The self-organized dynamics of vortex-like rotating waves, which are also known as scroll waves, are the basis of the formation of complex spatiotemporal patterns in many excitable chemical and biological systems. In the heart, filament-like phase singularities that are associated with three-dimensional scroll waves are considered to be the organizing centres of life-threatening cardiac arrhythmias. The mechanisms that underlie the onset, maintenance and control of electromechanical turbulence in the heart are inherently three-dimensional phenomena. However, it has not previously been possible to visualize the three-dimensional spatiotemporal dynamics of scroll waves inside cardiac tissues. Here we show that three-dimensional mechanical scroll waves and filament-like phase singularities can be observed deep inside the contracting heart wall using high-resolution four-dimensional ultrasound-based strain imaging. We found that mechanical phase singularities co-exist with electrical phase singularities during cardiac fibrillation. We investigated the dynamics of electrical and mechanical phase singularities by simultaneously measuring the membrane potential, intracellular calcium concentration and mechanical contractions of the heart. We show that cardiac fibrillation can be characterized using the three-dimensional spatiotemporal dynamics of mechanical phase singularities, which arise inside the fibrillating contracting ventricular wall. We demonstrate that electrical and mechanical phase singularities show complex interactions and we characterize their dynamics in terms of trajectories, topological charge and lifetime. We anticipate that our findings will provide novel perspectives for non-invasive diagnostic imaging and therapeutic applications.
Optical mapping is a widely used imaging technique for investigating cardiac electrophysiology in intact, Langendorff-perfused hearts. Mechanical contraction of cardiac tissue, however, may result in severe motion artifacts and significant distortion of the fluorescence signals. Therefore, pharmacological uncoupling is widely used to reduce tissue motion. Recently, various image processing algorithms have been proposed to reduce motion artifacts. We will review these technological developments. Furthermore, we will present a novel approach for the three-dimensional, marker-free reconstruction of contracting Langendorff-perfused intact hearts under physiological conditions. The algorithm allows disentangling the fluorescence signals (e.g. membrane voltage or intracellular calcium) from the mechanical motion (e.g. tissue strain). We will discuss the algorithms reconstruction accuracy, resolution, and robustness using experimental data from Langendorff-perfused rabbit hearts.
To behave properly in an unknown environment, animals or robots must distinguish external from selfgenerated stimuli on their sensors. The biologically inspired concepts of efference copy and internal model have been successfully applied to a number of robot control problems. Here we present an application of this for our dynamic walking robot RunBot. We use efference copies of the motor commands with a simple forward internal model to predict the expected self-generated acceleration during walking. The difference to the actually measured acceleration is then used to stabilize the walking on terrains with changing slopes through its upper body component controller. As a consequence, the controller drives the upper body component (UBC) to lean forwards/backwards as soon as an error occurs resulting in dynamical stable walking. We have evaluated the performance of the system on four different track configurations. Furthermore we believe that the experimental studies pursued here will sharpen our understanding of how the efference copies influence dynamic locomotion Electronic supplementary material The online version of this article (doi:10.1007/s10514-010-9199-7) contains supplementary material, which is available to authorized users.J. Schröder-Schetelig Max Planck Institute for Dynamics and Self-Organization, Bunsenstraße 10, 37073 Göttingen, Germany e-mail: johannes.schroeder-schetelig@ds.mpg.de P. Manoonpong · F. Wörgötter ( ) Bernstein Center for Computational Neuroscience (BCCN), University of Göttingen, Friedrich-Hund-Platz 1, 37077 Göttingen, Germany e-mail: worgott@physik3.gwdg.de P. Manoonpong e-mail: poramate@physik3.gwdg.de control to the benefit of modern neural control strategies in robots.
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