75 results found
Hampshire A, Trender W, Grant JE, et al., 2022, Item-level analysis of mental health symptom trajectories during the COVID-19 pandemic in the UK: Associations with age, sex and pre-existing psychiatric conditions, Comprehensive Psychiatry, Vol: 114, Pages: 1-18, ISSN: 0010-440X
BackgroundThere is widespread concern regarding how the COVID-19 pandemic has affected mental health. Emerging meta-analyses suggest that the impact on anxiety/depression may have been transient, but much of the included literature has major methodological limitations. Addressing this topic rigorously requires longitudinal data of sufficient scope and scale, controlling for contextual variables, with baseline data immediately pre-pandemic.AimsTo analyse self-report of symptom frequency from two largely UK-based longitudinal cohorts: Cohort 1 (N = 10,475, two time-points: winter pre-pandemic to UK first winter resurgence), and Cohort 2 (N = 10,391, two time-points, peak first wave to UK first winter resurgence).MethodMultinomial logistic regression applied at the item level identified sub-populations with greater probability of change in mental health symptoms. Permutation analyses characterised changes in symptom frequency distributions. Cross group differences in symptom stability were evaluated via entropy of response transitions.ResultsAnxiety was the most affected aspect of mental health. The profiles of change in mood symptoms was less favourable for females and older adults. Those with pre-existing psychiatric diagnoses showed substantially higher probability of very frequent symptoms pre-pandemic and elevated risk of transitioning to the highest levels of symptoms during the pandemic. Elevated mental health symptoms were evident across intra-COVID timepoints in Cohort 2.ConclusionsThese findings suggest that mental health has been negatively affected by the pandemic, including in a sustained fashion beyond the first UK lockdown into the first winter resurgence. Women, and older adults, were more affected relative to their own baselines. Those with diagnoses of psychiatric conditions were more likely to experience transition to the highest levels of symptom frequency.
Usher I, Hellyer P, Lee KS, et al., 2021, "It's not rocket science" and "It's not brain surgery"-"It's a walk in the park": prospective comparative study, BMJ: British Medical Journal, Vol: 375, Pages: 1-7, ISSN: 0959-535X
Objective To compare cognitive testing scores in neurosurgeons and aerospace engineers to help settle the age old argument of which phrase—“It’s not brain surgery” or “It’s not rocket science”—is most deserved.Design International prospective comparative study.Setting United Kingdom, Europe, the United States, and Canada.Participants 748 people (600 aerospace engineers and 148 neurosurgeons). After data cleaning, 401 complete datasets were included in the final analysis (329 aerospace engineers and 72 neurosurgeons).Main outcome measures Validated online test (Cognitron’s Great British Intelligence Test) measuring distinct aspects of cognition, spanning planning and reasoning, working memory, attention, and emotion processing abilities.Results The neurosurgeons showed significantly higher scores than the aerospace engineers in semantic problem solving (difference 0.33, 95% confidence interval 0.13 to 0.52). Aerospace engineers showed significantly higher scores in mental manipulation and attention (−0.29, −0.48 to −0.09). No difference was found between groups in domain scores for memory (−0.18, −0.40 to 0.03), spatial problem solving (−0.19, −0.39 to 0.01), problem solving speed (0.03, −0.20 to 0.25), and memory recall speed (0.12, −0.10 to 0.35). When each group’s scores for the six domains were compared with those in the general population, only two differences were significant: the neurosurgeons’ problem solving speed was quicker (mean z score 0.24, 95% confidence interval 0.07 to 0.41) and their memory recall speed was slower (−0.19, −0.34 to −0.04).Conclusions In situations that do not require rapid problem solving, it might be more correct to use the phrase “It’s not brain surgery.” It is possible that both neurosurgeons and aerospace engineers are unnecessarily placed on a pedestal and that “It&rsqu
Hampshire A, Hellyer PJ, Trender W, et al., 2021, Insights into the impact on daily life of the COVID-19 pandemic and effective coping strategies from free-text analysis of people's collective experiences, Interface Focus, Vol: 11, Pages: 1-11, ISSN: 2042-8901
There has been considerable speculation regarding how people cope during the COVID-19 pandemic; however, surveys requiring selection from prespecified answers are limited by researcher views and may overlook the most effective measures. Here, we apply an unbiased approach that learns from people's collective lived experiences through the application of natural-language processing of their free-text reports. At the peak of the first lockdown in the United Kingdom, 51 113 individuals provided free-text responses regarding self-perceived positive and negative impact of the pandemic, as well as the practical measures they had found helpful during this period. Latent Dirichlet Allocation identified, in an unconstrained data-driven manner, the most common impact and advice topics. We report that six negative topics and seven positive topics are optimal for capturing the different ways people reported being affected by the pandemic. Forty-five topics were required to optimally summarize the practical coping strategies that they recommended. General linear modelling showed that the prevalence of these topics covaried substantially with age. We propose that a wealth of coping measures may be distilled from the lived experiences of the general population. These may inform feasible individually tailored digital interventions that have relevance during and beyond the pandemic.
Hampshire A, Trender W, Chamberlain SR, et al., 2021, Cognitive deficits in people who have recovered from COVID-19, ECLINICALMEDICINE, Vol: 39
Hampshire A, Hellyer PJ, Soreq E, et al., 2021, Associations between dimensions of behaviour, personality traits, and mental-health during the COVID-19 pandemic in the United Kingdom (vol 12, 4111, 2021), NATURE COMMUNICATIONS, Vol: 12
Hampshire A, Hellyer P, Soreq E, et al., 2021, Associations between dimensions of behaviour, personality traits, and mental-health during the COVID-19 pandemic in the United Kingdom, Nature Communications, ISSN: 2041-1723
Chamberlain SR, Grant JE, Trender W, et al., 2021, Post-traumatic stress disorder symptoms in COVID-19 survivors: online population survey, BJPSYCH OPEN, Vol: 7, ISSN: 2056-4724
Donat C, Yanez Lopez M, Sastre M, et al., 2021, From biomechanics to pathology: predicting axonal injury from patterns of strain after traumatic brain injury., Brain: a journal of neurology, Vol: 144, Pages: 70-91, ISSN: 0006-8950
The relationship between biomechanical forces and neuropathology is key to understanding traumatic brain injury. White matter tracts are damaged by high shear forces during impact, resulting in axonal injury, a key determinant of long-term clinical outcomes. However, the relationship between biomechanical forces and patterns of white matter injuries, associated with persistent diffusion MRI abnormalities, is poorly understood. This limits the ability to predict the severity of head injuries and the design of appropriate protection. Our previously developed human finite element model of head injury predicted the location of post-traumatic neurodegeneration. A similar rat model now allows us to experimentally test whether strain patterns calculated by the model predicts in vivo MRI and histology changes. Using a Controlled Cortical Impact, mild and moderate injuries(1 and 2 mm) were performed. Focal and axonal injuries were quantified withvolumetric and diffusion 9.4T MRI two weeks post injury. Detailed analysis of the corpus callosum was conducted using multi-shell diffusion MRI and histopathology. Microglia and astrocyte density, including process parameters,along with white matter structural integrity and neurofilament expression were determined by quantitative immunohistochemistry. Linear mixed effects regression analyses for strain and strain rate with the employed outcome measures were used to ascertain how well immediate biomechanics could explain MRI and histology changes.The spatial pattern of mechanical strain and strain rate in the injured cortex shows good agreement with the probability maps of focal lesions derived from volumetric MRI. Diffusion metrics showed abnormalities in segments of the corpus callosum predicted to have a high strain, indicating white matter changes. The same segments also exhibited a severity-dependent increase in glia cell density, white matter thinning
Calzolari E, Chepisheva M, Smith RM, et al., 2021, Vestibular agnosia in traumatic brain injury and its link to imbalance, Brain, Vol: 144, Pages: 128-143, ISSN: 0006-8950
Vestibular dysfunction, causing dizziness and imbalance, is a common yet poorly understoodfeature in traumatic brain injury patients. Damage to the inner ear, nerve, brainstem, cerebellumand cerebral hemispheres may all affect vestibular functioning, hence, a multi-level assessment– from reflex to perception – is required. In a previous report, postural instability was thecommonest neurological feature in ambulating acute traumatic brain injury patients. We alsofrequently observe, during ward assessment of acute traumatic brain injury patients withcommon inner ear conditions and a related vigorous vestibular-ocular reflex nystagmus, a lossof vertigo sensation, suggesting a “vestibular agnosia”. Vestibular agnosia patients were alsomore unbalanced, however the link between vestibular agnosia and imbalance was confoundedby the presence of inner ear conditions. We investigated the brain mechanisms of imbalance inacute traumatic brain injury, its link with vestibular agnosia, and potential clinical impact, byprospective laboratory assessment of vestibular function, from reflex to perception, in patientswith preserved peripheral vestibular function. Assessment included vestibular-reflex function;vestibular-perception by participants’ report of their passive yaw rotations in the dark;objective balance via posturography; subjective symptoms via questionnaires; and structuralneuroimaging. We prospectively screened 918 acute admissions, assessed 146 and recruited37. Compared to 37 matched controls, patients showed elevated vestibular-perceptualthresholds (patients 12.92°/s vs. 3.87°/s) but normal vestibular-ocular reflex thresholds(patients 2.52°/s vs. 1.78°/s). Patients with elevated vestibular-perceptual thresholds (3standard deviations above controls’ average), were designated as having vestibular agnosia,and displayed worse posturography than non-vestibular-agnosia patients, despite no differencein vestibular symptom sc
Di Bella C, Trender W, Hellyer P, et al., 2020, Evaluating cognitive functioning in multiple sclerosis, compared to other neurological disorders, using an online cognitive battery, 8th Joint ACTRIMS-ECTRIMS Meeting (MSVirtual), Publisher: SAGE PUBLICATIONS LTD, Pages: 502-503, ISSN: 1352-4585
<jats:title>A<jats:sc>bstract</jats:sc></jats:title><jats:p>For most neuroimaging questions the huge range of possible analytic choices leads to the possibility that conclusions from any single analytic approach may be misleading. Examples of possible choices include the motion regression approach used and smoothing and threshold factors applied during the processing pipeline. Although it is possible to perform a multiverse analysis that evaluates all possible analytic choices, this can be computationally challenging and repeated sequential analyses on the same data can compromise inferential and predictive power. Here, we establish how active learning on a low-dimensional space that captures the inter-relationships between analysis approaches can be used to efficiently approximate the whole multiverse of analyses. This approach balances the benefits of a multiverse analysis without the accompanying cost to statistical power, computational power and the integrity of inferences. We illustrate this approach with a functional MRI dataset of functional connectivity across adolescence, demonstrating how a multiverse of graph theoretic and simple pre-processing steps can be efficiently navigated using active learning. Our study shows how this approach can identify the subset of analysis techniques (i.e., pipelines) which are best able to predict participants’ ages, as well as allowing the performance of different approaches to be quantified.</jats:p>
Hampshire A, Trender W, Chamberlain SR, et al., 2020, Cognitive deficits in people who have recovered from COVID-19 relative to controls: An N=84,285 online study
<jats:title>Abstract</jats:title><jats:p>Case studies have revealed neurological problems in severely affected COVID-19 patients. However, there is little information regarding the nature and broader prevalence of cognitive problems post-infection or across the full spread of severity. We analysed cognitive test data from 84,285 Great British Intelligence Test participants who completed a questionnaire regarding suspected and biologically confirmed COVID-19 infection. People who had recovered, including those no longer reporting symptoms, exhibited significant cognitive deficits when controlling for age, gender, education level, income, racial-ethnic group and pre-existing medical disorders. They were of substantial effect size for people who had been hospitalised, but also for mild but biologically confirmed cases who reported no breathing difficulty. Finer grained analyses of performance support the hypothesis that COVID-19 has a multi-system impact on human cognition.</jats:p><jats:sec><jats:title>Significance statement</jats:title><jats:p>There is evidence that COVID-19 may cause long term health changes past acute symptoms, termed ‘long COVID’. Our analyses of detailed cognitive assessment and questionnaire data from tens thousands of datasets, collected in collaboration with BBC2 Horizon, align with the view that there are chronic cognitive consequences of having COVID-19. Individuals who recovered from suspected or confirmed COVID-19 perform worse on cognitive tests in multiple domains than would be expected given their detailed age and demographic profiles. This deficit scales with symptom severity and is evident amongst those without hospital treatment. These results should act as a clarion call for more detailed research investigating the basis of cognitive deficits in people who have survived SARS-COV-2 infection.</jats:p></jats:sec>
Daws RE, Scott G, Soreq E, et al., 2020, Optimisation of functional network resources when learning behavioural strategies for performing complex tasks
<jats:p>We developed two novel self-ordered switching (SOS) fMRI paradigms to investigate how human behaviour and underlying network resources are optimised when learning to perform complex tasks with multiple goals. SOS was performed with detailed feedback and minimal pretraining (study 1) or with minimal feedback and substantial pretraining (study 2). In study 1, multiple-demand (MD) system activation became less responsive to routine trial demands but more responsive to the executive switching events with practice. Default Mode Network (DMN) activation showed the opposite relationship. Concomitantly, reaction time learning curves correlated with increased connectivity between functional brain networks and subcortical regions. This 'fine-tuning' of network resources correlated with progressively more routine and lower complexity behavioural structure. Furthermore, overall task performance was superior for people who applied structured behavioural routines with low algorithmic complexity. These behavioural and network signatures of learning were less evident in study 2, where task structure was established prior to entering the scanner. Together, these studies demonstrate how detailed feedback monitoring enables network resources to be progressively redeployed in order to efficiently manage concurrent demands.</jats:p>
Hughes SW, Hellyer PJ, Sharp DJ, et al., 2020, Diffusion tensor imaging of lumbar spinal nerves reveals changes in microstructural integrity following decompression surgery associated with improvements in clinical symptoms: A case report, Magnetic Resonance Imaging, Vol: 69, Pages: 65-70, ISSN: 0730-725X
The outcomes from spinal nerve decompression surgery are highly variable with a sizable proportion of elderly foraminal stenosis patients not regaining good pain relief. A better understanding of nerve root compression before and following decompression surgery and whether these changes are mirrored by improvements in symptoms may help to improve clinical decision-making processes. This case study used a combination of diffusion tensor imaging (DTI), clinical questionnaires and motor neurophysiology assessments before and up to 3 months following spinal decompression surgery. In this case report, a 70-year-old women with compression of the left L5 spinal nerve root in the L5-S1 exit foramina was recruited to the study. At 3 months following surgery, DTI revealed marked improvements in left L5 microstructural integrity to a similar level to that seen in the intact right L5 nerve root. This was accompanied by a gradual improvement in pain-related symptoms, mood and disability score by 3 months. Using this novel multimodal approach, it may be possible to track concurrent improvements in pain-related symptoms, function and microstructural integrity of compressed nerves in elderly foraminal stenosis patients undergoing decompression surgery.
Dafflon J, Pinaya WHL, Turkheimer F, et al., 2020, An automated machine learning approach to predict brain age from cortical anatomical measures, HUMAN BRAIN MAPPING, Vol: 41, Pages: 3555-3566, ISSN: 1065-9471
<ns4:p>In many clinical and scientific situations the optimal neuroimaging sequence may not be known prior to scanning and may differ for each individual being scanned, depending on the exact nature and location of abnormalities. Despite this, the standard approach to data acquisition, in such situations, is to specify the sequence of neuroimaging scans prior to data acquisition and to apply the same scans to all individuals. In this paper, we propose and illustrate an alternative approach, in which data would be analysed as it is acquired and used to choose the future scanning sequence: Active Acquisition. We propose three Active Acquisition scenarios based around multiple MRI modalities. In Scenario 1, we propose a simple use of near-real time analysis to decide whether to acquire more or higher resolution data, or acquire data with a different field<ns4:bold>-</ns4:bold>of<ns4:bold>-</ns4:bold>view. In Scenario 2, we simulate how multimodal MR data could be actively acquired and combined with a decision tree to classify a known outcome variable (in the simple example here, age). In Scenario 3, we simulate using Bayesian optimisation to actively search across multiple MRI modalities to find those which are most abnormal. These simulations suggest that by actively acquiring data, the scanning sequence can be adapted to each individual. We also consider the many outstanding practical and technical challenges involving normative data acquisition, MR physics, statistical modelling and clinical relevance. Despite these, we argue that Active Acquisition allows for potentially far more powerful, sensitive or rapid data acquisition, and may open up different perspectives on individual differences, clinical conditions, and biomarker discovery.</ns4:p>
Hampshire A, Sandrone S, Hellyer P, 2019, A large-scale, cross-sectional investigation into the efficacy of brain training, Frontiers in Neuroscience, Vol: 13, Pages: 1-13, ISSN: 1662-4548
Brain training is a large and expanding industry, and yet there is a recurrent and ongoing debate concerning its scientific basis or evidence for efficacy. Much of evidence for the efficacy of brain training within this debate is from small-scale studies that do not assess the type of “brain training,” the specificity of transfer effects, or the length of training required to achieve a generalized effect. To explore these factors, we analyze cross-sectional data from two large Internet-cohort studies (total N = 60,222) to determine whether cognition differs at the population level for individuals who report that they brain train on different devices, and across different timeframes, with programs in common use circa 2010–2013. Examining scores for an assessment of working-memory, reasoning and verbal abilities shows no cognitive advantages for individuals who brain train. This contrasts unfavorably with significant advantages for individuals who regularly undertake other cognitive pursuits such as computer, board and card games. However, finer grained analyses reveal a more complex relationship between brain training and cognitive performance. Specifically, individuals who have just begun to brain train start from a low cognitive baseline compared to individuals who have never engaged in brain training, whereas those who have trained for a year or more have higher working-memory and verbal scores compared to those who have just started, thus suggesting an efficacy for brain training over an extended period of time. The advantages in global function, working memory, and verbal memory after several months of training are plausible and of clinically relevant scale. However, this relationship is not evident for reasoning performance or self-report measures of everyday function (e.g., employment status and problems with attention). These results accord with the view that although brain training programs can produce benefits, these might extend to tasks
Hughes SW, Hellyer PJ, Sharp DJ, et al., 2019, Diffusion tensor imaging reveals changes in microstructural integrity along compressed nerve roots that correlate with chronic pain symptoms and motor deficiencies in elderly stenosis patients, NeuroImage: Clinical, Vol: 23, ISSN: 2213-1582
Age-related degenerative changes in the lumbar spine frequently result in nerve root compression causing severe pain and disability. Given the increasing incidence of lumbar spinal disorders in the aging population and the discrepancies between the use of current diagnostic imaging tools and clinical symptoms, novel methods of nerve root assessment are needed. We investigated elderly patients with stenosis at L4-L5 or L5-S1 levels. Diffusion tensor imaging (DTI) was used to quantify microstructure in compressed L5 nerve roots and investigate relationships to clinical symptoms and motor neurophysiology. DTI metrics (i.e. FA, MD, AD and RD) were measured at proximal, mid and distal segments along compressed (i.e. L5) and intact (i.e. L4 or S1) nerve roots. FA was significantly reduced in compressed nerve roots and MD, AD and RD were significantly elevated in the most proximal segment of the nerve root studied. FA was significantly correlated with electrophysiological measures of root function: minimum F-wave latency and peripheral motor conduction time (PMCT). In addition, FA along the compressed root also correlated with leg pain and depression score. There was also a relationship between RD and anxiety, leg pain and disability score and AD correlated with depression score. Taken together, these data show that DTI metrics are sensitive to nerve root compression in patients with stenosis as a result of age-related lumbar degeneration. Critically, they show that the changes in microstructural integrity along compressed L5 nerve roots are closely related to a number of clinical symptoms associated with the development of chronic pain as well as neurophysiological assessments of motor function. These inherent relationships between nerve root damage and phenotype suggest that the use DTI is a promising method as a way to stratify treatment selection and predict outcomes.
Barry EF, Vanes LD, Andrews DS, et al., 2019, Mapping cortical surface features in treatment resistant schizophrenia with in vivo structural MRI, PSYCHIATRY RESEARCH, Vol: 274, Pages: 335-344, ISSN: 0165-1781
Turkheimer FE, Hellyer P, Kehagia AA, et al., 2019, Conflicting emergences. Weak vs. strong emergence for the modelling of brain function, NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS, Vol: 99, Pages: 3-10, ISSN: 0149-7634
Low-dimensional yet rich dynamics often emerge in the brain. Examples include oscillations and chaotic dynamics during sleep, epilepsy, and voluntary movement. However, a general mechanism for the emergence of low dimensional dynamics remains elusive. Here, we consider Wilson-Cowan networks and demonstrate through numerical and analytical work that homeostatic regulation of the network firing rates can paradoxically lead to a rich dynamical repertoire. The dynamics include mixed-mode oscillations, mixed-mode chaos, and chaotic synchronization when the homeostatic plasticity operates on a moderately slower time scale than the firing rates. This is true for a single recurrently coupled node, pairs of reciprocally coupled nodes without self-coupling, and networks coupled through experimentally determined weights derived from functional magnetic resonance imaging data. In all cases, the stability of the homeostatic set point is analytically determined or approximated. The dynamics at the network level are directly determined by the behavior of a single node system through synchronization in both oscillatory and non-oscillatory states. Our results demonstrate that rich dynamics can be preserved under homeostatic regulation or even be caused by homeostatic regulation.When recordings from the brain are analyzed, rich dynamics such as oscillations or low-dimensional chaos are often present. However, a general mechanism for how these dynamics emerge remains unresolved. Here, we explore the potential that these dynamics are caused by an interaction between synaptic homeostasis, and the connectivity between distinct populations of neurons. Using both analytical and numerical approaches, we analyze how data derived connection weights interact with inhibitory synaptic homeostasis to create rich dynamics such chaos and oscillations operating on multiple time scales. We demonstrate that these rich dynamical states are present in simple systems such as single population of neurons
In many clinical and scientific situations the optimal neuroimaging sequence may not be known prior to scanning and may differ for each individual being scanned, depending on the exact nature and location of abnormalities. Despite this, the standard approach to data acquisition, in such situations, is to specify the sequence of neuroimaging scans prior to data acquisition and to apply the same scans to all individuals. In this paper, we propose and illustrate an alternative approach, in which data would be analysed as it is acquired and used to choose the future scanning sequence: Active Acquisition. We propose three Active Acquisition scenarios based around multiple MRI modalities. In Scenario 1, we propose a simple use of near-real time analysis to decide whether to acquire more or higher resolution data, or acquire data with a different field <b>-</b>of <b>-</b>view. In Scenario 2, we simulate how multimodal MR data could be actively acquired and combined with a decision tree to classify a known outcome variable (in the simple example here, age). In Scenario 3, we simulate using Bayesian optimisation to actively search across multiple MRI modalities to find those which are most abnormal. These simulations suggest that by actively acquiring data, the scanning sequence can be adapted to each individual. We also consider the many outstanding practical and technical challenges involving normative data acquisition, MR physics, statistical modelling and clinical relevance. Despite these, we argue that Active Acquisition allows for potentially far more powerful, sensitive or rapid data acquisition, and may open up different perspectives on individual differences, clinical conditions, and biomarker discovery.
In many clinical and scientific situations the optimal neuroimaging sequence may not be known prior to scanning and may differ for each individual being scanned, depending on the exact nature and location of abnormalities. Despite this, the standard approach to data acquisition, in such situations, is to specify the sequence of neuroimaging scans prior to data acquisition and to apply the same scans to all individuals. In this paper, we propose and illustrate an alternative approach, in which data would be analysed as it is acquired and used to choose the future scanning sequence: Active Acquisition. We propose three Active Acquisition scenarios based around multiple MRI modalities. In Scenario 1, we propose a simple use of near-real time analysis to decide whether to acquire more or higher resolution data, or acquire data with a different field -of -view. In Scenario 2, we simulate how multimodal MR data could be actively acquired and combined with a decision tree to classify a known outcome variable (in the simple example here, age). In Scenario 3, we simulate using Bayesian optimisation to actively search across multiple MRI modalities to find those which are most abnormal. These simulations suggest that by actively acquiring data, the scanning sequence can be adapted to each individual. We also consider the many outstanding practical and technical challenges involving normative data acquisition, MR physics, statistical modelling and clinical relevance. Despite these, we argue that Active Acquisition allows for potentially far more powerful, sensitive or rapid data acquisition, and may open up different perspectives on individual differences, clinical conditions, and biomarker discovery.
arora H, nila A, Vitharana K, et al., 2017, Microstructural consequences of blast lung injury characterised with digital volume correlation, Frontiers in Materials, Vol: 4, ISSN: 2296-8016
This study focuses on microstructural changes that occur within the mammalian lung when subject to blast and how these changes influence strain distributions within the tissue. Shock tube experiments were performed to generate the blast injured specimens (cadaveric Sprague-Dawley rats). Blast overpressures of 100 and 180 kPa were studied. Synchrotron tomography imaging was used to capture volumetric image data of lungs. Specimens were ventilated using a custom-built system to study multiple inflation pressures during each tomography scan. These data enabled the first digital volume correlation (DVC) measurements in lung tissue to be performed. Quantitative analysis was performed to describe the damaged architecture of the lung. No clear changes in the microstructure of the tissue morphology were observed due to controlled low- to moderate-level blast exposure. However, significant focal sites of injury were observed using DVC, which allowed the detection of bias and concentration in the patterns of strain level. Morphological analysis corroborated the findings, illustrating that the focal damage caused by a blast can give rise to diffuse influence across the tissue. It is important to characterize the non-instantly fatal doses of blast, given the transient nature of blast lung in the clinical setting. This research has highlighted the need for better understanding of focal injury and its zone of influence (alveolar interdependency and neighboring tissue burden as a result of focal injury). DVC techniques show great promise as a tool to advance this endeavor, providing a new perspective on lung mechanics after blast.
De Simoni S, Jenkins PO, Bourke N, et al., 2017, Altered caudate connectivity is associated with executive dysfunction after traumatic brain injury, Brain, Vol: 141, Pages: 148-164, ISSN: 1460-2156
Traumatic brain injury often produces executive dysfunction. This characteristic cognitive impairment often causes long-term problems with behaviour and personality. Frontal lobe injuries are associated with executive dysfunction, but it is unclear how these injuries relate to corticostriatal interactions that are known to play an important role in behavioural control. We hypothesized that executive dysfunction after traumatic brain injury would be associated with abnormal corticostriatal interactions, a question that has not previously been investigated. We used structural and functional MRI measures of connectivity to investigate this. Corticostriatal functional connectivity in healthy individuals was initially defined using a data-driven approach. A constrained independent component analysis approach was applied in 100 healthy adult dataset from the Human Connectome Project. Diffusion tractography was also performed to generate white matter tracts. The output of this analysis was used to compare corticostriatal functional connectivity and structural integrity between groups of 42 patients with traumatic brain injury and 21 age-matched controls. Subdivisions of the caudate and putamen had distinct patterns of functional connectivity. Traumatic brain injury patients showed disruption to functional connectivity between the caudate and a distributed set of cortical regions, including the anterior cingulate cortex. Cognitive impairments in the patients were mainly seen in processing speed and executive function, as well as increased levels of apathy and fatigue. Abnormalities of caudate functional connectivity correlated with these cognitive impairments, with reductions in right caudate connectivity associated with increased executive dysfunction, information processing speed and memory impairment. Structural connectivity, measured using diffusion tensor imaging between the caudate and anterior cingulate cortex was impaired and this also correlated with measures of ex
Ghajari M, Hellyer PJ, Sharp DJ, 2017, Predicting the location of chronic traumatic encephalopathy pathology, 2017 IRCOBI Conference, Publisher: International Research Council on Biomechanics of Injury (IRCOBI), Pages: 699-700, ISSN: 2235-3151
Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease linked to head impacts. Its distinctive neuropathologic feature is deposition of tau proteins in sulcal depths and in perivascular regions. Previous work has investigated pathological and clinical features of CTE, and here the authors report recent work on exploring the link between strain and strain rate distribution within the brain and location of CTE pathology. The authors used a high fidelity finite element (FE) model of traumatic brain injury (TBI) to test the hypothesis that strain and strain rate produced by head impacts are greatest in sulci, where neuropathology is prominently seen in CTE. The authors also analyzed diffusion tensor imaging (DTI) data from a large cohort of TBI patients to provide converging evidence from empirical neuroimaging data for the model’s prediction.
Hellyer P, Clopath C, Kehagia A, et al., 2017, From homeostasis to behavior: Balanced activity in an exploration of embodied dynamic environmental-neural interaction, PLoS Computational Biology, Vol: 13, ISSN: 1553-734X
In recent years, there have been many computational simulations of spontaneous neural dynamics. Here, we describe a simple model of spontaneous neural dynamics that controls an agent moving in a simple virtual environment. These dynamics generate interesting brain-environment feedback interactions that rapidly destabilize neural and behavioral dynamics demonstrating the need for homeostatic mechanisms. We investigate roles for homeostatic plasticity both locally (local inhibition adjusting to balance excitatory input) as well as more globally (regional “task negative” activity that compensates for “task positive”, sensory input in another region) balancing neural activity and leading to more stable behavior (trajectories through the environment). Our results suggest complementary functional roles for both local and macroscale mechanisms in maintaining neural and behavioral dynamics and a novel functional role for macroscopic “task-negative” patterns of activity (e.g., the default mode network).
Chiou SY, Hellyer PJ, Sharp DJ, et al., 2017, Relationships between the integrity and function of lumbar nerve roots as assessed by diffusion tensor imaging and neurophysiology, NEURORADIOLOGY, Vol: 59, Pages: 893-903, ISSN: 0028-3940
PurposeDiffusion tensor imaging (DTI) has shown promise in the measurement of peripheral nerve integrity, although the optimal way to apply the technique for the study of lumbar spinal nerves is unclear. The aims of this study are to use an improved DTI acquisition to investigate lumbar nerve root integrity and correlate this with functional measures using neurophysiology.MethodsTwenty healthy volunteers underwent 3 T DTI of the L5/S1 area. Regions of interest were applied to L5 and S1 nerve roots, and DTI metrics (fractional anisotropy, mean, axial and radial diffusivity) were derived. Neurophysiological measures were obtained from muscles innervated by L5/S1 nerves; these included the slope of motor-evoked potential input-output curves, F-wave latency, maximal motor response, and central and peripheral motor conduction times.ResultsDTI metrics were similar between the left and right sides and between vertebral levels. Conversely, significant differences in DTI measures were seen along the course of the nerves. Regression analyses revealed that DTI metrics of the L5 nerve correlated with neurophysiological measures from the muscle innervated by it.ConclusionThe current findings suggest that DTI has the potential to be used for assessing lumbar spinal nerve integrity and that parameters derived from DTI provide quantitative information which reflects their function.
Feeney C, Sharp DJ, Hellyer PJ, et al., 2017, Serum IGF-I levels are associated with improved white matter recovery after TBI., Annals of Neurology, Vol: 82, Pages: 30-43, ISSN: 0364-5134
OBJECTIVE: Traumatic brain injury (TBI) is a common disabling condition with limited treatment options. Diffusion tensor imaging (DTI) measures recovery of axonal injury in white matter (WM) tracts after TBI. Growth hormone deficiency (GHD) after TBI may impair axonal and neuropsychological recovery, and serum IGF-I may mediate this effect. We conducted a longitudinal study to determine the effects of baseline serum IGF-I concentrations on WM tract and neuropsychological recovery after TBI. METHODS: Thirty-nine adults after TBI (84.6% male; age median 30.5y; 87.2% moderate-severe; time since TBI median 16.3 months, n=4 with GHD) were scanned twice, 13.3 months (12.1-14.9) apart, and 35 healthy controls scanned once. Symptom and quality of life questionnaires and cognitive assessments were completed at both visits (n=33). Our main outcome measure was fractional anisotropy (FA), a measure of WM tract integrity, in a priori regions of interest: splenium of corpus callosum (SPCC), and posterior limb of internal capsule (PLIC). RESULTS: At baseline, FA was reduced in many WM tracts including SPCC and PLIC following TBI compared to controls, indicating axonal injury, with longitudinal increases indicating axonal recovery. There was a significantly greater increase in SPCC FA over time in patients with serum IGF-I above vs. below the median-for-age. Only the higher IGF-I group had significant improvements in immediate verbal memory recall over time. INTERPRETATION: WM recovery and memory improvements after TBI were greater in patients with higher serum IGF-I at baseline. These findings suggest that GH/IGF-I system may be a potential therapeutic target following TBI. This article is protected by copyright. All rights reserved.
Hellyer PJ, Barry EF, Pellizzon A, et al., 2017, Protein synthesis is associated with high-speed dynamics and broad-band stability of functional hubs in the brain., Neuroimage, Vol: 155, Pages: 209-216
L-[1-(11)C]leucine PET can be used to measure in vivo protein synthesis in the brain. However, the relationship between regional protein synthesis and on-going neural dynamics is unclear. We use a graph theoretical approach to examine the relationship between cerebral protein synthesis (rCPS) and both static and dynamical measures of functional connectivity (measured using resting state functional MRI, R-fMRI). Our graph theoretical analysis demonstrates a significant positive relationship between protein turnover and static measures of functional connectivity. We compared these results to simple measures of metabolism in the cortex using [(18)F]FDG PET). Whilst some relationships between [(18)F]FDG binding and graph theoretical measures was present, there remained a significant relationship between protein turnover and graph theoretical measures, which were more robustly explained by L-[1-(11)C]Leucine than [(18)F]FDG PET. This relationship was stronger in dynamics at a faster temporal resolution relative to dynamics measured over a longer epoch. Using a Dynamic connectivity approach, we also demonstrate that broad-band dynamic measures of Functional Connectivity (FC), are inversely correlated with protein turnover, suggesting greater stability of FC in highly interconnected hub regions is supported by protein synthesis. Overall, we demonstrate that cerebral protein synthesis has a strong relationship independent of tissue metabolism to neural dynamics at the macroscopic scale.
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