Complexity quantification of signals from the heart, the macrocirculation and the microcirculation through a multiscale entropy analysis

Quantifying and modelling the cardiovascular system (CVS) represent a challenge to improve our understanding of the CVS. To describe and quantify the CVS, several physiological signals have been analyzed through various signal processing methods. Recently, a quantitative descriptor – the multiscale entropy (MSE) – has been proposed to quantify time series complexity (i.e. the degree of regularity of signal fluctuations) over multiple time scales. Heart rate variability (HRV) signals (i.e. data from the heart) have largely been studied through MSE. By contrast, complexities of signals from the macrocirculation (i.e. elastic and muscular arteries) and the microcirculation (i.e. arterioles and capillaries), two other main components of the CVS, have rarely been investigated simultaneously with MSE. We therefore propose to quantify and compare complexity of signals from these three CVS subsystems: the heart, the macrocirculation and the microcirculation, using MSE. Electrocardiograms, electrical bio-impedance signals (macrocirculation), as well as laser Doppler flowmetry (LDF) signals from finger and forearm (microcirculation) have been recorded simultaneously in nine healthy subjects. The MSE values from these data have been computed and compared. We note a significant lower complexity on scales τ = 1, 2 and 3 (i.e. around 1.08 Hz, 0.54 Hz and 0.36 Hz respectively) for LDF signals from the finger compared to the ones of signals from the heart and the macrocirculation. On scale τ = 5 (i.e. 0.21 Hz), complexity value of signals from the macrocirculation is significantly lower than the ones of HRV and data from the microvascular blood flow in forearm. The three CVS subsystems present different complexity values depending on scales. It could now be of interest to investigate if these complexity differences are due to physiological activities. Moreover, our results could be compared with those obtained from data recorded on patients with vascular diseases

Non-linear tools and methodological concerns measuring human movement variability

In recent years, several works have explored variability using different approaches, trying to describe the variations in motor movement. Traditionally, movement variability was regarded as a system error due to noise of neuromuscular mechanisms, but alternative theories suggest that motor variability seems to reflect a functional behaviour improving motor control and enhancing learning. Controversial results have been reported about variability characteristics and its role in motor control and learning, and several works suggest that the main difficulty lies in how to measure this variability. In this work, we have outlined the most used non-linear tools to assess human variability, their applications, advantages and disadvantages. We have also suggested different methods about how to achieve a multidimensional approximation to motor variability. Finally, we have called attention to some methodological issues frequently reported as important aspects to take into account when measuring human movement variability.En los últimos años, varios trabajos han explorado la variabilidad desde diferentes enfoques con el objetivo de describir las variaciones del movimiento. Tradicionalmente, la variabilidad del movimiento fue considerada un error del sistema causado por el ruido de los mecanismos neuromusculares, pero actualmente, teorías alternativas sugieren que la variabilidad motora parece reflejar un comportamiento funcional que ayuda a mejorar el control del movimiento y el aprendizaje. Sin embargo, existen resultados controvertidos acerca de las características de la variabilidad motora y su rol en el aprendizaje y control motor. En la literatura se ha sugerido que uno de sus principales motivos puede ser las herramientas utilizadas para intenta analizar la variabilidad. En este trabajo hemos realizado un resumen sobre las herramientas no lineales más utilizadas para valorar la variabilidad humana, su aplicación, ventajas e inconvenientes. Además, sugerimos diferentes métodos para obtener una aproximación multidimensional de la variabilidad motora. Finalmente, hemos hecho hincapié en algunos problemas metodológicos que se han considerado importantes a la hora de medir la variabilidad del movimiento humano

Deformability-induced effects of red blood cells in flow

To ensure a proper health state in the human body, a steady transport of blood is necessary. As the main cellular constituent in the blood suspension, red blood cells (RBCs) are governing the physical properties of the entire blood flow. Remarkably, these RBCs can adapt their shape to the prevailing surrounding flow conditions, ultimately allowing them to pass through narrow capillaries smaller than their equilibrium diameter. However, several diseases such as diabetes mellitus or malaria are linked to an alteration of the deformability. In this work, we investigate the shapes of RBCs in microcapillary flow in vitro, culminating in a shape phase diagram of two distinct, hydrodynamically induced shapes, the croissant and the slipper. Due to the simplicity of the RBC structure, the obtained phase diagram leads to further insights into the complex interaction between deformable objects in general, such as vesicles, and the surrounding fluid. Furthermore, the phase diagram is highly correlated to the deformability of the RBCs and represents thus a cornerstone of a potential diagnostic tool to detect pathological blood parameters. To further promote this idea, we train a convolutional neural network (CNN) to classify the distinct RBC shapes. The benchmark of the CNN is validated by manual classification of the cellular shapes and yields very good performance. In the second part, we investigate an effect that is associated with the deformability of RBCs, the lingering phenomenon. Lingering events may occur at bifurcation apices and are characterized by a straddling of RBCs at an apex, which have been shown in silico to cause a piling up of subsequent RBCs. Here, we provide insight into the dynamics of such lingering events in vivo, which we consequently relate to the partitioning of RBCs at bifurcating vessels in the microvasculature. Specifically, the lingering of RBCs causes an increased intercellular distance to RBCs further downstream, and thus, a reduced hematocrit.Um die biologischen Funktionen im menschlichen Körper aufrechtzuerhalten ist eine stetige Versorgung mit Blut notwendig. Rote Blutzellen bilden den Hauptanteil aller zellulären Komponenten im Blut und beeinflussen somit maßgeblich dessen Fließeigenschaften. Eine bemerkenswerte Eigenschaft dieser roten Blutzellen ist ihre Deformierbarkeit, die es ihnen ermöglicht, ihre Form den vorherrschenden Strömungsbedingungen anzupassen und sogar durch Kapillaren zu strömen, deren Durchmesser kleiner ist als der Gleichgewichtsdurchmesser einer roten Blutzelle. Zahlreiche Erkrankungen wie beispielsweise Diabetes mellitus oder Malaria sind jedoch mit einer Veränderung dieser Deformierbarkeit verbunden. In der vorliegenden Arbeit untersuchen wir die hydrodynamisch induzierten Formen der roten Blutzellen in mikrokapillarer Strömung in vitro systematisch für verschiedene Fließgeschwindigkeiten. Aus diesen Daten erzeugen wir ein Phasendiagramm zweier charakteristischer auftretender Formen: dem Croissant und dem Slipper. Aufgrund der Einfachheit der Struktur der roten Blutzellen führt das erhaltene Phasendiagramm zu weiteren Erkenntnissen über die komplexe Interaktion zwischen deformierbaren Objekten im Allgemeinen, wie z.B. Vesikeln, und des sie umgebenden Fluids. Darüber hinaus ist das Phasendiagramm korreliert mit der Deformierbarkeit der Erythrozyten und stellt somit einen Eckpfeiler eines potentiellen Diagnosewerkzeugs zur Erkennung pathologischer Blutparameter dar. Um diese Idee weiter voranzutreiben, trainieren wir ein künstliches neuronales Netz, um die auftretenden Formen der Erythrozyten zu klassifizieren. Die Ausgabe dieses künstlichen neuronalen Netzes wird durch manuelle Klassifizierung der Zellformen validiert und weist eine sehr hohe Übereinstimmung mit dieser manuellen Klassifikation auf. Im zweiten Teil der Arbeit untersuchen wir einen Effekt, der sich direkt aus der Deformierbarkeit der roten Blutzellen ergibt, das Lingering-Phänomen. Diese Lingering-Ereignisse können an Bifurkationsscheiteln zweier benachbarter Kapillaren auftreten und sind durch ein längeres Verweilen von Erythrozyten an einem Scheitelpunkt gekennzeichnet. In Simulationen hat sich gezeigt, dass diese Dynamik eine Anhäufung von nachfolgenden roten Blutzellen verursacht. Wir analysieren die Dynamik solcher Verweilereignisse in vivo, die wir folglich mit der Aufteilung von Erythrozyten an sich gabelnden Gefäßen in der Mikrovaskulatur in Verbindung bringen. Insbesondere verursacht das Verweilen von Erythrozyten einen erhöhten interzellulären Abstand zu weiter stromabwärts liegenden Erythrozyten und damit einen reduzierten Hämatokrit

Whole-Mouse Brain Vascular Analysis Framework: Synthetic Model-Based Validation, Informatics Platform, and Queryable Database

The past decade has seen innovative advancements in light microscopy instrumentation that have afforded the acquisition of whole-brain datasets at micrometer resolution. As the hardware and software used to automate the traditional neuroanatomical workflow become more accessible to researchers around the globe, so will the tools needed to analyze whole-brain datasets. Only recently has the focus begun to shift from the development of instrumentation towards platforms for data-driven quantitative analyses. As a consequence of this, the tools required for large-scale quantitative studies across the whole brain are few and far between. In this dissertation, we aim to change this through the development of a standardized, quantitative approach to the study of whole-brain, cerebrovasculature datasets. Our standardized and quantitative approach has four components. The first is the construction of synthetic cerebrovasculature models that can be used in conjunction with the second component, a model-based validation system. Any cerebrovasculature study conducted using imaging data must first extract the filaments embedded within that dataset. The segmentation algorithms that are commonly used to do this are frequently validated on small-scale datasets that represent only a small selection of cerebrovasculature variability. The question is how do these algorithms perform when applied to large-scale datasets. Our model-based validation system uses biologically inspired, large-scale datasets that asses the accuracy of the segmentation algorithm output against ground truth data. Once the data is segmented, we have implemented an informatics platform that calculates descriptive statistics across the entire volume. Attributes describing each vascular filament are also calculated. These include measures of vascular radius, length, surface area, volume, tortuosity, and others. The result is a massive amount of data for the cerebrovasculature segments. The question becomes how can this be analyzed sensibly. Given that both cerebrovasculature topology and geometry can be capture in graph form, we construct the fourth component of our system: a graph database that stores the cerebrovasculature. The graph model of cerebrovasculature that we have developed allows segments to be searched across the whole-brain based on their attributes and/or location. We also implemented a means to reconstruct the segments returned by a specific query for visualizations. This means that a simple text-based query can retrieve cerebrovasculature geometry and topology of the specified vasculature. For example, a query can return all vessels within the frontal cortex, those with specific attribute(s) value range(s), or any combination of attribute and location. Complex graph algorithms can also be applied, such as the shortest path between two bifurcation points or measures of centrality that are important in determining the robust and fragile aspects of blood flow through the cerebrovasculature system. To illustrate the utility of our system, we construct a whole-brain database of vascular connectivity from the Knife-Edge Scanning Microscope India Ink dataset. Using our cerebrovasculature database, we were able to study the cerebrovasculature system by issuing text-based queries to extract the vessel segments that we were interested in. The outcome of our investigation was a wealth of information about the cerebrovasculature system as a whole, and about the different classifications of vessels comprising it. The results returned from these simple queries even generated some interesting and biologically relevant questions. For instance, the profound spikes in radius distribution for some classes of vessels that did not present in other classes. We expect that the methods described in this dissertation will open the door for data-driven, quantitative investigation across the whole-brain. At the time of writing – and to the best of our knowledge that prior to this work – there was not a systemic way to assess segmentation algorithm performance, calculate attributes for each segment of vasculature extracted across the whole brain, and store those results in a queryable database that also stores geometry and topology of the entire cerebrovasculature system. We believe that our method can and will set the standard for largescale cerebrovasculature research. Therefore, in conclusion, we state that our methods contribute a standardized, quantitative approach to the study of cerebrovasculature datasets acquired using modern imaging techniques