242 results found
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.
Baker CE, Martin PS, Wilson M, et al., 2022, Traumatic brain injury findings from Great Britain’s in-depth RAIDS database relating to delta-V, Pages: 726-727
Parkinson M, Doherty R, Curtis C, et al., 2022, Exploring interactions between traumatic brain injury, Association of British Neurologists
Ibitoye R, Mallas E-J, Bourke N, et al., 2022, The human vestibular cortex: functional anatomy of OP2, its connectivity and the effect of vestibular disease, Cerebral Cortex, 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.
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, BIORXIV
<jats:title>Abstract</jats:title><jats:p>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.</jats:p>
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, 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 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.
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
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
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.
Lima MR, Wairagkar M, Gupta M, et al., 2021, Conversational affective social robots for ageing and dementia support, IEEE Transactions on Cognitive and Developmental Systems, 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.
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
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.
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
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
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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.
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