Myocardial energy utilisation in relation to canine circulatory control, studied by multi-variate analysis and computer simulation

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Role of Fiber Orientation in Atrial Arrythmogenesis

Electrical wave-front propagation in the atria is determined largely by local fiber orientation. Recent study suggests that atrial fibrillation (AF) progresses with enhanced anisotropy. In this work, a 3D rabbit atrial anatomical model at 20 × 20 × 20 μm3 resolution with realistic fiber orientation was constructed based on the novel contrast-enhanced micro-CT imaging. The Fenton-Karma cellular activation model was adapted to reproduce rabbit atrial action potential period of 80 ms. Diffusivities were estimated for longitudinal and transverse directions of the fiber orientation respectively. Pacing was conducted in the 3D anisotropic atrial model with a reducing S2 interval to facilitate initiation of atrial arrhythmia. Multiple simulations were conducted with varying values of diffusion anisotropy and stimulus locations to evaluate the role of anisotropy in initiating AF. Under physiological anisotropy conditions, a rapid right atrial activation was followed by the left atrial activation. Excitation waves reached the atrio-ventricular border where they terminated. Upon reduction of conduction heterogeneity, re-entry was initiated by the rapid pacing and the activation of both atrial chambers was almost simultaneous. Myofiber orientation is an effective mechanism for regulating atrial activation. Modification of myoarchitecture is proarrhythmic

Loss of Side-to-Side Connections Affects the Relative Contributions of the Sodium and Calcium Current to Transverse Propagation Between Strands of Atrial Myocytes

Background: Atrial fibrillation (AF) leads to a loss of transverse connections between myocyte strands that is associated with an increased complexity and stability of AF. We have explored the interaction between longitudinal and transverse coupling, and the relative contribution of the sodium (INa) and calcium (ICa) current to propagation, both in healthy tissue and under diseased conditions using computer simulations.Methods: Two parallel strands of atrial myocytes were modeled (Courtemanche et al. ionic model). As a control condition, every single cell was connected both transversely and longitudinally. To simulate a loss of transverse connectivity, this number was reduced to 1 in 4, 8, 12, or 16 transversely. To study the interaction with longitudinal coupling, anisotropy ratios of 3, 9, 16, and 25:1 were used. All simulations were repeated for varying degrees of INa and ICa block and the transverse activation delay (TAD) between the paced and non-paced strands was calculated for all cases.Results: The TAD was highly sensitive to the transverse connectivity, increasing from 1 ms at 1 in 1, to 25 ms at 1 in 4, and 100 ms at 1 in 12 connectivity. The TAD also increased when longitudinal coupling was increased. Both decreasing transverse connectivity and increasing longitudinal coupling enhanced the synchronicity of activation of the non-paced strand and increased the propensity for transverse conduction block. Even after long TADs, the action potential upstroke in the non-paced strand was still mainly dependent on the INa. Nevertheless, ICa in the paced strand was essential to provide depolarizing current to the non-paced strand. Loss of transverse connections increased the sensitivity to both INa and ICa block. However, when longitudinal coupling was relatively high, transverse propagation was more sensitive to ICa block than to INa block.Conclusions: Although transverse propagation depends on both INa and ICa, their relative contribution, and sensitivity to channel blockade, depends on the distribution of transverse connections and the axial conductivity. This simple two-strand model helps to explain the nature of atrial discontinuous conduction during structural remodeling and provides an opportunity for more effective drug development

Dynamics of cardiac re-entry in micro-CT and serial histological sections based models of mammalian hearts

Cardiac re-entry regime of self-organised abnormal synchronisation underlie dangerous arrhythmias and fatal fibrillation. Recent advances in the theory of dissipative vortices, experimental studies, and anatomically realistic computer simulations, elucidated the role of cardiac re-entry interaction with fine anatomical features in the heart, and anatomy induced drift. The fact that anatomy and structural anisotropy of the heart is consistent within a species suggested its possible functional effect on spontaneous drift of cardiac re-entry. A comparative study of the anatomy induced drift could be used in order to predict evolution of atrial arrhythmia, and improve low-voltage defibrillation protocols and ablation strategies. Here, in micro-CT based model of rat pulmonary vein wall, and in sheep atria models based on high resolution serial histological sections, we demonstrate effects of heart geometry and anisotropy on cardiac re-entry anatomy induced drift, its pinning to fluctuations of thickness in the layer. The data sets of sheep atria and rat pulmonary vein wall are incorporated into the BeatBox High Performance Computing simulation environment. Re-entry is initiated at prescribed locations in the spatially homogeneous mono-domain models of cardiac tissue. Excitation is described by FitzHugh-Nagumo kinetics. In the in-silico models, isotropic and anisotropic conduction show specific anatomy effects and the interplay between anatomy and anisotropy of the heart. The main objectives are to demonstrate the functional role of the species hearts geometry and anisotropy on cardiac re-entry anatomy induced drift. In case of the rat pulmonary vein wall with ~90 degree transmural fibre rotation, it is shown that the joint effect of the PV wall geometry and anisotropy turns a plane excitation wave into a re-entry pinned to a small fluctuation of thickness in the wall

A Machine Learning Aided Systematic Review and Meta-Analysis of the Relative Risk of Atrial Fibrillation in Patients With Diabetes Mellitus

Background: Meta-analysis is a widely used tool in which weighted information from multiple similar studies is aggregated to increase statistical power. However, the exponential growth of publications in key areas of medical science has rendered manual identification of relevant studies increasingly time-consuming. The aim of this work was to develop a machine learning technique capable of robust automatic study selection for meta-analysis. We have validated this approach with an up-to-date meta-analysis to investigate the association between diabetes mellitus (DM) and new-onset atrial fibrillation (AF).Methods: The PubMed online database was searched from 1960 to September 2017 where 4,177 publications that mentioned both DM and AF were identified. Relevant studies were selected as follows. First, publications were clustered based on common text features using an unsupervised K-means algorithm. Clusters that best matched the selected set of potentially relevant studies (a “training” set of 139 articles) were then identified by using maximum entropy classification. The 139 articles selected automatically on this basis were screened manually to identify potentially relevant studies. To determine the validity of the automated process, a parallel set of studies was also assembled by manually screening all initially searched publications. Finally, detailed manual selection was performed on the full texts of the studies in both sets using standard criteria. Quality assessment, meta-regression random-effects models, sensitivity analysis and publication bias assessment were then conducted.Results: Machine learning-assisted screening identified the same 29 studies for meta-analysis as those identified by using manual screening alone. Machine learning enabled more robust and efficient study selection, reducing the number of studies needed for manual screening from 4,177 to 556 articles. A pooled analysis using the most conservative estimates indicated that patients with DM had ~49% greater risk of developing AF compared with individuals without DM. After adjusting for three additional risk factors i.e., hypertension, obesity and heart disease, the relative risk was 23%. Using multivariate adjusted models, the risk for developing AF in patients with DM was similar for all DM subtypes. Women with DM were 24% more likely to develop AF than men with DM. The risk for new-onset AF in patients with DM has also increased over the years.Conclusions: We have developed a novel machine learning method to identify publications suitable for inclusion in meta-analysis.This approach has the capacity to provide for a more efficient and more objective study selection process for future such studies. We have used it to demonstrate that DM is a strong, independent risk factor for AF, particularly for women

Comparison of diffusion tensor imaging by cardiovascular magnetic resonance and gadolinium enhanced 3D image intensity approaches to investigation of structural anisotropy in explanted rat hearts

Background: Cardiovascular magnetic resonance (CMR) can through the two methods 3D FLASH and diffusion tensor imaging (DTI) give complementary information on the local orientations of cardiomyocytes and their laminar arrays. Methods: Eight explanted rat hearts were perfused with Gd-DTPA contrast agent and fixative and imaged in a 9.4T magnet by two types of acquisition: 3D fast low angle shot (FLASH) imaging, voxels 50 × 50 × 50 μm, and 3D spin echo DTI with monopolar diffusion gradients of 3.6 ms duration at 11.5 ms separation, voxels 200 × 200 × 200 μm. The sensitivity of each approach to imaging parameters was explored. Results:The FLASH data showed laminar alignments of voxels with high signal, in keeping with the presumed predominance of contrast in the interstices between sheetlets. It was analysed, using structure-tensor (ST) analysis, to determine the most (v 1 ST ), intermediate (v 2 ST ) and least (v 3 ST ) extended orthogonal directions of signal continuity. The DTI data was analysed to determine the most (e 1 DTI ), intermediate (e 2 DTI ) and least (e 3 DTI ) orthogonal eigenvectors of extent of diffusion. The correspondence between the FLASH and DTI methods was measured and appraised. The most extended direction of FLASH signal (v 1 ST ) agreed well with that of diffusion (e 1 DTI ) throughout the left ventricle (representative discrepancy in the septum of 13.3 ± 6.7°: median ± absolute deviation) and both were in keeping with the expected local orientations of the long-axis of cardiomyocytes. However, the orientation of the least directions of FLASH signal continuity (v 3 ST ) and diffusion (e 3 ST ) showed greater discrepancies of up to 27.9 ± 17.4°. Both FLASH (v 3 ST ) and DTI (e 3 DTI ) where compared to directly measured laminar arrays in the FLASH images. For FLASH the discrepancy between the structure-tensor calculated v 3 ST and the directly measured FLASH laminar array normal was of 9 ± 7° for the lateral wall and 7 ± 9° for the septum (median ± inter quartile range), and for DTI the discrepancy between the calculated v 3 DTI and the directly measured FLASH laminar array normal was 22 ± 14° and 61 ± 53.4°. DTI was relatively insensitive to the number of diffusion directions and to time up to 72 hours post fixation, but was moderately affected by b-value (which was scaled by modifying diffusion gradient pulse strength with fixed gradient pulse separation). Optimal DTI parameters were b = 1000 mm/s2 and 12 diffusion directions. FLASH acquisitions were relatively insensitive to the image processing parameters explored. Conclusions: We show that ST analysis of FLASH is a useful and accurate tool in the measurement of cardiac microstructure. While both FLASH and the DTI approaches appear promising for mapping of the alignments of myocytes throughout myocardium, marked discrepancies between the cross myocyte anisotropies deduced from each method call for consideration of their respective limitations

Hodgkin-Huxley type ion channel characterization:an improved method of voltage clamp experiment parameter estimation

The Hodgkin-Huxley formalism for quantitative characterization of ionic channels is widely used in cellular electrophysiological models. Model parameters for these individual channels are determined from voltage clamp experiments and usually involve the assumption that inactivation process occurs on a time scale which is infinitely slow compared to the activation process. This work shows that such an assumption may lead to appreciable errors under certain physiological conditions and proposes a new numerical approach to interpret voltage clamp experiment results. In simulated experimental protocols the new method was shown to exhibit superior accuracy compared to the traditional least squares fitting methods. With noiseless input data the error in gating variables and time constants was less than 1%, whereas the traditional methods generated upwards of 10% error and predicted incorrect gating kinetics. A sensitivity analysis showed that the new method could tolerate up to approximately 15% perturbation in the input data without unstably amplifying error in the solution. This method could also assist in designing more efficient experimental protocols, since all channel parameters (gating variables, time constants and maximum conductance) could be determined from a single voltage step

Synaptic Plasticity in Cardiac Innervation and Its Potential Role in Atrial Fibrillation

Synaptic plasticity is defined as the ability of synapses to change their strength of transmission. Plasticity of synaptic connections in the brain is a major focus of neuroscience research, as it is the primary mechanism underpinning learning and memory. Beyond the brain however, plasticity in peripheral neurons is less well understood, particularly in the neurons innervating the heart. The atria receive rich innervation from the autonomic branch of the peripheral nervous system. Sympathetic neurons are clustered in stellate and cervical ganglia alongside the spinal cord and extend fibers to the heart directly innervating the myocardium. These neurons are major drivers of hyperactive sympathetic activity observed in heart disease, ventricular arrhythmias, and sudden cardiac death. Both pre- and postsynaptic changes have been observed to occur at synapses formed by sympathetic ganglion neurons, suggesting that plasticity at sympathetic neuro-cardiac synapses is a major contributor to arrhythmias. Less is known about the plasticity in parasympathetic neurons located in clusters on the heart surface. These neuronal clusters, termed ganglionated plexi, or “little brains,” can independently modulate neural control of the heart and stimulation that enhances their excitability can induce arrhythmia such as atrial fibrillation. The ability of these neurons to alter parasympathetic activity suggests that plasticity may indeed occur at the synapses formed on and by ganglionated plexi neurons. Such changes may not only fine-tune autonomic innervation of the heart, but could also be a source of maladaptive plasticity during atrial fibrillation