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

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

Three-dimensional transmural organization of perimysial collagen in the heart

There is strong support for the view that the ventricular myocardium has a laminar organization in which myocytes are grouped into branching layers separated by cleavage planes. However, understanding of the extent and functional implications of this architecture has been limited by the lack of a systematic three-dimensional description of the organization of myocytes and associated perimysial collagen. We imaged myocytes and collagen across the left ventricular wall at high resolution in seven normal rat hearts using extended volume confocal microscopy. We developed novel reconstruction and segmentation techniques necessary for the quantitative analysis of three-dimensional myocyte and perimysial collagen organization. The results confirm that perimysial collagen has an ordered arrangement and that it defines a laminar organization. Perimysial collagen is composed of three distinct forms: extensive meshwork on laminar surfaces, convoluted fibers connecting adjacent layers, and longitudinal cords. While myolaminae are the principal form of structural organization throughout most of the wall, they are not seen in the subepicardium, where perimysial collagen is present only as longitudinal cords

Progression of myocardial remodeling and mechanical and dysfunction in the spontaneously hypertensive rat

The progression of hypertensive heart disease (HHD) to heart failure (HF) is associated with myocardial remodeling. Corresponding changes in three-dimensional organization of cardiac extracellular matrix have not been quantified or related fully to the development of HF. Spontaneously hypertensive rats (SHRs) and Wistar-Kyoto controls were studied at 3, 12, 18, and 24 mo. Hemodynamic and morphological data, brain natriuretic peptide levels, and echocardiography demonstrate four distinct disease stages: systemic hypertension, diastolic dysfunction, early systolic failure, and decompensated HF. Passive left ventricular (LV) pressure-volume relationships were determined in vitro. Transmural specimens from the anterior LV free wall were imaged using extendedvolume confocal microscopy, and three-dimensional myocardial architecture was quantified. In SHRs, LV compliance was reduced at 12 mo and increased progressively thereafter. However, it was less than in controls for filling pressures 10 mmHg and not significantly different at 10 mmHg. Myocyte cross section was enlarged, with increased variability from 12 mo, while collagen fraction increased progressively. Perimysial collagen fraction remained unchanged with age, although endomysial collagen increased from 12 mo. Perimysial collagen between adjacent muscle layers fused at 12 mo and continued to thicken subsequently, while muscle layers became more dispersed and disordered. We conclude that LV dilatation, which accompanies decompensated HF in this model of HHD, is not due to LV “softening.” While perimysial (and endomysial) collagen networks are substantially remodeled, they are not dissolved, as has been proposed. We argue that progressive disruption of the laminar organization of LV myocardium may contribute to impaired systolic function in HHD