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Internally consistent and fully unbiased multimodal MRI brain template construction from UK Biobank: Oxford-MM
Anatomical magnetic resonance imaging (MRI) templates of the brain are essential to group-level analyses and image processing pipelines, as they provide a reference space for spatial normalisation. While it has become common for studies to acquire multimodal MRI data, many templates are still limited to one type of modality, usually either scalar or tensor based. Aligning each modality in isolation does not take full advantage of the available complementary information, such as strong contrast between tissue types in structural images, or axonal organisation in the white matter in diffusion tensor images. Most existing strategies for multimodal template construction either do not use all modalities of interest to inform the template construction process, or do not use them in a unified framework. Here, we present multimodal, cross-sectional templates constructed from UK Biobank data: the Oxford-MultiModal-1 (OMM-1) template and age-dependent templates for each year of life between 45 and 81 years. All templates are fully unbiased to represent the average shape of the populations they were constructed from, and internally consistent through jointly informing the template construction process with T1-weighted (T1), T2-weighted fluid-attenuated inversion recovery (T2-FLAIR), and diffusion tensor imaging (DTI) data. The OMM-1 template was constructed with a multiresolution, iterative approach using 240 individuals in the 50–55-year age range. The age-dependent templates were estimated using a Gaussian process, which describes the change in average brain shape with age in 37,330 individuals. All templates show excellent contrast and alignment within and between modalities. The global brain shape and size are not preconditioned on existing templates, although maximal possible compatibility with MNI-152 space was maintained through rigid alignment. We showed benefits in registration accuracy across two datasets (UK Biobank and HCP), when using the OMM-1 as the template compared with FSL’s MNI-152 template, and found that the use of age-dependent templates further improved accuracy to a small but detectable extent. All templates are publicly available and can be used as a new reference space for uni- or multimodal spatial alignment.
Stacking models of brain dynamics to improve prediction of subject traits in fMRI.
Beyond structural and time-averaged functional connectivity brain measures, modelling the way brain activity dynamically unfolds can add important information to our understanding and characterisation of individual cognitive traits. One approach to leveraging this information is to extract features from models of brain network dynamics to predict individual traits. However, these predictions are susceptible to variability due to factors such as variation in model estimation induced by the choice of hyperparameters. We suggest that, rather than merely being statistical noise, this variability may be useful in providing complementary information that can be leveraged to improve prediction accuracy. To leverage this variability, we propose the use of stacking, a prediction-driven approach for model selection. Specifically, we combine predictions developed from multiple hidden Markov models-a probabilistic generative model of network dynamics that identifies recurring patterns of brain activity-to demonstrate that stacking can slightly improve the accuracy and robustness of cognitive trait predictions. By comparing analysis from the Human Connectome Project and UK Biobank datasets, we show that stacking is relatively effective at improving prediction accuracy and robustness when there are enough subjects, and that the effectiveness of combining predictions from static and dynamic functional connectivity approaches depends on the length of scan per subject. We also show that the effectiveness of stacking predictions is driven by the accuracy and diversity in the underlying model estimations.
Neural correlates of cognitive ability and visuo-motor speed: Validation of IDoCT on UK Biobank Data
Automated online and App-based cognitive assessment tasks are becoming increasingly popular in large-scale cohorts and biobanks due to advantages in affordability, scalability, and repeatability. However, the summary scores that such tasks generate typically conflate the cognitive processes that are the intended focus of assessment with basic visuo-motor speeds, testing device latencies, and speed-accuracy tradeoffs. This lack of precision presents a fundamental limitation when studying brain-behaviour associations. Previously, we developed a novel modelling approach that leverages continuous performance recordings from large-cohort studies to achieve an iterative decomposition of cognitive tasks (IDoCT), which outputs data-driven estimates of cognitive abilities, and device and visuo-motor latencies, whilst recalibrating trial-difficulty scales. Here, we further validate the IDoCT approach with UK BioBank imaging data. First, we examine whether IDoCT can improve ability distributions and trial-difficulty scales from an adaptive picture-vocabulary task (PVT). Then, we confirm that the resultant visuo-motor and cognitive estimates associate more robustly with age and education than the original PVT scores. Finally, we conduct a multimodal brain-wide association study with free-text analysis to test whether the brain regions that predict the IDoCT estimates have the expected differential relationships with visuo-motor versus language and memory labels within the broader imaging literature. Our results support the view that the rich performance timecourses recorded during computerised cognitive assessments can be leveraged with modelling frameworks like IDoCT to provide estimates of human cognitive abilities that have superior distributions, re-test reliabilities, and brain-wide associations.
Increased c-Fos immunoreactivity in anxiety-related brain regions following paroxetine discontinuation.
Selective serotonin reuptake inhibitor (SSRI) therapy cessation often induces a disabling discontinuation syndrome, including increased anxiety. We recently reported that SSRI discontinuation induced behavioural changes in mice, which we hypothesise arose from activated anxiety circuitry. Here, we investigated the effect of discontinuation from the SSRI paroxetine on the expression of the activity-dependent gene c-fos in selected anxiety-related midbrain and forebrain regions. Male mice were injected daily with paroxetine (10 mg/kg) or saline for 12 days, then treatment was either continued or discontinued for two or five days. Mice were then tested on the elevated plus maze (EPM) and tissue collected 90 min later. Brain sections including the dorsal (DRN) and median raphe nucleus, periaqueductal grey, hippocampus, prefrontal cortex, and amygdala were processed for c-Fos immunoreactivity. Two days after paroxetine discontinuation, when mice showed elevated anxiety-like behaviour on the EPM, increased c-Fos immunoreactivity was evident in the DRN and ventral hippocampus, but not in any other region examined, compared to saline-treated controls. Increased c-Fos in the DRN was evident in TPH2-immunopositive neurons as well as neurons doubled-labelled for TPH2 and VGLUT3, suggesting activation of 5-HT-glutamate co-releasing neurons. Five days after paroxetine discontinuation, increased c-Fos immunoreactivity was evident in the DRN, but mice no longer exhibited increased anxiety. These findings suggest that, under these conditions, paroxetine discontinuation is associated with a short-lasting activation of anxiety-promoting circuitry limited to DRN 5-HT neurons and the hippocampus. This circuitry may contribute to symptoms such as anxiety that are a feature of SSRI discontinuation syndrome.
Dissecting metabolic dysfunction- and alcohol-associated liver disease (MetALD) using proteomic and metabolomic profiles.
BACKGROUND: & Aim, Metabolic dysfunction associated and alcohol associated liver disease (MetALD) is a poorly understood condition that bridges cardiometabolic and alcohol-related pathological characteristics. We aim to distinguish MetALD patients who share similar molecular signatures with alcohol-related liver disease (ALD) and those share signatures with metabolic dysfunction-associated steatotic liver disease (MASLD), and assess their prognostic risk for complications and mortality. METHODS: Our analysis involved 443,453 European participants from UK Biobank, including 34,147 with MetALD, 11,220 with ALD, and 124,034 with MASLD. We employed Elastic Net Regression to classify ALD and MASLD involving 249 plasma metabolites and/or 2,941 plasma proteins with various sensitivity analyses. We then used the selected concise model in MetALD patients to identify alcohol-predominant group (classified to ALD) and cardiometabolic-predominant group (classified to MASLD). Finally, we explored their 15-year risk of major outcomes (i.e., heart failure, myocardial infarction, stroke, cirrhosis, hepatocellular carcinoma and mortality) using Cox regression. RESULTS: The metabolome alone discriminated ALD from MASLD with an Area under the Curve (AUC) of 0.86, while the proteome alone achieved an AUC of 0.96. Adding age, sex, BMI, liver enzymes, or metabolome information did not enhance the AUC of the proteome model. A ten-protein model differentiated ALD and MASLD with an AUC of 0.93. This model identified that alcohol-predominant MetALD patients had significantly higher risks of mortality, and cirrhosis, along with elevated fibrosis scores and higher fibrosis stages, compared to cardiometabolic-predominant patients. CONCLUSIONS: This study emphasizes the importance of subtyping differentiation using proteome data for personalized treatment and improved prognostic outcomes in MetALD patients.
Research assistants' experiences recruiting patients with psychosis into clinical trials: a qualitative study.
OBJECTIVES: Treatments for patients diagnosed with psychosis need to be improved. Clinical trials are an important way of assessing the efficacy of new treatments. However, recruiting patients into trials is challenging. This study sought to better understand the reasons for this from the perspective of research assistants. DESIGN: A qualitative study underpinned by a critical realist ontology and contextualist epistemology. METHODS: Research assistants who had recruited patients with psychosis into trials, primarily of psychological interventions, were interviewed. Reflexive thematic analysis was used to identify themes. RESULTS: Overarching themes representing four types of factors influencing recruitment of patients with psychosis into clinical trials were generated: patient, clinical team, research team, and NHS infrastructure. Patients largely wished to take part in trials but needed time to build trust with research assistants. Clinical teams held the power in suggesting patients for trials; therefore, it was essential for research teams to build strong relationships with clinical staff. Research teams recruiting into trials benefited from lived experience expertise, support systems, and institutional knowledge. A key NHS infrastructure factor was that mental health staff had limited time to consider trials for their patients. CONCLUSIONS: Trial participation needs to be made more accessible to patients with psychosis, who often want to take part but lack opportunities. Methods of increasing accessibility could include identifying and addressing barriers to referral from clinical teams, employing multiple recruitment strategies, and flexible appointment formats. Qualitative research with clinical teams and patients will also help in developing the understanding of barriers to recruitment.
Systematic review and meta-analysis of microbiota-gut-astrocyte axis perturbation in neurodegeneration, brain injury, and mood disorders
Background: Astrocytes are essential for preserving homeostasis, maintaining the blood-brain barrier, and they are a key element of the tripartite neuronal synapse. Despite such multifaceted roles, their importance as contributors to the microbiota-gut-brain axis studies, which typically focus on microglia and neurons, has been largely overlooked. This meta-analysis provides the first systematic review of the microbiota-gut-astrocyte (MGA) axis in vivo, integrating findings across distinct neurological diseases. Methods: A systematic narrative review was conducted per PRISMA guidelines. The search term employed for PubMed was “Microbiota"[MeSH] AND (astrocyte OR glial) NOT (Review[Publication Type]) and for Web of Science, Embase, and Scopus, “Microbio∗ AND (astrocyte OR glial)” with filters applied to exclude review articles. Searches were completed by May 9th, 2024. Data extracted included study models, interventions, and outcomes related to astrocyte biology and rodent behaviour. SYRCLE's risk of bias tool was used to assess individual study designs. Results: 53 studies met the inclusion criteria, covering rodent models of stroke and traumatic (acute) brain injury, chronic neurodegenerative diseases including Alzheimer's and Parkinson's disease and other heterogeneous models of cognitive impairment and affective disorders. Significant heterogeneity in methodology was observed between studies. Five studies had a high risk of bias, and 15 were low risk. Astrocyte biology, typically measured by GFAP expression, was increased in neurodegeneration and acute brain injury models but varied significantly in mood disorder models, depending on the source of stress. Common findings across diseases included altered gut microbiota, particularly an increased Bacteroidetes/Firmicutes ratio and compromised gut barrier integrity, linked to increased GFAP expression. Faecal microbiota transplants and microbial metabolite analyses suggested a direct impact of the gut microbiota on astrocyte biology and markers of neuroinflammation. Conclusions: This review and meta-analysis describes the impact of the gut microbiota on astrocyte biology, and argues that the MGA axis is a promising therapeutic target for neurological disorders. However, it is clear that our understanding of the relationship between the gut microbiota and astrocyte behaviour is incomplete, including how different subtypes of astrocytes may be affected. Future studies must adopt new, multi-dimensional studies of astrocyte function and dysfunction, to elucidate their role in disease and explore the therapeutic potential of gut microbiota modulation.
Automated Assessment of Pain (AAP) and Multimodal Sensing Grand Challenge for Next-Gen Pain Assessment (AI4Pain)
Pain communication varies, with some individuals being highly expressive regarding their pain and others exhibiting stoic forbearance and minimal verbal account of discomfort. Considerable progress has been made in defining behavioral indices of pain [1]-[3]. An abundant literature shows that a limited subset of facial movements, in several non-human species, encode pain intensity across the lifespan [2]. To advance reliable pain monitoring, automated assessment of pain is emerging as a powerful mean to realize that goal. Though progress has been made, this field remains in its infancy. The workshop aims to promote current research and support growth of interdisciplinary collaborations to advance this groundbreaking research.
The AI4Pain Grand Challenge 2024: Advancing Pain Assessment with Multimodal fNIRS and Facial Video Analysis
The Multimodal Sensing Grand Challenge for NextGen Pain Assessment (AI4PAIN) is the first international competition focused on automating the recognition of acute pain using multimodal sensing technologies. Participants are tasked with classifying pain intensity into three categories: No Pain, Low Pain, and High Pain, utilising functional near-infrared spectroscopy (fNIRS) and facial video recordings. This paper presents the baseline results of our approach, examining both individual and combined modalities. Notably, this challenge represents a pioneering effort to advance pain recognition by integrating neurological information (fNIRS) with behavioural data (facial video). The AI4Pain Grand Challenge aims to generate a novel multimodal sensing dataset, facilitating benchmarking and serving as a valuable resource for future research in autonomous pain assessment. The results show that individual fNIRS data achieved the highest accuracy, with 43.2% for the validation set and 43.3% for the test set, while facial data yielded the lowest accuracy, with 40.0% for the validation set and 40.1% for the test set. The combined multimodal approach produced accuracies of 40.2% for the validation set and 41.7% for the test set. This challenge provides the research community with a significant opportunity to enhance the understanding of pain, ultimately aiming to improve the quality of life for many pain sufferers through advanced, automated pain assessment methods.
Bridging-mediated compaction of mitotic chromosomes.
Within living cells, chromosome shapes undergo a striking morphological transition, from loose and uncondensed fibers during interphase to compacted and cylindrical structures during mitosis. ATP driven loop extrusion performed by a specialized protein complex, condensin, has recently emerged as a key driver of this transition. However, while this mechanism can successfully recapitulate the compaction of chromatids during the early stages of mitosis, it cannot capture structures observed after prophase. Here we hypothesize that a condensin bridging activity plays an additional important role, and review evidence - obtained largely through molecular dynamics simulations - that, in combination with loop extrusion, it can generate compact metaphase cylinders. Additionally, the resulting model qualitatively explains the unusual elastic properties of mitotic chromosomes observed in micromanipulation experiments and provides insights into the role of condensins in the formation of abnormal chromosome structures associated with common fragile sites.
Pneumococcal serotype epidemiology
This chapter summarizes the relative prevalences of the most common serotypes prior to and following the introduction of the heptavalent pneumococcal capsular polysaccharide vaccine (PCV-7). It provides thoughts about the selection of serotypes for future-generation conjugate vaccines. A remarkable feature of the global epidemiology of pneumococcal carriage is the consistency of the dominant carriage serotypes in very different environments and at different times. Invasive disease potential, or invasiveness, is a measure of the ability of pneumococci to progress from nasopharyngeal carriage to invasive disease in humans. It differs from virulence in that the latter is often used to describe the ability of a pathogen to cause disease in laboratory animals. The 11-valent formulation prevented vaccine-related otitis media and was also shown to elicit antibodies with functional immunogenicity (opsonophagocytic activity) against 6A comparable to that seen with PCV-7. The incidence of invasive pneumococcal disease (IPD) due to vaccine serotypes has decreased substantially since the introduction of PCV-7 in the United States, in vaccinated children as well as all other age groups, indicating that pneumococcal transmission was interrupted as a result of the reduction in carriage in the vaccinated pediatric population. For mucosal disease, otitis media and nonbacteremic pneumonia, it is less clear which serotypes it would be most valuable to add since there appear to be less clearcut differences in invasiveness among serotypes. The only certain way of preventing mucosal disease is to sterilize the nasopharynx with respect to pneumococci.
Decoding dynamic brain networks in Parkinson's disease with temporal attention.
Detecting brief, clinically meaningful changes in brain activity is crucial for understanding neurological disorders. Conventional imaging analyses often overlook these subtle events due to computational demands. IMPACT (Integrative Multimodal Pipeline for Advanced Connectivity and Time-series) addresses this challenge by converting 3D/4D fMRI scans into time-series signals using a standardized brain atlas. This approach integrates regional signals, network patterns, and dynamic connectivity, and employs machine learning to detect subtle fluctuations. In Parkinson's disease diagnosis across two independent cohorts (n=43 and n=40), it achieves high accuracy (area under the curve = 0.97-0.98), outperforming conventional methods. Analyses reveal transient connectivity disruptions that align with dopaminergic mechanisms, while interpretability highlights the critical time windows and regions driving classification. This reproducible, standardized pipeline is readily adaptable to other conditions where short-lived brain changes serve as key diagnostic markers.
Resolution in super-resolution microscopy - facts, artifacts, technological advancements and biological applications.
Super-resolution microscopy (SRM) has undeniable potential for scientific discovery, yet still presents many challenges that hinder its widespread adoption, including technical trade-offs between resolution, speed and photodamage, as well as limitations in imaging live samples and larger, more complex biological structures. Furthermore, SRM often requires specialized expertise and complex instrumentation, which can deter biologists from fully embracing the technology. In this Perspective, a follow-up to our recent Q&A article, we aim to demystify these challenges by addressing common questions and misconceptions surrounding SRM. Experts offer practical insights into how biologists can maximize the benefits of SRM while navigating issues such as photobleaching, image artifacts and the limitations of existing techniques. We also highlight recent developments in SRM that continue to push the boundaries of resolution. Our goal is to equip researchers with the crucial knowledge they need to harness the full potential of SRM.
Leaf venation network evolution across clades and scales.
Leaf venation architecture varies greatly among living and fossil plants. However, we still have a limited understanding of when, why and in which clades new architectures arose and how they impacted leaf functioning. Using data from 1,000 extant and extinct (fossil) plants, we reconstructed approximately 400 million years of venation evolution across clades and vein sizes. Overall, venation networks evolved from having fewer veins and less smooth loops to having more veins and smoother loops, but these changes only occurred in small and medium vein sizes. The diversity of architectural designs increased biphasically, first peaking in the Paleozoic, then decreasing during the Cretaceous, then increasing again in the Cenozoic, when recent angiosperm lineages initiated a second and ongoing phase of diversification. Vein evolution was not associated with temperature and CO2 fluctuations but was associated with insect diversification. Our results highlight the complexity of the evolutionary trajectory and potential drivers of venation network architecture.
Language and economic behaviour: Future tense use causes less not more temporal discounting
Previous studies have found cross-cultural correlations between linguistic obligations for talking about future events and economic decisions like saving money. The hypothesis is that a grammatical obligation to use the future tense (e.g. will) causes speakers to perceive future rewards as temporally distal and therefore less valuable (“temporal discounting”). However, no studies have tested whether speakers actually temporally discount as a function of the extent to which they use the future tense. We present two studies which use a novel language-elicitation paradigm to do this, involving speakers of English (which obliges the future tense) and Dutch (which does not). We used mediation analysis to test how language-level differences in the grammatical obligation to use the future tense impact economic decisions via individual language use habits. However, we found that English speakers who habitually make greater use of the future tense actually discount less, not more. These results suggest obligatory future tense use is not responsible for previously-reported cross-cultural correlations. Instead, we suggest that a better explanation involves modal notions of certainty (the probability of an event occurring) rather than temporal distance (when an event will occur). Future tenses express high certainty, which makes the correct prediction that obligatory tense marking should cause less discounting. In contrast, the cross-cultural differences may be driven by variation in other aspects of future time reference, such as low-certainty modal terminology (e.g. may, might).