Publications
621 results found
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.
David M, Barnaghi P, Nilforooshan R, et al., 2021, Home monitoring of vital signs and generation of alerts in a cohort of people living with dementia, Alzheimer's & dementia : the journal of the Alzheimer's Association, Vol: 17
BACKGROUND: People with dementia (PwD) are at increased risk of adverse medical events (e.g. infections and falls). These often cause clinical deterioration, and potentially preventable admissions. Remote home monitoring of vital signs using internet-of-things technology can identify risk factors for these events - something particular pertinent during the COVID-19 pandemic. We present data from an on-going UK Dementia Research Institute and Technology Integrated Health Management (DRI-TIHM) project. We aim to define algorithms that generate automated alerts, like the hospital-based NEWS system (Morgan, 1997, Clin Intensive Car), and provide more proactive care for PwD. METHOD: PwD recorded their systolic/diastolic blood pressure (SBP/DBP), heart rate (HR), temperature (BTM), bodyweight (BW) and oxygen saturation (Sats) daily (figure 1) and data were collected centrally. A 'monitoring team' followed algorithms in response to alerts and advised medical attention if required. Events such as infections, were logged and correlated with the data. The dataset was then used to calculate the number of alerts that would have been raised if different thresholds were used. RESULT: 52 PwD living at home were included. Patients had a range of dementia diagnoses, most commonly Alzheimer's disease. In total, 89,894 measurements were collected over 556 days (figure 2). During the pandemic, a sub-group were given Sats probes and these produced a significant number of false positive results (figure 4f). On sub-group analysis, PwD with Parkinson's disease dementia had significantly lower SBP and DBP (p=0.004 and p=0.01 respectively, Mann-Whitney U) (figure 3a), one of whom suffered from repeated falls (figure 3b). Pilot data were used to set alert thresholds. Average values and number of alerts for each domain are shown (table 1). We then modelled the effect of different thresholds, based on NEWS, to the number of alerts generated (figure 4, table 2). This is informative in optimising
Rezvani R, Kouchaki S, Nilforooshan R, et al., 2021, Analysing behavioural changes in people with dementia using in-home monitoring technologies., Alzheimers & Dementia, Vol: 17 Suppl 11, Pages: e052181-e052181, ISSN: 1552-5260
BACKGROUND: Behavioural changes and neuropsychiatric symptoms such as agitation are common in people with dementia. These symptoms impact the quality of life of people with dementia and can increase the stress on caregivers. This study aims to identify the likelihood of having agitation in people affected by dementia (i.e., patients and carers) using routinely collected data from in-home monitoring technologies. We have used a digital platform and analytical methods, developed in our previous study, to generate alerts when changes occur in the digital markers collected using in-home sensing technologies (i.e., vital signs, environmental and activity data). A care monitoring team use the platform and interact with participants and caregivers when an alert is generated. METHOD: We have used connected sensory devices to collect environmental markers, including Passive Infra-Red (PIR), smart power plugs for monitoring home appliance use, motion and door sensors. The environmental marker data have been aggregated within each hour and used to train an agitation risk analysis model. We have trained a model using data collected from 88 homes (∼6 months of data from each home). The proposed model has two components: a self-supervised transformation learning and an ensemble classification model for agitation likelihood. Ten different neural network encoders are learned to create pseudo-labels using the samples from the unlabelled data. We use these pseudo-labels to train a classification model with a convolutional block and a decision layer. The trained convolutional block is then used to learn a latent representation of the data for an ensemble classification block. RESULTS: Comparing with baseline models such as LSTM network, Bidirectional LSTM (BiLSTM) network, VGG, ResNet, Inception, Random Forest (RF), Support Vector Machine (SVM) and Gaussian Process (GP) classifiers, the proposed model performs better in sensitivity (recall) and area under the precision-recall curv
Wairagkar M, De Lima MR, Harrison M, et al., 2021, Conversational artificial intelligence and affective social robot for monitoring health and well-being of people with dementia., Alzheimers & Dementia, Vol: 17 Suppl 11, Pages: e053276-e053276, ISSN: 1552-5260
BACKGROUND: Social robots are anthropomorphised platforms developed to interact with humans, using natural language, offering an accessible and intuitive interface suited to diverse cognitive abilities. Social robots can be used to support people with dementia (PwD) and carers in their homes managing medication, hydration, appointments, and evaluating mood, wellbeing, and potentially cognitive decline. Such robots have potential to reduce care burden and prolong independent living, yet translation into PwD use remains insignificant. METHOD: We have developed two social robots - a conversational robot and a digital social robot for mobile devices capable of communicating through natural language (powered by Amazon Alexa) and facial expressions that ask PwD daily questions about their health and wellbeing and also provide digital assistant functionality. We record data comprising of PwD's responses to daily questions, audio speech and text of conversations with Alexa to automatically monitor their health and wellbeing using machine learning. We followed user-centric development processes by conducting focus groups with 13 carers, 2 PwD and 5 clinicians to iterate the design. We are testing social robot with 3 PwD in their homes for ten weeks. RESULT: We received positive feedback on social robot from focus group participants. Ease of use, low maintenance, accessibility, assistance with medication, supporting with health and wellbeing were identified as the key opportunities for social robots. Based on responses to a daily questionnaire, our robots generate a report detailing PwD wellbeing that is automatically sent via email to family members or carers. This information is also stored systematically in a database that can help clinicians monitor their patients remotely. We use natural language processing to analyse conversations and identify topics of interest to PwD such that robot behaviour could be adapted. We process speech using signal processing and machine lear
Fletcher-Lloyd N, Soreq E, Wilson D, et al., 2021, Home monitoring of daily living activities and prediction of agitation risk in a cohort of people living with dementia., Alzheimers & Dementia, Vol: 17, Pages: 1-1, ISSN: 1552-5260
BACKGROUND: People living with dementia (PLWD) have an increased susceptibility to developing adverse physical and psychological events. Internet of Things (IoT) technologies provides new ways to remotely monitor patients within the comfort of their homes, particularly important for the timely delivery of appropriate healthcare. Presented here is data collated as part of the on-going UK Dementia Research Institute's Care Research and Technology Centre cohort and Technology Integrated Health Management (TIHM) study. There are two main aims to this work: first, to investigate the effect of the COVID-19 quarantine on the performance of daily living activities of PLWD, on which there is currently little research; and second, to create a simple classification model capable of effectively predicting agitation risk in PLWD, allowing for the generation of alerts with actionable information by which to prevent such outcomes. METHOD: A within-subject, date-matched study was conducted on daily living activity data using the first COVID-19 quarantine as a natural experiment. Supervised machine learning approaches were then applied to combined physiological and environmental data to create two simple classification models: a single marker model trained using ambient temperature as a feature, and a multi-marker model using ambient temperature, body temperature, movement, and entropy as features. RESULT: There are 102 PLWD total included in the dataset, with all patients having an established diagnosis of dementia, but with ranging types and severity. The COVID-19 study was carried out on a sub-group of 21 patient households. In 2020, PLWD had a significant increase in daily household activity (p = 1.40e-08), one-way repeated measures ANOVA). Moreover, there was a significant interaction between the pandemic quarantine and patient gender on night-time bed-occupancy duration (p = 3.00e-02, two-way mixed-effect ANOVA). On evaluating the models using 10-fold cross validation, both th
Liu KY, Howard R, Banerjee S, et al., 2021, Dementia wellbeing and COVID-19: Review and expert consensus on current research and knowledge gaps, INTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY, Vol: 36, Pages: 1597-1639, ISSN: 0885-6230
- Author Web Link
- Cite
- Citations: 42
Schubert JJ, Veronese M, Scott G, et al., 2021, Evidence of blood-to-cerebrospinal fluid alterations in traumatic brain injury, Publisher: SAGE PUBLICATIONS INC, Pages: 87-88, ISSN: 0271-678X
Ibitoye R, Castro P, Cooke J, et al., 2021, Frontal white matter integrity and idiopathic dizziness in cerebral small vessel disease, 25th World Congress of Neurology (WCN), Publisher: ELSEVIER, ISSN: 0022-510X
Ibitoye R, Mallas E-J, Bourke N, et al., 2021, The human vestibular cortex: Functional anatomy, connectivity and the effect of peripheral vestibular disease, 25th World Congress of Neurology (WCN), Publisher: ELSEVIER, ISSN: 0022-510X
Ibitoye R, Castro P, Cooke J, et al., 2021, Frontal white matter integrity and idiopathic dizziness in cerebral small vessel disease, 25th World Congress of Neurology (WCN), Publisher: ELSEVIER, ISSN: 0022-510X
Hebert DD, Naley MA, Cunningham CC, et al., 2021, Enabling Conducting Polymer Applications: Methods for Achieving High Molecular Weight in Chemical Oxidative Polymerization in Alkyl- and Ether-Substituted Thiophenes, MATERIALS, Vol: 14
- Author Web Link
- Cite
- Citations: 2
Graham NSN, Zimmerman KA, Moro F, et al., 2021, Axonal marker neurofilament light predicts long-term outcomes and progressive neurodegeneration after traumatic brain injury, Science Translational Medicine, Vol: 13, Pages: 1-15, ISSN: 1946-6234
Axonal injury is a key determinant of long-term outcomes after traumatic brain injury (TBI) but has been difficult to measure clinically. Fluid biomarker assays can now sensitively quantify neuronal proteins in blood. Axonal components such as neurofilament light (NfL) potentially provide a diagnostic measure of injury. In the multicenter BIO-AX-TBI study of moderate-severe TBI, we investigated relationships between fluid biomarkers, advanced neuroimaging, and clinical outcomes. Cerebral microdialysis was used to assess biomarker concentrations in brain extracellular fluid aligned with plasma measurement. An experimental injury model was used to validate biomarkers against histopathology. Plasma NfL increased after TBI, peaking at 10 days to 6 weeks but remaining abnormal at 1 year. Concentrations were around 10 times higher early after TBI than in controls (patients with extracranial injuries). NfL concentrations correlated with diffusion MRI measures of axonal injury and predicted white matter neurodegeneration. Plasma TAU predicted early gray matter atrophy. NfL was the strongest predictor of functional outcomes at 1 year. Cerebral microdialysis showed that NfL concentrations in plasma and brain extracellular fluid were highly correlated. An experimental injury model confirmed a dose-response relationship of histopathologically defined axonal injury to plasma NfL. In conclusion, plasma NfL provides a sensitive and clinically meaningful measure of axonal injury produced by TBI. This reflects the extent of underlying damage, validated using advanced MRI, cerebral microdialysis, and an experimental model. The results support the incorporation of NfL sampling subacutely after injury into clinical practice to assist with the diagnosis of axonal injury and to improve prognostication.
Evans M, Wade C, Hohenschurz-Schmidt D, et al., 2021, Magnetic resonance imaging as a biomarker in diabetic and HIV-associated peripheral neuropathy: A systematic review-based narrative, Frontiers in Neuroscience, Vol: 15, ISSN: 1662-453X
Background: Peripheral neuropathy can be caused by diabetes mellitus and HIV infection, and often leaves patients with treatment-resistant neuropathic pain. To better treat this condition, we need greater understanding of the pathogenesis, as well as objective biomarkers to predict treatment response. Magnetic resonance imaging (MRI) has a firm place as a biomarker for diseases of the central nervous system (CNS), but until recently has had little role for disease of the peripheral nervous system. Objectives: To review the current state-of-the-art of peripheral nerve MRI in diabetic and HIV symmetrical polyneuropathy. We used systematic literature search methods to identify all studies currently published, using this as a basis for a narrative review to discuss major findings in the literature. We also assessed risk of bias, as well as technical aspects of MRI and statistical analysis. Methods: Protocol was pre-registered on NIHR PROSPERO database. MEDLINE, Web of Science and EMBASE databases were searched from 1946 to 15th August 2020 for all studies investigating either diabetic or HIV neuropathy and MRI, focusing exclusively on studies investigating symmetrical polyneuropathy. The NIH quality assessment tool for observational and cross-sectional cohort studies was used for risk of bias assessment. Results: The search resulted in 18 papers eligible for review, 18 for diabetic neuropathy and 0 for HIV neuropathy. Risk of bias assessment demonstrated that studies generally lacked explicit sample size justifications, and some may be underpowered. Whilst most studies made efforts to balance groups for confounding variables (age, gender, BMI, disease duration), there was lack of consistencybetween studies. Overall, the literature provides convincing evidence that DPN is associated with larger nerve cross sectional area, T2-weighted hyperintense and hypointense lesions, evidence of nerve oedema on Dixon imaging, decreased fractional anisotropy and increased apparent dif
Kurtin DL, Violante IR, Zimmerman K, et al., 2021, Investigating the interaction between white matter and brain state on tDCS-induced changes in brain network activity, Brain Stimulation, Vol: 14, Pages: 1261-1270, ISSN: 1876-4754
BACKGROUND: Transcranial direct current stimulation (tDCS) is a form of noninvasive brain stimulation whose potential as a cognitive therapy is hindered by our limited understanding of how participant and experimental factors influence its effects. Using functional MRI to study brain networks, we have previously shown in healthy controls that the physiological effects of tDCS are strongly influenced by brain state. We have additionally shown, in both healthy and traumatic brain injury (TBI) populations, that the behavioral effects of tDCS are positively correlated with white matter (WM) structure. OBJECTIVES: In this study we investigate how these two factors, WM structure and brain state, interact to shape the effect of tDCS on brain network activity. METHODS: We applied anodal, cathodal and sham tDCS to the right inferior frontal gyrus (rIFG) of healthy (n = 22) and TBI participants (n = 34). We used the Choice Reaction Task (CRT) performance to manipulate brain state during tDCS. We acquired simultaneous fMRI to assess activity of cognitive brain networks and used Fractional Anisotropy (FA) as a measure of WM structure. RESULTS: We find that the effects of tDCS on brain network activity in TBI participants are highly dependent on brain state, replicating findings from our previous healthy control study in a separate, patient cohort. We then show that WM structure further modulates the brain-state dependent effects of tDCS on brain network activity. These effects are not unidirectional - in the absence of task with anodal and cathodal tDCS, FA is positively correlated with brain activity in several regions of the default mode network. Conversely, with cathodal tDCS during CRT performance, FA is negatively correlated with brain activity in a salience network region. CONCLUSIONS: Our results show that experimental and participant factors interact to have unexpected effects on brain network activity, and that these effects are not fully predictable by studying the fa
Pasternak AO, Vroom J, Kootstra NA, et al., 2021, Non-nucleoside reverse transcriptase inhibitor-based combination antiretroviral therapy is associated with lower cell- associated HIV RNA and DNA levels compared to protease inhibitor-based therapy, eLife, Vol: 10, Pages: 1-21, ISSN: 2050-084X
Background:It remains unclear whether combination antiretroviral therapy (ART) regimens differ in their ability to fully suppress human immunodeficiency virus (HIV) replication. Here, we report the results of two cross-sectional studies that compared levels of cell-associated (CA) HIV markers between individuals receiving suppressive ART containing either a non-nucleoside reverse transcriptase inhibitor (NNRTI) or a protease inhibitor (PI).Methods:CA HIV unspliced RNA and total HIV DNA were quantified in two cohorts (n = 100, n = 124) of individuals treated with triple ART regimens consisting of two nucleoside reverse transcriptase inhibitors (NRTIs) plus either an NNRTI or a PI. To compare CA HIV RNA and DNA levels between the regimens, we built multivariable models adjusting for age, gender, current and nadir CD4+ count, plasma viral load zenith, duration of virological suppression, NRTI backbone composition, low-level plasma HIV RNA detectability, and electronically measured adherence to ART.Results:In both cohorts, levels of CA HIV RNA and DNA strongly correlated (rho = 0.70 and rho = 0.54) and both markers were lower in NNRTI-treated than in PI-treated individuals. In the multivariable analysis, CA RNA in both cohorts remained significantly reduced in NNRTI-treated individuals (padj = 0.02 in both cohorts), with a similar but weaker association between the ART regimen and total HIV DNA (padj = 0.048 and padj = 0.10). No differences in CA HIV RNA or DNA levels were observed between individual NNRTIs or individual PIs, but CA HIV RNA was lower in individuals treated with either nevirapine or efavirenz, compared to PI-treated individuals.Conclusions:All current classes of antiretroviral drugs only prevent infection of new cells but do not inhibit HIV RNA transcription in long-lived reservoir cells. Therefore, these differences in CA HIV RNA and DNA levels by treatment regimen suggest that NNRTIs are more potent in suppressing HIV residual replication than PIs, whi
Hadi Z, Pondeca YJ, Calzolari E, et al., 2021, Vestibular Agnosia Linked to Widespread Abnormality of Functional Brain Networks, BNA 2021, Publisher: SAGE Publications, ISSN: 2398-2128
Ibitoye RT, Mallas E-J, Bourke NJ, et al., 2021, The human vestibular cortex: functional anatomy, connectivity and the effect of vestibular disease
<jats:title>Abstract</jats:title><jats:p>Area OP2 in the posterior peri-sylvian cortex has been proposed to be the core human vestibular cortex. We defined the functional anatomy of OP2 using spatially constrained independent component analysis of functional MRI data from the Human Connectome Project. Ten distinct subregions were identified. Most subregions showed significant connectivity to other areas with vestibular function: the parietal opercula, the primary somatosensory cortex, the supracalcarine cortex, the left inferior parietal lobule and the anterior cingulate cortex. OP2 responses to vestibular and visual-motion were analysed in 17 controls and 17 right-sided unilateral vestibular lesion patients (vestibular neuritis) 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. Patients showed abnormal OP2 activity, with an absence of visual or caloric activation of the healthy ear and no correlations with dizziness or visual dependence – despite normal brainstem responses to caloric stimulation (slow-phase nystagmus velocity). A lateral part of right OP2 showed activity that correlated with chronic dizziness (situational vertigo) in patients. Our results define the functional anatomy of OP2 in health and disease. A posterior subregion of right OP2 shows strong functional connectivity to other vestibular regions and a visuo-vestibular profile that becomes profoundly disrupted after vestibular disease. In vestibular patients, a lateral subregion of right OP2 shows responses linked to the challenging long-term symptoms which define poorer clinical outcomes.</jats:p><jats:sec><jats:title>Significance statement</jats:title><jats:p>The human
Zimmerman K, Laverse E, Samra R, et al., 2021, White matter abnormalities in active elite adult rugby players, Brain Communications, Vol: 3, Pages: 1-19, ISSN: 2632-1297
The recognition, diagnosis and management of mild traumatic brain injuries is difficult and confusing. It is unclear how the severity and number of injuries sustained relate to brain injuries such as diffuse axonal injury, diffuse vascular injury and progressive neurodegeneration. Advances in neuroimaging techniques enable the investigation of neuropathologies associated with acute and long-term effects of injury. Head injuries are the most commonly reported injury seen during professional rugby. There is increased vigilance for the immediate effects of these injuries in matches, but there has been surprisingly little research investigating the longer-term effects of rugby participation.Here we present a longitudinal observational study investigating the relationship of exposure to rugby participation and sub-acute head injuries in professional adult male and female rugby union and league players using advanced MRI. Diffusion tensor imaging and susceptibility weighted imaging was used to assess white matter structure and evidence of axonal and diffuse vascular injury. We also studied changes in brain structure over time using Jacobian Determinant statistics extracted from serial volumetric imaging. We tested 41 male and 3 female adult elite rugby players, of whom 21 attended study visits after a head injury, alongside 32 non-sporting controls, 15 non-collision-sport athletic controls and 16 longitudinally assessed controls. 18 rugby players participated in the longitudinal arm of the study, with a second visit at least 6 months after their first scan.Neuroimaging evidence of either axonal injury or diffuse vascular injury was present in 23% (10/44) of players. In the non-acutely injured group of rugby players, abnormalities of fractional anisotropy and other diffusion measures were seen. In contrast, non-collision-sport athletic controls were not classified as showing abnormalities. A group level contrast also showed evidence of sub-acute injury using diffusion te
Li LM, Bourke NJ, Lai HHL, et al., 2021, Conferences in the time of COVID: notes on organizing and delivering the first Brain Conference, Brain Communications, Vol: 3, ISSN: 2632-1297
To further fulfil their missions of promoting teaching, education and research in neurology and related clinical-academic disciplines, the Guarantors of Brain and the Brain journal family invited delegates to the first Brain Conference in Spring of this year. This event aimed to deliver excellent teaching and scientific presentations across a broad spectrum of neuroscience fields, with the key aim of making the content as accessible as possible. We hoped to capitalize on the benefits of an online format, whilst trying to capture a little of the joy of the in-person meeting. This article reports on the approach and practical choices made to achieve these goals, and we hope this will provide some guidance and advice to others organizing their own online conference.
Farajzadeh Khosroshahi S, Yin X, Donat C, et al., 2021, Multiscale modelling of cerebrovascular injury reveals the role of vascular anatomy and parenchymal shear stresses, Scientific Reports, Vol: 11, ISSN: 2045-2322
Neurovascular injury is often observed in traumatic brain injury (TBI). However, the relationship between mechanical forces and vascular injury is still unclear. A key question is whether the complex anatomy of vasculature plays a role in increasing forces in cerebral vessels and producing damage. We developed a high-fidelity multiscale finite element model of the rat brain featuring a detailed definition of the angioarchitecture. Controlled cortical impacts were performed experimentally and in-silico. The model was able to predict the pattern of blood–brain barrier damage. We found strong correlation between the area of fibrinogen extravasation and the brain area where axial strain in vessels exceeds 0.14. Our results showed that adjacent vessels can sustain profoundly different axial stresses depending on their alignment with the principal direction of stress in parenchyma, with a better alignment leading to larger stresses in vessels. We also found a strong correlation between axial stress in vessels and the shearing component of the stress wave in parenchyma. Our multiscale computational approach explains the unrecognised role of the vascular anatomy and shear stresses in producing distinct distribution of large forces in vasculature. This new understanding can contribute to improving TBI diagnosis and prevention.
Baker L, Tar M, Kramer AH, et al., 2021, Fidgetin-like 2 negatively regulates axonal growth and can be targeted to promote functional nerve regeneration, JCI INSIGHT, Vol: 6
- Author Web Link
- Cite
- Citations: 3
Molero Y, Sharp DJ, Onofrio BMD, et al., 2021, Psychotropic and pain medication use in individuals with traumatic brain injury-a Swedish total population cohort study of 240 000 persons, JOURNAL OF NEUROLOGY NEUROSURGERY AND PSYCHIATRY, Vol: 92, Pages: 519-527, ISSN: 0022-3050
- Author Web Link
- Cite
- Citations: 3
Popescu SG, Sharp DJ, Cole JH, et al., 2021, Distributional gaussian process layers for outlier detection in imagesegmentation, Information Processing in Medical Imaging (IPMI) 2021, Publisher: arXiv
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 never been achievedwith previous 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.
Duckworth H, Sharp DJ, Ghajari M, 2021, Smoothed particle hydrodynamic modelling of the cerebrospinal fluid for brain biomechanics: accuracy and stability, International Journal for Numerical Methods in Biomedical Engineering, Vol: 37, ISSN: 1069-8299
The Cerebrospinal Fluid (CSF) can undergo shear deformations under head motions. Finite Element (FE) models, which are commonly used to simulate biomechanics of the brain, including traumatic brain injury, employ solid elements to represent the CSF. However, the limited number of elements paired with shear deformations in CSF can decrease the accuracy of their predictions. Large deformation problems can be accurately modelled using the mesh-free Smoothed Particle Hydrodynamics (SPH) method, but there is limited previous work on using this method for modelling the CSF. Here we explored the stability and accuracy of key modelling parameters of an SPH model of the CSF when predicting relative brain/skull displacements in a simulation of an in vivo mild head impact in human. The Moving Least Squares (MLS) SPH formulation and Ogden rubber material model were found to be the most accurate and stable. The strain and strain rate in the brain differed across the SPH and FE models of CSF. The FE mesh anchored the gyri, preventing them from experiencing the level of strains seen in the in vivo brain experiments and predicted by the SPH model. Additionally, SPH showed higher levels of strains in the sulci compared to the FE model. However, tensile instability was found to be a key challenge of the SPH method, which needs to be addressed in future. Our study provides a detailed investigation of the use of SPH and shows its potential for improving the accuracy of computational models of brain biomechanics.
Olsen A, Babikian T, Bigler ED, et al., 2021, Toward a global and reproducible science for brain imaging in neurotrauma: the ENIGMA adult moderate/severe traumatic brain injury working group, BRAIN IMAGING AND BEHAVIOR, Vol: 15, Pages: 526-554, ISSN: 1931-7557
- Author Web Link
- Cite
- Citations: 11
Zimmerman K, Kim J, Karton C, et al., 2021, Player position in American Football influences the magnitude of mechanical strains produced in the location of chronic traumatic encephalopathy pathology: a computational modelling study, Journal of Biomechanics, Vol: 118, ISSN: 0021-9290
American football players are frequently exposed to head impacts, which can cause concussions and may lead to neurodegenerative diseases such as chronic traumatic encephalopathy (CTE). Player position appears to influence the risk of concussion but there is limited work on its effect on the risk of CTE. Computational modelling has shown that large brain deformations during head impacts co-localise with CTE pathology in sulci. Here we test whether player position has an effect on brain deformation within the sulci, a possible biomechanical trigger for CTE. We physically reconstructed 148 head impact events from video footage of American Football games. Players were separated into 3 different position profiles based on the magnitude and frequency of impacts. A detailed finite element model of TBI was then used to predict Green-Lagrange strain and strain rate across the brain and in sulci. Using a one-way ANOVA, we found that in positions where players were exposed to large magnitude and low frequency impacts (e.g. defensive back and wide receiver), strain and strain rate across the brain and in sulci were highest. We also found that rotational head motion is a key determinant in producing large strains and strain rates in the sulci. Our results suggest that player position has a significant effect on impact kinematics, influencing the magnitude of deformations within sulci, which spatially corresponds to where CTE pathology is observed. This work can inform future studies investigating different player-position risks for concussion and CTE and guide design of prevention systems.
Li LM, Dilley MD, Carson A, et al., 2021, Management of traumatic brain injury (TBI): a clinical neuroscience-led pathway for the NHS., Clinical medicine (London, England), Vol: 21, Pages: e198-e205, ISSN: 1470-2118
Following hyperacute management after traumatic brain injury (TBI), most patients receive treatment which is inadequate or inappropriate, and delayed. This results in suboptimal rehabilitation outcome and avoidable detrimental chronic effects on patients' recovery. This worsens long-term disability, and magnifies costs to the individual and society. We believe that accurate diagnosis (at the level of pathology, impairment and function) of the causes of disability is a prerequisite for appropriate care and for accessing effective rehabilitation. An expert-led, integrated care pathway is needed to deliver accurate and timely diagnosis and optimal treatment at all stages during a TBI patient's care.We propose the introduction of a specialist interdisciplinary traumatic brain injury team, led by a neurosciences-trained brain injury consultant. This team would engage acutely and for a longer term after TBI to provide accurate diagnoses, which guides subsequent management and rehabilitation. This approach would also encourage more efficient collaboration between research and the clinic. We propose that the current major trauma network is leveraged to introduce and evaluate this proposal. Improvements to patient outcomes through this approach would lead to reduced personal, societal and economic impact of TBI.
Graham NSN, Junghans C, McLaren R, et al., 2021, High rates of SARS-CoV-2 seropositivity in nursing home residents, Journal of Infection, Vol: 82, Pages: 310-312, ISSN: 0163-4453
Ladhani SN, Chow JY, Atkin S, et al., 2021, Regular mass screening for SARS-CoV-2 infection in care homes already affected by COVID-19 outbreaks: Implications of false positive test results, Journal of Infection, Vol: 82, Pages: 299-301, ISSN: 0163-4453
Bourke N, Yanez-Lopez M, Jenkins P, et al., 2021, Traumatic brain injury: a comparison of diffusion and volumetric magnetic resonance imaging measures, Brain Communications, Vol: 3, ISSN: 2632-1297
Cognitive impairment following traumatic brain injury remains hard to predict. This is partly because axonal injury, which is of fundamental importance, is difficult to measure clinically. Advances in MRI allow axonal injury to be detected after traumatic brain injury, but the most sensitive approach is unclear. Here we compare the performance of diffusion tensor imaging, neurite orientation dispersion and density-imaging and volumetric measures of brain atrophy in the identification of white matter abnormalities after traumatic brain injury.Thirty patients with moderate-severe traumatic brain injury in the chronic phase and 20 age-matched controls had T1-weighted and diffusion MRI. Neuropsychological tests of processing speed, executive functioning and memory were used to detect cognitive impairment.Extensive abnormalities in neurite density index and orientation dispersion index were observed, with distinct spatial patterns. Fractional anisotropy and mean diffusivity also indicated widespread abnormalities of white matter structure. Neurite density index was significantly correlated with processing speed. Slower processing speed was also related to higher mean diffusivity in the cortico-spinal tracts. Lower white matter volumes were seen following brain injury with greater effect sizes compared to diffusion metrics however volume was not sensitive to changes in cognitive performance.Volume was the most sensitive at detecting change between groups but was not specific for determining relationships with cognition. Abnormalities in fractional anisotropy and mean diffusivity were the most sensitive diffusion measures, however neurite density index and orientation dispersion index may be more spatially specific. Lower neurite density index may be a useful metric for examining slower processing speed.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.