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Professor Andrea Németh has successfully applied to the John Fell Fund to establish the Oxford Neurodevelopment Consortium (OxNDC).
Analysis of infection rates in neuromyelitis optica spectrum disorder: Comparing satralizumab treatment in SAkuraMoon, post-marketing, and US-based health claims data.
Satralizumab showed a comparable safety profile versus placebo in 2 pivotal neuromyelitis optica spectrum disorder (NMOSD) studies. We analyzed infection rates with long-term satralizumab treatment in the open-label study, SAkuraMoon, and in a post-marketing setting (PMS), comparing frequencies with US-based health claims real-world data (US-RWD). Incidence rates of infection per 100 patient-years (IR/100 PY) were analyzed in the SAkura studies (clinical cut-off date: 31 January 2023). Reported rates of infection ( %) in a PMS using Periodic Benefit-Risk Evaluation Reports (2020-2023), and cumulative incidence of infections ( %) from the US PharMetrics claims data in NMOSD patients (2017-2022) were analyzed. 166 patients (SAkura studies), 2951 patients (PMS) and 2872 patients (US-RWD) were included. In the SAkura studies, the incidence rates of infection, serious infection, and sepsis were lower versus the double-blind period (IR/100 PY [95 % confidence intervals (Tur, C. et al.)] infection 91.7 [85.5-98.3] vs 113.0 [98.6-129.0]; serious infection 2.6 [1.7-3.9] vs 4.1 [1.8-8.1]; sepsis 0.6 [0.2-1.3] vs 1.0 [0.1-3.7], respectively). In a PMS, reported rates of infection, serious infection, and sepsis were 7.3 %, 3.8 %, and 0.6 %, respectively. In the US-RWD, cumulative incidence of infection, serious infection, and sepsis in NMOSD were 67.3 %, 8.4 %, and 4.9 %, respectively. Concomitant IST use, comorbidities, Expanded Disability Status Scale score ≥4.0, and age >65 years were potential confounders of sepsis. US-RWD indicated infection is a major comorbidity in NMOSD, independent of satralizumab treatment. Infection rates were consistently lower in satralizumab-treated patients compared with US-RWD. Trial Registration: NCT04660539(SAkuraMoon), NCT02028884(SAkuraSky), NCT02073279(SAkuraStar).
Automated quality control of T1-weighted brain MRI scans for clinical research datasets: methods comparison and design of a quality prediction classifier
Abstract T1-weighted (T1w) MRI is widely used in clinical neuroimaging for studying brain structure and its changes, including those related to neurodegenerative diseases, and as anatomical reference for analysing other modalities. Ensuring high-quality T1w scans is vital as image quality affects reliability of outcome measures. However, visual inspection can be subjective and time-consuming, especially with large datasets. The effectiveness of automated quality control (QC) tools for clinical cohorts remains uncertain. In this study, we used T1w scans from elderly participants within ageing and clinical populations to test the accuracy of existing QC tools with respect to visual QC and to establish a new quality prediction framework for clinical research use. Four datasets acquired from multiple scanners and sites were used (N = 2438, 11 sites, 39 scanner manufacturer models, 3 field strengths – 1.5T, 3T, 2.9T, patients and controls, average age 71 ± 8 years). All structural T1w scans were processed with two standard automated QC pipelines (MRIQC and CAT12). The agreement of the accept-reject ratings was compared between the automated pipelines and with visual QC. We then designed a quality prediction framework that combines the QC measures from the existing automated tools and is trained on clinical research datasets. We tested the classifier performance using cross-validation on data from all sites together, also examining the performance across diagnostic groups. We then tested the generalisability of our approach when leaving one site out and explored how well our approach generalises to data from a different scanner manufacturer and/or field strength from those used for training, as well as on an unseen new dataset of healthy young participants with movement related artefacts. Our results show significant agreement between automated QC tools and visual QC (Kappa=0.30 with MRIQC predictions; Kappa=0.28 with CAT12’s rating) when considering the entire dataset, but the agreement was highly variable across datasets. Our proposed robust undersampling boost (RUS) classifier achieved 87.7% balanced accuracy on the test data combined from different sites (with 86.6% and 88.3% balanced accuracy on scans from patients and controls respectively). This classifier was also found to be generalisable on different combinations of training and test datasets (average balanced accuracy of leave-one-site-out = 78.2%; exploratory models on field strengths and manufacturers = 77.7%; movement related artefact dataset when including 1% scans in the training = 88.5%). While existing QC tools may not be robustly applicable to datasets comprised of older adults, they produce quality metrics that can be leveraged to train a more robust quality control classifiers for ageing and clinical cohorts.
Designing better systems to navigate the sepsis-antimicrobial stewardship tension.
Sepsis is a leading cause of preventable death and requires timely antimicrobial treatment to reduce mortality. Despite extensive sepsis management guidelines, high-income countries continue to have considerable rates of sepsis mortality, indicating a gap between guideline quality, usability, and practical application. Simultaneously, the rise of antimicrobial resistance threatens the efficacy of antimicrobial therapies for infection control, underscoring the tension between sepsis management and antimicrobial stewardship. This Personal View explores how system factors, such as people, environments, tools, technologies, and tasks, influence the sepsis-antimicrobial stewardship tension. With the Systems Engineering Initiative for Patient Safety, we use a case study to highlight how organisational pressures, inadequate diagnostic tools, and sociocultural factors drive the gap between work-as-imagined and work-as-done. These latent safety risks that impede guideline adherence and contribute to unintended antimicrobial use highlight the need to design better systems, not blame individuals for non-compliance. We argue that addressing sepsis and antimicrobial resistance requires a holistic systems approach and that every discipline, including policy makers, clinicians, researchers, and drug developers, should adopt systems thinking in the design of interventions intended to address this problem. This shift is essential to ensuring effective care for patients today while safeguarding the effectiveness of antimicrobials tomorrow.
Tracing mitochondrial marks of neuronal aging in iPSCs-derived neurons and directly converted neurons
Abstract This study aims to determine if neurons derived from induced pluripotent stem cells (iPSCsNs) and directly converted neurons (iNs) from the same source cells exhibit changes in mitochondrial properties related to aging. This research addresses the uncertainty around whether aged iPSCsNs retain aging-associated mitochondrial impairments upon transitioning through pluripotency while direct conversion maintains these impairments. We observe that both aged models exhibit characteristics of aging, such as decreased ATP, mitochondrial membrane potential, respiration, NAD+/NADH ratio, and increased radicals and mitochondrial mass. In addition, both neuronal models show a fragmented mitochondrial network. However, aged iPSCsNs do not exhibit a metabolic shift towards glycolysis, unlike aged iNs. Furthermore, mRNA expression differed significantly between aged iPSCsNs and aged iNs. The study concludes that aged iPSCsNs may differ in transcriptomics and the aging-associated glycolytic shift but can be a valuable tool for studying specific feature of mitochondrial neuronal aging in vitro alongside aged iNs.
AnchorInv: Few-Shot Class-Incremental Learning of Physiological Signals via Feature Space-Guided Inversion
Deep learning models have demonstrated exceptional performance in a variety of real-world applications. These successes are often attributed to strong base models that can generalize to novel tasks with limited supporting data while keeping prior knowledge intact. However, these impressive results are based on the availability of a large amount of high-quality data, which is often lacking in specialized biomedical applications. In such fields, models are usually developed with limited data that arrive incrementally with novel categories. This requires the model to adapt to new information while preserving existing knowledge. Few-Shot Class-Incremental Learning (FSCIL) methods offer a promising approach to addressing these challenges, but they also depend on strong base models that face the same aforementioned limitations. To overcome these constraints, we propose AnchorInv following the straightforward and efficient buffer-replay strategy. Instead of selecting and storing raw data, AnchorInv generates synthetic samples guided by anchor points in the feature space. This approach protects privacy and regularizes the model for adaptation. When evaluated on three public physiological time series datasets, AnchorInv exhibits efficient knowledge forgetting prevention and improved adaptation to novel classes, surpassing state-of-the-art baselines.
Dreaming of Better Treatments: Advances in Drug Development for Sleep Medicine and Chronotherapy.
Throughout history, the development of new sleep medicines has been driven by progress in our understanding of the mechanisms underlying sleep. Ancient civilisations used their understanding of the sedative nature of natural herbs and compounds to induce sleep. The discovery of barbiturates and bromides heralded a new era of synthetic sleep medicine in the 19th century. This was followed by the development of benzodiazepines that were used to inhibit signalling throughout the brain by promoting gamma-amino butyric acid release and thereby produce loss of consciousness. As our understanding of sleep has deepened, newer therapies have more specifically targeted the wake-inducing neurotransmitter orexin with fewer side effects. Given the newly highlighted role of kinases in sleep/wake regulation, we predict that the next breakthroughs in sleep medicine will likely target these kinases. Given the fundamental role that sleep plays in maintaining brain health through processes such as glymphatic clearance, sleep medicine has therapeutic potential beyond just sleep. Recent evidence suggests that sleep disruptions directly contribute to the build-up of pathological neuronal proteins in neurodegenerative disorders. Therefore, sleep medicine could improve prognosis in disorders such as these. Great attention must be paid to the mechanism of action of each sleep medicine, however, as sleep medicines which do not fully mimic sleep could actually worsen disease progression.
In Vivo Quantification of Creatine Kinase Kinetics in Mouse Brain Using 31P-MRS at 7 T
31P-MRS is a method of choice for studying neuroenergetics in vivo, but its application in the mouse brain has been limited, often restricted to ultrahigh field (> 7 T) MRI scanners. Establishing its feasibility on more readily available preclinical 7-T scanners would create new opportunities to study metabolism and physiology in murine models of brain disorders. Here, we demonstrate that the apparent forward rate constant (kf) of creatine kinase (CK) can be accurately quantified using a progressive saturation-transfer approach in the mouse brain at 7 T. We also find that a 20% reduction in respiration of anesthetized mice can lead to 36% increase in kf attributable to a drop in cellular pH and mitochondrial ATP production. To achieve this, we used a test–retest analysis to assess the reliability and repeatability of 31P-MRS acquisition, analysis, and experimental design protocols. We report that many 31P-containing metabolites can be reliably measured using a localized 3D-ISIS sequence, which showed highest SNR amplitude, SNR consistency, and minimal T2 relaxation signal loss. Our study identifies key physiological factors influencing mouse brain energy homeostasis in vivo and provides a methodological basis to guide future studies interested in implementing 31P-MRS on preclinical 7-T scanners.
The Effects of Facilitation and Inhibition During Multimodal Somatosensory Integration
The somatosensory system, including modalities such as touch, temperature, and pain, is essential for perceiving and interacting with the environment. When individuals encounter different somatosensory modalities, they interact through a process called multimodal somatosensory integration. This integration is essential for accurate perception, motor coordination, pain management, and adaptive behavior. Disruptions in this process can lead to a variety of sensory disorders and complicate rehabilitation efforts. However, research on the behavioral patterns and neural mechanisms underlying multimodal somatosensory integration remains limited. According to previous studies, multimodal somatosensory integration can result in facilitative or inhibitory effects depending on factors like stimulus type, intensity, and spatial proximity. Facilitative effects are observed primarily when stimuli from the same sensory modality (e. g., two touch or temperature stimuli) are presented simultaneously, leading to amplified perceptual strength and quicker reaction times. Additionally, certain external factors, such as cooling, can increase sensitivity to other sensory inputs, further promoting facilitative integration. In contrast, inhibitory effects may also emerge when stimuli from different sensory modalities interact, particularly between touch and pain. Under such conditions, one sensory input (e.g., vibration or non-noxious temperature stimulation) can effectively reduce the perceived intensity of the other, often resulting in reduced pain perception. These facilitative and inhibitory interactions are critical for efficient processing in a multi-stimulus environment and play a role in modulating the experience of somatosensory inputs in both normal and clinical contexts. The neural mechanisms underlying multimodal somatosensory integration are multi-tiered, encompassing peripheral receptors, the spinal cord, and various cortical structures. Facilitative integration relies on the synchronous activation of peripheral receptors, which transmit enhanced signals to higher processing centers. At the cortical level, areas such as the primary and secondary somatosensory cortex, through multimodal neuron responses, facilitate combined representation and amplification of sensory signals. In particular, the thalamus is a significant relay station where multisensory neurons exhibit superadditive responses, contributing to facilitation by enhancing signal strength when multiple inputs are present. Inhibitory integration, on the other hand, is mediated by mechanisms within the spinal cord, such as gating processes that limit transmission of competing sensory signals, thus diminishing the perceived intensity of certain inputs. At the cortical level, lateral inhibition within the somatosensory cortex plays a key role in reducing competing signals from non-target stimuli, enabling prioritized processing of the most relevant sensory input. This layered neural architecture supports the dynamic modulation of sensory inputs, balancing facilitation and inhibition to optimize perception. Understanding the neural pathways involved in somatosensory integration has potential clinical implications for diagnosing sensory disorders and developing therapeutic strategies. Future research should focus on elucidating the specific neural circuitry and mechanisms that contribute to these complex interactions, providing insights into the broader implications of somatosensory integration on behavior and cognition. In summary, this review highlights the importance of multimodal somatosensory integration in enhancing sensory perception. It also underscores the need for further exploration into the neural underpinnings of these processes to advance our understanding of sensory integration and its applications in clinical settings.
Exploration of brain-spinal cord-gut axis abnormalities and the mechanism of acupuncture therapy in irritable bowel syndrome based on magnetic resonance imaging
Irritable bowel syndrome (IBS) is a functional gastrointestinal disorder triggered by the disorder of brain-gut interaction and characterized by abdominal pain, bloating, and altered bowel habits. It is estimated to affect between 5% and 10% of the global population. Although IBS does not have an excessive mortality rate, the disease significantly affects the quality of life and can lead to significant disability. Current treatments mainly focus on relieving abdominal pain and improving bowel habits. However, the effect of drug therapy on the overall symptoms of patients is limited, and the majority of therapeutic drugs are associated with the risk of adverse reactions. Consequently, many patients turn to complementary and alternative therapies to achieve more favorable treatment outcomes. Acupuncture, as a complementary and alternative therapy, has shown potential in the treatment of IBS. Although clinical trials have confirmed the therapeutic effect of acupuncture, its mechanism of action remains unclear, leading to controversy in the global medical community. Researchers, leveraging magnetic resonance imaging (MRI) technology, strive to delve deeply into the biological mechanisms underlying the alleviation of irritable bowel syndrome symptoms through acupuncture therapy, aiming to provide solid support for the scientific basis and efficacy of this treatment method. However, current imaging research primarily focuses on changes in brain structure and function, relatively neglecting the close connection between spinal structure and function and IBS. The spinal cord plays a crucial role in brain-gut interaction, and the development of MRI technology provides a new perspective for exploring the pathogenesis of IBS and the mechanism of acupuncture based on the brain-spinal cord-gut axis. This paper reviews MRI-based studies on abnormalities in brain-spinal cord-gut axis interaction in IBS and acupuncture treatment. Although there have been significant advancements in understanding the causes and using acupuncture to treat IBS, there are still several limitations that need to be addressed. One limitation is the insufficient number of imaging studies on the spinal cord, which hinders our comprehensive understanding of the development of IBS and the underlying mechanisms of acupuncture therapy. In the future, it is necessary to enhance the imaging study of the spinal cord and conduct a thorough analysis of the brain-spinal cord-gut axis mechanism. This will enable us to establish a scientific foundation for understanding the pathogenesis of IBS and the effectiveness of acupuncture treatment. Furthermore, the current research on the impact of acupuncture on IBS primarily concentrates on describing the phenomenon and comparing data but fails to incorporate the principles of neuroscience pain theory. In the future, it is important to prioritize the integration of pain theory and thoroughly investigate the impact of acupuncture on the primary pathways of pain transmission and processing. This will help us understand the intricate mechanism of acupuncture analgesia and facilitate the broader application of acupuncture therapy.
Revolutionizing treatment for disorders of consciousness: a multidisciplinary review of advancements in deep brain stimulation
AbstractAmong the existing research on the treatment of disorders of consciousness (DOC), deep brain stimulation (DBS) offers a highly promising therapeutic approach. This comprehensive review documents the historical development of DBS and its role in the treatment of DOC, tracing its progression from an experimental therapy to a detailed modulation approach based on the mesocircuit model hypothesis. The mesocircuit model hypothesis suggests that DOC arises from disruptions in a critical network of brain regions, providing a framework for refining DBS targets. We also discuss the multimodal approaches for assessing patients with DOC, encompassing clinical behavioral scales, electrophysiological assessment, and neuroimaging techniques methods. During the evolution of DOC therapy, the segmentation of central nuclei, the recording of single-neurons, and the analysis of local field potentials have emerged as favorable technical factors that enhance the efficacy of DBS treatment. Advances in computational models have also facilitated a deeper exploration of the neural dynamics associated with DOC, linking neuron-level dynamics with macroscopic behavioral changes. Despite showing promising outcomes, challenges remain in patient selection, precise target localization, and the determination of optimal stimulation parameters. Future research should focus on conducting large-scale controlled studies to delve into the pathophysiological mechanisms of DOC. It is imperative to further elucidate the precise modulatory effects of DBS on thalamo-cortical and cortico-cortical functional connectivity networks. Ultimately, by optimizing neuromodulation strategies, we aim to substantially enhance therapeutic outcomes and greatly expedite the process of consciousness recovery in patients.
Clinical Perspectives on Using Remote Measurement Technology in Assessing Epilepsy, Multiple Sclerosis, and Depression: Delphi Study
Background: Multiple sclerosis (MS), epilepsy, and depression are chronic central nervous system conditions in which remote measurement technology (RMT) may offer benefits compared with usual assessment. We previously worked with clinicians, patients, and researchers to develop 13 use cases for RMT: 5 in epilepsy (seizure alert, seizure counting, risk scoring, triage support, and trend analysis), 3 in MS (detecting silent progression, detecting depression in MS, and donating data to a biobank), and 5 in depression (detecting trends, reviewing treatment, self-management, comorbid monitoring, and carer alert). Objective: In this study, we aimed to evaluate the use cases and related implementation issues with an expert panel of clinicians external to our project consortium. Methods: We used a Delphi exercise to validate the use cases and suggest a prioritization among them and to ascertain the importance of a variety of implementation issues related to RMT. The expert panel included clinicians from across Europe who were external to the project consortium. The study had 2 survey rounds (n=23 and n=17) and a follow-up interview round (n=9). Data were analyzed for consensus between participants and for stability between survey rounds. The interviews explored the reasons for answers given in the survey. Results: The findings showed high stability between rounds on questions related to specific use cases but lower stability on questions relating to wider issues around the implementation of RMT. Overall, questions on wider issues also had less consensus. All 5 use cases for epilepsy (seizure alert, seizure counting, risk scoring, triage support, and trend analysis) were considered beneficial, with consensus among participants above the a priori threshold for most questions, although use case 3 (risk scoring) was considered less likely to facilitate or catalyze care. There was very little consensus on the benefits of the use cases in MS, although this may have resulted from a higher dropout rate of MS clinicians (50%). Participants agreed that there would be benefits for all 5 of the depression use cases, although fewer questions on use case 4 (triage support) reached consensus agreement than for depression use cases 1 (detecting trends), 2 (reviewing treatment), 3 (self-management), and 5 (carer alert). The qualitative analysis revealed further insights into each use case and generated 8 themes on practical issues related to implementation. Conclusions: Overall, these findings inform the prioritization of use cases for RMT that could be developed in future work, which may include clinical trials, cost-effectiveness studies, and the commercial development of RMT products and services. Priorities for further development include the use of RMT to provide more accurate records of symptoms and treatment response than is currently possible and to provide data that could help inform patient triage and generate timely alerts for patients and carers.
Cholinergic degeneration in prodromal and early Parkinson's: a link to present and future disease states.
The neuropathological process in Parkinson's disease (PD) and Lewy body disorders has been shown to extend well beyond the degeneration of the dopaminergic system, affecting other neuromodulatory systems in the brain which play crucial roles in the clinical expression and progression of these disorders. Here, we investigate the role of the macrostructural integrity of the nucleus basalis of Meynert (NbM), the main source of cholinergic input to the cerebral cortex, in cognitive function, clinical manifestation, and disease progression in non-demented subjects with PD and individuals with isolated REM sleep behaviour disorder (iRBD). Using structural MRI data from 393 early PD patients, 128 iRBD patients, and 186 controls from two longitudinal cohorts, we found significantly lower NbM grey matter volume in both PD (β=-12.56, p=0.003) and iRBD (β=-16.41, p=0.004) compared to controls. In PD, higher NbM volume was associated with better higher-order cognitive function (β=0.10, p=0.045), decreased non-motor (β=-0.66, p=0.026) and motor (β=-1.44, p=0.023) symptom burden, and lower risk of future conversion to dementia (Hazard ratio (HR)<0.400, p<0.004). Higher NbM volume in iRBD was associated with decreased future risk of phenoconversion to PD or dementia with Lewy bodies (DLB) (HR<0.490, p<0.016). However, despite similar NbM volume deficits to those seen in PD, associations between NbM structural deficits and current disease burden or clinical state were less pronounced in iRBD. These findings identify NbM volume as a potential biomarker with dual utility: predicting cognitive decline and disease progression in early PD, while also serving as an early indicator of phenoconversion risk in prodromal disease. The presence of structural deficits before clear clinical correlates in iRBD suggests complex compensatory mechanisms may initially mask cholinergic dysfunction, with subsequent failure of these mechanisms potentially contributing to clinical conversion.
Dopamine D2 receptor upregulation in dorsal striatum in the LRRK2-R1441C rat model of early Parkinson's disease revealed by in vivo PET imaging.
We conducted PET imaging with [18F]FDOPA and dopamine D2/3 receptor ligand [18F]fallypride in aged transgenic rats carrying human pathogenic LRRK2 R1441C or G2019S mutations. These rats have mild age-dependent deficits in dopamine release restricted to dorsal striatum despite no overt loss of dopamine neurons or dopamine content and demonstrate L-DOPA-responsive movement deficits.LRRK2 mutant rats displayed no deficit in [18F]FDOPA uptake, consistent with intact dopamine synthesis in striatal axons. However, LRRK2-R1441C rats demonstrated greater binding of [18F]fallypride than LRRK2-G2019S or non-transgenic controls, from a regionally selective increase in dorsal striatum. Immunocytochemical labelling post-mortem confirmed a greater density of D2 receptors in LRRK2-R1441C than other genotypes restricted to dorsal striatum, consistent with upregulation of D2-receptors as a compensatory response to the greater dopamine release deficit previously demonstrated in this genotype.These results show that [18F]fallypride PET imaging is sensitive to dysregulation of dopamine signalling in the LRRK2-R1441C rat, revealing upregulation of D2 receptors that parallels observations in human putamen in early sporadic PD. Future studies of candidate therapies could exploit this non-invasive approach to assess treatment efficacy.