Thalamic inflammation after brain trauma is associated with thalamo-cortical white matter damage

Background Traumatic brain injury can trigger chronic neuroinflammation, which may predispose to neurodegeneration. Animal models and human pathological studies demonstrate persistent inflammation in the thalamus associated with axonal injury, but this relationship has never been shown in vivo. Findings Using [11C]-PK11195 positron emission tomography, a marker of microglial activation, we previously demonstrated thalamic inflammation up to 17 years after traumatic brain injury. Here, we use diffusion MRI to estimate axonal injury and show that thalamic inflammation is correlated with thalamo-cortical tract damage. Conclusions These findings support a link between axonal damage and persistent inflammation after brain injury

Inferring individual-level variations in the functional parcellation of the cerebral cortex

Objective: Functional parcellation of the cerebral cortex is variable across different subjects or between cognitive states. Ignoring individual - or state - dependent variations in the functional parcellation may lead to inaccurate representations of individual functional connectivity, limiting the precision of interpretations of differences in individual connectivity profiles. However, it is difficult to infer the individual-level variations due to the relatively low robustness of methods for parcellation of individual subjects. Methods: We propose a method called “joint K-means” to robustly parcellate the cerebral cortex using fMRI data for contrasts between two states or subjects that intended to characterize variance in individual functional parcellations. The key idea of the proposed method is to jointly infer parcellations in contrasted datasets by iterative descent, while constraining the similarity of the two pathways in searches for local minima to reduce spurious variations. Results: Parcellations of resting-state fMRI datasets from the Human Connectome Project show that the similarity of parcellations for an individual subject studied on two sessions is greater than that between different subjects. Differences in parcellations between subjects are non-uniformly distributed across the cerebral cortex, with clusters of higher variance in the prefrontal, lateral temporal and occipito-parietal cortices. This pattern is reproducible across sessions, between groups and using different numbers of parcels. Conclusion: The individual-level variations inferred by the proposed method are plausible and consistent with the previously reported functional connectivity variability. Significance: The proposed method is a promising tool for investigating relationships between the cerebral functional organization and behavioral differences

New lesion segmentation for multiple sclerosis brain images with imaging and lesion-aware augmentation

Multiple sclerosis (MS) is an inflammatory and demyelinating neurological disease of the central nervous system. Image-based biomarkers, such as lesions defined on magnetic resonance imaging (MRI), play an important role in MS diagnosis and patient monitoring. The detection of newly formed lesions provides crucial information for assessing disease progression and treatment outcome. Here, we propose a deep learning-based pipeline for new MS lesion detection and segmentation, which is built upon the nnU-Net framework. In addition to conventional data augmentation, we employ imaging and lesion-aware data augmentation methods, axial subsampling and CarveMix, to generate diverse samples and improve segmentation performance. The proposed pipeline is evaluated on the MICCAI 2021 MS new lesion segmentation challenge (MSSEG-2) dataset. It achieves an average Dice score of 0.510 and F1 score of 0.552 on cases with new lesions, and an average false positive lesion number nFP of 0.036 and false positive lesion volume VFP of 0.192 mm3 on cases with no new lesions. Our method outperforms other participating methods in the challenge and several state-of-the-art network architectures

Imaging the neuroendocrinology of appetite.

Functional magnetic resonance imaging has become a powerful tool to investigate the neuroendocrinology of appetite. In a recent study, we demonstrated that the brain activation pattern seen following the infusion of the anorectic gut hormones PYY3-36and GLP-17-36 amideto fasted individuals resembles the brain activation pattern seen in the physiological satiated state. This commentary discusses the significance of these findings and compares them with other landmark studies in the field, with specific reference to the brain areas involved in appetite regulation. We highlight the importance of this type of research in order to pave the way for the development of efficacious and safe anti-obesity therapies

Survey of Research Approaches Utilised in The Scholarship of Teaching and Learning Publications

The Scholarship of Teaching and Learning (SoTL) has been described as the fastest growing academic development movement in higher education. As this field of inquiry matures, there is a need to understand how SoTL research is conducted. The purpose of our study was to inform this debate by investigating research approaches used in SoTL publications. We analysed 223 empirical research studies published from 2012 to 2014 in three explicitly focused SoTL journals. We classified the studies as either qualitative, quantitative, or mixed methods using an analytical framework devised from existing literature on research methods. We found that the use of the three research designs was fairly evenly distributed across the papers examined: qualitative (37.2%), quantitative (29.6%), and mixed methods (33.2%). However, there was an over-reliance on data collection from a single source in 83.9% of papers analysed, and this source was primarily students. There was some, but limited, evidence of the use of triangulation through the use of multiple data collection instruments (e.g. survey, assessment tasks, grade databases). Similarly, only one-third of publications classified as mixed methods integrated the analysis and interpretation of the qualitative and quantitative data equally within the study. We conclude that current SoTL research is characterised by methodological pluralism but could be advanced through inclusion of more diverse approaches, such as close reading, and adoption of strategies known to enhance the quality of research, for example, triangulation and visual representation

Towards a standard MRI protocol for multiple sclerosis across the UK.

Multiple sclerosis (MS) is a chronic inflammatory demyelinating and degenerative disease of the central nervous system. It is the most common non-traumatic cause of chronic disability in young adults. An early and accurate diagnosis, and effective disease modifying treatment (DMT) are key elements of optimum care for people with MS (pwMS). Magnetic resonance imaging (MRI) has become a critical tool to confirm the presence of dissemination in space and time of lesions characteristic of inflammatory demyelination, a cornerstone of MS diagnosis, over and above exclusion of numerous differential diagnoses. In the modern era of early and highly effective DMT, follow-up of pwMS also relies heavily on MRI, to both confirm efficacy and for pharmacovigilance. Since criteria for MS rely heavily on MRI, an agreed standardized acquisition and reporting protocol enabling efficient and equitable application across the UK is desirable. Following a recent meeting of MS experts in London (UK), we make recommendations for a standardized UK MRI protocol that captures the diagnostic phase as well as monitoring for safety and treatment efficacy once the diagnosis is established. Our views take into account issues arising from the (repeated) use of contrast agents as well as the advent of (semi-) automated tools to further optimise disease monitoring in pwMS.Edmond J Safra FoundationLily SafraMRCUK Dementia Research InstituteImperial College Healthcare Trust Biomedical Research Centr

A real-time Global Warming Index

We propose a simple real-time index of global human-induced warming and assess its robustness to uncertainties in climate forcing and short-term climate fluctuations. This index provides improved scientific context for temperature stabilisation targets and has the potential to decrease the volatility of climate policy. We quantify uncertainties arising from temperature observations, climate radiative forcings, internal variability and the model response. Our index and the associated rate of human-induced warming is compatible with a range of other more sophisticated methods to estimate the human contribution to observed global temperature change

Ocean temperature and salinity components of the Madden-Julian oscillation observed by Argo floats

New diagnostics of the Madden-Julian Oscillation (MJO) cycle in ocean temperature and, for the first time, salinity are presented. The MJO composites are based on 4 years of gridded Argo float data from 2003 to 2006, and extend from the surface to 1,400 m depth in the tropical Indian and Pacific Oceans. The MJO surface salinity anomalies are consistent with precipitation minus evaporation fluxes in the Indian Ocean, and with anomalous zonal advection in the Pacific. The Argo sea surface temperature and thermocline depth anomalies are consistent with previous studies using other data sets. The near-surface density changes due to salinity are comparable to, and partially offset, those due to temperature, emphasising the importance of including salinity as well as temperature changes in mixed-layer modelling of tropical intraseasonal processes. The MJO-forced equatorial Kelvin wave that propagates along the thermocline in the Pacific extends down into the deep ocean, to at least 1,400 m. Coherent, statistically significant, MJO temperature and salinity anomalies are also present in the deep Indian Ocean

Estimating Carbon Budgets for Ambitious Climate Targets

Carbon budgets, which define the total allowable CO2 emissions associated with a given global climate target, are a useful way of framing the climate mitigation challenge. In this paper, we review the geophysical basis for the idea of a carbon budget, showing how this concept emerges from a linear climate response to cumulative CO2 emissions. We then discuss the difference between a “CO2-only carbon budget” associated with a given level of CO2-induced warming and an “effective carbon budget” associated with a given level of warming caused by all human emissions. We present estimates for the CO2-only and effective carbon budgets for 1.5 and 2 °C, based on both model simulations and updated observational data. Finally, we discuss the key contributors to uncertainty in carbon budget estimates and suggest some implications of this uncertainty for decision-making. Based on the analysis presented here, we argue that while the CO2-only carbon budget is a robust upper bound on allowable emissions for a given climate target, the size of the effective carbon budget is dependent on the how quickly we are able to mitigate non-CO2 greenhouse gas and aerosol emissions. This suggests that climate mitigation efforts could benefit from being responsive to a changing effective carbon budget over time, as well as to potential new information that could narrow uncertainty associated with the climate response to CO2 emissions