Traumatic brain injury disrupts large-scale brain networks. Dynamic functional connectivity captures time-varying network interactions from functional MRI (fMRI) and provides insights into the brain’s dynamic patterns of integration and segregation. Here, we investigate dynamic functional connectivity in subacute moderate-severe traumatic brain injury patients (10 days to 6 weeks post-injury) and explore the relationships with blood and imaging biomarkers of injury and propofol sedation. We hypothesized that traumatic brain injury patients would show less complex brain state dynamics that would be associated with greater injury severity measured by white matter integrity and blood biomarkers. Sixty-five subacute traumatic brain injury patients and 48 healthy controls underwent structural and resting-state fMRI. Patients were followed-up at 6 and 12 months post-injury. Plasma concentrations of neurofilament-light chain, microtubule-associated protein, glial fibrillary acidic protein, ubiquitin carboxyl-terminal hydrolase L1, and serum S100 calcium-binding protein B were measured. Fractional anisotropy (FA), a measure of white matter integrity, was derived from diffusion-weighted imaging for a set of white matter tracts. Dynamic functional connectivity analysis was performed using a sliding-window approach. Correlations between time courses of 19 regions of interest representing the default mode network, bilateral frontaloparietal networks, and the salience network were calculated, and k -means clustering was applied to these connectivity matrices. Temporal characteristics of the resulting brain states, including fraction time, dwell time, number of transitions, and entropy of state transitions, were calculated. Four distinct brain states were identified. Brief periods of anticorrelation between key large-scale networks that support cognitive control were a dominant feature. Traumatic brain injury resulted in reduced temporal flexibility, less anticorrelated activity, and fewer transitions. Reduced entropy of state transitions was significantly associated with elevated blood-based biomarkers and reduced white matter integrity. Propofol sedation markedly reduced entropy. Dominance analysis identified glial fibrillary acidic protein, an astroglial plasma marker, as the strongest predictor of entropy. Preserved entropy during the subacute period was a significant predictor of 12-month functional outcomes. Entropy normalization at 6 months was associated with changes in glial fibrillary acidic protein, ubiquitin carboxyl-terminal hydrolase L1, and microtubule-associated protein over the same time period. We show that dynamic functional connectivity is disrupted following moderate-to-severe traumatic brain injury and these effects are exacerbated by sedation. The observed reductions in brain state entropy indicate a loss of network segregation and a shift toward less complex and more predictable brain activity, with important implications for prognosis.

There is widespread concern among former athletes about the link between head injury and dementia. Neurologists are increasingly assessing ex-contact sports athletes with cognitive and behavioural issues following repetitive head impacts and traumatic brain injury. Their assessment and management can be challenging due to the broad differential diagnosis, including psychiatric issues, trauma-related impairment and, in some cases, neurodegeneration. There may be a range of pathologies present after trauma exposure, including Alzheimer’s disease and chronic traumatic encephalopathy. Currently, we have only limited understanding of specific clinical phenotypes for distinct types of post-traumatic dementia, nor are there in vivo tests for many of the pathologies. Informed by our experience running a midlife brain health clinic for retired elite contact sport athletes, we describe a practical framework for the workup of athletes with cognitive concerns, highlighting key clinical features, an approach to investigation including neuroimaging and advanced fluid biomarkers, symptomatic management strategies and research directions.

INTRODUCTION:The brain is a complex dynamical system, influenced by arousal state. Cortical synchrony supports information processing and is disrupted in Alzheimer's disease (AD). Locus coeruleus (LC) integrity and pupillometry index arousal system structure and function. METHODS:Sixty-four AD and 26 controls underwent resting-state pupillometry-fMRI. Neuromelanin MRI and Addenbrooke's Cognitive Examination were conducted. Mean and standard deviation of blood oxygen level dependent (BOLD) phase coherence yielded synchrony and metastability, respectively. Leading Eigenvector Dynamics Analysis (LEiDA) produced coherence-based states. RESULTS:AD had reduced global synchrony [b = -0.90, p < 0.001], metastability [b = -0.61, p < 0.01], LEiDA "global coherence state" occupancy [b = -0.06, p < 0.01], and LC integrity [b = -0.37, p = 0.01]. Synchrony [b = 0.19, p = 0.01] and LC integrity [b = 0.17, p < 0.01] related to cognition and one another [b = 0.27, p = 0.01]. Pupil-linked arousal correlated with synchrony and global coherence state maintenance. DISCUSSION:In health, cortical activity shows widespread but dynamic synchrony across regions to meet changing demands. In AD, arousal dysfunction appears to disrupt these dynamics, impacting cognition.

Background: Improvements in health technology offer opportunities for remote disease screening, diagnosis and monitoring. The Withings Sleep Analyzer (WSA), an under mattress ballistocardiograph sensor able to detect body movement, breathing, and cardiac ejection is a promising technology for the non-invasive detection and monitoring of neurodegenerative diseases. InSleep46 aims to evaluate whether the WSA is able to detect preclinical Alzheimer's disease in members of the 1946 British Birth cohort, now in their late 70s. Objectives: To assess feasibility of deployment of a remote sleep, circadian and physiological monitoring device in a population of older adults. Participants: 356 participants from the Insight 46 neuroimaging sub-study (1946 British Birth Cohort), all born in one week in March 1946. Methods: We describe remote recruitment, device installation, and troubleshooting protocols. Feasibility analysis examined participant characteristics associated with recruitment and successful device set-up using logistic regression. Troubleshooting events for device installation and maintenance were recorded over a mean 14-month follow-up period. Results: During the feasibility analysis period, 263 (74%) participants, mean (SD) age 77 years (0.47) agreed to take part, of whom 245 (93%) successfully set up the WSA. Recruitment and successful set up of the WSA were not dependent on cognitive ability, socioeconomic position, or educational attainment. 162 (62%) of recruited individuals required ≥1 troubleshooting call (mean 2.3 per participant, range 0-16). 603 calls were required in total. Conclusion: Deployment of a remote sleep and physiological monitoring device in an older adult population is feasible. Most participants required individualised assistance to set up the device. For the technology to be widely implemented, the set up must be accessible, with dedicated support available. ### Competing Interest Statement JMS has received research funding and PET tracer from AVID Radiopharmaceuticals (a wholly owned subsidiary of Eli Lilly) and Alliance Medical; has consulted for Roche, Eli Lilly, Biogen, AVID, Merck and GE; and received royalties from Oxford University Press and Henry Stewart Talks. He is Chief Medical Officer for Alzheimer's Research UK. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the Health Research Authority Research Ethics Committee (HRA REC) London (REC reference 14/LO/1173, PI Schott). All participants provided written informed consent. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The NSHD data-sharing policy is available on the NSHD Data Sharing website (https://nshd.mrc.ac.uk/data-sharing). Data generated as part of Insight 46, including inSleep46, will be made available one year after data collection is complete, allowing sufficient time for quality control. Data will be made available to bona fide researchers through UCL's Unit for Lifelong Health and Ageing's standard security process, and in accordance to established NSHD data sharing guidelines (http://www.nshd.mrc.ac.uk/data). doi: 10.5522/NSHD/Q101, doi: 10.5522/NSHD/Q102, and doi: 10.5522/NSHD/Q103. NIHR, NIHR204286, NIHR301677, NIHR207100, NIHR-SSCR-DP27, NIHR-SSCR-DP-CDA30, NIHR Senior Investigator Alzheimer’s Research UK, https://ror.org/02ymzm013, ARUK-PG2014-1946, ARUK-PG2017-1946 Medical Research Council, https://ror.org/03x94j517, CSUB19166, MR/Y009452/1, MC\_UU\_00019/1, MC\_UU\_00019/3 Wolfson Foundation, https://ror.org/0333xzh65, PR/ylr/18575 Alzheimer's Association, https://ror.org/0375f4d26, SG-666374-UK BIRTH COHORT Brain Research UK, https://ror.org/04q4ck618, UCC14191 British Heart Foundation, https://ror.org/02wdwnk04, PG/17/90/33415, RE/24/130013 National Brain Appeal UK Dementia Research Institute, https://ror.org/02wedp412 UCL Biomedical Research Centre, https://ror.org/03r9qc142 AVID Radiopharmaceuticals Life Molecular Imaging NIHR Newcastle Biomedical Research Centre

Traumatic Brain Injury (TBI) triggers an acute systemic inflammatory response, which may impact outcomes. This response may interact with pre-existing factors linked to inflammation, such as age, to influence outcomes. Previous studies have typically measured few cytokines, but high-dimensional proteomic approaches can sensitively detect a broad range of inflammatory markers, to better characterise post-TBI inflammation. We analysed plasma from BIO-AX-TBI study participants (n = 37 acute moderate-severe TBI (Mayo Criteria), n = 22 acute non-TBI trauma (NTT), n = 28 non-injured controls (CON)) using the Alamar NULISA™ panel (>200 inflammatory markers). The NTT group enabled differentiation of TBI-specific versus general injury-related responses. Inflammatory markers were correlated with plasma NFL, GFAP, total tau, UCH-L1 (Simoa®), S100B (Millipore), and subacute (10 days-6 weeks) 3T MRI measures of lesion volume and white matter injury. Differential expression analysis identified four markers elevated specifically in TBI (VSNL1, IL1RN/IL-1Ra, GFAP, IKBKG), while other derangements reflected non-specific injury responses. Higher VSNL1 correlated with greater lesion volume (rs = 0.53) and higher IL1RN/IL-1Ra with greater white matter injury (rs = -0.66, both FDR-adjusted p < 0.05). IL33, part of the non-specific injury response was higher in participants with good (GOS-E 5-8) versus poor (GOS-E 1-4) outcomes (W = 47, FDR-adjusted p = 0.0024). Using an Elastic Net model trained on healthy controls, we show that "inflammation age" exceeded chronological age in TBI, particularly in younger participants. In summary, acute post-TBI inflammation includes both TBI-specific and non-specific components, linked to structural brain injury and functional outcome. Age modulates the inflammatory response. VSNL1, IL1RN/IL-1Ra, and IL33 are potential mediators of post-TBI pathophysiology.

Abstract Blood-based biomarkers are increasingly used to investigate brain health, but collecting venous blood is difficult in remote and field settings. Capillary microsampling offers a practical alternative, although the ability to delay processing and its agreement with gold-standard venous blood require validation. We evaluated Tasso+, a minimally invasive upper-arm capillary blood collection system, for measuring neurological and host-response biomarkers in plasma and serum during an exercise-based protocol. Sampling occurred before, immediately after, and approximately 24-to-36 hours after exercise; Tasso+ samples were processed with or without a 72-hour room-temperature delay. Tasso+ samples were compared with matched venous blood, and Capitainer SEP10 dried plasma spots were also evaluated, using Quanterix Simoa 4plexD+ and Alamar Biosciences NULISAseq CNS panel. Tasso+ enabled reliable measurement of several key biomarkers, even after delayed processing. These findings support capillary microsampling for neurological biomarker studies where venepuncture is challenging, including field-based research and participant-led remote sampling.

There is limited contemporary evidence on head impact conditions and injuries in motorcyclists, despite substantial recent changes in vehicles, helmet design and test standards. This constrains evidence-driven improvements to helmet protection and impact test protocols, particularly for underrepresented facial impacts. We quantified head impact locations and associated head and facial injury distributions in 12 years of motorcycle collisions from Great Britain’s Road Accident In-Depth Studies (RAIDS) database (1 April 2013–31 March 2025). Injuries were classified using Abbreviated Injury Scale codes augmented with free-text identification of clinically utilised Mayo-classified brain injury, and primary helmet impact location was derived from investiga-tor summaries and helmet photographs. Most of the 353 motorcyclists were injured (93%) and male (90%), and 2% were unhelmeted. One third sustained at least one head injury and 24.9% sustained Mayo-classified traumatic brain injury (19.0% moderate–severe). Facial injuries occurred in 12.2%, including 4.4% with facial fracture. Skull fractures (including basilar) and intracranial haemorrhage were also common. Head and facial injuries were more prevalent in fatally injured motorcyclists than survivors. Primary helmet impact location was determined for 125 motorcyclists with 50.4% of impacts were to the facial region. Head injury rates and patterns were similar across primary impact locations. When primary facial impacts caused head injury, upper face and chinbar impacts dominated visor impacts. Facial impacts are both frequent and associated with clinically important head injuries. Helmet standards and consumer ratings should incorporate facial impact assessments and adopt injury risk criteria re-flecting skull fractures, focal brain injury and intracranial haemorrhage.

Children's cycle helmets are certified using the same impact conditions as adult helmets, which can overlook important factors contributing to child head injuries. Our objective is to identify common patterns in traumatic brain injury pathologies, age, sex, riding environment, cause of injury, helmet use, and helmet injury reduction in child cyclists to inform child-specific test methods. We reviewed 48,074 head injury cases in cyclists under 17 years across 24 studies. An aggregate data meta-analysis was conducted to identify recurring patterns overall and in studies with a high proportion of severe injuries (n = 3,542 cases). Cases most often involved male riders (71.8%, CI: 71.6-72.1%), aged 10-13 years (40.2%, CI: 39.1-41.3%), occurring on paved roads (75.0%, CI: 74.2-75.9%) without prior collision (84.4%, CI: 84.1-84.8%). Injuries were predominantly intracranial (73.7%, CI: 71.6-75.8%). Studies with mostly severe injuries included significantly more males, on-road incidents, motor vehicle collisions, intracranial haemorrhages, and skull fractures. Helmets reduced odds of head injuries (OR = 0.44, CI = 0.41-0.47), but the efficacy was lower for severe injuries (OR = 0.61, CI = 0.58-0.65), which contrasts most findings for adult helmets. The identified factors associated with severe injuries in child cyclists, such as vehicle collisions and intracranial injuries with rotational mechanisms, are not represented in current child helmet test procedures. This work provides a foundation for further work aimed at quantifying representative head impact biomechanics in typical and severe child cycling incidents, with the ultimate goal of developing helmet test procedures tailored specifically to children.

OBJECTIVE:To assess the utility, accessibility, and equivalence to supervised scales of online cognitive assessment in older individuals with cognitive impairment. METHODS:Patients with Alzheimer's disease (AD, n = 31), idiopathic normal pressure hydrocephalus (iNPH, n = 26), and traumatic brain injury (TBI, n = 23) completed online cognitive tasks (Cognitron). We evaluated cognition relative to a large normative dataset (N ≈ 400,000), adjusting for device and demographics which can affect performance. Principal Component Analysis (PCA) was used to derive domain-specific and total composite scores. We compared clinical groups and correlated performance with standard assessments. RESULTS:Uptake was ~70%. PCA identified components across memory, processing speed, language, and executive functions. AD showed memory and language impairments compared with the norms and other groups. iNPH had greater executive and processing speed deficits, consistent with a subcortical impairment profile. TBI showed milder deficits in memory, working memory, and language. Cognitron total composite was associated with standard supervised tests (ADAS-Cog: β = -0.76, p < 0.001 and ACE-III: β = 0.69, p < 0.001). In iNPH, Cognitron composite predicted walking speed (estimate = 1.10, p < 0.001), a core clinical feature of the disease which is difficult to evaluate remotely. We selected five tasks with high completion rates, discriminability between conditions, and broad cognitive coverage. The derived short composite showed very high accuracy in separating AD (AUC = 0.94) and iNPH (AUC = 0.90) from age-matched norms; performance was weaker for TBI (AUC = 0.66). INTERPRETATION:Online assessment in older clinical populations is feasible and sensitive to subtle disease-specific cognitive deficits. A demographically adjusted, 15-min battery offers a scalable adjunct to standard testing, with potential to reduce burden on patients and healthcare systems.

Abstract Idiopathic normal pressure hydrocephalus (iNPH) is a globally growing neurological disorder in older adults, radiologically characterised by enlargement of ventricles. However, it remains unknown whether ventricular enlargement can produce biomechanical loading large enough to drive brain morphological changes and tissue damage. Here, we develop an anatomically detailed biomechanical model of ageing human brain and apply ventricular enlargement using a three-dimensional displacement field derived from MRI of iNPH patients and age-matched controls. The model accurately reproduces radiological markers of iNPH, including Evans index, callosal angle and high-convexity sulcal narrowing. It further predicts large mechanical strains in periventricular white matter, particularly within the corpus callosum and anterior thalamic radiations, tracts consistently implicated in iNPH imaging abnormalities. These findings provide strong evidence that ventricular enlargement induces mechanical strain that contributes to iNPH brain abnormalities, which can potentially be reversed by reducing strain following shunting surgery. The biomechanical brain model forms the foundation of a predictive digital platform and future “digital twin” technology to support diagnosis, patient stratification and treatment planning in iNPH. Key Points We develop an anatomically detailed biomechanical model of the brain, incorporating sulci, septum pellucidum and all four ventricles. A novel data-driven loading approach is introduced which uses 3D displacement fields from finite element-based registration of healthy and iNPH patient MRI, replacing the arbitrary pressure gradients of previous models. The model predictions closely match established radiological markers measured in iNPH patients, including Evans index, callosal angle and high-convexity sulcal narrowing. Ventricular enlargement generates large mechanical strains concentrated in periventricular white matter, providing a biomechanical explanation for the structural abnormalities observed in iNPH.

Abstract Neuroinflammation is a hallmark of numerous neurodegenerative, psychiatric, and chronic pain disorders and can be assessed in vivo with 18 kDa translocator protein (TSPO) positron emission tomography (PET). However, conventional quantification methods of TSPO PET are limited and often overlook the spatial relationships between regional signals. The application of network-based approaches to TSPO PET imaging may provide a novel framework to capture disease-specific neuroinflammatory patterns. To address this question, here we developed a data-driven, network-based approach to generate individual brain-wide TSPO PET matrices, employing Euclidean distance to quantify inter-regional pharmacokinetics similarity. We applied this approach to a large multicenter dataset of 528 PET scans utilizing three different TSPO tracers ([11C]-PBR28, [18F]-DPA714, [11C]-PK11195), including healthy controls and patients with different diseases such as multiple sclerosis, traumatic brain injury, schizophrenia, depression, and chronic low back pain. Statistical modelling and machine learning classifiers were applied to evaluate the impact of experimental and biological factors on TSPO similarity patterns and to investigate their potential for capturing disease-specific signatures. TSPO similarity patterns demonstrated high biological specificity and reproducibility, with strong test–retest correlations (mean Spearman’s ρ = 0.84). Average precision of disease classification exceeded chance performance by 23–89% across conditions and was driven by condition-specific regional hubs whose topological distributions closely mirrored disease pathophysiology. This specificity was further supported by minimal overlap in feature importance values across conditions. Altogether, our findings show that network-based analysis of human TSPO PET data can detect disease-specific neuroinflammatory signatures. Such methodologies underscore the biological significance of TSPO PET and enhance its translational value, supporting precision medicine strategies for neuroinflammatory disorders.

Inflammation following traumatic brain injury (TBI) may contribute to long-term morbidity. We aimed to characterize plasma interleukin-6 (IL6) trajectory after TBI and assess associations with imaging, biomarkers, and outcomes. Secondary analysis of three prospective multicenter observational cohorts: BIO-AX-TBI (United Kingdom/Europe), CREACTIVE (Europe), and TRACK-TBI (United States). Adults (≥18 years) with TBI were enrolled at trauma centers, with non-TBI trauma (NTT) and non-injured controls (CON) included in BIO-AX-TBI and TRACK-TBI. Blood was obtained at acute (≤10 days), subacute (10 days-6 weeks), and chronic (6 and 12 months) timepoints. IL6 was measured on OLINK® (BIO-AX-TBI, CREACTIVE) or MSD S-PLEX (TRACK-TBI) platforms. The Glasgow Outcome Scale-Extended (GOS-E) assessed functional outcome at chronic time points, dichotomized as unfavorable (1-4) versus favorable (5-8). Additional outcomes included neuropsychiatric symptom scores, magnetic resonance imaging (MRI) measures (lesion volume, fractional anisotropy [FA]), and neuronal/astroglial injury markers. BIO-AX-TBI included n = 195 TBI, n = 24 NTT, and n = 89 CON; CREACTIVE included n = 1146 TBI, TRACK-TBI included n = 387 TBI, n = 98 NTT, and n = 67 CON. IL6 was significantly elevated acutely in both TBI and NTT compared with CON but highest in TBI. In BIO-AX-TBI, IL6 remained elevated at 6 months (TBI median = 2.47, IQR = 1.98-2.87 vs. CON median = 2.13, IQR = 1.74-2.58; t = 2.50; p = 0.014) and 12 months (TBI median = 2.53, IQR = 2.08-3.06; t = 4.11; p < 0.001). Acute IL6 correlated with intracranial injury (GFAP; t/z = 5.14-8.14; p < 0.001), extracranial injury (t/z = 3.89-9.08; p < 0.005), and other plasma markers (rs = 0.2-0.67; false discovery rate-corrected p < 0.05). Higher peak IL-6 was associated with greater lesion volume (t = 2.82; p = 0.0057) and reduced white matter FA (t = 2.47-2.54; p < 0.05). Elevated subacute IL6 was associated with unfavorable GOS-E across all cohorts. No associations were observed with neuropsychiatric symptoms. Post-TBI IL6 elevation persists up to 12 months and is associated with greater tissue injury and worse outcomes, suggesting IL6 as a potential therapeutic target.

Abstract Purpose Identifying head impacts linked to brain injury in sport remains challenging. Instrumented mouthguards quantify head-impact kinematics, and finite element (FE) modelling can transform these data into brain strain estimates, which may better reflect injury risk than kinematics alone. Here, we examined associations between mouthguard-measured kinematics, FE-derived strain, and plasma brain injury biomarker GFAP following head impacts. Methods We analysed 41 video-verified impacts from male Australian football players, including 22 assessed for concussion (17 diagnosed) and 19 unassessed. Instrumented mouthguards recorded peak linear acceleration (PLA), peak rotational acceleration, and peak rotational velocity (PRV). Brain strain was estimated using the Imperial College FE brain model, and plasma GFAP was quantified using Simoa. Biomechanical-GFAP associations were examined using Spearman correlations and segmented regression. Results For impacts overall, plasma GFAP was moderately correlated with PLA (ρ=0.46, 95% CI: 0.20–0.66), PRV (ρ=0.53, 95% CI: 0.20–0.78), and strain (ρ=0.60, 95% CI: 0.32–0.80). Associations were stronger within concussion cases for strain (ρ=0.86, 95% CI: 0.58–0.97) and PRV (ρ=0.64, 95% CI: 0.15–0.93). Piecewise regression identified strain levels above which strain-GFAP relationships steepened across the whole-brain and brainstem. In concussion cases, supra-threshold brainstem strain was associated with greater symptoms. Conclusion Finite element brain strain may better predict brain injury risk following a sport-related head impact than peak acceleration metrics. Stronger associations with plasma GFAP, particularly among concussion cases, and evidence of a biomechanical threshold, support the use of biomarker-informed strain measures in future risk modelling and the development of brain injury screening thresholds.

BACKGROUND:Contact sports, including rugby union, are associated with higher rates of neurodegenerative dementia, due to various underlying pathologies such as Alzheimer's disease (AD) and chronic traumatic encephalopathy (CTE). New ultrasensitive multiplexed immunoassays may clarify disease mechanisms after repetitive head impacts (RHI) and traumatic brain injury, potentially aiding risk-stratification, early diagnosis and dementia treatment. METHODS:Midlife participants in the ABHC cohort underwent plasma biomarker quantification (NULISA - NUcleic acid Linked Immuno-Sandwich Assay; n=124 markers), 3T MRI, trauma exposure ascertainment and phenotyping. Regressions quantified exposure-specific protein expression, relationship to trauma (including position) and brain atrophy, using cluster analysis to test correlates of traumatic encephalopathy syndrome (TES). RESULTS:197 former elite rugby players and 33 controls were assessed. 24 (12.2%) met criteria for TES but none had dementia. Ex-players returned reduced plasma glial fibrillary acidic protein (GFAP), kallikrein-6 (KLK6) and synaptosomal-associated protein 25 (SNAP25). Ex-forwards specifically showed reduced plasma beta-site amyloid precursor protein cleaving enzyme 1 (BACE1), amyloid beta-38 (Aβ38), and increased phospho-tau181 (p-tau181). KLK6 was lower in ex-backs than controls. No biomarkers related to career duration, concussion load or regional brain volume, nor did clustering relate to TES. CONCLUSIONS:Ex-players showed distinctive plasma biomarker changes, more prominently in ex-forwards, possibly reflecting greater RHI exposure. Plasma KLK6, an endothelial serine protease, was reduced across the ex-player group, with potential diagnostic or prognostic utility in future. Reduced GFAP and SNAP25 in ex-forwards has an uncertain basis, while elevated p-tau-181 more so than p-tau217 points towards non-AD tau pathology. Our findings motivate longitudinal characterisation, including comparison with other neurodegenerative diseases.

Neurofilament light (NfL) is a discriminative blood biomarker for many neurological diseases. Current accurate analysis relating to NfL relies on state-of-the-art technologies such as the single-molecule array (Simoa) and immunoprecipitation-mass spectrometry (IP-MS), which require complicated machinery, skilled operational personnel, and well-equipped laboratories. Herein, we demonstrate a robust on-chip graphene field-effect transistor (GFET) biosensing platform for the ultrasensitive detection of NfL. This work utilizes smaller antibody fragments F(ab')2 to mitigate Debye screening and enhance sensing performance, alongside quantitative characterization of 1-pyrenebutyric acid N-hydroxysuccinimide ester (PBASE) surface density to support controlled antibody immobilization. Compared with whole antibody-based GFETs, this F(ab')2-modified GFET platform is shown to achieve a 114% increase in sensitivity, a fivefold improvement in the limit-of-detection (LoD) down to 0.18 pg/mL, and a wide dynamic detection range from 0.18 to 1500 pg/mL, together with good selectivity, stability, and reproducibility. This biosensing platform is validated against Simoa technology for the detection of NfL in clinical plasma samples, yielding a high correlation coefficient of 0.99. These results demonstrate the potential of GFETs for point-of-care diagnosis and the monitoring of neurological diseases in frontline clinical settings, outperforming conventional immunoassays and approaching Simoa sensitivity.

ABSTRACT Introduction and aims Dementia is a growing public health challenge affecting millions of people worldwide. It is a progressive condition that increases the risk of infections, falls, hospital admissions, dependence in activities of daily living, safety issues such as wandering, care home transfers, and death. New ways of supporting people living with dementia (PLWD) at home are urgently needed. We describe the MinderCare study which evaluates a digitally enabled care model that integrates low-burden sensor-based remote monitoring within a nurse-led clinical service. Methods and analysis In this mixed-methods study, we will recruit 100 people with confirmed or suspected dementia living at home and deploy the Minder remote monitoring system for at least 12 months. A detailed characterisation of the cohort will be obtained, including cognition, frailty, participant and carer wellbeing, functioning, and quality of life. The feasibility, acceptability, sustainability, and resource requirements of the service will also be assessed. Low-cost sensors provide information about behaviour, environment and physiology from the home. Machine-learning algorithms have been used to develop digital biomarkers of infection, sleep, night-time behaviours, daily activities and routines, and the effects of clinical events and treatment. These will be assessed through clinical reports of sensor-derived data that include anomaly alerts provided to the clinical teams. Algorithms will be assessed for their clinical utility and acceptability. The comparative-effectiveness component will be designed as a target trial emulation using linked electronic health-record data to construct a time-indexed external usual-care control cohort. The primary comparative outcome will be Days Alive and Out of Hospital (DAOH) over 12 months from the activation-index date, with healthcare utilisation, costs, institutionalisation and mortality assessed as secondary outcomes. DAOH and estimated MinderCare effects will also be examined across prespecified strata of baseline inpatient utilisation. Ethics and dissemination Ethical approval has been granted by the North East – Newcastle and North Tyneside 2 Research Ethics Committee, and the study has received confirmation of capacity and capability by the Imperial College Healthcare NHS Trust. Study findings will be disseminated to patients, health and social care professionals, and policymakers through peer-reviewed publications and conference presentations. Study registration number: ISRCTN14997677 and NIHR portfolio CPMSID 63023. Strengths and limitations of this study This study evaluates a digitally enabled remote monitoring model for people living with dementia that integrates passive in-home sensors and algorithm-derived digital biomarkers to detect clinically relevant changes such as infection risk, behavioural disturbance and physiological deterioration. The study is embedded within the North West London Integrated Care System, allowing evaluation of a remote monitoring service implemented within routine NHS and social care pathways in a large and socio-demographically diverse population. The mixed-methods design integrates continuous sensor data, standardised clinical assessments, linked electronic health records and qualitative interviews to examine algorithm performance alongside service feasibility, acceptability and sustainability. The comparative-effectiveness component is structured as a target trial emulation using linked electronic health records, with explicit specification of eligibility, treatment strategies, activation-index date, follow-up, outcomes, causal estimand and analysis plan. As a non-randomised target trial emulation using an externally matched comparator, the study may remain susceptible to residual confounding, particularly from unmeasured factors such as carer engagement, home suitability, willingness to accept monitoring and referral-route effects; attrition due to death, care home transition, and variability in home environments or device connectivity may also affect data completeness and interpretation.

Systemic infection such as urinary tract infection (UTI) causes delirium and faster cognitive decline in patients with Alzheimer's disease (AD). Changes in brain cytokine levels in response to systemic infection influence amyloid-β, tau and glial pathology and exacerbate cerebrovascular dysfunction in animal models. Here we investigate the relationships between blood inflammatory and neurodegenerative markers, urinary tract infections and disease progression in people living with AD. We analysed longitudinal blood samples from 84 AD patients and 29 elderly controls using two platforms: OLINK®Target-48 Inflammation and ultrasensitive single-molecule array (Simoa®) assay. We stratified AD patients according to the presence/absence of UTIs confirmed on longitudinal urine sampling for urine microscopy and culture. Repeated ADAS-COG, NPI and BADL were used to assess disease progression. AD patients had elevated NfL, GFAP, p -tau217, as well as a range of inflammatory proteins (Figure 1). NfL, a marker of axonal injury, correlated with multiple cytokines in AD group (Figure 2a), including IL-17A, which was also associated with cognitive performance measured by ADAS-COG in cross-sectional and longitudinal analyses (Figure 2b,c). The presence of urinary tract infections was associated with higher GFAP (Figure 3a), elevated IL-17A (Figure 3b) and faster rates of cognitive decline on MMSE, ADAS-COG, and BADL scales (mean MMSE score change = -3.11points/year (SD=1.5) in UTI group versus -1.77 points/year (SD=1.22) in No_UTI group, U=84, p  = 0.005). We show that blood inflammatory cytokines are elevated in AD, correlate with NfL plasma levels and cognitive performance, and are influenced by urinary tract infections. AD patients with chronic UTIs have elevated levels of GFAP and IL-17A and progress faster than those with no history of UTIs. Recent AD mouse models support the mechanistic link between IL-17A accumulation, cognitive deficits and neurodegeneration. These findings highlight systemic infection as an important contributor to dementia, requiring early identification and treatment in this vulnerable population.

Following traumatic brain injury, the ability of conventional diffusion-weighted MRI analysis techniques to resolve tract-specific white-matter damage, particularly in crossing fibre regions, is limited. Using fixel-based analysis, this study aimed to identify white-matter abnormalities in chronic traumatic brain injury patients and to resolve the effects of traumatic brain injury on distinct white-matter tracts, especially in crossing fibre regions. In this cross-sectional study, diffusion-weighted MRI were acquired from adults with chronic moderate-to-severe traumatic brain injury (N=29; median time since injury 1.9 years) and matched healthy controls (N=17). Whole-brain and tract-of-interest analyses compared differences in white-matter connectivity represented by fixel-wise metrics (fibre density, fibre bundle cross-section, and combined fibre-density and bundle cross-section) between groups. Regions where crossing white-matter fibres demonstrates differential damage were identified. Significant reductions were found in all corrected fixel-wise metrics in traumatic brain injury patients, with distinct spatial distributions between metrics. Combined fibre-density and bundle cross-section demonstrated the highest sensitivity out of the fixel-wise metrics and fractional anisotropy, detecting abnormalities in 73.6% of examined tracts. Fixel-based analysis resolved the distinct effects of traumatic brain injuries on crossing fibres with 14% of tract pairings containing crossing fibres (131/927) demonstrating robust evidence of differential damage (i.e. significant difference between groups in the fixel-wise metric of one tract in the pair but not the other tract within the same voxel). Fixel-based analysis identified variabilities in white-matter abnormalities in traumatic brain injury patients. Crucially, fixel-based analysis was able to resolve injury-related tract-specific alterations even in crossing fibre regions, supporting further exploration of fixel-wise metrics as more specific biomarkers of white-matter alterations in traumatic brain injury.

Abstract Converting medical images into anatomically detailed, subject-specific finite element (FE) models is a long-standing bottleneck in brain computational modelling. These models are used to predict brain tissue deformation, e.g. in traumatic brain injury, particle diffusion in brain drug delivery, and other biophysical phenomena across neurological disorders. However, existing model creation workflows depend on manual image segmentation, proprietary meshing software, and labourintensive repair of meningeal and interface structures, limiting reproducibility and cohort analysis. Here we present PARS, a fully automated, open-source pipeline that converts a T1-weighted MRI scan into a simulation-ready FE head model. PARS combines anatomical parcellation with tissue maps and uses iterative neighbourhood-based reclassification, yielding a gap-free whole-head label volume. The volume is directly converted into a hexahedral mesh, augmented with algorithmically reconstructed falx, tentorium, pia and dura mater, and refined by Laplacian smoothing under a node-locking scheme that controls element quality and the explicit-solver stable timestep. We evaluated PARS on 23 subjects spanning cranial volumes of 832–1,329 cm³, at 1.0, 1.5 and 2.0 mm MRI resolutions. At 1 mm, meshes achieved a median Scaled Jacobian of 0.976±0.012, and total intracranial volume error of 0.54±0.19%; quality remained high at 1.5 mm (SJ: 0.933±0.018) and 2 mm (SJ: 0.921±0.016). Model creation runtime ranged from 9 to 38 minutes per subject. Models generated by PARS have been validated against cadaveric brain displacement data and demonstrated utility across traumatic brain injury and normal pressure hydrocephalus research. PARS provides an open-access, reproducible resource that substantially lowers the barriers to subject-specific brain modelling.