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  • Journal article
    Underwood J, Cole JH, Leech R, Sharp DJ, Winston Aet al., 2018,

    Multivariate pattern analysis of volumetric neuroimaging data and its relationship with cognitive function in treated HIV-disease

    , Journal of Acquired Immune Deficiency Syndromes, Vol: 78, Pages: 429-436, ISSN: 1525-4135

    BACKGROUND: Accurate prediction of longitudinal changes in cognitive function would potentially allow targeted intervention in those at greatest risk of cognitive decline. We sought to build a multivariate model using volumetric neuroimaging data alone to accurately predict cognitive function. METHODS: Volumetric T1-weighted neuroimaging data from virally suppressed HIV-positive individuals from the CHARTER cohort (n=139) were segmented into grey and white matter and spatially normalised before were entering into machine learning models. Prediction of cognitive function at baseline and longitudinally was determined using leave-one-out cross validation. Additionally, a multivariate model of brain ageing was used to measure the deviation of apparent brain age from chronological age and assess its relationship with cognitive function. RESULTS: Cognitive impairment, defined using the global deficit score, was present in 37.4%. However, it was generally mild and occurred more commonly in those with confounding comorbidities (p<0.001). Although multivariate prediction of cognitive impairment as a dichotomous variable at baseline was poor (AUC 0.59), prediction of the global T-score was better than a comparable linear model (adjusted R=0.08, p<0.01 vs. adjusted R=0.01, p=0.14). Accurate prediction of longitudinal changes in cognitive function was not possible (p=0.82).Brain-predicted age exceeded chronological age by mean (95% confidence interval) 1.17 (-0.14-2.53) years, but was greatest in those with confounding comorbidities (5.87 [1.74-9.99] years) and prior AIDS (3.03 [0.00-6.06] years). CONCLUSION: Accurate prediction of cognitive impairment using multivariate models using only T1-weighted data was not achievable, which may reflect the small sample size, heterogeneity of the data or that impairment was usually mild.

  • Journal article
    Deb S, Leeson V, Aimola L, Bodani M, Li L, Weaver T, Sharp D, Crawford Met al., 2018,

    Aggression following traumatic brain injury: effectiveness of Risperidone (AFTER): study protocol for a feasibility randomised controlled trial

    , Trials, Vol: 19, ISSN: 1745-6215

    BackgroundTraumatic brain injury (TBI) is a major public health concern and many people develop long-lasting physical and neuropsychiatric consequences following a TBI. Despite the emphasis on physical rehabilitation, it is the emotional and behavioural consequences that have greater impact on people with TBI and their families. One such problem behaviour is aggression which can be directed towards others, towards property or towards the self. Aggression is reported to be common after TBI (37–71%) and causes major stress for patients and their families. Both drug and non-drug interventions are used to manage this challenging behaviour, but the evidence-base for these interventions is poor and no drugs are currently licensed for the treatment of aggression following TBI. The most commonly used drugs for this purpose are antipsychotics, particularly second-generation drugs such as risperidone. Despite this widespread use, randomised controlled trials (RCTs) of antipsychotic drugs, including risperidone, have not been conducted. We have, therefore, set out to test the feasibility of conducting an RCT of this drug for people who have aggressive behaviour following TBI.Methods/designWe will examine the feasibility of conducting a placebo-controlled, double-blind RCT of risperidone for the management of aggression in adults with TBI and also assess participants’ views about their experience of taking part in the study.We will randomise 50 TBI patients from secondary care services in four centres in London and Kent to up to 4 mg of risperidone orally or an inert placebo and follow them up 12 weeks later. Participants will be randomised to active or control treatment in a 1:1 ratio via an external and remote web-based randomisation service. Participants will be assessed at baseline and 12-week follow-up using a battery of assessment scales to measure changes in aggressive behaviour (MOAS, IRQ) as well as global functioning (GOS-E, CGI), quality of life (EQ-5D-5L

  • Conference paper
    Li W, Lao-Kaim NP, Roussakis A, Martin-Bastida A, Valle-Guzman N, Paul G, Soreq E, Daws RE, Foltynie T, Barker R, Hampshire A, Piccini Pet al., 2018,

    Functional connectivity changes in relation to dopaminergic decline in Parkinson's over time: a resting-state fMRI and 11C-PE2I PET imaging study

    , 4th Congress of the European-Academy-of-Neurology (EAN), Publisher: WILEY, Pages: 345-345, ISSN: 1351-5101
  • Journal article
    Cole JH, caan M, Underwood J, de Francesco D, van Zoest R, Wit F, Mutsaerts H, Leech R, Geurtsen G, Portigies P, Majoie C, Schim van der Loeff M, Sabin C, Reiss P, Winston A, Sharp Det al., 2018,

    No evidence for accelerated ageing-related brain pathology in treated HIV: longitudinal neuroimaging results from the Comorbidity in Relation to AIDS (COBRA) project

    , Clinical Infectious Diseases, Vol: 66, Pages: 1899-1909, ISSN: 1058-4838

    BackgroundDespite successful antiretroviral therapy people living with HIV (PLWH) experience higher rates of age-related morbidity, including abnormal brain structure, brain function and cognitive impairment. This has raised concerns that PLWH may experience accelerated ageing-related brain pathology.MethodsWe performed a multi-centre longitudinal study of 134 virologically-suppressed PLWH (median age = 56.0 years) and 79 demographically-similar HIV-negative controls (median age = 57.2 years). To measure cognitive performance and brain pathology, we conducted detailed neuropsychological assessments and multi-modality neuroimaging (T1-weighted, T2-weighted, diffusion-MRI, resting-state functional-MRI, spectroscopy, arterial spin labelling) at baseline and after two-year follow-up. Group differences in rates of change were assessed using linear mixed effects models.Results123 PLWH and 78 HIV-negative controls completed longitudinal assessments (median interval = 1.97 years). There were no differences between PLWH and HIV-negative controls in age, sex, years of education, smoking, alcohol use, recreational drug use, blood pressure, body-mass index or cholesterol levels.At baseline, PLWH had poorer global cognitive performance (P<0.01), lower grey matter volume (P=0.04), higher white matter hyperintensity load (P=0.02), abnormal white-matter microstructure (P<0.005) and greater ‘brain-predicted age difference’ (P=0.01). Longitudinally, there were no significant differences in rates of change in any neuroimaging measure between PLWH and HIV-negative controls (P>0.1). Cognitive performance was stable across the study period in both groups.ConclusionsOur finding indicate that when receiving successful treatment, middle-aged PLWH are not at increased risk of accelerated ageing-related brain changes or cognitive decline over two years, when compared to closely-matched HIV-negative controls.

  • Conference paper
    Puyol-Anton E, Ruijsink B, Bai W, Langet H, De Craene M, Schnabel JA, Piro P, King AP, Sinclair Met al., 2018,

    Fully automated myocardial strain estimation from cine MRI using convolutional neural networks

    , International Symposium on Biomedical Imaging, Pages: 1139-1143, ISSN: 1945-7928

    © 2018 IEEE. Cardiovascular magnetic resonance myocardial feature tracking (CMR-FT) is a promising method for quantification of cardiac function from standard steady-state free precession (SSFP) images. However, currently available techniques require operator dependent and time-consuming manual intervention, limiting reproducibility and clinical use. In this paper, we propose a fully automated pipeline to compute left ventricular (LV) longitudinal and radial strain from 2- and 4-chamber cine acquisitions, and LV circumferential and radial strain from the short-axis imaging. The method employs a convolutional neural network to automatically segment the myocardium, followed by feature tracking and strain estimation. Experiments are performed using 40 healthy volunteers and 40 ischemic patients from the UK Biobank dataset. Results show that our method obtained strain values that were in excellent agreement with the commercially available clinical CMR-FT software CVI42(Circle Cardiovascular Imaging, Calgary, Canada).

  • Journal article
    Whittington A, Sharp DJ, Gunn RN, 2018,

    Spatiotemporal distribution of β-amyloid in Alzheimer's disease results from heterogeneous regional carrying capacities

    , Journal of Nuclear Medicine, Vol: 59, Pages: 822-827, ISSN: 1535-5667

    β-amyloid (Aβ) accumulation in the brain is one of two pathological hallmarks of Alzheimer's Disease (AD) and its spatial distribution has been studied extensively ex vivo. We apply mathematical modelling to Aβ in vivo PET imaging data in order to investigate competing theories of Aβ spread in AD. Our results provide evidence that Aβ accumulation starts in all brain regions simultaneously and that its spatiotemporal distribution is a result of heterogeneous regional carrying capacities (regional maximum possible concentration of Aβ) for the aggregated protein rather than longer term spreading from seed regions.

  • Journal article
    Sandrone S, van Gijn J, 2018,

    Macdonald Critchley (1900-1997)

    , JOURNAL OF NEUROLOGY, Vol: 265, Pages: 1244-1245, ISSN: 0340-5354
  • Journal article
    Lorenz R, Ribeiro Violante I, Monti R, Montana G, Hampshire A, Leech Ret al.,

    Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization

    , Nature Communications, Vol: 9, ISSN: 2041-1723

    Understanding the unique contributions of frontoparietal networks (FPN) in cognition is challenging because they overlap spatially and are co-activated by diverse tasks. Characterizing these networks therefore involves studying their activation across many different cognitive tasks, which previously was only possible with meta-analyses. Here, we use neuroadaptive Bayesian optimization, an approach combining real-time analysis of functional neuroimaging data with machine-learning, to discover cognitive tasks that segregate ventral and dorsal FPN activity. We identify and subsequently refine two cognitive tasks, Deductive Reasoning and Tower of London, which maximally dissociate the dorsal from ventral FPN. We subsequently investigate these two FPNs in the context of a wider range of FPNs and demonstrate the importance of studying the whole activity profile across tasks to uniquely differentiate any FPN. Our findings deviate from previous meta-analyses and hypothesized functional labels for these FPNs. Taken together the results form the starting point for a neurobiologically-derived cognitive taxonomy.

  • Journal article
    Oliveira V, Kumutha JR E N, Somanna J, Benkappa N, Bandya P, Chandrasekeran M, Swamy R, Mondkar J, Dewang K, Manerkar S, Sundaram M, Chinathambi K, Bharadwaj S, Bhat V, Madhava V, Nair M, Lally PJ, Montaldo P, Atreja G, Mendoza J, Bassett P, Ramji S, Shankaran S, Thayyil Set al., 2018,

    Hypothermia for encephalopathy in low-income and middle-income countries: feasibility of whole-body cooling using a low-cost servo-controlled device

    , BMJ Paediatrics Open, Vol: 2, ISSN: 2399-9772

    Although therapeutic hypothermia (TH) is the standard of care for hypoxic ischaemic encephalopathy in high-income countries, the safety and efficacy of this therapy in low-income and middle-income countries (LMICs) is unknown. We aimed to describe the feasibility of TH using a low-cost servo-controlled cooling device and the short-term outcomes of the cooled babies in LMIC. Design: We recruited babies with moderate or severe hypoxic ischaemic encephalopathy (aged <6 hours) admitted to public sector tertiary neonatal units in India over a 28-month period. We administered whole-body cooling (set core temperature 33.5°C) using a servo-controlled device for 72 hours, followed by passive rewarming. We collected the data on short-term neonatal outcomes prior to hospital discharge. Results: Eighty-two babies were included-61 (74%) had moderate and 21 (26%) had severe encephalopathy. Mean (SD) hypothermia cooling induction time was 1.7 hour (1.5) and the effective cooling time 95% (0.08). The mean (SD) hypothermia induction time was 1.7 hour (1.5 hour), core temperature during cooling was 33.4°C (0.2), rewarming rate was 0.34°C (0.16°C) per hour and the effective cooling time was 95% (8%). Twenty-five (51%) babies had gastric bleeds, 6 (12%) had pulmonary bleeds and 21 (27%) had meconium on delivery. Fifteen (18%) babies died before discharge from hospital. Heart rate more than 120 bpm during cooling (P=0.01) and gastric bleeds (P<0.001) were associated with neonatal mortality. Conclusions: The low-cost servo-controlled cooling device maintained the core temperature well within the target range. Adequately powered clinical trials are required to establish the safety and efficacy of TH in LMICs. Clinical trial registration number: NCT01760629.

  • Journal article
    Mason SL, Daws RE, Soreq E, Johnson EB, Scahill RI, Tabrizi SJ, Barker RA, Hampshire Aet al., 2018,

    Predicting clinical diagnosis in Huntington's disease: An imaging polymarker

    , ANNALS OF NEUROLOGY, Vol: 83, Pages: 532-543, ISSN: 0364-5134

    ObjectiveHuntington's disease (HD) gene carriers can be identified before clinical diagnosis; however, statistical models for predicting when overt motor symptoms will manifest are too imprecise to be useful at the level of the individual. Perfecting this prediction is integral to the search for disease modifying therapies. This study aimed to identify an imaging marker capable of reliably predicting real‐life clinical diagnosis in HD.MethodA multivariate machine learning approach was applied to resting‐state and structural magnetic resonance imaging scans from 19 premanifest HD gene carriers (preHD, 8 of whom developed clinical disease in the 5 years postscanning) and 21 healthy controls. A classification model was developed using cross‐group comparisons between preHD and controls, and within the preHD group in relation to “estimated” and “actual” proximity to disease onset. Imaging measures were modeled individually, and combined, and permutation modeling robustly tested classification accuracy.ResultsClassification performance for preHDs versus controls was greatest when all measures were combined. The resulting polymarker predicted converters with high accuracy, including those who were not expected to manifest in that time scale based on the currently adopted statistical models.InterpretationWe propose that a holistic multivariate machine learning treatment of brain abnormalities in the premanifest phase can be used to accurately identify those patients within 5 years of developing motor features of HD, with implications for prognostication and preclinical trials.

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