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  • Journal article
    Kariholu U, Montaldo P, Markati T, Lally PJ, Teiserskas J, Liow N, Oliveira V, Soe A, Shankaran S, Thayyil Set al., 2020,

    Therapeutic hypothermia for mild neonatal encephalopathy: A systematic review and meta-analysis

    , Archives of Disease in Childhood. Fetal and Neonatal Edition, Vol: 105, Pages: 225-228, ISSN: 1359-2998

    Objectives To examine if therapeutic hypothermia reduces the composite outcome of death, moderate or severe disability at 18 months or more after mild neonatal encephalopathy (NE).Data source MEDLINE, Cochrane database, Scopus and ISI Web of Knowledge databases, using ‘hypoxic ischaemic encephalopathy’, ‘newborn’ and ‘hypothermia’, and ‘clinical trials’ as medical subject headings and terms. Manual search of the reference lists of all eligible articles and major review articles and additional data from the corresponding authors of selected articles.Study selection Randomised and quasirandomised controlled trials comparing therapeutic hypothermia with usual care.Data extraction Safety and efficacy data extracted independently by two reviewers and analysed.Results We included the data on 117 babies with mild NE inadvertently recruited to five cooling trials (two whole-body cooling and three selective head cooling) of moderate and severe NE, in the meta-analysis. Adverse outcomes occurred in 11/56 (19.6%) of the cooled babies and 12/61 (19.7%) of the usual care babies (risk ratio 1.11 (95% CIs 0.55 to 2.25)).Conclusions Current evidence is insufficient to recommend routine therapeutic hypothermia for babies with mild encephalopathy and significant benefits or harm cannot be excluded.

  • Journal article
    Montaldo P, Lally P, Oliveira V, Swamy R, Mendoza J, Atreja G, Kariholu U, Shivamurthappa V, Liow N, Teiserskas J, Pryce R, Soe A, Shankaran S, Thayyil Set al., 2019,

    Therapeutic hypothermia initiated within 6 hours of birth is associated with reduced brain injury on MR biomarkers in mild hypoxic ischemic encephalopathy: a non-randomised cohort study

    , Archives of Disease in Childhood. Fetal and Neonatal Edition, Vol: 104, Pages: F515-F520, ISSN: 1359-2998

    Objective To examine the effect of therapeutic hypothermia on MR biomarkers and neurodevelopmental outcomes in babies with mild hypoxic-ischaemic encephalopathy (HIE).Design Non-randomised cohort study.Setting Eight tertiary neonatal units in the UK and the USA.Patients 47 babies with mild HIE on NICHD neurological examination performed within 6 hours after birth.Interventions Whole-body cooling for 72 hours (n=32) or usual care (n=15; of these 5 were cooled for <12 hours).Main outcome measures MRI and MR spectroscopy (MRS) within 2 weeks after birth, and a neurodevelopmental outcome assessment at 2 years.Results The baseline characteristics in both groups were similar except for lower 10 min Apgar scores (p=0.02) in the cooled babies. Despite this, the mean (SD) thalamic NAA/Cr (1.4 (0.1) vs 1.6 (0.2); p<0.001) and NAA/Cho (0.67 (0.08) vs 0.89 (0.11); p<0.001) ratios from MRS were significantly higher in the cooled group. Cooled babies had lower white matter injury scores than non-cooled babies (p=0.02). Four (27%) non-cooled babies with mild HIE developed seizures after 6 hours of age, while none of the cooled babies developed seizures (p=0.008). Neurodevelopmental outcomes at 2 years were available in 40 (85%) of the babies. Adverse outcomes were seen in 2 (14.3%) non-cooled babies, and none of the cooled babies (p=0.09).Conclusions Therapeutic hypothermia may have a neuroprotective effect in babies with mild HIE, as demonstrated by improved MRS biomarkers and reduced white matter injury on MRI. This may warrant further evaluation in adequately powered randomised controlled trials.

  • Journal article
    Duan J, Bello G, Schlemper J, Bai W, Dawes TJW, Biffi C, Marvao AD, Doumou G, O'Regan DP, Rueckert Det al., 2019,

    Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

    , IEEE Transactions on Medical Imaging, Vol: 38, Pages: 2151-2164, ISSN: 0278-0062

    Deep learning approaches have achieved state-of-the-art performance incardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, the refinement step is designed to explicitly enforce a shape constraint and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular3D models, despite the artefacts in input CMR volumes.

  • Journal article
    Hampshire A, Daws RE, Neves ID, Soreq E, Sandrone S, Violante IRet al., 2019,

    Probing cortical and sub-cortical contributions to instruction-based learning: Regional specialisation and global network dynamics

    , NeuroImage, Vol: 192, Pages: 88-100, ISSN: 1053-8119

    Diverse cortical networks and striatal brain regions are implicated in instruction-based learning (IBL); however, their distinct contributions remain unclear. We use a modified fMRI paradigm to test two hypotheses regarding the brain mechanisms that underlie IBL. One hypothesis proposes that anterior caudate and frontoparietal regions transiently co-activate when new rules are being bound in working memory. The other proposes that they mediate the application of the rules at different stages of the consolidation process. In accordance with the former hypothesis, we report strong activation peaks within and increased connectivity between anterior caudate and frontoparietal regions when rule-instruction slides are presented. However, similar effects occur throughout a broader set of cortical and sub-cortical regions, indicating a metabolically costly reconfiguration of the global brain state. The distinct functional roles of cingulo-opercular, frontoparietal and default-mode networks are apparent from their activation throughout, early and late in the practice phase respectively. Furthermore, there is tentative evidence of a peak in anterior caudate activity mid-way through the practice stage. These results demonstrate how performance of the same simple task involves a steadily shifting balance of brain systems as learning progresses. They also highlight the importance of distinguishing between regional specialisation and global dynamics when studying the network mechanisms that underlie cognition and learning.

  • Journal article
    Cox DJ, Bai W, Price AN, Edwards AD, Rueckert D, Groves AMet al., 2019,

    Ventricular remodeling in preterm infants: computational cardiac magnetic resonance atlasing shows significant early remodeling of the left ventricle

    , PEDIATRIC RESEARCH, Vol: 85, Pages: 807-815, ISSN: 0031-3998
  • Journal article
    Tarroni G, Oktay O, Bai W, Schuh A, Suzuki H, Passerat-Palmbach J, de Marvao A, O'Regan D, Cook S, Glocker B, Matthews P, Rueckert Det al., 2019,

    Learning-based quality control for cardiac MR images

    , IEEE Transactions on Medical Imaging, Vol: 38, Pages: 1127-1138, ISSN: 0278-0062

    The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artefacts such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images: however, this procedure is strongly operatordependent, cumbersome and sometimes incompatible with the time constraints in clinical settings and large-scale studies. We propose a fast, fully-automated, learning-based quality control pipeline for CMR images, specifically for short-axis image stacks. Our pipeline performs three important quality checks: 1) heart coverage estimation, 2) inter-slice motion detection, 3) image contrast estimation in the cardiac region. The pipeline uses a hybrid decision forest method - integrating both regression and structured classification models - to extract landmarks as well as probabilistic segmentation maps from both long- and short-axis images as a basis to perform the quality checks. The technique was tested on up to 3000 cases from the UK Biobank as well as on 100 cases from the UK Digital Heart Project, and validated against manual annotations and visual inspections performed by expert interpreters. The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e.g. on UK Biobank, sensitivity and specificity respectively 88% and 99% for heart coverage estimation, 85% and 95% for motion detection), allowing their exclusion from the analysed dataset or the triggering of a new acquisition.

  • Journal article
    Scott G, Carhart-Harris R, 2019,

    Psychedelics as a treatment for disorders of consciousness

    , Neuroscience of Consciousness, Vol: 2019, Pages: 1-8, ISSN: 2057-2107

    Based on its ability to increase brain complexity, a seemingly reliable index of conscious level, we proposetesting the capacity ofthe classic psychedelic, psilocybin,to increase conscious awarenessin patients with disorders of consciousness.We alsoconfrontthe considerable ethical and practical challengesthis proposal must address, if this hypothesis is to be directly assessed.

  • Journal article
    Sandrone S, Moreno-Zambrano D, Kipnis J, van Gijn Jet al., 2019,

    A (delayed) history of the brain lymphatic system.

    , Nat Med, Vol: 25, Pages: 538-540
  • Journal article
    Robinson R, Valindria VV, Bai W, Oktay O, Kainz B, Suzuki H, Sanghvi MM, Aung N, Paiva JÉM, Zemrak F, Fung K, Lukaschuk E, Lee AM, Carapella V, Kim YJ, Piechnik SK, Neubauer S, Petersen SE, Page C, Matthews PM, Rueckert D, Glocker Bet al., 2019,

    Automated quality control in image segmentation: application to the UK Biobank cardiac MR imaging study

    , Journal of Cardiovascular Magnetic Resonance, Vol: 21, ISSN: 1097-6647

    Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools, e.g. image segmentation methods, are employed to derive quantitative measures or biomarkers for later analyses. Manual inspection and visual QC of each segmentation isn't feasible at large scale. However, it's important to be able to automatically detect when a segmentation method fails so as to avoid inclusion of wrong measurements into subsequent analyses which could lead to incorrect conclusions. Methods: To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4,800 cardiac magnetic resonance scans. We then apply our method to a large cohort of 7,250 cardiac MRI on which we have performed manual QC. Results: We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4,800 scans for which manual segmentations were available. We mimic real-world application of the method on 7,250 cardiac MRI where we show good agreement between predicted quality metrics and manual visual QC scores. Conclusions: We show that RCA has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study.

  • Journal article
    Soreq E, Leech R, Hampshire A, 2019,

    Dynamic network coding of working-memory domains and working-memory processes

    , Nature Communications, Vol: 10, ISSN: 2041-1723

    The classic mapping of distinct aspects of working memory (WM) to mutually exclusive brain areas is at odds with the distributed processing mechanisms proposed by contemporary network science theory. Here, we use machinelearning to determine how aspects of WM are dynamically coded in the human brain. Using cross-validation across independent fMRI studies, we demonstrate that stimulus domains (spatial, number and fractal) and WM processes(encode, maintain, probe) are classifiable with high accuracy from the patterns of network activity and connectivitythat they evoke. This is the case even when focusing on ‘multiple demands’ brain regions, which are active across all WM conditions. Contrary to early neuropsychological perspectives, these aspects of WM do not map exclusively tobrain areas or processing streams; however, the mappings from that literature form salient features within the corresponding multivariate connectivity patterns. Furthermore, connectivity patterns provide the most precise basis for classification and become fine-tuned as maintenance load increases. These results accord with a network-codingmechanism, where the same brain regions support diverse WM demands by adopting different connectivity states.

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