State-dependent activity dynamics of hypothalamic stress effector neurons

The stress response necessitates an immediate boost in vital physiological functions from their homeostatic operation to an elevated emergency response. However, the neural mechanisms underlying this state-dependent change remain largely unknown. Using a combination of in vivo and ex vivo electrophysiology with computational modeling, we report that corticotropin releasing hormone (CRH) neurons in the paraventricular nucleus of the hypothalamus (PVN), the effector neurons of hormonal stress response, rapidly transition between distinct activity states through recurrent inhibition. Specifically, in vivo optrode recording shows that under non-stress conditions, CRHPVN neurons often fire with rhythmic brief bursts (RB), which, somewhat counterintuitively, constrains firing rate due to long (~2 s) interburst intervals. Stressful stimuli rapidly switch RB to continuous single spiking (SS), permitting a large increase in firing rate. A spiking network model shows that recurrent inhibition can control this activity-state switch, and more broadly the gain of spiking responses to excitatory inputs. In biological CRHPVN neurons ex vivo, the injection of whole-cell currents derived from our computational model recreates the in vivo-like switch between RB and SS, providing direct evidence that physiologically relevant network inputs enable state-dependent computation in single neurons. Together, we present a novel mechanism for state-dependent activity dynamics in CRHPVN neurons

Cardiac cell modelling: Observations from the heart of the cardiac physiome project

In this manuscript we review the state of cardiac cell modelling in the context of international initiatives such as the IUPS Physiome and Virtual Physiological Human Projects, which aim to integrate computational models across scales and physics. In particular we focus on the relationship between experimental data and model parameterisation across a range of model types and cellular physiological systems. Finally, in the context of parameter identification and model reuse within the Cardiac Physiome, we suggest some future priority areas for this field

Characterizing Cardiac Electrophysiology during Radiofrequency Ablation : An Integrative Ex vivo, In silico, and In vivo Approach

Catheter ablation is a major treatment for atrial tachycardias. Hereby, the precise monitoring of the lesion formation is an important success factor. This book presents computational, wet-lab, and clinical studies with the aim of evaluating the signal characteristics of the intracardiac electrograms (IEGMs) recorded around ablation lesions from different perspectives. The detailed analysis of the IEGMs can optimize the description of durable and complex lesions during the ablation procedure

VIOLA - A multi-purpose and web-based visualization tool for neuronal-network simulation output

Neuronal network models and corresponding computer simulations are invaluable tools to aid the interpretation of the relationship between neuron properties, connectivity and measured activity in cortical tissue. Spatiotemporal patterns of activity propagating across the cortical surface as observed experimentally can for example be described by neuronal network models with layered geometry and distance-dependent connectivity. The interpretation of the resulting stream of multi-modal and multi-dimensional simulation data calls for integrating interactive visualization steps into existing simulation-analysis workflows. Here, we present a set of interactive visualization concepts called views for the visual analysis of activity data in topological network models, and a corresponding reference implementation VIOLA (VIsualization Of Layer Activity). The software is a lightweight, open-source, web-based and platform-independent application combining and adapting modern interactive visualization paradigms, such as coordinated multiple views, for massively parallel neurophysiological data. For a use-case demonstration we consider spiking activity data of a two-population, layered point-neuron network model subject to a spatially confined excitation originating from an external population. With the multiple coordinated views, an explorative and qualitative assessment of the spatiotemporal features of neuronal activity can be performed upfront of a detailed quantitative data analysis of specific aspects of the data. Furthermore, ongoing efforts including the European Human Brain Project aim at providing online user portals for integrated model development, simulation, analysis and provenance tracking, wherein interactive visual analysis tools are one component. Browser-compatible, web-technology based solutions are therefore required. Within this scope, with VIOLA we provide a first prototype.Comment: 38 pages, 10 figures, 3 table

Development and Validation of Intracardiac Electrograms Software Tool for Analysis of Atrial Fibrillation Biomarkers

Atrial Fibrillation (AF) is the most common cardiac arrhythmia and is associated with a drastically increase of mortality risk. Mechanisms underlying the initiation and maintenance of AF are not understood yet and current treatments for AF are not completely efficient. Most effective one, around 50% of effectiveness, is the catheter ablation therapy but its long-term effects are reduced. Target ablation is thought to be a solution if AF focal sources were identified. Associated AF biomarkers to identify such zones are shortened action potential durations (APDs) and slow conduction velocities (CV). Identification of those biomarkers could be made through intracardiac electrogram (EGM) signals, however, they are very complex to interpret and there is still no validated method to perform it. The aim of this bachelor thesis is to develop and validate a software tool able to obtain APDs and CV from intracardiac EGM signals recorded directly from a swine heart. Electrical recording results are validated with optical mapping (OM) data recordings. The code was implemented with the purpose to work for nearby sinus rhythms, that is when it can be useful in clinical application to identify in patients with paroxystic AF the regions associated to the trigger of the AF. Simultaneous electrical and OM recordings of isolated swine heart are performed in a Langendorff system set-up. Software code processes electrical recordings by computing APDs with Botteron algorithm and computing phase maps. Once phase maps are obtained, trajectories are computed and thus CV. Electrical results are validated with OM data. Two catheters of different sizes are tested as well as results using 15 or 5 out of the 15 electrodes available in each catheter. APDs results were highly accurate, they were very similar to the OM data independently of whether catheter or number of electrodes was used. CV results were also similar to the OM data, varying inside the acceptable range proposed. Best results of CV were obtained with the small catheter and specifically for 5 electrode interpolation. Both APDs and CV results did not show statistically significative differences with the analysis of the T-student test. It can be concluded that the software code developed in this thesis demonstrates that is possible to compute both APD and CV values from intracardiac EGM signals with an acceptable accuracy. Optimal results for CV computation were obtained with the small catheter (2.5 cm radius) and using only 5 out of the 15 electrodes. This opens new insights into the use of EGM signals for the clinical estimation of proarrhythmic areas.Ingeniería Biomédic

Toward a multiscale modeling framework for understanding serotonergic function

Despite its importance in regulating emotion and mental wellbeing, the complex structure and function of the serotonergic system present formidable challenges toward understanding its mechanisms. In this paper, we review studies investigating the interactions between serotonergic and related brain systems and their behavior at multiple scales, with a focus on biologically-based computational modeling. We first discuss serotonergic intracellular signaling and neuronal excitability, followed by neuronal circuit and systems levels. At each level of organization, we will discuss the experimental work accompanied by related computational modeling work. We then suggest that a multiscale modeling approach that integrates the various levels of neurobiological organization could potentially transform the way we understand the complex functions associated with serotonin