Publications
263 results found
Graham NSN, Blissitt G, Zimmerman K, et al., 2023, ADVANCE-TBI study protocol: traumatic brain injury outcomes in UK military personnel serving in Afghanistan between 2003 and 2014 - a longitudinal cohort study., BMJ Open, Vol: 13
INTRODUCTION: Outcomes of traumatic brain injury (TBI) are highly variable, with cognitive and psychiatric problems often present in survivors, including an increased dementia risk in the long term. Military personnel are at an increased occupational risk of TBI, with high rates of complex polytrauma including TBI characterising the UK campaign in Afghanistan. The ArmeD SerVices TrAuma and RehabilitatioN OutComE (ADVANCE)-TBI substudy will describe the patterns, associations and long-term outcomes of TBI in the established ADVANCE cohort. METHODS AND ANALYSIS: The ADVANCE cohort comprises 579 military personnel exposed to major battlefield trauma requiring medical evacuation, and 566 matched military personnel without major trauma. TBI exposure has been captured at baseline using a standardised interview and registry data, and will be refined at first follow-up visit with the Ohio State Method TBI interview (a National Institute of Neurological Disorders and Stroke TBI common data element). Participants will undergo blood sampling, MRI and detailed neuropsychological assessment longitudinally as part of their follow-up visits every 3-5 years over a 20-year period. Biomarkers of injury, neuroinflammation and degeneration will be quantified in blood, and polygenic risk scores calculated for neurodegeneration. Age-matched healthy volunteers will be recruited as controls for MRI analyses. We will describe TBI exposure across the cohort, and consider any relationship with advanced biomarkers of injury and clinical outcomes including cognitive performance, neuropsychiatric symptom burden and function. The influence of genotype will be assessed. This research will explore the relationship between military head injury exposure and long-term outcomes, providing insights into underlying disease mechanisms and informing prevention interventions. ETHICS AND DISSEMINATION: The ADVANCE-TBI substudy has received a favourable opinion from the Ministry of Defence Research Eth
Ibitoye R, Mallas E-J, Bourke N, et al., 2023, The human vestibular cortex: functional anatomy of OP2, its connectivity and the effect of vestibular disease, Cerebral Cortex, Vol: 33, Pages: 567-582, ISSN: 1047-3211
Area OP2 in the posterior peri-sylvian cortex has been proposed to be the core human vestibular cortex. We investigated the functional anatomy of OP2 and adjacent areas (OP2+) using spatially constrained independent component analysis of functional MRI data from the Human ConnectomeProject. Ten ICA-derived subregions were identified. OP2+ responses to vestibular and visual-motion were analysed in 17 controls and 17 right-sided vestibular neuritis patients who had previously undergone caloric and optokinetic stimulation during functional MRI. In controls, a posterior part of right OP2+ showed: (a) direction-selective responses to visual motion; and (b) activation during caloric stimulation that correlated positively with perceived self-motion, and negatively with visual dependence and peak slow phase nystagmus velocity. Patients showed abnormal OP2+ activity, with an absence of visual or caloric activation of the healthy ear and no correlations with vertigo or visual dependence – despite normal slow-phase nystagmus responses to caloric stimulation. Activity in a lateral part of right OP2+ correlated with chronic visually-induced dizziness in patients. In summary, distinct functional subregions of right OP2+ show strong connectivity to other vestibular areas and a profile of caloric and visual responses suggesting a central role for vestibular function in health and disease.
Graham NSN, Cole JH, Bourke NJ, et al., 2023, Distinct patterns of neurodegeneration after TBI and in Alzheimer's disease, Alzheimer's and Dementia, ISSN: 1552-5260
IntroductionTraumatic brain injury (TBI) is a dementia risk factor, with Alzheimer's disease (AD) more common following injury. Patterns of neurodegeneration produced by TBI can be compared to AD and aging using volumetric MRI.MethodsA total of 55 patients after moderate to severe TBI (median age 40), 45 with AD (median age 69), and 61 healthy volunteers underwent magnetic resonance imaging over 2 years. Atrophy patterns were compared.ResultsAD patients had markedly lower baseline volumes. TBI was associated with increased white matter (WM) atrophy, particularly involving corticospinal tracts and callosum, whereas AD rates were increased across white and gray matter (GM). Subcortical WM loss was shared in AD/TBI, but deep WM atrophy was TBI-specific and cortical atrophy AD-specific. Post-TBI atrophy patterns were distinct from aging, which resembled AD.DiscussionPost-traumatic neurodegeneration 1.9–4.0 years (median) following moderate-severe TBI is distinct from aging/AD, predominantly involving central WM. This likely reflects distributions of axonal injury, a neurodegeneration trigger.HighlightsWe compared patterns of brain atrophy longitudinally after moderate to severe TBI in late-onset AD and healthy aging.Patients after TBI had abnormal brain atrophy involving the corpus callosum and other WM tracts, including corticospinal tracts, in a pattern that was specific and distinct from AD and aging.This pattern is reminiscent of axonal injury following TBI, and atrophy rates were predicted by the extent of axonal injury on diffusion tensor imaging, supporting a relationship between early axonal damage and chronic neurodegeneration.
Zimmerman K, 2022, The biomechanical signature of loss of consciousness: computational modelling of elite athlete head injuries, Brain: a journal of neurology, ISSN: 0006-8950
Mallas E-J, Gorgoraptis N, Dautricourt S, et al., 2022, Pathological slow-wave activity and impaired working memory binding in post-traumatic amnesia, The Journal of Neuroscience, Vol: 42, Pages: 9193-9210, ISSN: 0270-6474
Associative binding is key to normal memory function and is transiently disrupted during periods of post-traumatic amnesia (PTA) following traumatic brain injury (TBI). Electrophysiological abnormalities including low-frequency activity are common following TBI. Here, we investigate associative memory binding during PTA and test the hypothesis that misbinding is caused by pathological slowing of brain activity disrupting cortical communication. Thirty acute moderate-severe TBI patients (25 males; 5 females) and 26 healthy controls (20 males; 6 females) were tested with a precision working memory paradigm requiring the association of object and location information. Electrophysiological effects of TBI were assessed using resting-state EEG in a subsample of 17 patients and 21 controls. PTA patients showed abnormalities in working memory function and made significantly more misbinding errors than patients who were not in PTA and controls. The distribution of localisation responses was abnormally biased by the locations of non-target items for patients in PTA suggesting a specific impairment of object and location binding. Slow wave activity was increased following TBI. Increases in the delta-alpha ratio indicative of an increase in low-frequency power specifically correlated with binding impairment in working memory. Connectivity changes in TBI did not correlate with binding impairment. Working memory and electrophysiological abnormalities normalised at six-month follow-up. These results show that patients in PTA show high rates of misbinding that are associated with a pathological shift towards lower frequency oscillations.
Lima MR, Wairagkar M, Gupta M, et al., 2022, Conversational affective social robots for ageing and dementia support, IEEE Transactions on Cognitive and Developmental Systems, Vol: 14, Pages: 1378-1397, ISSN: 2379-8920
Socially assistive robots (SAR) hold significant potential to assist older adults and people with dementia in human engagement and clinical contexts by supporting mental health and independence at home. While SAR research has recently experienced prolific growth, long-term trust, clinical translation and patient benefit remain immature. Affective human-robot interactions are unresolved and the deployment of robots with conversational abilities is fundamental for robustness and humanrobot engagement. In this paper, we review the state of the art within the past two decades, design trends, and current applications of conversational affective SAR for ageing and dementia support. A horizon scanning of AI voice technology for healthcare, including ubiquitous smart speakers, is further introduced to address current gaps inhibiting home use. We discuss the role of user-centred approaches in the design of voice systems, including the capacity to handle communication breakdowns for effective use by target populations. We summarise the state of development in interactions using speech and natural language processing, which forms a baseline for longitudinal health monitoring and cognitive assessment. Drawing from this foundation, we identify open challenges and propose future directions to advance conversational affective social robots for: 1) user engagement, 2) deployment in real-world settings, and 3) clinical translation.
Kolanko MA, Soreq E, Lai H, et al., 2022, Clinically relevant monitoring of long-term night-time behaviour and physiology from the homes of people living with dementia., Alzheimers Dement, Vol: 18 Suppl 2
BACKGROUND: Disturbances of sleep and night-time behaviours are amongst the most disabling symptoms of dementia. They often increase carers' burden and the risk of institutionalization. The causes are complex and are difficult to investigate because of a lack of acceptable methods for monitoring behaviours in the home. Here we show that a passive under-mattress can be used to track changes in night-time behaviour and physiology, and that a range of digital biomarkers produced are informative in understanding the effects of medication changes, disease progression and intercurrent illness in patients living with dementia (PLWD). METHOD: We used contactless Withings Sleep Mattress (WSM) to monitor bed-occupancy in 4 PLWD (age 74-93, 3males) enrolled into the CR&T MINDER cohort study. Each participant was tracked over 1000 nights between 2019 and 2021. Minute-to-minute timeseries were extracted from WSM to calculate bed occupancy metrics and nocturnal physiology measures (heart and breathing rates (HR/BR)). Raw measures were standardized within subjects by comparing each time point to the mean of the time points that preceded it. We then investigated the relationship between these metrics and clinical events such as infections and medication. RESULT: The 4 case studies illustrate the potential of this technology for passive health monitoring in PLWD. High levels of intraindividual variability in behavioural and physiological metrics were observed. Progressive changes in bed occupancy were observed in two patients with frontotemporal dementia and Alzheimer's disease (Cases 1&2). Intercurrent illness and medications changes influenced the measures. For example, Patient 1 showed progressive night-time wandering with increasing time spent out of bed, which improved following the initiation of risperidone. Case 2 showed recurrent episodes of heart failure accompanied by increased nocturnal HR. Cases 3 and 4 showed urinary tract infections, which were accompanied by t
Maas AIR, Menon DK, Manley GT, et al., 2022, Traumatic brain injury: progress and challenges in prevention, clinical care, and research, LANCET NEUROLOGY, Vol: 21, Pages: 1004-1060, ISSN: 1474-4422
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- Citations: 7
Serban A-I, Soreq E, Barnaghi P, et al., 2022, The effect of COVID-19 on the home behaviours of people affected by dementia, npj Digital Medicine, Vol: 5, ISSN: 2398-6352
The COVID-19 pandemic has dramatically altered the behaviour of most of the world’s population, particularly affecting the elderly, including people living with dementia (PLwD). Here we use remote home monitoring technology deployed into 31 homes of PLwD living in the UK to investigate the effects of COVID-19 on behaviour within the home, including social isolation. The home activity was monitored continuously using unobtrusive sensors for 498 days from 1 December 2019 to 12 April 2021. This period included six distinct pandemic phases with differing public health measures, including three periods of home ‘lockdown’. Linear mixed-effects modelling is used to examine changes in the home activity of PLwD who lived alone or with others. An algorithm is developed to quantify time spent outside the home. Increased home activity is observed from very early in the pandemic, with a significant decrease in the time spent outside produced by the first lockdown. The study demonstrates the effects of COVID-19 lockdown on home behaviours in PLwD and shows how unobtrusive home monitoring can be used to track behaviours relevant to social isolation.
Rosnati M, Soreq E, Monteiro M, et al., 2022, Automatic lesion analysis for increased efficiency in outcome prediction of traumatic brain injury, 5th International Workshop, MLCN 2022, Publisher: Springer Nature Switzerland, Pages: 135-146, ISSN: 0302-9743
The accurate prognosis for traumatic brain injury (TBI) patients is difficult yet essential to inform therapy, patient management, and long-term after-care. Patient characteristics such as age, motor and pupil responsiveness, hypoxia and hypotension, and radiological findings on computed tomography (CT), have been identified as important variables for TBI outcome prediction. CT is the acute imaging modality of choice in clinical practice because of its acquisition speed and widespread availability. However, this modality is mainly used for qualitative and semi-quantitative assessment, such as the Marshall scoring system, which is prone to subjectivity and human errors. This work explores the predictive power of imaging biomarkers extracted from routinely-acquired hospital admission CT scans using a state-of-the-art, deep learning TBI lesion segmentation method. We use lesion volumes and corresponding lesion statistics as inputs for an extended TBI outcome prediction model. We compare the predictive power of our proposed features to the Marshall score, independently and when paired with classic TBI biomarkers. We find that automatically extracted quantitative CT features perform similarly or better than the Marshall score in predicting unfavourable TBI outcomes. Leveraging automatic atlas alignment, we also identify frontal extra-axial lesions as important indicators of poor outcome. Our work may contribute to a better understanding of TBI, and provides new insights into how automated neuroimaging analysis can be used to improve prognostication after TBI.
Bethlehem RAI, Seidlitz J, White SR, et al., 2022, Brain charts for the human lifespan (vol 604, pg 525, 2022), NATURE, Vol: 610, Pages: E6-E6, ISSN: 0028-0836
Parkinson M, Curtis F, Dani M, et al., 2022, MTBI PREDICT: A PROSPECTIVE BIOMARKER STUDY TO PREDICT OUTCOMES IN MILD TRAUMATIC BRAIN INJURY, Association-of-British-Neurologists (ABN) Annual Meeting, Publisher: BMJ PUBLISHING GROUP, ISSN: 0022-3050
Soreq E, Kolanko M, Guruswamy Ravindran KK, et al., 2022, Longitudinal assessment of sleep/wake behaviour in dementia patients living at home, Association-of-British-Neurologists (ABN) Annual Meeting, Publisher: BMJ PUBLISHING GROUP, ISSN: 0022-3050
Parker T, Zimmerman K, Laverse E, et al., 2022, ACTIVE ELITE RUGBY PARTICIPATION PREDICTS ALTERATIONS IN CORTICAL THICKNESS, Publisher: BMJ PUBLISHING GROUP, ISSN: 0022-3050
Parkinson M, Curtis F, Dani M, et al., 2022, EXPLORING INTERACTIONS BETWEEN TRAUMATIC BRAIN INJURY AND COGNITIVE CO-MORBIDITY: DESCRIPTIVE CASE ANALYSIS FROM REAL-WORLD MONITORING, Association-of-British-Neurologists (ABN) Annual Meeting, Publisher: BMJ PUBLISHING GROUP, ISSN: 0022-3050
Bourke N, Demarchi C, De Simoni S, et al., 2022, Brain volume abnormalities and clinical outcomes following paediatric traumatic brain injury, Brain: a journal of neurology, Vol: 145, Pages: 2920-2934, ISSN: 0006-8950
Long-term outcomes are difficult to predict after paediatric traumatic brain injury. The presence or absence of focal brain injuries often do not explain cognitive, emotional and behavioural disabilities that are common and disabling. In adults, traumatic brain injury produces progressive brain atrophy that can be accurately measured and is associated with cognitive decline. However, the effect of paediatric traumatic brain injury on brain volumes is more challenging to measure because of its interaction with normal brain development. Here we report a robust approach to the individualised estimation of brain volume following paediatric traumatic brain injury and investigate its relationship to clinical outcomes. We first used a large healthy control dataset (N>1200, age 8-22) to describe the healthy development of white and grey matter regions through adolescence. Individual estimates of grey and white matter regional volume were then generated for a group of moderate/severe traumatic brain injury patients injured in childhood (N=39, mean age 13.53±1.76, median time since injury = 14 months, range 4 – 168 months) by comparing brain volumes in patients to age matched controls. Patients were individually classified as having low or normal brain volume. Neuropsychological and neuropsychiatric outcomes were assessed using standardised testing and parent/carer assessments. Relative to head size, grey matter regions decreased in volume during normal adolescence development whereas white matter tracts increased in volume. Traumatic brain injury disrupted healthy brain development, producing reductions in both grey and white matter brain volumes after correcting for age. Of the 39 patients investigated, 11 (28%) had at least one white matter tract with reduced volume and seven (18%) at least one area of grey matter with reduced volume. Those classified as having low brain volume had slower processing speed compared to healthy controls, emotional impairments
Li LM, Dilley MD, Carson A, et al., 2022, Response to: Management of traumatic brain injury: practical development of a recent proposal, CLINICAL MEDICINE, Vol: 22, Pages: 358-359, ISSN: 1470-2118
Ibitoye RT, Castro P, Cooke J, et al., 2022, A link between frontal white matter integrity and dizziness in cerebral small vessel disease, NeuroImage: Clinical, Vol: 35, Pages: 1-12, ISSN: 2213-1582
One in three older people (>60 years) complain of dizziness which often remains unexplained despite specialist assessment. We investigated if dizziness was associated with vascular injury to white matter tracts relevant to balance or vestibular self-motion perception in sporadic cerebral small vessel disease (age-related microangiopathy). We prospectively recruited 38 vestibular clinic patients with idiopathic (unexplained) dizziness and 36 age-matched asymptomatic controls who underwent clinical, cognitive, balance, gait and vestibular assessments, and structural and diffusion brain MRI. Patients had more vascular risk factors, worse balance, worse executive cognitive function, and worse ankle vibration thresholds in association with greater white matter hyperintensity in frontal deep white matter, and lower fractional anisotropy in the genu of the corpus callosum and the right inferior longitudinal fasciculus. A large bihemispheric white matter network had less structural connectivity in patients. Reflex and perceptual vestibular function was similar in patients and controls. Our results suggest cerebral small vessel disease is involved in the genesis of dizziness through its effect on balance.
Gil Rosa B, Akingbade OE, Guo X, et al., 2022, Multiplexed immunosensors for point-of-care diagnostic applications, Biosensors and Bioelectronics, Vol: 203, ISSN: 0956-5663
Accurate, reliable, and cost-effective immunosensors are clinically important for the early diagnosis and monitoring of progressive diseases, and multiplexed sensing is a promising strategy for the next generation of diagnostics. This strategy allows for the simultaneous detection and quantification of multiple biomarkers with significantly enhanced reproducibility and reliability, whilst requiring smaller sample volumes, fewer materials, and shorter average analysis time for individual biomarkers than individual tests. In this opinionated review, we compare different techniques for the development of multiplexed immunosensors. We review the state-of-the-art approaches in the field of multiplexed immunosensors using electrical, electrochemical, and optical methods. The barriers that prevent translating this sensing strategy into clinics are outlined together with the potential solutions. We also share our vision on how multiplexed immunosensors will continue their evolution in the coming years.
Duckworth H, Azor A, Wischmann N, et al., 2022, A finite element model of cerebral vascular injury for predicting microbleeds location, Frontiers in Bioengineering and Biotechnology, Vol: 10, ISSN: 2296-4185
Finite Element (FE) models of brain mechanics have improved our understanding of the brain response to rapid mechanical loads that produce traumatic brain injuries. However, these models have rarely incorporated vasculature, which limits their ability to predict the response of vessels to head impacts. To address this shortcoming, here we used high-resolution MRI scans to map the venous system anatomy at a submillimetre resolution. We then used this map to develop an FE model of veins and incorporated it in an anatomically detailed FE model of the brain. The model prediction of brain displacement at different locations was compared to controlled experiments on post-mortem human subject heads, yielding over 3,100 displacement curve comparisons, which showed fair to excellent correlation between them. We then used the model to predict the distribution of axial strains and strain rates in the veins of a rugby player who had small blood deposits in his white matter, known as microbleeds, after sustaining a head collision. We hypothesised that the distribution of axial strain and strain rate in veins can predict the pattern of microbleeds. We reconstructed the head collision using video footage and multi-body dynamics modelling and used the predicted head accelerations to load the FE model of vascular injury. The model predicted large axial strains in veins where microbleeds were detected. A region of interest analysis using white matter tracts showed that the tract group with microbleeds had 95th percentile peak axial strain and strain rate of 0.197 and 64.9 s−1 respectively, which were significantly larger than those of the group of tracts without microbleeds (0.163 and 57.0 s−1). This study does not derive a threshold for the onset of microbleeds as it investigated a single case, but it provides evidence for a link between strain and strain rate applied to veins during head impacts and structural damage and allows for future work to generate threshold valu
Bethlehem RAI, Seidlitz J, White SR, et al., 2022, Brain charts for the human lifespan, NATURE, Vol: 604, Pages: 525-+, ISSN: 0028-0836
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- Citations: 49
Al-Diwani A, Theorell J, Damato V, et al., 2022, Cervical lymph nodes and ovarian teratomas as germinal centres in NMDA receptor-antibody encephalitis, BRAIN, Vol: 145, Pages: 2742-2754, ISSN: 0006-8950
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- Citations: 5
Popescu SG, Sharp DJ, Cole JH, et al., 2022, Distributional Gaussian Processes Layers for Out-of-Distribution Detection, Journal of Machine Learning for Biomedical Imaging
Machine learning models deployed on medical imaging tasks must be equippedwith out-of-distribution detection capabilities in order to avoid erroneouspredictions. It is unsure whether out-of-distribution detection models relianton deep neural networks are suitable for detecting domain shifts in medicalimaging. Gaussian Processes can reliably separate in-distribution data pointsfrom out-of-distribution data points via their mathematical construction.Hence, we propose a parameter efficient Bayesian layer for hierarchicalconvolutional Gaussian Processes that incorporates Gaussian Processes operatingin Wasserstein-2 space to reliably propagate uncertainty. This directlyreplaces convolving Gaussian Processes with a distance-preserving affineoperator on distributions. Our experiments on brain tissue-segmentation showthat the resulting architecture approaches the performance of well-establisheddeterministic segmentation algorithms (U-Net), which has not been achieved withprevious hierarchical Gaussian Processes. Moreover, by applying the samesegmentation model to out-of-distribution data (i.e., images with pathologysuch as brain tumors), we show that our uncertainty estimates result inout-of-distribution detection that outperforms the capabilities of previousBayesian networks and reconstruction-based approaches that learn normativedistributions. To facilitate future work our code is publicly available.
Parkinson M, Doherty R, Curtis C, et al., 2022, Exploring interactions between traumatic brain injury, Association of British Neurologists
Bourke N, Trender W, Hampshire A, et al., 2022, Assessing prospective and retrospective metacognitive accuracy following traumatic brain injury remotely across cognitive domains, Neuropsychological Rehabilitation, ISSN: 0960-2011
The ability to monitor one's behaviour is frequently impaired following TBI, impacting on patients’ rehabilitation. Inaccuracies in judgement or self-reflection of one’s performance provides a useful marker of metacognition. However, metacognition is rarely measured during routine neuropsychology assessments and how it varies across cognitive domains is unclear. A cohort of participants consisting of 111 TBI patients [mean age = 45.32(14.15), female = 29] and 84 controls [mean age = 31.51(12.27), female = 43] was studied. Participants completed cognitive assessments via a bespoke digital platform on their smartphones. Included in the assessment were a prospective evaluation of memory and attention, and retrospective confidence judgements of task performance. Metacognitive accuracy was calculated from the difference between confidence judgement of task performance and actual performance. Prospective judgment of attention and memory was correlated with task performance in these domains for controls but not patients. TBI patients had lower task performance in processing speed, executive functioning and working memory compared to controls, maintaining high confidence, resulting in overestimation of cognitive performance compared to controls. Additional judgments of task performance complement neuropsychological assessments with little additional time–cost. These results have important theoretical and practical implications for evaluation of metacognitive impairment in TBI patients and neurorehabilitation.
Baker C, Martin P, Wilson M, et al., 2022, The relationship between road traffic collision dynamics and traumatic brain injury pathology, Brain Communications, Vol: 4, ISSN: 2632-1297
Road traffic collisions are a major cause of traumatic brain injury. However, the relationship between road traffic collision dynamics and traumatic brain injury risk for different road users is unknown. We investigated 2,065 collisions from Great Britain’s Road Accident In-depth Studies collision database involving 5,374 subjects (2013-20). 595 subjects sustained a traumatic brain injury (20.2% of 2,940 casualties), including 315 moderate-severe and 133 mild-probable. Key pathologies included skull fracture (179, 31.9%), subarachnoid haemorrhage (171, 30.5%), focal brain injury (168, 29.9%) and subdural haematoma (96, 17.1%). These results were extended nationally using >1,000,000 police-reported collision casualties. Extrapolating from the in-depth data we estimate that there are ~20,000 traumatic brain injury casualties (~5,000 moderate-severe) annually on Great Britain’s roads, accounting for severity differences. Detailed collision investigation allows vehicle collision dynamics to be understood and the change-in-velocity (known as delta-V) to be estimated for a subset of in-depth collision data. Higher delta-V increased the risk of moderate-severe brain injury for all road users. The four key pathologies were not observed below 8km/h delta-V for pedestrians/cyclists and 19km/h delta-V for car occupants (higher delta-V threshold for focal injury in both groups). Traumatic brain injury risk depended on road user type, delta-V and impact direction. Accounting for delta-V, pedestrians/cyclists had a 6-times higher likelihood of moderate-severe brain injury than car occupants. Wearing a cycle helmet was protective against overall and mild-to-moderate-severe brain injury, particularly skull fracture and subdural haematoma. Cycle helmet protection was not due to travel or impact speed differences between helmeted and non-helmeted cyclist groups. We additionally examined the influence of delta-V direction. Car occupants exposed to a higher latera
Hadi Z, Pondeca Y, Calzolari E, et al., 2022, The human brain networks mediating the vestibular sensation of self-motion, Publisher: Cold Spring Harbor Laboratory
Vestibular Agnosia - where peripheral vestibular activation triggers the usual reflex nystagmus response but with attenuated or no self-motion perception - is found in brain disease with disrupted cortical network functioning, e.g. traumatic brain injury (TBI) or neurodegeneration (Parkinson’s Disease). Patients with acute focal hemispheric lesions (e.g. stroke) do not manifest vestibular agnosia. Thus brain network mapping techniques, e.g. resting state functional MRI (rsfMRI), are needed to interrogate functional brain networks mediating vestibular agnosia. Whole-brain rsfMRI was acquired from 39 prospectively recruited acute TBI patients with preserved peripheral vestibular function, along with self-motion perceptual thresholds during passive yaw rotations in the dark. Following quality-control checks, 25 patient scans were analyzed. TBI patients were classified as having vestibular agnosia (n = 11) or not (n = 14) via laboratory testing of self-motion perception. Using independent component analysis, we found altered functional connectivity in the right superior longitudinal fasciculus and left rostral prefrontal cortex in vestibular agnosia. Moreover, regions of interest analyses showed both inter-hemispheric and intra-hemispheric network disruption in vestibular agnosia. In conclusion, our results show that vestibular agnosia is mediated by bilateral anterior and posterior network dysfunction and reveal the distributed brain mechanisms mediating vestibular self-motion perception.
Azor AM, Sharp DJ, Jolly AE, et al., 2022, Automation and standardization of subject-specific region-of-interest segmentation for investigation of diffusion imaging in clinical populations., PLoS One, Vol: 17
Diffusion weighted imaging (DWI) is key in clinical neuroimaging studies. In recent years, DWI has undergone rapid evolution and increasing applications. Diffusion magnetic resonance imaging (dMRI) is widely used to analyse group-level differences in white matter (WM), but suffers from limitations that can be particularly impactful in clinical groups where 1) structural abnormalities may increase erroneous inter-subject registration and 2) subtle differences in WM microstructure between individuals can be missed. It also lacks standardization protocols for analyses at the subject level. Region of Interest (ROI) analyses in native diffusion space can help overcome these challenges, with manual segmentation still used as the gold standard. However, robust automated approaches for the analysis of ROI-extracted native diffusion characteristics are limited. Subject-Specific Diffusion Segmentation (SSDS) is an automated pipeline that uses pre-existing imaging analysis methods to carry out WM investigations in native diffusion space, while overcoming the need to interpolate diffusion images and using an intermediate T1 image to limit registration errors and guide segmentation. SSDS is validated in a cohort of healthy subjects scanned three times to derive test-retest reliability measures and compared to other methods, namely manual segmentation and tract-based spatial statistics as an example of group-level method. The performance of the pipeline is further tested in a clinical population of patients with traumatic brain injury and structural abnormalities. Mean FA values obtained from SSDS showed high test-retest and were similar to FA values estimated from the manual segmentation of the same ROIs (p-value > 0.1). The average dice similarity coefficients (DSCs) comparing results from SSDS and manual segmentations was 0.8 ± 0.1. Case studies of TBI patients showed robustness to the presence of significant structural abnormalities, indicating its potential clinica
Li B, Xu L, Ramadan S, et al., 2021, Detection of glial fibrillary acidic protein in patient plasma using on-chip graphene field-effect biosensors, in comparison with ELISA and single molecule array, ACS Sensors, Vol: 7, Pages: 253-262, ISSN: 2379-3694
Glial fibrillary acidic protein (GFAP) is a discriminative blood biomarker for many neurological diseases, such as traumatic brain injury. Detection of GFAP in buffer solutions using biosensors has been demonstrated, but accurate quantification of GFAP in patient samples has not been reported, yet in urgent need. Herein, we demonstrate a robust on-chip graphene field-effect transistor (GFET) biosensing method for sensitive and ultrafast detection of GFAP in patient plasma. Patients with moderate–severe traumatic brain injuries, defined by the Mayo classification, are recruited to provide plasma samples. The binding of target GFAP with the specific antibodies that are conjugated on a monolayer GFET device triggers the shift of its Dirac point, and this signal change is correlated with the GFAP concentration in the patient plasma. The limit of detection (LOD) values of 20 fg/mL (400 aM) in buffer solution and 231 fg/mL (4 fM) in patient plasma have been achieved using this approach. In parallel, for the first time, we compare our results to the state-of-the-art single-molecule array (Simoa) technology and the classic enzyme-linked immunosorbent assay (ELISA) for reference. The GFET biosensor shows competitive LOD to Simoa (1.18 pg/mL) and faster sample-to-result time (<15 min), and also it is cheaper and more user-friendly. In comparison to ELISA, GFET offers advantages of total detection time, detection sensitivity, and simplicity. This GFET biosensing platform holds high promise for the point-of-care diagnosis and monitoring of traumatic brain injury in GP surgeries and patient homes.
Popescu SG, Glocker B, Sharp DJ, et al., 2021, Local brain-age: A u-net model, Frontiers in Aging Neuroscience, Vol: 13, Pages: 1-17, ISSN: 1663-4365
We propose a new framework for estimating neuroimaging-derived “brain-age” at a local level within the brain, using deep learning. The local approach, contrary to existing global methods, provides spatial information on anatomical patterns of brain ageing. We trained a U-Net model using brain MRI scans from n = 3,463 healthy people (aged 18–90 years) to produce individualised 3D maps of brain-predicted age. When testing on n = 692 healthy people, we found a median (across participant) mean absolute error (within participant) of 9.5 years. Performance was more accurate (MAE around 7 years) in the prefrontal cortex and periventricular areas. We also introduce a new voxelwise method to reduce the age-bias when predicting local brain-age “gaps.” To validate local brain-age predictions, we tested the model in people with mild cognitive impairment or dementia using data from OASIS3 (n = 267). Different local brain-age patterns were evident between healthy controls and people with mild cognitive impairment or dementia, particularly in subcortical regions such as the accumbens, putamen, pallidum, hippocampus, and amygdala. Comparing groups based on mean local brain-age over regions-of-interest resulted in large effects sizes, with Cohen's d values >1.5, for example when comparing people with stable and progressive mild cognitive impairment. Our local brain-age framework has the potential to provide spatial information leading to a more mechanistic understanding of individual differences in patterns of brain ageing in health and disease.
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