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  • Journal article
    Fallon SJ, Bor D, Hampshire A, Barker RA, Owen AMet al., 2017,

    Spatial structure normalises working memory performance in Parkinson's disease.

    , Cortex, Vol: 96, Pages: 73-82

    Cognitive deficits are a frequent symptom of Parkinson's disease (PD), particularly in the domain of spatial working memory (WM). Despite numerous demonstrations of aberrant WM in patients, there is a lack of understanding about how, if at all, their WM is fundamentally altered. Most notably, it is unclear whether span - the yardstick upon which most WM models are built - is compromised by the disease. Moreover, it is also unknown whether WM deficits occur in all patients or only exist in a sub-group who are executively impaired. We assessed the factors that influenced spatial span in medicated patients by varying the complexity of to-be-remembered items. Principally, we manipulated the ease with which items could enter - or be blocked from - WM by varying the level of structure in memoranda. Despite having similar levels of executive performance to controls, PD patients were only impaired when remembering information that lacked spatial, easy-to-chunk, structure. Patients' executive function, however, did not influence this effect. The ease with which patients could control WM was further examined by presenting irrelevant information during encoding, varying the level of structure in irrelevant information and manipulating the amount of switching between relevant and irrelevant information. Disease did not significantly alter the effect of these manipulations. Rather, patients' executive performance constrained the detrimental effect of irrelevant information on WM. Thus, PD patients' spatial span is predominantly determined by level of structure in to-be-remembered information, whereas their level of executive function may mitigate against the detrimental effect of irrelevant information.

  • Journal article
    Bai W, Sinclair M, Tarroni G, Oktay O, Rajchl M, Vaillant G, Lee AM, Aung N, Lukaschuk E, Sanghvi MM, Zemrak F, Fung K, Paiva JM, Carapella V, Kim YJ, Suzuki H, Kainz B, Matthews PM, Petersen SE, Piechnik SK, Neubauer S, Glocker B, Rueckert Det al., 2017,

    Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

    Cardiovascular magnetic resonance (CMR) imaging is a standard imagingmodality for assessing cardiovascular diseases (CVDs), the leading cause ofdeath globally. CMR enables accurate quantification of the cardiac chambervolume, ejection fraction and myocardial mass, providing information fordiagnosis and monitoring of CVDs. However, for years, clinicians have beenrelying on manual approaches for CMR image analysis, which is time consumingand prone to subjective errors. It is a major clinical challenge toautomatically derive quantitative and clinically relevant information from CMRimages. Deep neural networks have shown a great potential in image patternrecognition and segmentation for a variety of tasks. Here we demonstrate anautomated analysis method for CMR images, which is based on a fullyconvolutional network (FCN). The network is trained and evaluated on alarge-scale dataset from the UK Biobank, consisting of 4,875 subjects with93,500 pixelwise annotated images. The performance of the method has beenevaluated using a number of technical metrics, including the Dice metric, meancontour distance and Hausdorff distance, as well as clinically relevantmeasures, including left ventricle (LV) end-diastolic volume (LVEDV) andend-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolicvolume (RVEDV) and end-systolic volume (RVESV). By combining FCN with alarge-scale annotated dataset, the proposed automated method achieves a highperformance on par with human experts in segmenting the LV and RV on short-axisCMR images and the left atrium (LA) and right atrium (RA) on long-axis CMRimages.

  • Journal article
    Van Zoest RA, Underwood J, De Francesco D, Sabin CA, Cole JH, Wit FW, Caan MWA, Kootstra NA, Fuchs D, Zetterberg H, Majoie CBLM, Portegies P, Winston A, Sharp DJ, Gisslén M, Reiss P, Co-morBidity in Relation to AIDS COBRA Collaborationet al., 2017,

    Structural brain abnormalities in successfully treated HIV infection: associations with disease and cerebrospinal fluid biomarkers.

    , Journal of Infectious Diseases, Vol: 217, Pages: 69-81, ISSN: 0022-1899

    Background: Brain structural abnormalities have been reported in persons with HIV (PWH) on suppressive combination antiretroviral therapy (cART), but their pathophysiology remains unclear. Methods: We investigated factors associated with brain tissue volumes and white matter microstructure (fractional anisotropy) in 134 PWH on suppressive cART and 79 comparable HIV-negative controls, aged ≥45 years from the Co-morBidity in Relation to AIDS (COBRA) cohort, using multimodal neuroimaging and cerebrospinal fluid (CSF) biomarkers. Results: Compared to controls, PWH had lower grey matter volumes (-13.7 mL [95%-confidence interval -25.1, -2.2 mL]) and fractional anisotropy (-0.0073 [-0.012, -0.0024]), with the largest differences observed in those with prior clinical AIDS. Hypertension and CSF soluble CD14 concentration were associated with lower fractional anisotropy. These associations were independent of HIV serostatus (Pinteraction=0.32 and Pinteraction=0.59, respectively) and did not explain the greater abnormalities in brain structure in relation to HIV. Conclusions: The presence of lower grey matter volumes and more white matter microstructural abnormalities in well-treated PWH partly reflect a combination of historical effects of AIDS, as well as the more general influence of systemic factors such as hypertension and ongoing neuroinflammation. Additional mechanisms explaining the accentuation of brain structure abnormalities in treated HIV infection remain to be identified.

  • Conference paper
    Sharp D, 2017,

    Long-term inflammatory and neurodegenerative consequences of traumatic brain injury

    , 23rd World Congress of Neurology (WCN), Publisher: ELSEVIER SCIENCE BV, Pages: 11-11, ISSN: 0022-510X
  • Conference paper
    Sharp D, 2017,

    Precision medicine in TBI: Lessons from dopaminergic treatment of cognitive impairment

    , 23rd World Congress of Neurology (WCN), Publisher: ELSEVIER SCIENCE BV, Pages: 1-2, ISSN: 0022-510X
  • Conference paper
    Sinclair M, Bai W, Puyol-Antón E, Oktay O, Rueckert D, King APet al., 2017,

    Fully automated segmentation-based respiratory motion correction of multiplanar cardiac magnetic resonance images for large-scale datasets

    , International Conference On Medical Image Computing & Computer Assisted Intervention, Pages: 332-340, ISSN: 0302-9743

    © Springer International Publishing AG 2017. Cardiac magnetic resonance (CMR) can be used for quantitative analysis of heart function. However, CMR imaging typically involves acquiring 2D image planes during separate breath-holds, often resulting in misalignment of the heart between image planes in 3D. Accurate quantitative analysis requires a robust 3D reconstruction of the heart from CMR images, which is adversely affected by such motion artifacts. Therefore, we propose a fully automated method for motion correction of CMR planes using segmentations produced by fully convolutional neural networks (FCNs). FCNs are trained on 100 UK Biobank subjects to produce short-axis and long-axis segmentations, which are subsequently used in an iterative registration algorithm for correcting breath-hold induced motion artifacts. We demonstrate significant improvements in motion-correction over image-based registration, with strong correspondence to results obtained using manual segmentations. We also deploy our automatic method on 9,353 subjects in the UK Biobank database, demonstrating significant improvements in 3D plane alignment.

  • Journal article
    Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, Cook S, de Marvao A, Dawes T, O'Regan D, Kainz B, Glocker B, Rueckert Det al., 2017,

    Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation

    , IEEE Transactions on Medical Imaging, Vol: 37, Pages: 384-395, ISSN: 0278-0062

    Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning based techniques. However, in most recent and promising techniques such as CNN based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end. The new framework encourages models to follow the global anatomical properties of the underlying anatomy (e.g. shape, label structure) via learnt non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks (e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac datasets and public benchmarks. Additionally, we demonstrate how the learnt deep models of 3D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.

  • Journal article
    Oliveira V, Singhvi DP, Montaldo P, Lally PJ, Mendoza J, Manerkar S, Shankaran S, Thayyil Set al., 2017,

    Therapeutic hypothermia in mild neonatal encephalopathy: a national survey of practice in the UK

    , Archives of Disease in Childhood. Fetal and Neonatal Edition, Vol: 103, Pages: F388-F390, ISSN: 1359-2998

    Although major cooling trials (and subsequent guidelines) excluded babies with mild encephalopathy, anecdotal evidence suggests that cooling is often offered to these infants. We report a national survey on current cooling practices for babies with mild encephalopathy in the UK. From 74 neonatal units contacted, 68 were cooling centres. We received 54 responses (79%) and included 48 (five excluded due to incomplete data and one found later not to offer cooling). Of these, 36 centres (75%) offered cooling to infants with mild encephalopathy. Although most of the participating units reported targeting 33-34°C core temperature, seven (19%) considered initiating cooling beyond 6 hours of age and 13 (36%) discontinued cooling prior to 72 hours. Babies were ventilated for cooling in two (6%) units and 13 (36%) sedated all cooled babies. Enteral feeding was withheld in 15 (42%) units and reduced below 25% of requirements in eight (22%) units. MRI and neurodevelopmental outcome evaluation were offered to all cooled babies in 29 (80%) and 27 (75%) units, respectively. Further research is necessary to ensure optimal neuroprotection in mild encephalopathy.

  • Journal article
    Thayyil S, Oliveira V, Lally PJ, Swamy R, Bassett P, Chandrasekaran M, Mondkar J, Mangalabharathi S, Benkappa N, Seeralar A, Shahidullah M, Montaldo P, Herberg J, Manerkar S, Kumaraswami K, Kamalaratnam C, Prakash V, Chandramohan R, Bandya P, Mannan MA, Rodrigo R, Nair M, Ramji S, Shankaran S, HELIX Trial groupet al., 2017,

    Hypothermia for encephalopathy in low and middle-income countries (HELIX): study protocol for a randomised controlled trial.

    , Trials, Vol: 18, ISSN: 1745-6215

    BACKGROUND: Therapeutic hypothermia reduces death and disability after moderate or severe neonatal encephalopathy in high-income countries and is used as standard therapy in these settings. However, the safety and efficacy of cooling therapy in low- and middle-income countries (LMICs), where 99% of the disease burden occurs, remains unclear. We will examine whether whole body cooling reduces death or neurodisability at 18-22 months after neonatal encephalopathy, in LMICs. METHODS: We will randomly allocate 408 term or near-term babies (aged ≤ 6 h) with moderate or severe neonatal encephalopathy admitted to public sector neonatal units in LMIC countries (India, Bangladesh or Sri Lanka), to either usual care alone or whole-body cooling with usual care. Babies allocated to the cooling arm will have core body temperature maintained at 33.5 °C using a servo-controlled cooling device for 72 h, followed by re-warming at 0.5 °C per hour. All babies will have detailed infection screening at the time of recruitment and 3 Telsa cerebral magnetic resonance imaging and spectroscopy at 1-2 weeks after birth. Our primary endpoint is death or moderate or severe disability at the age of 18 months. DISCUSSION: Upon completion, HELIX will be the largest cooling trial in neonatal encephalopathy and will provide a definitive answer regarding the safety and efficacy of cooling therapy for neonatal encephalopathy in LMICs. The trial will also provide important data about the influence of co-existent perinatal infection on the efficacy of hypothermic neuroprotection. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02387385 . Registered on 27 February 2015.

  • Conference paper
    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.

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