Design and Validation of a Novel Method to Measure Cross-Sectional Area of Neck Muscles Included during Routine MR Brain Volume Imaging

Low muscle mass secondary to disease and ageing is an important cause of excess mortality and morbidity. Many studies include a MR brain scan but no peripheral measure of muscle mass. We developed a technique to measure posterior neck muscle cross-sectional area (CSA) on volumetric MR brain scans enabling brain and muscle size to be measured simultaneously.We performed four studies to develop and test: feasibility, inter-rater reliability, repeatability and external validity. We used T1-weighted MR brain imaging from young and older subjects, obtained on different scanners, and collected mid-thigh MR data.After developing the technique and demonstrating feasibility, we tested it for inter-rater reliability in 40 subjects. Intraclass correlation coefficients (ICC) between raters were 0.99 (95% confidence intervals (CI) 0.98-1.00) for the combined group (trapezius, splenius and semispinalis), 0.92 (CI 0.85-0.96) for obliquus and 0.92 (CI 0.85-0.96) for sternocleidomastoid. The first unrotated principal component explained 72.2% of total neck muscle CSA variance and correlated positively with both right (r = 0.52, p = .001) and left (r = 0.50, p = .002) grip strength. The 14 subjects in the repeatability study had had two MR brain scans on three different scanners. The ICC for between scanner variation for total neck muscle CSA was high at 0.94 (CI 0.86-0.98). The ICCs for within scanner variations were also high, with values of 0.95 (CI 0.86-0.98), 0.97 (CI 0.92-0.99) and 0.96 (CI 0.86-0.99) for the three scanners. The external validity study found a correlation coefficient for total thigh CSA and total neck CSA of 0.88.We present a feasible, valid and reliable method for measuring neck muscle CSA on T1-weighted MR brain scans. Larger studies are needed to validate and apply our technique with subjects differing in age, ethnicity and geographical location

An Iterative Jackknife Approach for Assessing Reliability and Power of fMRI Group Analyses

For functional magnetic resonance imaging (fMRI) group activation maps, so-called second-level random effect approaches are commonly used, which are intended to be generalizable to the population as a whole. However, reliability of a certain activation focus as a function of group composition or group size cannot directly be deduced from such maps. This question is of particular relevance when examining smaller groups (<20–27 subjects). The approach presented here tries to address this issue by iteratively excluding each subject from a group study and presenting the overlap of the resulting (reduced) second-level maps in a group percent overlap map. This allows to judge where activation is reliable even upon excluding one, two, or three (or more) subjects, thereby also demonstrating the inherent variability that is still present in second-level analyses. Moreover, when progressively decreasing group size, foci of activation will become smaller and/or disappear; hence, the group size at which a given activation disappears can be considered to reflect the power necessary to detect this particular activation. Systematically exploiting this effect allows to rank clusters according to their observable effect size. The approach is tested using different scenarios from a recent fMRI study (children performing a “dual-use” fMRI task, n = 39), and the implications of this approach are discussed

Mapping the human genetic architecture of COVID-19

The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3,4,5,6,7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease