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Bereaved relatives described the ongoing pain of being absent at the end of a loved-one's life. Many had not seen their relative for weeks or months due to the pandemic. Opportunities must be prioritised for essential connections between families at end-of-life care.
Critical components of ‘Early Intervention in Psychosis’: national retrospective cohort study
Background Psychotic disorders are severe mental health conditions frequently associated with long-term disability, reduced quality of life and premature mortality. Early Intervention in Psychosis (EIP) services aim to provide timely, comprehensive packages of care for people with psychotic disorders. However, it is not clear which components of EIP services contribute most to the improved outcomes they achieve. Aims We aimed to identify associations between specific components of EIP care and clinically significant outcomes for individuals treated for early psychosis in England. Method This national retrospective cohort study of 14 874 EIP individuals examined associations between 12 components of EIP care and outcomes over a 3-year follow-up period, by linking data from the National Clinical Audit of Psychosis (NCAP) to routine health outcome data held by NHS England. The primary outcome was time to relapse, defined as psychiatric inpatient admission or referral to a crisis resolution (home treatment) team. Secondary outcomes included duration of admissions, detention under the Mental Health Act, emergency department and general hospital attendances and mortality. We conducted multilevel regression analyses incorporating demographic and service-level covariates. Results Smaller care coordinator case-loads and the use of clozapine for eligible people were associated with reduced relapse risk. Physical health interventions were associated with reductions in mortality risk. Other components, such as cognitive–behavioural therapy for psychosis (CBTp), showed associations with improvements in secondary outcomes. Conclusions Smaller case-loads should be prioritised and protected in EIP service design and delivery. Initiatives to improve the uptake of clozapine should be integrated into EIP care. Other components, such as CBTp and physical health interventions, may have specific benefits for those eligible. These findings highlight impactful components of care and should guide resource allocation to optimise EIP service delivery.
Cannabis Withdrawal and Psychiatric Intensive Care.
IMPORTANCE: Cannabis use is common in people with severe mental illness and its adverse effects on outcomes are well established. However, adverse outcomes may also result from cannabis withdrawal syndrome (CWS). CWS includes symptoms such as agitation, irritability, and aggression, and typically peaks after 3 to 5 days of abstinence. OBJECTIVE: To assess whether cannabis use prior to admission is associated with an increase in the risk of transfer to a psychiatric intensive care unit (PICU) during the cannabis withdrawal risk period. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study used clinical data from a secondary mental health care database and took place at 4 psychiatric hospitals in London, United Kingdom, between January 2008 and December 2023. Patients included adults admitted to general psychiatric wards and PICUs. Data were analyzed from June 2023 to February 2025. EXPOSURE: Cannabis use was determined from clinical records, using natural language processing and manual review. MAIN OUTCOMES AND MEASURES: The primary outcome was transfer from a general ward to PICU during the cannabis withdrawal risk period (3 to 5 days after presentation to the hospital). Secondary outcomes included admission to PICU at any time point. Outcomes were analyzed according to cannabis use status with multivariable models, which adjusted for age, gender, ethnicity, diagnosis, tobacco use, stimulant use, comorbid alcohol or substance use disorder, and admission year. RESULTS: There were 52 088 hospital admissions identified, of which 4691 involved admission to a PICU (9.0%). Cannabis users were more likely to be admitted to a PICU than nonusers (adjusted odds ratio [aOR], 1.44; 95% CI, 1.33-1.55; P
Integrating the environmental and genetic architectures of aging and mortality.
Both environmental exposures and genetics are known to play important roles in shaping human aging. Here we aimed to quantify the relative contributions of environment (referred to as the exposome) and genetics to aging and premature mortality. To systematically identify environmental exposures associated with aging in the UK Biobank, we first conducted an exposome-wide analysis of all-cause mortality (n = 492,567) and then assessed the associations of these exposures with a proteomic age clock (n = 45,441), identifying 25 independent exposures associated with mortality and proteomic aging. These exposures were also associated with incident age-related multimorbidity, aging biomarkers and major disease risk factors. Compared with information on age and sex, polygenic risk scores for 22 major diseases explained less than 2 percentage points of additional mortality variation, whereas the exposome explained an additional 17 percentage points. Polygenic risk explained a greater proportion of variation (10.3-26.2%) compared with the exposome for incidence of dementias and breast, prostate and colorectal cancers, whereas the exposome explained a greater proportion of variation (5.5-49.4%) compared with polygenic risk for incidence of diseases of the lung, heart and liver. Our findings provide a comprehensive map of the contributions of environment and genetics to mortality and incidence of common age-related diseases, suggesting that the exposome shapes distinct patterns of disease and mortality risk, irrespective of polygenic disease risk.
Adversarial testing of global neuronal workspace and integrated information theories of consciousness.
Different theories explain how subjective experience arises from brain activity1,2. These theories have independently accrued evidence, but have not been directly compared3. Here we present an open science adversarial collaboration directly juxtaposing integrated information theory (IIT)4,5 and global neuronal workspace theory (GNWT)6-10 via a theory-neutral consortium11-13. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes and interpretation thereof12. Human participants (n = 256) viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography and intracranial electroencephalography. We found information about conscious content in visual, ventrotemporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results align with some predictions of IIT and GNWT, while substantially challenging key tenets of both theories. For IIT, a lack of sustained synchronization within the posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in the prefrontal cortex. These challenges extend to other theories of consciousness that share some of the predictions tested here14-17. Beyond challenging the theories, we present an alternative approach to advance cognitive neuroscience through principled, theory-driven, collaborative research and highlight the need for a quantitative framework for systematic theory testing and building.
Classification of asthma based on nonlinear analysis of breathing pattern
Normal human breathing exhibits complex variability in both respiratory rhythm and volume. Analyzing such nonlinear fluctuations may provide clinically relevant information in patients with complex illnesses such as asthma. We compared the cycle-by-cycle fluctuations of inter-breath interval (IBI) and lung volume (LV) among healthy volunteers and patients with various types of asthma. Continuous respiratory datasets were collected from forty agematched men including 10 healthy volunteers, 10 patients with controlled atopic asthma, 10 patients with uncontrolled atopic asthma, and 10 patients with uncontrolled non-atopic asthma during 60 min spontaneous breathing. Complexity of breathing pattern was quantified by calculating detrended fluctuation analysis, largest Lyapunov exponents, sample entropy, and cross-sample entropy. The IBI as well as LV fluctuations showed decreased long-range correlation, increased regularity and reduced sensitivity to initial conditions in patients with asthma, particularly in uncontrolled state. Our results also showed a strong synchronization between the IBI and LV in patients with uncontrolled asthma. Receiver operating characteristic (ROC) curve analysis showed that nonlinear analysis of breathing pattern has a diagnostic value in asthma and can be used in differentiating uncontrolled from controlled and non-atopic from atopic asthma.We suggest that complexity analysis of breathing dynamics may represent a novel physiologic marker to facilitate diagnosis and management of patients with asthma. However, future studies are needed to increase the validity of the study and to improve these novel methods for better patient management.
FLUX: A pipeline for MEG analysis.
Magnetoencephalography (MEG) allows for quantifying modulations of human neuronal activity on a millisecond time scale while also making it possible to estimate the location of the underlying neuronal sources. The technique relies heavily on signal processing and source modelling. To this end, there are several open-source toolboxes developed by the community. While these toolboxes are powerful as they provide a wealth of options for analyses, the many options also pose a challenge for reproducible research as well as for researchers new to the field. The FLUX pipeline aims to make the analyses steps and setting explicit for standard analysis done in cognitive neuroscience. It focuses on quantifying and source localization of oscillatory brain activity, but it can also be used for event-related fields and multivariate pattern analysis. The pipeline is derived from the Cogitate consortium addressing a set of concrete cognitive neuroscience questions. Specifically, the pipeline including documented code is defined for MNE Python (a Python toolbox) and FieldTrip (a Matlab toolbox), and a data set on visuospatial attention is used to illustrate the steps. The scripts are provided as notebooks implemented in Jupyter Notebook and MATLAB Live Editor providing explanations, justifications and graphical outputs for the essential steps. Furthermore, we also provide suggestions for text and parameter settings to be used in registrations and publications to improve replicability and facilitate pre-registrations. The FLUX can be used for education either in self-studies or guided workshops. We expect that the FLUX pipeline will strengthen the field of MEG by providing some standardization on the basic analysis steps and by aligning approaches across toolboxes. Furthermore, we also aim to support new researchers entering the field by providing education and training. The FLUX pipeline is not meant to be static; it will evolve with the development of the toolboxes and with new insights. Furthermore, with the anticipated increase in MEG systems based on the Optically Pumped Magnetometers, the pipeline will also evolve to embrace these developments.
Modulation of alpha oscillations by attention is predicted by hemispheric asymmetry of subcortical regions.
Evidence suggests that subcortical structures play a role in high-level cognitive functions such as the allocation of spatial attention. While there is abundant evidence in humans for posterior alpha band oscillations being modulated by spatial attention, little is known about how subcortical regions contribute to these oscillatory modulations, particularly under varying conditions of cognitive challenge. In this study, we combined MEG and structural MRI data to investigate the role of subcortical structures in controlling the allocation of attentional resources by employing a cued spatial attention paradigm with varying levels of perceptual load. We asked whether hemispheric lateralization of volumetric measures of the thalamus and basal ganglia predicted the hemispheric modulation of alpha-band power. Lateral asymmetry of the globus pallidus, caudate nucleus, and thalamus predicted attention-related modulations of posterior alpha oscillations. When the perceptual load was applied to the target and the distractor was salient caudate nucleus asymmetry predicted alpha-band modulations. Globus pallidus was predictive of alpha-band modulations when either the target had a high load, or the distractor was salient, but not both. Finally, the asymmetry of the thalamus predicted alpha band modulation when neither component of the task was perceptually demanding. In addition to delivering new insight into the subcortical circuity controlling alpha oscillations with spatial attention, our finding might also have clinical applications. We provide a framework that could be followed for detecting how structural changes in subcortical regions that are associated with neurological disorders can be reflected in the modulation of oscillatory brain activity.
Retinal morphological differences in atypical Parkinsonism: A cross-sectional analysis of the AlzEye cohort
Objective: Atypical Parkinsonian syndrome (APS) describes a heterogeneous group of disorders mimicking the clinical presentation of Parkinson disease (PD) but with disparate natural history and pathophysiology. While retinal markers of PD are increasingly described, APS has been afforded less attention possibly owing to its lower prevalence. Here, we investigate retinal morphological differences in individuals with APS in a large real world cohort. Methods: We conducted a cross-sectional analysis of the AlzEye study, a retrospective cohort where ophthalmic data of individuals attending Moorfields Eye Hospital between January 2008 and March 31st 2018 (inclusive) has been linked with systemic disease data through national hospital admissions. Retinal features were extracted from macula-centered color fundus photography (CFP) and optical coherence tomography (OCT) and compared between individuals with APS and those unaffected. Individuals with idiopathic PD were excluded. Retinal neural and vascular features were measured using automated segmentation and analyzed with multivariable-adjusted regression models. Results: Among a cohort of 91,170 patients, there were 51 patients with APS and 91,119 controls. Individuals with APS were older and more likely to have hypertension and diabetes mellitus. After adjusting for age, sex, hypertension and diabetes melitus, individuals with APS had a thinner ganglion cell-inner plexiform layer (-3.95 microns, 95% CI: −7.53, −0.37, p = 0.031) but no difference in other retinoneural or retinovascular indices. Optic nerve cup-to-disc ratio was similar between groups. Conclusion: Our cross-sectional analysis of the AlzEye cohort reveals distinct retinal morphological characteristics in APS compared to healthy controls. The study notably identifies a thinner ganglion cell-inner plexiform layer in APS patients, without accompanying changes in the inner nuclear layer or significant alterations in retinovascular indices and optic nerve cup-disc ratio. These changes are distinct from those observed in PD, where thinning of the inner nuclear layer (INL) is a characteristic feature. Significance: These findings demonstrate a retinal phenotype in APS, markedly different from both healthy controls and idiopathic Parkinson's disease, highlighting the potential of retinal imaging in differentiating neurodegenerative disorders. By establishing a distinct retinal phenotype for APS, our findings underscore the potential of retinal imaging as a valuable, non-invasive diagnostic tool. This advancement is particularly significant for enhancing diagnostic accuracy, facilitating early detection, and offering a window into the underlying disease mechanisms in APS, thereby aiding in the development of targeted therapeutic interventions and personalized patient care strategies.
The long arm of childhood socioeconomic deprivation on mid- to later-life cognitive trajectories: A cross-cohort analysis
AbstractINTRODUCTIONEarlier studies of the effects of childhood socioeconomic status (SES) on later life cognitive function consistently report a social gradient in later life cognitive function. Evidence for their effects on cognitive decline is, however, less clear.METHODSThe sample consists of 5,324 participants in the Whitehall II Study, 8,572 in the Health and Retirement Study, and 1,413 in the Kame Project, who completed self-report questionnaires on their early-life experiences and underwent repeated cognitive assessments. We characterised cognitive trajectories using latent class mixed models, and explored associations between childhood SES and latent class membership using logistic regressions.RESULTSWe identified distinct trajectories classes for all cognitive measures examined. Childhood socioeconomic deprivation was associated with an increased likelihood of being in a lower trajectory class.DISCUSSIONOur findings support the notions that cognitive ageing is a heterogeneous process and early-life circumstances may have lasting effects on cognition across the life-course.Research in contextSystematic review: We reviewed the literature on childhood socioeconomic status (SES) as a predictor for cognitive decline in mid- to later-life using PubMed. Studies generally reported lower childhood SES is associated with poorer baseline cognition, but not a faster rate of decline. These studies generally focused on the mean rate of decline in the population; no study to date has explored associations between childhood SES and different cognitive trajectories. Relevant studies have been appropriately cited.Interpretation: Our findings suggest that cognitive trajectories differ between individuals and across cognitive domains. Individuals of lower childhood SES were more likely to be in a lower cognitive trajectory class, which may or may not involve more rapid decline.Future directions: Future studies should include more cognitive outcomes and longer follow-ups, as well as investigate the impact of social mobility to further improve our understanding on how early-life circumstances influence cognitive decline.
Evaluating the harmonisation potential of diverse cohort datasets.
Data discovery, the ability to find datasets relevant to an analysis, increases scientific opportunity, improves rigour and accelerates activity. Rapid growth in the depth, breadth, quantity and availability of data provides unprecedented opportunities and challenges for data discovery. A potential tool for increasing the efficiency of data discovery, particularly across multiple datasets is data harmonisation.A set of 124 variables, identified as being of broad interest to neurodegeneration, were harmonised using the C-Surv data model. Harmonisation strategies used were simple calibration, algorithmic transformation and standardisation to the Z-distribution. Widely used data conventions, optimised for inclusiveness rather than aetiological precision, were used as harmonisation rules. The harmonisation scheme was applied to data from four diverse population cohorts.Of the 120 variables that were found in the datasets, correspondence between the harmonised data schema and cohort-specific data models was complete or close for 111 (93%). For the remainder, harmonisation was possible with a marginal a loss of granularity.Although harmonisation is not an exact science, sufficient comparability across datasets was achieved to enable data discovery with relatively little loss of informativeness. This provides a basis for further work extending harmonisation to a larger variable list, applying the harmonisation to further datasets, and incentivising the development of data discovery tools.
Semantic Harmonization of Alzheimer's Disease Datasets Using AD-Mapper.
BACKGROUND: Despite numerous past endeavors for the semantic harmonization of Alzheimer's disease (AD) cohort studies, an automatic tool has yet to be developed. OBJECTIVE: As cohort studies form the basis of data-driven analysis, harmonizing them is crucial for cross-cohort analysis. We aimed to accelerate this task by constructing an automatic harmonization tool. METHODS: We created a common data model (CDM) through cross-mapping data from 20 cohorts, three CDMs, and ontology terms, which was then used to fine-tune a BioBERT model. Finally, we evaluated the model using three previously unseen cohorts and compared its performance to a string-matching baseline model. RESULTS: Here, we present our AD-Mapper interface for automatic harmonization of AD cohort studies, which outperformed a string-matching baseline on previously unseen cohort studies. We showcase our CDM comprising 1218 unique variables. CONCLUSION: AD-Mapper leverages semantic similarities in naming conventions across cohorts to improve mapping performance.