent an important predictor of negative long-term outcomes. Rest-activity rhythm disturbance is also common, however, few studies have investigated associations between these factors. We employed a novel AI-based automatic WML segmentation tool and diffusion-weighted tractography to investigate associations between tract specific WML volumes and non-parametric actigraphy measures in older adults at risk for cognitive decline. The primary non-parametric measures of interest were inter-daily stability (IS), intra-daily variability and relative amplitude, with the anterior thalamic radiation (ATR), superior longitudinal fasciculus (SLF) and inferior longitudinal fasciculus (ILF) selected as tracts of interest. One hundred and eight participants at risk for cognitive decline (classified as experiencing subjective or objective cognitive decline) were included (mean age = 68.85 years, SD = 8.91). Of the primary non-parametric measures of interest, results showed that lower IS was associated with a greater likelihood of higher WML burden in the ATR (OR = 1.82, 95% CI [1.12,3.15]). Analysis of secondary non-parametric measures revealed later onset of the least active period to be associated with greater likelihood of high WML burden in the SLF (OR = 1.55, 95% CI [1.00,2.53]) and increased activity during the least active 5-h period to be associated with a greater likelihood of high whole-brain WML burden (OR = 1.83, 95% CI [1.06,3.47]). This study shows integrity of the ATR and SLF, and overall WML burden is linked to altered rest-activity rhythms in older adults at risk for cognitive decline, with those demonstrating altered rest-activity rhythms showing 50%-80% higher odds of having high WML burden.

Rest-activity functioning is related to white matter microarchitecture and modifiable risk factors in older adults at-risk for dementia.

STUDY OBJECTIVES: Growing evidence demonstrates pronounced alterations in rest-activity functioning in older adults at-risk for dementia. White matter degeneration, poor cardiometabolic functioning, and depression have also been linked to a greater risk of decline; however, limited studies have examined the white matter in relation to rest-activity functioning in at-risk older adults. METHODS: We investigated associations between nonparametric actigraphy measures and white matter microarchitecture using whole-brain fixel-based analysis of diffusion-weighted imaging in older adults (aged 50 years or older) at-risk for cognitive decline and dementia. The fixel-based metrics assessed were fiber density, fiber cross-section, and combined fiber-density, and cross-section. Interactions between rest-activity functioning and known clinical risk factors, specifically body mass index (BMI), vascular risk factors, depressive symptoms and self-reported exercise, and their association with white matter properties were then investigated. RESULTS: Sixty-seven older adults were included (mean = 65.78 years, SD = 7.89). Lower relative amplitude, poorer 24-h synchronization and earlier onset of the least active 5-h period were associated with reductions in markers of white matter atrophy in widespread regions, including cortico-subcortical and cortical association pathways. Preliminary evidence was also found indicating more pronounced white matter alterations in those with lower amplitude and higher BMI (ß = 0.25, 95% CI [0.05, 0.46]), poorer 24-h synchronization and more vascular risk factors (ß = 0.17, 95% CI [-0.02, 0.36]) and earlier onset of inactivity and greater depressive symptoms (ß = 0.17, 95% CI [0.03, 0.30]). CONCLUSIONS: These findings highlight the complex interplay between rest-activity rhythms, white matter, and clinical risk factors in individuals at-risk for dementia that should be considered in future studies.

Relative rate of change in cognitive score network dynamics via Bayesian hierarchical models reveal spatial patterns of neurodegeneration.

The degeneration of the human brain is a complex process, which often affects certain brain regions due to healthy aging or disease. This degeneration can be evaluated on regions of interest (ROI) in the brain through probabilistic networks and morphological estimates. Current approaches for finding such networks are limited to analyses at discrete neuropsychological stages, which cannot appropriately account for connectivity dynamics over the onset of cognitive deterioration, and morphological changes are seldom unified with connectivity networks, despite known dependencies. To overcome these limitations, a probabilistic wombling model is proposed to simultaneously estimate ROI cortical thickness and covariance networks contingent on rates of change in cognitive decline. This proposed model was applied to analyze longitudinal data from healthy control (HC) and Alzheimer's disease (AD) groups and found connection differences pertaining to regions, which play a crucial role in lasting cognitive impairment, such as the entorhinal area and temporal regions. Moreover, HC cortical thickness estimates were significantly higher than those in the AD group across all ROIs. The analyses presented in this work will help practitioners jointly analyze brain tissue atrophy at the ROI-level conditional on neuropsychological networks, which could potentially allow for more targeted therapeutic interventions.

How many streamlines are required for reliable probabilistic tractography? Solutions for microstructural measurements and neurosurgical planning.

Diffusion MRI tractography is commonly used to delineate white matter tracts. These delineations can be used for planning neurosurgery or for identifying regions of interest from which microstructural measurements can be taken. Probabilistic tractography produces different delineations each time it is run, potentially leading to microstructural measurements or anatomical delineations that are not reproducible. Generating a sufficiently large number of streamlines is required to avoid this scenario, but what constitutes "sufficient" is difficult to assess and so streamline counts are typically chosen in an arbitrary or qualitative manner. This work explores several factors influencing tractography reliability and details two methods for estimating this reliability. The first method automatically estimates the number of streamlines required to achieve reliable microstructural measurements, whilst the second estimates the number of streamlines required to achieve a reliable binarised trackmap than can be used clinically. Using these methods, we calculated the number of streamlines required to achieve a range of quantitative reproducibility criteria for three anatomical tracts in 40 Human Connectome Project datasets. Actual reproducibility was checked by repeatedly generating the tractograms with the calculated numbers of streamlines. We found that the required number of streamlines varied strongly by anatomical tract, image resolution, number of diffusion directions, the degree of reliability desired, the microstructural measurement of interest, and/or the specifics on how the tractogram was converted to a binary volume. The proposed methods consistently predicted streamline counts that achieved the target reproducibility. Implementations are made available to enable the scientific community to more-easily achieve reproducible tractography.

An efficient algorithm for estimating brain covariance networks.

Often derived from partial correlations or many pairwise analyses, covariance networks represent the inter-relationships among regions and can reveal important topological structures in brain measures from healthy and pathological subjects. However both approaches are not consistent network estimators and are sensitive to the value of the tuning parameters. Here, we propose a consistent covariance network estimator by maximising the network likelihood (MNL) which is robust to the tuning parameter. We validate the consistency of our algorithm theoretically and via a simulation study, and contrast these results against two well-known approaches: the graphical LASSO (gLASSO) and Pearson pairwise correlations (PPC) over a range of tuning parameters. The MNL algorithm had a specificity equal to and greater than 0.94 for all sample sizes in the simulation study, and the sensitivity was shown to increase as the sample size increased. The gLASSO and PPC demonstrated a specificity-sensitivity trade-off over a range of values of tuning parameters highlighting the discrepancy in the results for misspecified values. Application of the MNL algorithm to the case study data showed a loss of connections between healthy and impaired groups, and improved ability to identify between lobe connectivity in contrast to gLASSO networks. In this work, we propose the MNL algorithm as an effective approach to find covariance brain networks, which can inform the organisational features in brain-wide analyses, particularly for large sample sizes.

Comparisons of neurodegeneration over time between healthy ageing and Alzheimer's disease cohorts via Bayesian inference.

OBJECTIVES: In recent years, large-scale longitudinal neuroimaging studies have improved our understanding of healthy ageing and pathologies including Alzheimer's disease (AD). A particular focus of these studies is group differences and identification of participants at risk of deteriorating to a worse diagnosis. For this, statistical analysis using linear mixed-effects (LME) models are used to account for correlated observations from individuals measured over time. A Bayesian framework for LME models in AD is introduced in this paper to provide additional insight often not found in current LME volumetric analyses. SETTING AND PARTICIPANTS: Longitudinal neuroimaging case study of ageing was analysed in this research on 260 participants diagnosed as either healthy controls (HC), mild cognitive impaired (MCI) or AD. Bayesian LME models for the ventricle and hippocampus regions were used to: (1) estimate how the volumes of these regions change over time by diagnosis, (2) identify high-risk non-AD individuals with AD like degeneration and (3) determine probabilistic trajectories of diagnosis groups over age. RESULTS: We observed (1) large differences in the average rate of change of volume for the ventricle and hippocampus regions between diagnosis groups, (2) high-risk individuals who had progressed from HC to MCI and displayed similar rates of deterioration as AD counterparts, and (3) critical time points which indicate where deterioration of regions begins to diverge between the diagnosis groups. CONCLUSIONS: To the best of our knowledge, this is the first application of Bayesian LME models to neuroimaging data which provides inference on a population and individual level in the AD field. The application of a Bayesian LME framework allows for additional information to be extracted from longitudinal studies. This provides health professionals with valuable information of neurodegeneration stages, and a potential to provide a better understanding of disease pathology.

Infección por citomegalovirus y parálisis facial periférica aguda / Infection by citomegalovirus and peripheric sharp facial passy

Las infecciones por citomegalovirus son muy frecuentes en todas las poblaciones; sin embargo, la mayoría son asintomáticas. Son severas en los pacientes inmunocomprometidos o en los individuos con respuesta inmunológica inmadura. En las personas previamente sanas, se han identificado múltiples cuadros clínicos, incluyendo manifestaciones neurológicas variadas. La parálisis facial periférica aguda o parálisis de Bell es una neuropatía inflamatoria frecuente en Costa Rica. Diversos estudios han demostrado una asociación de esta entidad con algunos agentes infecciosos, entre ellos, los virus de la familia Herpesviridae. Existen pocos datos sobre el papel del citomegalovirus en esta neuropatía. En el presente estudio, utilizando grupos de control y por medio de la técnica ELISA para la detección de anticuerpos altamente sensible y específica, se logró demostrar una infección aguda por citomegalovirus en 10/62 (16 por ciento ) de los pacientes con parálisis facial periférica aguda. No se observó diferencias en la incidencia por sexo. En 7/10 pacientes la edad fue menor de 35 años. No obstante, en dos pacientes mayores de 55 años, fue posible demostrar dicha asociación. Se discuten estos hallazgos así como la patogénesis de esta entidad.