Stephen M. Smith

Automated online and App-based cognitive assessment tasks are becoming increasingly popular in large-scale cohorts and biobanks due to advantages in affordability, scalability, and repeatability. However, the summary scores that such tasks generate typically conflate the cognitive processes that are the intended focus of assessment with basic visuo-motor speeds, testing device latencies, and speed-accuracy tradeoffs. This lack of precision presents a fundamental limitation when studying brain-behaviour associations. Previously, we developed a novel modelling approach that leverages continuous performance recordings from large-cohort studies to achieve an iterative decomposition of cognitive tasks (IDoCT), which outputs data-driven estimates of cognitive abilities, and device and visuo-motor latencies, whilst recalibrating trial-difficulty scales. Here, we further validate the IDoCT approach with UK …

BackgroundAmyotrophic lateral sclerosis (ALS) is a disease of the motor network associated with brain structure and functional connectivity alterations that are implicated in disease progression. Whether such changes have a causal role in ALS, fitting with a postulated influence of premorbid cerebral architecture on the phenotypes associated with neurodegenerative disorders is not known.MethodsThis study considered causal effects and shared genetic risk of 2240 structural and functional MRI brain scan imaging-derived phenotypes (IDPs) on ALS using two sample Mendelian randomisation, with putative associations further examined with extensive sensitivity analysis. Shared genetic predisposition between IDPs and ALS was explored using genetic correlation analysis.ResultsIncreased white matter volume in the cerebral hemispheres was causally associated with ALS. Weaker causal associations were …

The spectrum, pathophysiology, and recovery trajectory of persistent post-COVID-19 cognitive deficits are unknown, limiting our ability to develop prevention and treatment strategies. We report the one-year cognitive, serum biomarker, and neuroimaging findings from a prospective, national longitudinal study of cognition in 351 COVID-19 patients who had required hospitalisation, compared to 2,927 normative matched controls. Cognitive deficits were global and associated with elevated brain injury markers and reduced anterior cingulate cortex volume one year after admission. The severity of the initial infective insult, post-acute psychiatric symptoms, and a history of encephalopathy were associated with greatest deficits. There was strong concordance between subjective and objective cognitive deficits. Treatment with corticosteroids during the acute phase appeared protective against cognitive deficits. Together, these findings support the hypothesis that brain injury in moderate to severe COVID-19 is immune-mediated, and should guide the development of therapeutic strategies.

We have previously identified a network of higher-order brain regions particularly vulnerable to the ageing process, schizophrenia and Alzheimer’s disease. However, it remains unknown what the genetic influences on this fragile brain network are, and whether it can be altered by the most common modifiable risk factors for dementia. Here, in ~40,000 UK Biobank participants, we first show significant genome-wide associations between this brain network and seven genetic clusters implicated in cardiovascular deaths, schizophrenia, Alzheimer’s and Parkinson’s disease, and with the two antigens of the XG blood group located in the pseudoautosomal region of the sex chromosomes. We further reveal that the most deleterious modifiable risk factors for this vulnerable brain network are diabetes, nitrogen dioxide – a proxy for traffic-related air pollution – and alcohol intake frequency. The extent of these associations …

The potential value of large scale datasets is constrained by the ubiquitous problem of missing data, arising in either a structured or unstructured fashion. While there is considerable work on imputation methods, much is focused on small-scale datasets with just tens of variables. When imputation methods are proposed for large scale data, one limitation is the simplicity of existing evaluation methods. Specifically, most evaluations create synthetic data with only a simple, unstructured missing data mechanism, and do not resemble the missing data patterns found in real data. For example, in UK Biobank missing data tends to appear in blocks, because non-participation in one of the sub-studies leads to missingness for all of the sub-study variables.

Carbon dioxide removal (CDR) is a critical component of any strategy to limit global warming to well below 2 C and rapidly gaining attention in climate research and policymaking. Despite its importance, there have been few attempts to systematically evaluate the scientific evidence on CDR. Here we use an approach rooted in artificial intelligence to produce a comprehensive systematic map of the CDR literature. In particular, we hand-label 5,339 documents to train machine learning classifiers with high levels of precision and recall to identify a total of 28,976 CDR studies across different technology domains and disciplines published in the period 1990-2022 which is at least 2-3 times more than previous studies suggested. We paint a granular picture of available CDR research in terms of the CDR methods studied, the geographical focus of research, the research method applied, and the broad area of research. The field has grown considerably faster than the climate change literature as a whole. This is driven mainly by the rapid expansion of literature on biochar, which made up about 62% of CDR publications in 2022. Beyond this stark concentration of CDR research on a few individual CDR methods, we find that most studies (86%) focus on improving the CDR methods themselves, but there is little research on their societal implications and ethical foundations. Citations patterns from the most recent IPCC report strongly differ from publication patterns on CDR in terms of its attention to CDR methods, research design and methodological context, as does attention to CDR methods in policy and practice in terms of real-world deployments …

The ventral intermediate nucleus of the thalamus (Vim) is a well-established surgical target in functional neurosurgery for the treatment of tremor. As the structure lacks intrinsic contrast on conventional MRI sequences, targeting the Vim has predominantly relied on standardised Vim atlases which can fail to account for individual anatomical variability. To overcome this limitation, recent studies define the Vim using its structural connectivity profile generated via tractography. Although successful in accounting for individual variability, these connectivity-based methods are sensitive to variations in image acquisition and processing, and require high-quality diffusion imaging protocols which are usually not available in clinical settings. Here we propose a novel transfer learning approach to accurately target the Vim particularly on clinical-quality data. The approach transfers anatomical information from publicly-available high-quality datasets to a wide range of white matter connectivity features in low-quality data to augment inference on the Vim. We demonstrate that the approach can robustly and reliably identify Vim even with compromised data quality and is generalisable to datasets acquired with different protocols, outperforming previous surgical targeting methods. The approach is not limited to targeting Vim and can be adapted to other deep brain structures.

Introduction The eye is a well-established model of brain structure and function, yet region-specific structural correlations between the retina and the brain remain underexplored.Aims To explore and describe the relationships between the retinal layer thicknesses and brain magnetic resonance image (MRI) derived phenotypes in UK Biobank.Methods Participants with both quality-controlled optical coherence tomography (OCT) and brain magnetic resonance imaging (MRI) were eligible. Retinal sub-layer thicknesses and total macular thicknesses were derived from OCT scans. Brain image-derived phenotypes (IDPs) of 153 cortical and subcortical regions were processed from MRI scans. In this hypothesis-free study, we examined pairwise retinal-brain associations using multivariable linear regression models. All analyses were corrected for multiple testing and adjusted for confounders.Results Data from 6,446 …