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The characterisation of dynamic electrophysiological brain networks, which form and dissolve in order to support ongoing cognitive function, is one of the most important goals in neuroscience. Here, we introduce a method for measuring such networks in the human brain using magnetoencephalography (MEG). Previous network analyses look for brain regions that share a common temporal profile of activity. Here distinctly, we exploit the high spatio-temporal resolution of MEG to measure the temporal evolution of connectivity between pairs of parcellated brain regions. We then use an ICA based procedure to identify networks of connections whose temporal dynamics covary. We validate our method using MEG data recorded during a finger movement task, identifying a transient network of connections linking somatosensory and primary motor regions, which modulates during the task. Next, we use our method to image the networks which support cognition during a Sternberg working memory task. We generate a novel neuroscientific picture of cognitive processing, showing the formation and dissolution of multiple networks which relate to semantic processing, pattern recognition and language as well as vision and movement. Our method tracks the dynamics of functional connectivity in the brain on a timescale commensurate to the task they are undertaking. •A method is developed to track dynamic electrophysiological networks using MEG.•Method based on ICA applied to timecourses measuring evolution of connectivity.•Method allows a unique picture of transient networks that support cognition.•Method validated in MEG data recorded during a Sternberg working memory task.•Sensory networks observed include visual and sensorimotor.•Cognitive networks relate to semantic processing, pattern recognition and language.

Patterns of intrinsic human brain activity exhibit a profile of functional connectivity that is associated with behaviour and cognitive performance, and deteriorates with disease. This paper investigates the relative importance of genetic factors and the common environment between twins in determining this functional connectivity profile. Using functional magnetic resonance imaging (fMRI) on 820 subjects from the Human Connectome Project, and magnetoencephalographic (MEG) recordings from a subset, the heritability of connectivity among 39 cortical regions was estimated. On average over all connections, genes account for about 15% of the observed variance in fMRI connectivity (and about 10% in alpha-band and 20% in beta-band oscillatory power synchronisation), which substantially exceeds the contribution from the environment shared between twins. Therefore, insofar as twins share a common upbringing, it appears that genes, rather than the developmental environment, have the dominant role in determining the coupling of neuronal activity.

A Bayesian model for sparse, hierarchical, inver-covariance estimation is presented, and applied to multi-subject functional connectivity estimation in the human brain. It enables simultaneous inference of the strength of connectivity between brain regions at both subject and population level, and is applicable to fMRI, MEG and EEG data. Two versions of the model can encourage sparse connectivity, either using continuous priors to suppress irrelevant connections, or using an explicit description of the network structure to estimate the connection probability between each pair of regions. A large evaluation of this model, and thirteen methods that represent the state of the art of inverse covariance modelling, is conducted using both simulated and resting-state functional imaging datasets. Our novel Bayesian approach has similar performance to the best extant alternative, Ng et al.'s Sparse Group Gaussian Graphical Model algorithm, which also is based on a hierarchical structure. Using data from the Human Connectome Project, we show that these hierarchical models are able to reduce the measurement error in MEG beta-band functional networks by 10%, producing concomitant increases in estimates of the genetic influence on functional connectivity.

Background Hospitalisation during the last weeks of life when there is no medical need or desire to be there is distressing and expensive. This study sought palliative care initiatives which may avoid or shorten hospital stay at the end of life and analysed their success in terms reducing bed days. Methods Part 1 included a search of literature in PubMed and Google Scholar between 2013 and 2018, an examination of governmental and organisational publications plus discussions with external and co-author experts regarding other sources. This initial sweep sought to identify and categorise relevant palliative care initiatives. In Part 2, we looked for publications providing data on hospital admissions and bed days for each category. Results A total of 1252 abstracts were reviewed, resulting in ten broad classes being identified. Further screening revealed 50 relevant publications describing a range of multi-component initiatives. Studies were generally small and retrospective. Most researchers claim their service delivered benefits. In descending frequency, benefits identified were support in the community, integrated care, out-of-hours telephone advice, care home education and telemedicine. Nurses and hospices were central to many initiatives. Barriers and factors underpinning success were rarely addressed. Conclusions A wide range of initiatives have been introduced to improve end-of-life experiences. Formal evidence supporting their effectiveness in reducing inappropriate/non-beneficial hospital bed days was generally limited or absent. Trial registration N/A

BackgroundA current concern is the number of people dying in hospital who have no medical need, or wish, to be there (Marie Curie Cancer Care, 2012). 72% of people would prefer to die at home (ComRes, 2014), yet just 25% do so, with 50% dying in hospital (Gomes, Calanzani, Higginson, 2011). Instinctively, hospice-led initiatives play an important role in minimising inappropriate hospital usage at the end of life, but there is a lack of robust data.AimTo establish the impact of different hospice led innovations on a) reducing the number of hospital bed days during the last 90 days of life, b) place of death and other secondary outcome measures: the number of emergency and inpatient admissions and discharges to a hospice in the last 90 days of life.MethodMixed methods study with a quantitative quasi-experimental longitudinal design employing a ‘difference of difference’ analysis of HES data to assess the impact on hospital utilisation in the last 90 days of life. Any encountered differences are compared to control cohorts. Stakeholders were qualitatively interviewed through open-ended, semi-structured and structured interviews followed by narrative, framework and content analysis respectively.ResultsQuantitative: ongoing, however, we anticipate data showing a reduction in the number of hospital beds days, in the last 90 days of life, within the locality of the intervention hospice. Qualitative: interviewed 188 people, including 24 patients and carers, across 27 interventions at 25 sites providing 31 recurrent topics of which the five most relatively frequent were the process of development, collaboration, the intervention group, staff roles and professional culture.ConclusionsQualitative evaluation of these innovations shows benefit to the patient experience and factors critical to success and replicability. Quantitative data will show the impact on NHS resources, and together the findings will enable better evidence-based commissioning, supporting service redesign at a local level. Final report due Sept. 2018.

MEG and fMRI offer complementary insights into connected human brain function. Evidence from the use of both techniques in the study of networked activity indicates that functional connectivity reflects almost every measurable aspect of human reality, being indicative of ability and deteriorating with disease. Functional network analyses may offer improved prediction of dysfunction and characterisation of cognition. Three factors holding back progress are the difficulty in synthesising information from multiple imaging modalities; a need for accurate modelling of connectivity in individual subjects, not just average effects; and a lack of scalable solutions to these problems that are applicable in a big-data setting. I propose two methodological advances that tackle these issues. A confound to network analysis in MEG, the artificial correlations induced across the brain by the process of source reconstruction, prevents the transfer of connectivity models from fMRI to MEG. The first advance is a fast correction for this confound, allowing comparable analyses to be performed in both modalities. A comparative study demonstrates that this new approach for MEG shows better repeatability for connectivity estimation, both within and between subjects, than a wide range of alternative models in popular use. A case-study analysis uses both fMRI and MEG recordings from a large dataset to determine the genetic basis for functional connectivity in the human brain. Genes account for 20% - 65% of the variation in connectivity, and outweigh the influence of the developmental environment. The second advance is a Bayesian hierarchical model for sparse functional networks that is applicable to both modalities. By sharing information over a group of subjects, more accurate estimates can be constructed for individuals' connectivity patterns. The approach scales to large datasets, outperforms state-of-the-art methods, and can provide a 50% noise reduction in MEG resting-state networks.

Objectives Hospitalisation during the last weeks of life when there is no medical need or desire to be there is distressing and expensive. This study sought palliative care initiatives which may avoid or shorten hospital stay at the end of life and analysed their success in terms reducing bed days. Methods Part 1 included a search of literature in PubMed and Google Scholar between 2013 and 2018, an examination of governmental and organisational publications plus discussions with external and co-author experts regarding other sources. This initial sweep sought to identify and categorise relevant palliative care initiatives. In Part 2, we looked for publications providing data on hospital admissions and bed days for each category. Results A total of 1252 abstracts were reviewed, resulting in ten broad classes being identified. Further screening revealed 50 relevant publications describing a range of multi-component initiatives. Studies were generally small and retrospective. Most researchers claim their service delivered benefits. In descending frequency, benefits identified were support in the community, integrated care, out-of-hours telephone advice, care home education and telemedicine. Nurses and hospices were central to many initiatives. Barriers and factors underpinning success were rarely addressed. Conclusions A wide range of initiatives have been introduced to improve end-of-life experiences. Formal evidence supporting their effectiveness in reducing inappropriate/non-beneficial hospital bed days was generally limited or absent.