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Background Little is known about the long-term mental health consequences of the pandemic in children and young people (CYP), despite extremely high levels of exposure to the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus and the disruption to schooling and leisure activities due to the resultant restrictions. There are mixed findings from systematic reviews of how the pandemic affected CYP's mental health, which may be due to heterogeneous methods and poor quality studies. Most, but not all, suggest deterioration in mental health but population level studies may obscure the differing experiences of subgroups. The study questions are: (i) are there subgroups of CYP with distinct mental health profiles over the course of the second year of the Coronavirus Disease 2019 (COVID-19) pandemic (between April 2021 and May 2022); and (ii) do vulnerability factors influence CYP's mental health trajectories. Methods and findings A matched longitudinal cohort study of non-hospitalised test-positive and test-negative 11- to 17-year-old CYP in England were recruited from the UK Health Security Agency having undergone PCR testing for COVID-19. They completed the Strengths and Difficulties Questionnaire (SDQ) at least twice over a 12-month follow-up period. Overall, 8,518 of 17,918 (47.5%) CYP who returned their first SDQ at 3 or 6 months post-testing were included in the analytical sample. Associations between age, sex, ethnicity, socioeconomic status (SES), and an educational health and care plan (EHCP, indicating special educational needs) on SDQ score trajectories were examined separately, after adjusting for PCR test result. Findings from multilevel mixed-effects linear regression model showed that on average mental health symptoms as measured by the total SDQ score increased over time (B = 0.11 (per month), 95% CI = 0.09 to 0.12, p < 0.001) although this increase was small and not clinically significant. However, associations with time varied by age, such that older participants reported greater deterioration in mental health over time (B = 0.12 (per month), 95% CI = 0.10 to 0.14 for 15 to 17y; 0.08 (95% CI = 0.06 to 0.10) for 11 to 14y; p.sub.interaction = 0.002) and by sex, with greater deterioration in girls. Children with an EHCP experienced less deterioration in their mental health compared to those without an EHCP. There was no evidence of differences in rate of change in total SDQ by ethnicity, SES, or physical health. Those with worse prior mental health did not appear to be disproportionately negatively affected over time. There are several limitations of the methodology including relatively low response rates in CLoCk and potential for recall bias. Conclusions Overall, there was a statistically but not clinically significant decline in mental health during the pandemic. Sex, age, and EHCP status were important vulnerability factors that were associated with the rate of mental health decline, whereas ethnicity, SES, and prior poor physical health were not. The research highlights individual factors that could identify groups of CYP vulnerable to worsening mental health., Author(s): Laura Panagi 1,*, Simon R. White 1, Snehal M. Pinto Pereira 2, Manjula D. Nugawela 3, Isobel Heyman 3, Kishan Sharma 4, Terence Stephenson 3, Trudie Chalder 5, Natalia [...]

Neuro‐imaging data can often be represented as statistical networks, especially for functional magnetic resonance imaging (fMRI) data, where brain regions are defined as nodes and the functional interactions between those regions are taken as edges. Such networks are commonly divided into classes depending on the type of edges, namely binary or weighted. A binary network means edges can either be present or absent. Whereas the edges of a weighted network are associated with weight values, and fMRI networks belong to weighted networks. Statistical methods are often adopted to analyse such networks, among which, the exponential random graph model (ERGM) is an important network analysis approach. Typically ERGMs are applied to binary networks, and weighted networks often need to be binarised by arbitrarily selecting a threshold value to define the presence of the edges, which can lead to non‐robustness and loss of valuable edge weight information representing the strength of fMRI interaction in fMRI networks. While it is therefore important to gain deeper insight in adopting ERGM on weighted networks, there only exists a few different ERGM frameworks for weighted networks; some of these are not directly implementable on fMRI networks based on their original proposal. We systematically review, implement, analyse and compare five such frameworks via a simulation study and provide guidelines on each modelling framework as well as conclude the suitability of them on fMRI networks based on a range of criteria. We concluded that Multi‐Layered ERGM is currently the most suitable framework. [ABSTRACT FROM AUTHOR]

Adolescent development of human brain structural and functional networks is increasingly recognized as fundamental to emergence of typical and atypical adult cognitive and emotional processes. We analysed multimodal magnetic resonance imaging (MRI) data collected from N ~ 300 healthy adolescents (51%; female; 14 to 26 y) each scanned repeatedly in an accelerated longitudinal design, to provide an analyzable dataset of 469 structural scans and 448 functional MRI scans. We estimated the morphometric similarity between each possible pair of 358 cortical areas on a feature vector comprising six macro- and microstructural MRI metrics, resulting in a morphometric similarity network (MSN) for each scan. Over the course of adolescence, we found that morphometric similarity increased in paralimbic cortical areas, e.g., insula and cingulate cortex, but generally decreased in neocortical areas, and these results were replicated in an independent developmental MRI cohort (N~304). Increasing hubness of paralimbic nodes in MSNs was associated with increased strength of coupling between their morphometric similarity and functional connectivity. Decreasing hubness of neocortical nodes in MSNs was associated with reduced strength of structure-function coupling and increasingly diverse functional connections in the corresponding fMRI networks. Neocortical areas became more structurally differentiated and more functionally integrative in a metabolically expensive process linked to cortical thinning and myelination, whereas paralimbic areas specialized for affective and interoceptive functions became less differentiated, as hypothetically predicted by a developmental transition from periallocortical to proisocortical organization of the cortex. Cytoarchitectonically distinct zones of the human cortex undergo distinct neurodevelopmental programs during typical adolescence. [ABSTRACT FROM AUTHOR]

The statistical concept of sampling is often given little direct attention, typically reduced to the mantra "take a random sample". This low resource and adaptable activity demonstrates sampling and explores issues that arise due to biased sampling.

We describe an activity that introduces students to population modelling, enables them to use estimates obtained from a sample to infer back to the population, and understands how the findings are translatable via penguins and their poo!

Probability and chance are essential concepts, not just in statistics but in real life. We present an adaptable activity which investigates what we mean by bias, how we can identify bias, and how we can use it to our advantage!

The identification of sets of co-regulated genes that share a common function is a key question of modern genomics. Bayesian profile regression is a semi-supervised mixture modelling approach that makes use of a response to guide inference toward relevant clusterings. Previous applications of profile regression have considered univariate continuous, categorical, and count outcomes. In this work, we extend Bayesian profile regression to cases where the outcome is longitudinal (or multivariate continuous) and provide PReMiuMlongi, an updated version of PReMiuM, the R package for profile regression. We consider multivariate normal and Gaussian process regression response models and provide proof of principle applications to four simulation studies. The model is applied on budding-yeast data to identify groups of genes co-regulated during the Saccharomyces cerevisiae cell cycle. We identify four distinct groups of genes associated with specific patterns of gene expression trajectories, along with the bound transcriptional factors, likely involved in their co-regulation process. [ABSTRACT FROM AUTHOR]

The development of dementia is a devastating aspect of Parkinson's disease (PD), affecting nearly half of patients within 10 years post-diagnosis. For effective therapies to prevent and slow progression to PD dementia (PDD), the key mechanisms that determine why some people with PD develop early dementia, while others remain cognitively unaffected, need to be understood. Neuroinflammation and tau protein accumulation have been demonstrated in post-mortem PD brains, and in many other neurodegenerative disorders leading to dementia. However, whether these processes mediate dementia risk early on in the PD disease course is not established. To this end, we used PET neuroimaging with 11C-PK11195 to index neuroinflammation and 18F-AV-1451 for misfolded tau in early PD patients, stratified according to dementia risk in our 'Neuroinflammation and Tau Accumulation in Parkinson's Disease Dementia' (NET-PDD) study. The NET-PDD study longitudinally assesses newly-diagnosed PD patients in two subgroups at low and high dementia risk (stratified based on pentagon copying, semantic fluency, MAPT genotype), with comparison to age- and sex-matched controls. Non-displaceable binding potential (BPND) in 43 brain regions (Hammers' parcellation) was compared between groups (pairwise t -tests), and associations between BPND of the tracers tested (linear-mixed-effect models). We hypothesized that people with higher dementia risk have greater inflammation and/or tau accumulation in advance of significant cognitive decline. We found significantly elevated neuroinflammation (11C-PK11195 BPND) in multiple subcortical and restricted cortical regions in the high dementia risk group compared with controls, while in the low-risk group this was limited to two cortical areas. The high dementia risk group also showed significantly greater neuroinflammation than the low-risk group concentrated on subcortical and basal ganglia regions. Neuroinflammation in most of these regions was associated with worse cognitive performance (Addenbrooke's Cognitive Examination-III score). Overall neuroinflammation burden also correlated with serum levels of pro-inflammatory cytokines. In contrast, increases in 18F-AV-1451 (tau) BPND in PD versus controls were restricted to subcortical regions where off-target binding is typically seen, with no relationship to cognition found. Whole-brain 18F-AV-1451 burden correlated with serum phosphorylated tau181 levels. Although there was minimal regional tau accumulation in PD, regional neuroinflammation and tau burden correlated in PD participants, with the strongest association in the high dementia risk group, suggesting possible co-localization of these pathologies. In conclusion, our findings suggest that significant regional neuroinflammation in early PD might underpin higher risk for PDD development, indicating neuroinflammation as a putative early modifiable aetiopathological disease factor to prevent or slow dementia development using immunomodulatory strategies. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Although many cognitive measures have been developed to assess cognitive decline due to Alzheimer's disease (AD), there is little consensus on optimal measures, leading to varied assessments across research cohorts and clinical trials making it difficult to pool cognitive measures across studies. METHODS: We used a two‐stage approach to harmonize cognitive data across cohorts and derive a cross‐cohort score of cognitive impairment due to AD. First, we pool and harmonize cognitive data from international cohorts of varying size and ethnic diversity. Next, we derived cognitive composites that leverage maximal data from the harmonized dataset. RESULTS: We show that our cognitive composites are robust across cohorts and achieve greater or comparable sensitivity to AD‐related cognitive decline compared to the Mini‐Mental State Examination and Preclinical Alzheimer Cognitive Composite. Finally, we used an independent cohort validating both our harmonization approach and composite measures. DISCUSSION: Our easy to implement and readily available pipeline offers an approach for researchers to harmonize their cognitive data with large publicly available cohorts, providing a simple way to pool data for the development or validation of findings related to cognitive decline due to AD. [ABSTRACT FROM AUTHOR]

Background: People with dementia frequently develop behavioural and psychological symptoms, sometimes necessitating care in specialist dementia mental health wards. There has been little research on their life expectancy following admission or need for palliative care. The work presented here explores the mortality of these patients and whether this can be predicted at their time of admission to the ward. Method: We conducted a retrospective analysis of 576 patients admitted to the Cambridgeshire and Peterborough NHS Foundation Trust dementia mental health wards in the United Kingdom, and built a Kaplan‐Meier survival curve as well as machine learning models. Next, to examine changes in deaths occurring over time, a retrospective service evaluation was conducted involving four mental health wards for people with dementia in the United Kingdom, encompassing a further 1,976 patients. Result: The median survival length post‐admission was 1201 days. Clinical data collected on admission did not predict mortality in machine learning models at a level of accuracy likely to have clinical utility. Data from four different wards show that the number of patients dying in dementia mental health wards has increased over time. Conclusion: Our cohort had a high mortality, although with a wide range of survival times. We suggest all people admitted to these units should have discussions and access to high‐quality end‐of‐life care. [ABSTRACT FROM AUTHOR]