Experimental Medicine 2 – Integration of clinical and cellular phenotypes in the DPUK Deep and Frequent Phenotyping cohort
The team undertaking this project has pioneered the techniques necessary to culture induced pluripotent stem cells (iPSCs) from a group of individuals (the MRC NIHR Deep and Frequent Phenotyping cohort) with very early AD and Mild Cognitive Impairment and use these cells for further studies. Significant cognitive and biological testing has been conducted on the cohort to provide a wealth of data on individuals. The team aim to test whether these iPSC cells can be used as model systems that will replicate the cognitive and biological processes being undertaken in cohort members and hence provide insight into neurodegenerative diseases.
Introduction: The rising burden of dementia is a global concern, and there is a need to study its causes, natural history and outcomes. The Secure Anonymised Information Linkage (SAIL) Databank contains anonymised, routinely-collected healthcare data for the population of Wales, UK. It has potential to be a valuable resource for dementia research owing to its size, long follow-up time and prospective collection of data during clinical care. Objectives: We aimed to apply reproducible methods to create the SAIL dementia e-cohort (SAIL-DeC). We created SAIL-DeC with a view to maximising its utility for a broad range of research questions whilst minimising duplication of effort for researchers. Methods: SAIL contains individual-level, linked primary care, hospital admission, mortality and demographic data. Data are currently available until 2018 and future updates will extend participant follow-up time. We included participants who were born between 1st January 1900 and 1st January 1958 and for whom primary care data were available. We applied algorithms consisting of International Classification of Diseases (versions 9 and 10) and Read (version 2) codes to identify participants with and without all-cause dementia and dementia subtypes. We also created derived variables for comorbidities and risk factors. Results: From 4.4 million unique participants in SAIL, 1.2 million met the cohort inclusion criteria, resulting in 18.8 million person-years of follow-up. Of these, 129,650 (10%) developed all-cause dementia, with 77,978 (60%) having dementia subtype codes. Alzheimer's disease was the most common subtype diagnosis (62%). Among the dementia cases, the median duration of observation time was 14 years. Conclusion: We have created a generalisable, national dementia e-cohort, aimed at facilitating epidemiological dementia research.
Experimental Medicine 1 – How do peripheral and central vascular markers relate to cognitive decline?
This pilot exploratory study aimed to investigate statistical relations between measures of plasma lipidomics and lipoproteins and cognitive and neurovascular imaging parameters. The main hypothesis being addressed was that the lipidomic/lipoprotein markers will correlate with, and allow the stratification of, declines in the cognitive and neurovascular parameters.
© Springer Nature Switzerland AG 2019. All rights are reserved. Researchers are beginning to appreciate the brain as more than a mere collection of loosely connected, highly specialized components. While there is clear specialization among regions of the cortex, the true power of the brain appears to arise from the ability of those regions to work together across a range of spatial scales as a richly interconnected and complex network. On all levels, the study of brain connectivity seeks to understand how different regions of the cortex communicate, what the emerging networks signify functionally, and why these are important for normal behavior. The use ofMEG in this endeavor is an attempt to understand these processes on the broad, interregional scale, and in that respect MEG is an ideal tool. It has a good deal of spatial resolution, enough to distinguish between brain areas ∼1 cm apart, and exquisite temporal resolution, enough to record even the fastest electrical oscillations the brain can generate. This chapter begins with a brief overview of the history of electrophysiological measures and their application to the study of brain connectivity. We then describe some of the core theory underlying the measurement of magnetic fields generated by the brain and practical considerations of measuring correlated activity with MEG. Some notable applications of MEG to the study of brain networks will then be described, and a comparison will be made between MEG to other methods such as ECoG. The chapter will also explore some of the principal mathematical techniques used by researchers to probe different aspects of connectivity ranging from simple correlational approaches to more involved concepts such as multivariate autoregressive models (MARs). Finally, we will discuss limitations of using MEG to study connectivity and also give some insight into the exciting prospects the future might hold for MEG connectivity research.
Recent work has highlighted the scale and ubiquity of subject variability in observations from functional MRI data (fMRI). Furthermore, it is highly likely that errors in the estimation of either the spatial presentation of, or the coupling between, functional regions can confound cross-subject analyses, making accurate and unbiased representations of functional data essential for interpreting any downstream analyses. Here, we extend the framework of probabilistic functional modes (PFMs) (Harrison et al., 2015) to capture cross-subject variability not only in the mode spatial maps, but also in the functional coupling between modes and in mode amplitudes. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets, and the combined inference and analysis package, PROFUMO, is available from git.fmrib.ox.ac.uk/samh/profumo. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets. Using simulated data, resting-state data from 1000 subjects collected as part of the Human Connectome Project (Van Essen et al., 2013), and an analysis of 14 subjects in a variety of continuous task-states (Kieliba et al., 2019), we demonstrate how PFMs are able to capture, within a single model, a rich description of how the spatio-temporal structure of resting-state fMRI activity varies across subjects. We also compare the new PFM model to the well established independent component analysis with dual regression (ICA-DR) pipeline. This reveals that, under PFM assumptions, much more of the (behaviorally relevant) cross-subject variability in fMRI activity should be attributed to the variability in spatial maps, and that after accounting for this functional coupling between modes primarily reflects current cognitive state. This has fundamental implications for the interpretation of cross-sectional studies of functional connectivity that do not capture cross-subject variability to the same extent as PFMs.
Stroke is a major risk factor for dementia and declining cognition is a major risk factor for stroke. However, the links between vascular health and brain health remain poorly understood. The “Rates, Risks and Routes to Reduce Vascular Dementia study” (R4VaD) aims to recruit 2,000 stroke survivors to shed light on vascular and brain health.
Psychiatric comorbid disorders of cognition: a machine learning approach using 1175 UK Biobank participants.
BACKGROUND: Conceptualising comorbidity is complex and the term is used variously. Here, it is the coexistence of two or more diagnoses which might be defined as 'chronic' and, although they may be pathologically related, they may also act independently. Of interest here is the comorbidity of common psychiatric disorders and impaired cognition. OBJECTIVES: To examine whether anxiety and/or depression are/is important longitudinal predictors of cognitive change. METHODS: UK Biobank participants used at three time points (n=502 664): baseline, first follow-up (n=20 257) and first imaging study (n=40 199). Participants with no missing data were 1175 participants aged 40-70 years, 41% women. Machine learning was applied and the main outcome measure of reaction time intraindividual variability (cognition) was used. FINDINGS: Using the area under the receiver operating characteristic curve, the anxiety model achieves the best performance with an area under the curve (AUC) of 0.68, followed by the depression model with an AUC of 0.63. The cardiovascular and diabetes model, and the covariates model have weaker performance in predicting cognition, with an AUC of 0.60 and 0.56, respectively. CONCLUSIONS: Outcomes suggest that psychiatric disorders are more important comorbidities of long-term cognitive change than diabetes and cardiovascular disease, and demographic factors. Findings suggest that psychiatric disorders (anxiety and depression) may have a deleterious effect on long-term cognition and should be considered as an important comorbid disorder of cognitive decline. CLINICAL IMPLICATIONS: Important predictive effects of poor mental health on longitudinal cognitive decline should be considered in secondary and also primary care.
This work package focused on developing and applying state-of-the-art genetic data analysis methods to DPUK cohorts to demonstrate the potential of informatics portals for conducting integrated genetic analyses. It led to the successfully development of the DPUK Genetics Portal to operate alongside the DPUK Data Portal and Imaging Portal. The team has successfully generated novel findings in the genetic risk for dementias and implicated other inflammation biomarkers in the prediction of dementia.