Statistical analysis plan for the SOLUTIONS randomised controlled trial with internal pilot: Solution Focused Brief Therapy (SFBT) in 10-17-year-olds presenting at policy custody.

BACKGROUND: Within England, children and young people entering police custody are referred to Liaison and Diversion (L&D) teams. These teams liaise with healthcare and other support services aiming to divert children and young people away from the criminal justice system. Although targeted psychological interventions are not typically offered to children and young people by L&D teams, evidence suggests that Solution Focused Brief Therapy (SFBT) leads to a reduction in internalising and externalising behaviour problems. METHODS: A two-arm individually randomised controlled trial (RCT) with internal pilot and process evaluation will be conducted with approximately 448 children and young people aged 10-17 years presenting at police custody suites who are referred to the L&D team or recruited via online routes if they have previously presented at any police custody suite in England. The primary outcome is the Self-Report Delinquency Measure (SRDM) at 6 months post-randomisation. Analyses will be performed using intention-to-treat. RESULTS: The statistical analysis plan (SAP) for the trial is described. The plan details of analyses to be undertaken which will be reported in the primary and any secondary publications. The plan was developed and published prior to locking our database and unblinding to treatment allocation. DISCUSSION: This RCT will evaluate the effectiveness of SFBT in reducing offending behaviours in CYP presenting at police custody suites including testing of moderating factors and sensitivity of the primary analyses. TRIAL REGISTRATION: ClinicalTrials.gov ISRCTN14195235. Registered on June 16, 2023.

BundleAGE: Predicting White Matter Age using Along-Tract Microstructural Profiles from Diffusion MRI.

Brain Age Gap Estimation (BrainAGE) is an estimate of the gap between a person's chronological age (CA) and a measure of their brain's 'biological age' (BA). This metric is often used as a marker of accelerated aging, albeit with some caveats. Age prediction models trained on brain structural and functional MRI have been employed to derive BrainAGE biomarkers, for predicting the risk of neurodegeneration. While voxel-based and along-tract microstructural maps from diffusion MRI have been used to study brain aging, no studies have evaluated along-tract microstructure for computing BrainAGE. In this study, we train machine learning models to predict a person's age using along-tract microstructural profiles from diffusion tensor imaging. We were able to demonstrate differential aging patterns across different white matter bundles and microstructural measures. The novel Bundle Age Gap Estimation (BundleAGE) biomarker shows potential in quantifying risk factors for neurodegenerative diseases and aging, while incorporating finer scale information throughout white matter bundles.

Association between Adiposity and Iron Status in Women of Reproductive Age: Data from the UK National Diet and Nutrition Survey (NDNS) 2008-2019.

BACKGROUND: Overweight/obesity and iron deficiency (ID) are highly prevalent in women of reproductive age (WRA), impacting on women's health. Obesity is a risk factor for nutritional deficiencies but its association with ID is unclear. OBJECTIVES: To determine the association between adiposity and markers of iron status and ID prevalence in WRA. METHODS: This cross-sectional study analyzed the National Diet and Nutrition Survey (2008-2019) data, focusing on women aged 18-49 y with body mass index (BMI) ≥18.5 kg/m2. Prevalence of anemia, iron deficiency anemia (IDA), and ID were analyzed. Ferritin was adjusted for C-reactive protein. Iron status was assessed across high and low BMI, waist circumference (WC), waist-to-height ratio (WHtR), and waist-to-hip ratio (WHR). χ2, linear and logistic regressions were performed adjusting for covariates. RESULTS: Among 1098 WRA, 496 normal weight and 602 overweight/obesity, prevalence rates were: anemia 9.2% and IDA 6.8%. Anemia was more prevalent in those with higher WHtR and WHR (11.9% compared with 5.9% and 16.7% compared with 6.5%, both P < 0.001). WRA with increased WC, WHtR, and WHR had higher IDA prevalence than those with lower adiposity (8.5% compared with 4.3%, P = 0.005; 9.4% compared with 3.3%, P < 0.001; 12.1% compared with 4.9%, P < 0.001). ID prevalence was 49.7% (ferritin cutoff 30 µg/L) and 19.6% (ferritin cutoff 15 µg/L), showing similar rates across adiposity groups. ID prevalence defined by soluble transferrin receptor (sTfR) was higher in women with increased WHR (P = 0.001). Higher WHR predicted ID categorized by sTfR (adjusted odds ratio [aOR]: 2.104, P = 0.004), and WHtR and WHR predicted anemia and IDA (anemia: WHtR aOR: 2.006, P = 0.036; WHR aOR: 4.489, P < 0.001 and IDA: WHtR aOR: 2.942, P = 0.012; WHR aOR: 4.142, P < 0.001). CONCLUSIONS: At least 1 in 5 WRA in the UK are iron deficient, highlighting the need to revise current policies. Greater central adiposity was strongly associated with impaired iron status and the development of anemia, IDA, and ID.

Generalized models for estimating cerebral lateralisation of young children using functional transcranial Doppler ultrasound.

Thompson et al., 2023 (Generalized models for quantifying laterality using functional transcranial Doppler ultrasound. Human Brain Mapping, 44(1), 35-48) introduced generalised model-based analysis methods for determining cerebral lateralisation from functional transcranial Doppler ultrasound (fTCD) data which substantially decreased the uncertainty of individual lateralisation estimates across several large adult samples. We aimed to assess the suitability of these methods for increasing precision in lateralisation estimates for child fTCD data. We applied these methods to adult fTCD data to establish the validity of two child-friendly language and visuospatial tasks. We also applied the methods to fTCD data from 4- to 7-year-old children. For both samples, the laterality estimates from the complex generalised additive model (GAM) approach correlated strongly with the traditional methods while also decreasing individual standard errors compared to the popular period-of-interest averaging method. We recommend future research using fTCD with young children consider using GAMs to reduce the noise in their LI estimates.

Overview of ADNI MRI.

The magnetic resonance imaging (MRI) Core has been operating since Alzheimer's Disease Neuroimaging Initiative's (ADNI) inception, providing 20 years of data including reliable, multi-platform standardized protocols, carefully curated image data, and quantitative measures provided by expert investigators. The overarching purposes of the MRI Core include: (1) optimizing and standardizing MRI acquisition methods, which have been adopted by many multicenter studies and trials worldwide and (2) providing curated images and numeric summary values from relevant MRI sequences/contrasts to the scientific community. Over time, ADNI MRI has become increasingly complex. To remain technically current, the ADNI MRI protocol has changed substantially over the past two decades. The ADNI 4 protocol contains nine different imaging types (e.g., three dimensional [3D] T1-weighted and fluid-attenuated inversion recovery [FLAIR]). Our view is that the ADNI MRI data are a greatly underutilized resource. The purpose of this paper is to educate the scientific community on ADNI MRI methods and content to promote greater awareness, accessibility, and use. HIGHLIGHTS: The MRI Core provides multi-platform standardized protocols, carefully curated image data, and quantitative analysis by expert groups. The ADNI MRI protocol has undergone major changes over the past two decades to remain technically current. As of April 25, 2024, the following numbers of image series are available: 17,141 3D T1w; 6877 FLAIR; 3140 T2/PD; 6623 GRE; 3237 dMRI; 2846 ASL; 2968 TF-fMRI; and 2861 HighResHippo (see Table 1 for abbreviations). As of April 25, 2024, the following numbers of quantitative analyses are available: FreeSurfer 10,997; BSI 6120; tensor based morphometry (TBM) and TBM-SYN 12,019; WMH 9944; dMRI 1913; ASL 925; TF-fMRI NFQ 2992; and medial temporal subregion volumes 2726 (see Table 4 for abbreviations). ADNI MRI is an underutilized resource that could be more useful to the research community.

The influence of therapy quality on outcomes from behavioural activation and guided self-help treatments for depression in adults with intellectual disabilities.

OBJECTIVES: We report the effect of quality of therapy delivery on outcomes in a randomized, controlled trial of behavioural activation (BA) and guided self-help (GSH) for depression in adults with intellectual disabilities. METHODS: A study specific measure of quality was used in a linear mixed effect model to determine the effects therapy and therapy quality on therapy outcome. RESULTS: There was a significant interaction between quality and treatment type, with lower quality therapy associated with better outcome for GSH but poorer outcome for BA, with little difference in outcomes at higher levels of therapy quality. CONCLUSIONS: Factors suggesting high quality in individualized BA may indicate problematic engagement for GSH. More research into processes in therapy for people with intellectual disabilities is required.

Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility.

Neuroimaging plays a crucial role in understanding brain structure and function, but the lack of transparency, reproducibility, and reliability of findings is a significant obstacle for the field. To address these challenges, there are ongoing efforts to develop reporting checklists for neuroimaging studies to improve the reporting of fundamental aspects of study design and execution. In this review, we first define what we mean by a neuroimaging reporting checklist and then discuss how a reporting checklist can be developed and implemented. We consider the core values that should inform checklist design, including transparency, repeatability, data sharing, diversity, and supporting innovations. We then share experiences with currently available neuroimaging checklists. We review the motivation for creating checklists and whether checklists achieve their intended objectives, before proposing a development cycle for neuroimaging reporting checklists and describing each implementation step. We emphasize the importance of reporting checklists in enhancing the quality of data repositories and consortia, how they can support education and best practices, and how emerging computational methods, like artificial intelligence, can help checklist development and adherence. We also highlight the role that funding agencies and global collaborations can play in supporting the adoption of neuroimaging reporting checklists. We hope this review will encourage better adherence to available checklists and promote the development of new ones, and ultimately increase the quality, transparency, and reproducibility of neuroimaging research.

A federated learning architecture for secure and private neuroimaging analysis.

The amount of biomedical data continues to grow rapidly. However, collecting data from multiple sites for joint analysis remains challenging due to security, privacy, and regulatory concerns. To overcome this challenge, we use federated learning, which enables distributed training of neural network models over multiple data sources without sharing data. Each site trains the neural network over its private data for some time and then shares the neural network parameters (i.e., weights and/or gradients) with a federation controller, which in turn aggregates the local models and sends the resulting community model back to each site, and the process repeats. Our federated learning architecture, MetisFL, provides strong security and privacy. First, sample data never leave a site. Second, neural network parameters are encrypted before transmission and the global neural model is computed under fully homomorphic encryption. Finally, we use information-theoretic methods to limit information leakage from the neural model to prevent a "curious" site from performing model inversion or membership attacks. We present a thorough evaluation of the performance of secure, private federated learning in neuroimaging tasks, including for predicting Alzheimer's disease and for brain age gap estimation (BrainAGE) from magnetic resonance imaging (MRI) studies in challenging, heterogeneous federated environments where sites have different amounts of data and statistical distributions.

Comparison of Explainable AI Models for MRI-based Alzheimer's Disease Classification.

Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer's disease or infer dementia severity from 3D T1-weighted brain MRI scans. Here, we examine the value of adding occlusion sensitivity analysis (OSA) and gradient-weighted class activation mapping (Grad-CAM) to these models to make the results more interpretable. Much research in this area focuses on specific datasets such as the Alzheimer's Disease Neuroimaging Initiative (ADNI) or National Alzheimer's Coordinating Center (NACC), which assess people of North American, predominantly European ancestry, so we examine how well models trained on these data generalize to a new population dataset from India (NIMHANS cohort). We also evaluate the benefit of using a combined dataset to train the CNN models. Our experiments show feature localization consistent with knowledge of AD from other methods. OSA and Grad-CAM resolve features at different scales to help interpret diagnostic inferences made by CNNs.

The Pattern and Staging of Brain Atrophy in Spinocerebellar Ataxia Type 2 (SCA2): MRI Volumetrics from ENIGMA-Ataxia.

Objective: Spinocerebellar ataxia type 2 (SCA2) is a rare, inherited neurodegenerative disease characterised by progressive deterioration in both motor coordination and cognitive function. Atrophy of the cerebellum, brainstem, and spinal cord are core features of SCA2, however the evolution and pattern of whole-brain atrophy in SCA2 remain unclear. We undertook a multi-site, structural magnetic resonance imaging (MRI) study to comprehensively characterize the neurodegeneration profile of SCA2. Methods: Voxel-based morphometry analyses of 110 participants with SCA2 and 128 controls were undertaken to assess groupwise differences in whole-brain volume. Correlations with clinical severity and genotype, and cross-sectional profiling of atrophy patterns at different disease stages, were also performed. Results: Atrophy in SCA2 relative to controls was greatest (Cohen's d >2.5) in the cerebellar white matter (WM), middle cerebellar peduncle, pons, and corticospinal tract. Very large effects ( d >1.5) were also evident in the superior cerebellar, inferior cerebellar, and cerebral peduncles. In cerebellar grey matter (GM), large effects ( d >0.8) mapped to areas related to both motor coordination and cognitive tasks. Strong correlations (| r |>0.4) between volume and disease severity largely mirrored these groupwise outcomes. Stratification by disease severity showed a degeneration pattern beginning in cerebellar and pontine WM in pre-clinical subjects; spreading to the cerebellar GM and cerebro-cerebellar/corticospinal WM tracts; then finally involving the thalamus, striatum, and cortex in severe stages. Interpretation: The magnitude and pattern of brain atrophy evolves over the course of SCA2, with widespread, non-uniform involvement across the brainstem, cerebellar tracts, and cerebellar cortex; and late involvement of the cerebral cortex and striatum.

Data-driven biomarkers better associate with stroke motor outcomes than theory-based biomarkers.

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have associated motor outcomes with the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to model chronic motor outcomes after stroke and compares the accuracy of these associations to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients from the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA) Stroke Recovery Working Group. Using the explained variance metric to measure the strength of the association between chronic motor outcomes and imaging biomarkers, we compared theory-based biomarkers, like lesion load to known motor tracts, to three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had stronger associations with chronic motor outcomes accuracy than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R 2 = 0.210, P < 0.001), performing significantly better than the theory-based biomarkers of lesion load of the corticospinal tract (R 2 = 0.132, P < 0.001) and of multiple descending motor tracts (R 2 = 0.180, P < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R 2 = 0.200, P < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R 2 = 0.167, P < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved the strength of associations for theory-based and data-driven biomarkers. Combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R 2 = 0.241, P < 0.001. Overall, these results demonstrate that out-of-sample associations between chronic motor outcomes and data-driven imaging features, particularly when lesion data is represented in terms of structural disconnection, are stronger than associations between chronic motor outcomes and theory-based biomarkers. However, combining both theory-based and data-driven models provides the most robust associations.

Amyloid, Tau, and APOE in Alzheimer's Disease: Impact on White Matter Tracts.

Alzheimer's disease (AD) is characterized by cognitive decline and memory loss due to the abnormal accumulation of amyloid-beta (Aß) plaques and tau tangles in the brain; its onset and progression also depend on genetic factors such as the apolipoprotein E (APOE) genotype. Understanding how these factors affect the brain's neural pathways is important for early diagnostics and interventions. Tractometry is an advanced technique for 3D quantitative assessment of white matter tracts, localizing microstructural abnormalities in diseased populations in vivo. In this work, we applied BUAN (Bundle Analytics) tractometry to 3D diffusion MRI data from 730 participants in ADNI3 (phase 3 of the Alzheimer's Disease Neuroimaging Initiative; age range: 55-95 years, 349M/381F, 214 with mild cognitive impairment, 69 with AD, and 447 cognitively healthy controls). Using along-tract statistical analysis, we assessed the localized impact of amyloid, tau, and APOE genetic variants on the brain's neural pathways. BUAN quantifies microstructural properties of white matter tracts, supporting along-tract statistical analyses that identify factors associated with brain microstructure. We visualize the 3D profile of white matter tract associations with tau and amyloid burden in Alzheimer's disease; strong associations near the cortex may support models of disease propagation along neural pathways. Relative to the neutral genotype, APOE ϵ3/ϵ3, carriers of the AD-risk conferring APOE ϵ4 genotype show microstructural abnormalities, while carriers of the protective ϵ2 genotype also show subtle differences. Of all the microstructural metrics, mean diffusivity (MD) generally shows the strongest associations with AD pathology, followed by axial diffusivity (AxD) and radial diffusivity (RD), while fractional anisotropy (FA) is typically the least sensitive metric. Along-tract microstructural metrics are sensitive to tau and amyloid accumulation, showing the potential of diffusion MRI to track AD pathology and map its impact on neural pathways.

Contrasting association pattern of plasma low-density lipoprotein with white matter integrity in APOE4 carriers versus non-carriers.

Apolipoprotein E ε4 (APOE4) is a strong genetic risk factor of Alzheimer's disease and metabolic dysfunction. However, whether APOE4 and markers of metabolic dysfunction synergistically impact the deterioration of white matter (WM) integrity in older adults remains unknown. In the UK Biobank data, we conducted a multivariate analysis to investigate the interactions between APOE4 and 249 plasma metabolites (measured using nuclear magnetic resonance spectroscopy) with whole-brain WM integrity (measured by diffusion-weighted magnetic resonance imaging) in a cohort of 1917 older adults (aged 65.0-81.0 years; 52.4 % female). Although no main association was observed between either APOE4 or metabolites with WM integrity (adjusted P > 0.05), significant interactions between APOE4 and metabolites with WM integrity were identified. Among the examined metabolites, higher concentrations of low-density lipoprotein and very low-density lipoprotein were associated with a lower level of WM integrity (b=-0.12, CI=-0.14,-0.10) among APOE4 carriers. Conversely, among non-carriers, they were associated with a higher level of WM integrity (b=0.05, CI=0.04,0.07), demonstrating a significant moderation role of APOE4 (b =-0.18, CI=-0.20,-0.15, P<0.00001).

CAT: a computational anatomy toolbox for the analysis of structural MRI data.

A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT)-a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike, providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams-illustrated on an example dataset-allow for voxel-based, surface-based, and region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT while offering a citable standard for the neuroscience community.

The Relationship of Cardiorespiratory Fitness, Physical Activity, and Coronary Artery Calcification to Cardiovascular Disease Events in CARDIA Participants.

AIMS: Moderate-to-vigorous-intensity physical activity (MVPA), cardiorespiratory fitness (CRF), and coronary artery calcification (CAC) are associated with cardiovascular disease (CVD) risk. While a U-shaped relationship between CRF or MVPA and CAC has been reported, the presence of CAC among highly fit individuals might be benign. We examined interactive associations of CRF or MVPA and CAC with outcomes and evaluated the relationship of CRF and MVPA to CAC incidence. METHODS: CARDIA participants with CAC assessed in 2005-06 were included (n=3,141, mean age 45). MVPA was assessed by self-report and accelerometer. CRF was estimated with a maximal graded exercise test. Adjudicated CVD events and mortality data were obtained through 2019. CAC was reassessed in 2010-11. Cox models were constructed to assess hazard ratios (HRs) for CVD, coronary heart disease (CHD), and mortality in groups defined by CAC presence/absence and lower/higher CRF or MVPA levels. Logistic models were constructed to assess associations with CAC incidence. Adjustment was made for sociodemographic and CVD risk factors. RESULTS: Relative to participants with no CAC and higher CRF, the adjusted HRs for CVD were 4.68 for CAC and higher CRF, 2.22 for no CAC and lower CRF, and 3.72 for CAC and lower CRF. For CHD, the respective HRs were 9.98, 2.28, and 5.52. For mortality, the HRs were 1.15, 1.58, and 3.14, respectively. Similar findings were observed when MVPA, measured either by self-report or accelerometer, was substituted for CRF. A robust inverse association of CRF and accelerometer-derived MVPA with CAC incidence was partly accounted for by adjusting for CVD risk factors. CONCLUSIONS: In middle-aged adults, CRF and MVPA demonstrated an inverse association with CAC incidence but did not mitigate the increased cardiovascular risk associated with CAC, indicating that CAC is not benign in individuals with higher CRF or MVPA levels.

This study explored the relationship between physical fitness, physical activity, and coronary artery calcification (CAC) in predicting heart disease risk. CAC is the build-up of calcium deposits in the coronary arteries, indicating the presence of atherosclerosis. Involving approximately 3,000 adults with an average age of 45, the study measured physical activity through self-report and accelerometer, fitness via treadmill tests, and CAC at two time points, five years apart. Being fit and active was associated with a lower chance of developing new CAC. Similarly, higher fitness and physical activity levels were associated with a lower risk of experiencing heart disease events and death over 13 years of follow-up. In contrast, the presence of CAC strongly predicted elevated heart disease risk and death. Furthermore, having CAC eliminated the heart health benefits of being physically active or fit. The study concludes that while being fit and active is beneficial, CAC remains a serious risk factor for heart disease, even in individuals with higher fitness and physical activity levels. In middle-aged adults, being aerobically fit and physically active is associated with an overall benefit regarding heart disease events and mortality.Despite this, having CAC significantly increases the risk of heart disease events, even for those who are fit and active.

ComBatLS: A location- and scale-preserving method for multi-site image harmonization.

Recent work has leveraged massive datasets and advanced harmonization methods to construct normative models of neuroanatomical features and benchmark individuals' morphology. However, current harmonization tools do not preserve the effects of biological covariates including sex and age on features' variances; this failure may induce error in normative scores, particularly when such factors are distributed unequally across sites. Here, we introduce a new extension of the popular ComBat harmonization method, ComBatLS, that preserves biological variance in features' locations and scales. We use UK Biobank data to show that ComBatLS robustly replicates individuals' normative scores better than other ComBat methods when subjects are assigned to sex-imbalanced synthetic "sites". Additionally, we demonstrate that ComBatLS significantly reduces sex biases in normative scores compared to traditional methods. Finally, we show that ComBatLS successfully harmonizes consortium data collected across over 50 studies. R implementation of ComBatLS is available at https://github.com/andy1764/ComBatFamily.

Mega-analysis of the brain-age gap in substance use disorder: An ENIGMA Addiction working group study.

BACKGROUND AND AIMS: The brain age gap (BAG), calculated as the difference between a machine learning model-based predicted brain age and chronological age, has been increasingly investigated in psychiatric disorders. Tobacco and alcohol use are associated with increased BAG; however, no studies have compared global and regional BAG across substances other than alcohol and tobacco. This study aimed to compare global and regional estimates of brain age in individuals with substance use disorders and healthy controls. DESIGN: This was a cross-sectional study. SETTING: This is an Enhancing Neuro Imaging through Meta-Analysis Consortium (ENIGMA) Addiction Working Group study including data from 38 global sites. PARTICIPANTS: This study included 2606 participants, of whom 1725 were cases with a substance use disorder and 881 healthy controls. MEASUREMENTS: This study used the Kaufmann brain age prediction algorithms to generate global and regional brain age estimates using T1 weighted magnetic resonance imaging (MRI) scans. We used linear mixed effects models to compare global and regional (FreeSurfer lobestrict output) BAG (i.e. predicted minus chronological age) between individuals with one of five primary substance use disorders as well as healthy controls. FINDINGS: Alcohol use disorder (ß = -5.49, t = -5.51, p < 0.001) was associated with higher global BAG, whereas amphetamine-type stimulant use disorder (ß = 3.44, t = 2.42, p = 0.02) was associated with lower global BAG in the separate substance-specific models. CONCLUSIONS: People with alcohol use disorder appear to have a higher brain-age gap than people without alcohol use disorder, which is consistent with other evidence of the negative impact of alcohol on the brain.

Supporting the working life exposome: Annotating occupational exposure for enhanced literature search.

An individual's likelihood of developing non-communicable diseases is often influenced by the types, intensities and duration of exposures at work. Job exposure matrices provide exposure estimates associated with different occupations. However, due to their time-consuming expert curation process, job exposure matrices currently cover only a subset of possible workplace exposures and may not be regularly updated. Scientific literature articles describing exposure studies provide important supporting evidence for developing and updating job exposure matrices, since they report on exposures in a variety of occupational scenarios. However, the constant growth of scientific literature is increasing the challenges of efficiently identifying relevant articles and important content within them. Natural language processing methods emulate the human process of reading and understanding texts, but in a fraction of the time. Such methods can increase the efficiency of both finding relevant documents and pinpointing specific information within them, which could streamline the process of developing and updating job exposure matrices. Named entity recognition is a fundamental natural language processing method for language understanding, which automatically identifies mentions of domain-specific concepts (named entities) in documents, e.g., exposures, occupations and job tasks. State-of-the-art machine learning models typically use evidence from an annotated corpus, i.e., a set of documents in which named entities are manually marked up (annotated) by experts, to learn how to detect named entities automatically in new documents. We have developed a novel annotated corpus of scientific articles to support machine learning based named entity recognition relevant to occupational substance exposures. Through incremental refinements to the annotation process, we demonstrate that expert annotators can attain high levels of agreement, and that the corpus can be used to train high-performance named entity recognition models. The corpus thus constitutes an important foundation for the wider development of natural language processing tools to support the study of occupational exposures.

The Genetic Architecture of the Human Corpus Callosum and its Subregions.

The corpus callosum (CC) is the largest set of white matter fibers connecting the two hemispheres of the brain. In humans, it is essential for coordinating sensorimotor responses, performing associative/executive functions, and representing information in multiple dimensions. Understanding which genetic variants underpin corpus callosum morphometry, and their shared influence on cortical structure and susceptibility to neuropsychiatric disorders, can provide molecular insights into the CC's role in mediating cortical development and its contribution to neuropsychiatric disease. To characterize the morphometry of the midsagittal corpus callosum, we developed a publicly available artificial intelligence based tool to extract, parcellate, and calculate its total and regional area and thickness. Using the UK Biobank (UKB) and the Adolescent Brain Cognitive Development study (ABCD), we extracted measures of midsagittal corpus callosum morphometry and performed a genome-wide association study (GWAS) meta-analysis of European participants (combined N = 46,685). We then examined evidence for generalization to the non-European participants of the UKB and ABCD cohorts (combined N = 7,040). Post-GWAS analyses implicate prenatal intracellular organization and cell growth patterns, and high heritability in regions of open chromatin, suggesting transcriptional activity regulation in early development. Results suggest programmed cell death mediated by the immune system drives the thinning of the posterior body and isthmus. Global and local genetic overlap, along with causal genetic liability, between the corpus callosum, cerebral cortex, and neuropsychiatric disorders such as attention-deficit/hyperactivity and bipolar disorders were identified. These results provide insight into variability of corpus callosum development, its genetic influence on the cerebral cortex, and biological mechanisms related to neuropsychiatric dysfunction.

Prevalence of Tendon Rupture and Tendinopathies Among Patients with Atherosclerotic Cardiovascular Disease Derived From United States Administrative Claims Data.

INTRODUCTION: The prevalence of tendon rupture and tendinopathies (TRT) has not been determined in a large population of patients with atherosclerotic cardiovascular disease (ASCVD). We investigated TRT prevalence among patients with ASCVD and in the general population, using data from the Symphony Health Integrated Dataverse, a large US medical and pharmacy claims database. METHODS: This retrospective, observational study included patients aged ≥ 19 years from the claims database during the identification period (January 2019 to December 2020) and 12 months of continuous enrollment. The primary outcome was evidence of TRT in the 12 months following the index date (first ASCVD diagnosis in the ASCVD cohort; first claim in the claims database in the overall population). Diagnostic codes (ICD-10 and/or CPT) were used to define ASCVD and TRT diagnosis. RESULTS: The ASCVD cohort and overall population included 5,589,273 and 61,715,843 patients, respectively. In the ASCVD cohort, use of medications with a potential or known association with TRT was identified in 67.9% (statins), 17.7% (corticosteroids), and 16.7% (fluoroquinolones) of patients. Bempedoic acid use was reported in 1556 (< 0.1%) patients. TRT prevalence during 12-month follow-up was 3.4% (ASCVD cohort) and 1.9% (overall population). Among patients with ASCVD, 83.5% experienced TRT in only one region of the body. Factors most associated with TRT in the ASCVD cohort were increasing age, most notably in those aged 45-|64 years (odds ratio [OR] 2.19; 95% confidence interval [CI] 2.07-2.32), obesity (OR 1.51; 95% CI 1.50-1.53), and rheumatoid arthritis (OR 1.47; 95% CI 1.45-1.79). Use of statins or bempedoic acid was not associated with increased TRT risk. CONCLUSION: Patients with ASCVD may have greater risk of TRT than the general population, which may be driven by an increased prevalence of comorbidities and use of medications with a potential or known association with TRT.

Patients with atherosclerosis, the main cause of heart attacks, strokes, and peripheral vascular disease, typically require several drugs to control the disease. Some of the drugs used to treat atherosclerosis have been linked to a higher occurrence of tendon tears (or ruptures) or swelling/inflammation of the tendons (tendinopathies). However, there may be other factors present in these patients that increase the risk of tendon injuries that are not related to these drugs. This study used the medical records of over 5.5 million patients with atherosclerosis and over 63 million patients reflecting the general population in the United States to determine the prevalence of tendon injury. Additionally, the researchers looked at other factors that might be related to a higher risk of tendon injury in each group. Over a 12-month period, tendon injuries occurred in 3.4% of patients with atherosclerosis and 1.8% of patients in the general population. In patients with atherosclerosis, factors such as being obese, older (45­64 years), or having rheumatoid arthritis were also linked to an increased risk of tendon injuries. There was no association seen between statin or bempedoic acid use and tendon injuries. These results may help healthcare providers to determine the underlying risk of tendon injuries and guide treatment of this patient population.