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  • Journal article
    Bai W, Suzuki H, Huang J, Francis C, Wang S, Tarroni G, Guitton F, Aung N, Fung K, Petersen SE, Piechnik SK, Neubauer S, Evangelou E, Dehghan A, O'Regan DP, Wilkins MR, Guo Y, Matthews PM, Rueckert Det al., 2020,

    A population-based phenome-wide association study of cardiac and aortic structure and function

    , Nature Medicine, Vol: 26, Pages: 1654-1662, ISSN: 1078-8956

    Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart–brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers.

  • Journal article
    Meyer H, Dawes T, Serrani M, Bai W, Tokarczuk P, Cai J, Simoes Monteiro de Marvao A, Henry A, Lumbers T, Gierten J, Thumberger T, Wittbrodt J, Ware J, Rueckert D, Matthews P, Prasad S, Costantino M, Cook S, Birney E, O'Regan Det al., 2020,

    Genetic and functional insights into the fractal structure of the heart

    , Nature, Vol: 584, Pages: 589-594, ISSN: 0028-0836

    The inner surfaces of the human heart are covered by a complex network of muscular strands that is thought to be a vestigeof embryonic development.1,2 The function of these trabeculae in adults and their genetic architecture are unknown. Toinvestigate this we performed a genome-wide association study using fractal analysis of trabecular morphology as animage-derived phenotype in 18,096 UK Biobank participants. We identified 16 significant loci containing genes associatedwith haemodynamic phenotypes and regulation of cytoskeletal arborisation.3,4 Using biomechanical simulations and humanobservational data, we demonstrate that trabecular morphology is an important determinant of cardiac performance. Throughgenetic association studies with cardiac disease phenotypes and Mendelian randomisation, we find a causal relationshipbetween trabecular morphology and cardiovascular disease risk. These findings suggest an unexpected role for myocardialtrabeculae in the function of the adult heart, identify conserved pathways that regulate structural complexity, and reveal theirinfluence on susceptibility to disease

  • Journal article
    Creswell A, Bharath AA, 2019,

    Denoising adversarial autoencoders

    , IEEE Transactions on Neural Networks and Learning Systems, Vol: 30, Pages: 968-984, ISSN: 2162-2388

    Unsupervised learning is of growing interest because it unlocks the potential held in vast amounts of unlabeled data to learn useful representations for inference. Autoencoders, a form of generative model, may be trained by learning to reconstruct unlabeled input data from a latent representation space. More robust representations may be produced by an autoencoder if it learns to recover clean input samples from corrupted ones. Representations may be further improved by introducing regularization during training to shape the distribution of the encoded data in the latent space. We suggest denoising adversarial autoencoders (AAEs), which combine denoising and regularization, shaping the distribution of latent space using adversarial training. We introduce a novel analysis that shows how denoising may be incorporated into the training and sampling of AAEs. Experiments are performed to assess the contributions that denoising makes to the learning of representations for classification and sample synthesis. Our results suggest that autoencoders trained using a denoising criterion achieve higher classification performance and can synthesize samples that are more consistent with the input data than those trained without a corruption process.

  • Journal article
    Dawes T, Cai J, Quinlan M, Simoes Monteiro de Marvao A, Ostowski P, Tokarczuk P, Watson G, Wharton J, Howard L, Gibbs J, Cook S, Wilkins M, O'Regan DPet al., 2018,

    Fractal analysis of right ventricular trabeculae in pulmonary hypertension

    , Radiology, Vol: 288, Pages: 386-395, ISSN: 0033-8419

    Purpose: To measure right ventricular (RV) trabecular complexity by its fractal dimension (FD) in healthy subjects and patients with pulmonary hypertension (PH) and assess its relationship to hemodynamic and functional parameters, and future cardiovascular events. Materials and methods: This retrospective study used data acquired from May 2004 to October 2013 for 256 patients with newly-diagnosed PH that underwent cardiac magnetic resonance (CMR) imaging, right heart catheterization and six-minute walk distance testing with a median follow-up of 4.0 years. 256 healthy controls underwent CMR only. Biventricular FD, volumes and function were assessed on short-axis cine images. Reproducibility was assessed by intraclass correlation coefficient, correlation between variables was assessed by Pearson’s correlation test, and mortality prediction compared by univariable and multivariable Cox regression analysis. Results: RV-FD reproducibility had an intraclass correlation coefficient of 0.97 (95% confidence interval [CI]: 0.96, 0.98).RV-FD was higher in PH patients than healthy subjects (median 1.310, inter-quartile range [IQR] 1.281-1.341 vs 1.264, 1.242-1.295, P<.001) with the greatest difference near the apex. RV-FD was associated with pulmonary vascular resistance (r=0.30, P<.001). In univariable Cox regression analysis, RV-FD was a significant predictor of death (hazards ratio [HR]: 1.256, CI: 1.011, 1.560, P=.04), but in a multivariable analysis did not predict survival independently of conventional parameters of RV remodeling (HR: 1.179, CI: 0.871, 1.596, P=0.29). Conclusion: Fractal analysis of RV trabecular complexity is a highly reproducible measure of remodeling in PH associated with afterload, although the gain in survival prediction over traditional markers is not significant.

  • Journal article
    Dawes T, Simoes monteiro de marvao A, Shi W, Fletcher T, Watson G, Wharton J, Rhodes C, Howard L, Gibbs J, Rueckert D, Cook S, Wilkins M, O'Regan DPet al., 2017,

    Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study

    , Radiology, Vol: 283, Pages: 381-390, ISSN: 1527-1315

    Purpose: To determine if patient survival and mechanisms of right ventricular (RV) failure in pulmonary hypertension (PH) could be predicted using supervised machine learning of three dimensional patterns of systolic cardiac motion. Materials and methods: The study was approved by a research ethics committee and participants gave written informed consent. 256 patients (143 females, mean age 63 ± 17) with newly diagnosed PH underwent cardiac MR imaging, right heart catheterization (RHC) and six minute walk testing (6MWT) with a median follow up of 4.0 years. Semi automated segmentation of short axis cine images was used to create a three dimensional model of right ventricular motion. Supervised principal components analysis identified patterns of systolic motion which were most strongly predictive of survival. Survival prediction was assessed by the difference in median survival time and the area under the curve (AUC) using time dependent receiver operator characteristic for one year survival. Results: At the end of follow up 33% (93/256) died and one underwent lung transplantation. Poor outcome was predicted by a loss of effective contraction in the septum and freewall coupled with reduced basal longitudinal motion. When added to conventional imaging, hemodynamic, functional and clinical markers, three dimensional cardiac motion improved survival prediction (area under the curve 0.73 vs 0.60, p<0.001) and provided greater differentiation by difference in median survival time between high and low risk groups (13.8 vs 10.7 years, p<0.001). Conclusion:Three dimensional motion modeling with machine learning approaches reveal the adaptations in function that occur early in right heart failure and independently predict outcomes in newly diagnosed PH patients.

  • Journal article
    Ciaccio EJ, Coromilas J, Wit AL, Peters NS, Garan Het al., 2016,

    Formation of reentrant circuits in the mid-myocardial infarct border zone

    , COMPUTERS IN BIOLOGY AND MEDICINE, Vol: 71, Pages: 205-213, ISSN: 0010-4825
  • Journal article
    Ma ZB, Yang Y, Liu YX, Bharath AAet al., 2016,

    Recurrently decomposable 2-D convolvers for FPGA-based digital image processing

    , IEEE Transactions on Circuits and Systems, Vol: 63, Pages: 979-983, ISSN: 1549-7747

    Two-dimensional (2-D) convolution is a widely used operation in image processing and computer vision, characterized by intensive computation and frequent memory accesses. Previous efforts to improve the performance of field-programmable gate array (FPGA) convolvers focused on the design of buffering schemes and on minimizing the use of multipliers. A recently proposed recurrently decomposable (RD) filter design method can reduce the computational complexity of 2-D convolutions by splitting the convolution between an image and a large mask into a sequence of convolutions using several smaller masks. This brief explores how to efficiently implement RD based 2-D convolvers using FPGA. Three FPGA architectures are proposed based on RD filters, each with a different buffering scheme. The conclusion is that RD based architectures achieve higher area efficiency than other previously reported state-of-the-art methods, especially for larger convolution masks. An area efficiency metric is also suggested, which allows the most appropriate architecture to be selected.

  • Journal article
    Luther V, Linton NW, Koa-Wing M, Lim PB, Jamil-Copley S, Qureshi N, Ng FS, Hayat S, Whinnett Z, Davies DW, Peters NS, Kanagaratnam Pet al., 2016,

    A prospective study of ripple mapping in atrial tachycardias: a novel approach to interpreting activation in low-voltage areas

    , Circulation: Arrhythmia and Electrophysiology, Vol: 9, Pages: 1-13, ISSN: 1941-3084

    BACKGROUND: Post ablation atrial tachycardias are characterized by low-voltage signals that challenge current mapping methods. Ripple mapping (RM) displays every electrogram deflection as a bar moving from the cardiac surface, resulting in the impression of propagating wavefronts when a series of bars move consecutively. RM displays fractionated signals in their entirety thereby helping to identify propagating activation in low-voltage areas from nonconducting tissue. We prospectively used RM to study tachycardia activation in the previously ablated left atrium.METHODS AND RESULTS: Patients referred for atrial tachycardia ablation underwent dense electroanatomic point collection using CARTO3v4. RM was played over a bipolar voltage map and used to determine the voltage "activation threshold" that differentiated functional low voltage from nonconducting areas for each map. Ablation was guided by RM, but operators could perform entrainment or review the isochronal activation map for diagnostic uncertainty. Twenty patients were studied. Median RM determined activation threshold was 0.3 mV (0.19-0.33), with nonconducting tissue covering 33±9% of the mapped surface. All tachycardias crossed an isthmus (median, 0.52 mV, 13 mm) bordered by nonconducting tissue (70%) or had a breakout source (median, 0.35 mV) moving away from nonconducting tissue (30%). In reentrant circuits (14/20) the path length was measured (87-202 mm), with 9 of 14 also supporting a bystander circuit (path lengths, 147-234 mm). In breakout tachycardias, splitting of wavefronts resulted in 2 to 4 incomplete circuits. RM-guided ablation interrupted the tachycardia in 19 of 20 cases with the first ablation set. CONCLUSIONS: RM helps to define activation through low-voltage regions and aids ablation of atrial tachycardias.

  • Journal article
    Creswell A, Bharath AA, 2016,

    Task Specific Adversarial Cost Function

    The cost function used to train a generative model should fit the purpose ofthe model. If the model is intended for tasks such as generating perceptuallycorrect samples, it is beneficial to maximise the likelihood of a sample drawnfrom the model, Q, coming from the same distribution as the training data, P.This is equivalent to minimising the Kullback-Leibler (KL) distance, KL[Q||P].However, if the model is intended for tasks such as retrieval or classificationit is beneficial to maximise the likelihood that a sample drawn from thetraining data is captured by the model, equivalent to minimising KL[P||Q]. Thecost function used in adversarial training optimises the Jensen-Shannon entropywhich can be seen as an even interpolation between KL[Q||P] and KL[P||Q]. Here,we propose an alternative adversarial cost function which allows easy tuning ofthe model for either task. Our task specific cost function is evaluated on adataset of hand-written characters in the following tasks: Generation,retrieval and one-shot learning.

  • Journal article
    Desplantez T, Grikscheit K, Thomas NM, Peters NS, Severs NJ, Dupont Eet al., 2015,

    Relating specific connexin co-expression ratio to connexon composition and gap junction function

    , Journal of Molecular and Cellular Cardiology, Vol: 89, Pages: 195-202, ISSN: 1095-8584

    Cardiac connexin 43 (Cx43), Cx40 and Cx45 are co-expressed at distinct ratios in myocytes. This pattern is considered a key factor in regulating the gap junction channels composition, properties and function and remains poorly understood.This work aims to correlate gap junction function with the connexin composition of the channels at accurate ratios Cx43:Cx40 and Cx43:Cx45.Rat liver epithelial cells that endogenously express Cx43 were stably transfected to induce expression of accurate levels of Cx40 or Cx45 that may be present in various areas of the heart (e.g. atria and ventricular conduction system). Induction of Cx40 does not increase the amounts of junctional connexins (Cx43 and Cx40), whereas induction of Cx45 increases the amounts of junctional connexins (Cx43 and Cx45). Interestingly, the non-junctional fraction of Cx43 remains unaffected upon induction of Cx40 and Cx45. Co-immunoprecipitation studies show low level of Cx40/Cx43 heteromerisation and undetectable Cx45/Cx43 heteromerisation. Functional characterisation shows that induction of Cx40 and Cx45 decreases Lucifer Yellow transfer. Electrical coupling is decreased by Cx45 induction, whereas it is decreased at low induction of Cx40 and increased at high induction.These data indicate a fine regulation of the gap junction channel make-up in function of the type and the ratio of co-expressed Cxs that specifically regulates chemical and electrical coupling. This reflects specific gap junction function in regulating impulse propagation in the healthy heart, and a pro-arrhythmic potential of connexin remodelling in the diseased heart.

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