Multi-compartment Simulation Research Articles

Comparison of gradient echo and gradient echo sampling of spin echo sequence for the quantification of the oxygen extraction fraction from a combined quantitative susceptibility mapping and quantitative BOLD (QSM+qBOLD) approach.

To compare gradient echo (GRE) and gradient echo sampling of spin echo (GESSE) sequences for the quantification of the oxygen extraction fraction (OEF) from combined quantitative BOLD and quantitative susceptibility mapping (QSM) with regard to accuracy, precision and parameter initialization. GRE and GESSE data were acquired from 7 healthy volunteers. QSM was applied to the GRE data and used as a regularization for the single-compartment quantitative BOLD fit to the GESSE and GRE data, respectively, to quantify OEF, deoxygenated blood volume (ν), R2 , and non-blood susceptibility (χnb ). Intersubject means within gray and white matter, respectively, were compared between GESSE and GRE (Student's t) and gray-white matter contrast was determined for each sequence separately. A single- and multi-compartment simulation was used to compare reconstruction accuracy. Intersubject means and SDs for gray and white matter were OEF = 32.4 ± 1.6%, ν = 2.9 ± 0.1%, R2 = 14.2 ± 0.5 Hz, χnb = -43 ± 5 ppb for GESSE and OEF = 43.0 ± 5.4%, ν = 3.5 ± 0.4%, R2 = 14.4 ± 0.7 Hz, χnb = -43 ± 8 ppb for GRE with a significant difference (P < 0.05) for OEF and ν. Gray-white matter contrast was significant (P < 0.05) in all parameters for GESSE but only in ν and R2 for GRE. All parameters reconstructed from GESSE had higher accuracy than from GRE in the single- but not multi-compartment simulation. GESSE yields higher parameter accuracy in simulated gray matter but produces unphysiological gray-white matter contrast in OEF in vivo. GRE produces uniform OEF maps in vivo and is more efficient, which could facilitate a clinical implementation, but revealed biases in simulation. The appropriate sequence should be chosen depending on application.

A framework supporting multi-compartment stochastic simulation and parameter optimisation for investigating biological system development

In this paper we propose a simulation framework specifically suited for developmental biology studies. It is mainly composed of three parts. First, it is based on a multiscale computational model, and related logic-oriented specification language compiler, supporting large-scale networks of compartments and an enhanced model of chemical reactions addressing molecule transfer. Second, we rely on a simulation engine based on an optimised version of the Gillespie stochastic simulation algorithm, which is able to simulate fine events at intracellular and multicellular level. Third, a metaheuristic-based module for automatically calibrating model parameters (such as reaction rates) is exploited. As a case study we model the first stages of Drosophila melanogaster development, which generate the early spatial pattern of gap gene expression. Results show the formation of a precise spatial pattern which has been successfully compared with observations acquired from the real embryo gene expressions. In particular, adopting the Covariance Matrix Adaptation Evolution Strategy for parameter estimation is crucial for the quality of the results achieved, reducing the error of a 60% from the initial formulation of parameters. The main contribution of this paper is the enhancement of a stochastic, multi-compartment simulator by means of a metaheuristic-based module for parameter estimation. This is the first such application available in literature, where the subject of parameter estimation is well-established only for deterministic single-compartment models. Moreover this is the first work demonstrating the ability of the Gillespie stochastic simulation algorithm, when properly equipped with additional parameter optimisation techniques, to model large-scale, complex biological systems.

Estimation method for biophysical properties of insect neurons in the combination of suitable stimulation and multi-compartment simulation with supercomputers

Event Abstract Back to Event Estimation method for biophysical properties of insect neurons in the combination of suitable stimulation and multi-compartment simulation with supercomputers Daisuke Miyamoto1*, Tomoki Kazawa2, Stephan Haupt2, Masashi Tabuchi3, Tomoaki Mori1, Kei Nakatani4 and Ryohei Kanzaki2 1 Graduate School of Information Science and Technology, The University of Tokyo , Japan 2 Research Center for Advanced Science and Technology, The University of Tokyo , Japan 3 Graduate School of Engineering, The University of Tokyo , Japan 4 Graduate School of Life and Environmental Science, Tsukuba University , Japan The importance of local dendritic activicty in information processing of single neurons has been generally recognized. Constructing multi-compartment simulation models that also reflect the distribution of active conductances in real neurons is the one of the big challenges to achieve realistic modeling due to the paucity of experimental data. As a remedy to this problem, we propose an estimation method combining suitable electrical stimulation and massively parallel computation using genetic algorithms. We applied this method to projection neurons in antennal lobes of silkmoth (Bombyx mori) as the first model neurons. We obtained morphological tree models containing about 4,000 cylindrical compartments from confocal microscopy images. We assumed the presence of known insect K+, Na+ and Ca2+ channels and used candidate distribution models to estimate the corresponding conductances. Several types of electrical stimuli (current ramps, optionally with added sine waves of different frequencies, and voltage clamp pulses) stimuli, were evaluated for their efficiency in providing multi-compartment models reproducing the electrophysiological data. Such estimation methods need to run multi-compartment simulations many times. To meet the computational demands of the method, we used the Real Coded Genetics Algorithm (RCGA) for parallel efficiency and MPI/OpenMP hybrid implementations on the K computer which has 640,000 SPARC64VIIIfx CPU cores and on the RIKEN Integrated Cluster of Clusters (RICC).For estimating the position of a high-conductance area in an axon, injecting a current ramp with added sine wave showed good performance even after a small number of generations of the genetic algorithm (Fig. 1). This could be related to the frequency transmission characteristics of the axon. With 32,768 genes and 200 generations per estimation, we achieved parallel efficiency as high as p=0.99987 comparing the 1,024 cores and 8,192 cores cases (Fig. 2). We showed here that 1) RCGA is suitable for estimating biophysical properties for realistic multi-compartment models with high efficiency in massively parallel computation and that 2) suitable choice of stimulation paradigms can improve the efficiency of estimation. The interaction between experimental data simulation and parameter estimation can be a useful tool to improve multi-compartment models. Figure 1 Keywords: computational neuroscience, Biophysical methods, neuron models, supercomputer simulation, genetic algorithms Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Poster Topic: Neuroinformatics Citation: Miyamoto D, Kazawa T, Haupt S, Tabuchi M, Mori T, Nakatani K and Kanzaki R (2014). Estimation method for biophysical properties of insect neurons in the combination of suitable stimulation and multi-compartment simulation with supercomputers. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2014.08.00011 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Mar 2013; Published Online: 27 Feb 2014. * Correspondence: Dr. Daisuke Miyamoto, Graduate School of Information Science and Technology, The University of Tokyo, Meguro-ku, Japan, miyamoto@brain.imi.i.u-tokyo.ac.jp Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Daisuke Miyamoto Tomoki Kazawa Stephan Haupt Masashi Tabuchi Tomoaki Mori Kei Nakatani Ryohei Kanzaki Google Daisuke Miyamoto Tomoki Kazawa Stephan Haupt Masashi Tabuchi Tomoaki Mori Kei Nakatani Ryohei Kanzaki Google Scholar Daisuke Miyamoto Tomoki Kazawa Stephan Haupt Masashi Tabuchi Tomoaki Mori Kei Nakatani Ryohei Kanzaki PubMed Daisuke Miyamoto Tomoki Kazawa Stephan Haupt Masashi Tabuchi Tomoaki Mori Kei Nakatani Ryohei Kanzaki Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. 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Mechanisms and Distribution of Ion Channels in Retinal Ganglion Cells: Using Temperature as an Independent Variable

Trains of action potentials of rat and cat retinal ganglion cells (RGCs) were recorded intracellularly across a temperature range of 7-37 degrees C. Phase plots of the experimental impulse trains were precision fit using multicompartment simulations of anatomically reconstructed rat and cat RGCs. Action potential excitation was simulated with a "Five-channel model" [Na, K(delayed rectifier), Ca, K(A), and K(Ca-activated) channels] and the nonspace-clamped condition of the whole cell recording was exploited to determine the channels' distribution on the dendrites, soma, and proximal axon. At each temperature, optimal phase-plot fits for RGCs occurred with the same unique channel distribution. The "waveform" of the electrotonic current was found to be temperature dependent, which reflected the shape changes in the experimental action potentials and confirmed the channel distributions. The distributions are cell-type specific and adequate for soma and dendritic excitation with a safety margin. The highest Na-channel density was found on an axonal segment some 50-130 microm distal to the soma, as determined from the temperature-dependent "initial segment-somadendritic (IS-SD) break." The voltage dependence of the gating rate constants remains invariant between 7 and 23 degrees C and between 30 and 37 degrees C, but undergoes a transition between 23 and 30 degrees C. Both gating-kinetic and ion-permeability Q10s remain virtually constant between 23 and 37 degrees C (kinetic Q10s = 1.9-1.95; permeability Q10s = 1.49-1.64). The Q10s systematically increase for T <23 degrees C (kinetic Q10 = 8 at T = 8 degrees C). The Na channels were consistently "sleepy" (non-Arrhenius) for T <8 degrees C, with a loss of spiking for T <7 degrees C.

Developmental Changes in Dendritic Shape and Synapse Location Tune Single-Neuron Computations to Changing Behavioral Functions

During nervous system development, different classes of neurons obtain different dendritic architectures, each of which receives a large number of input synapses. However, it is not clear whether synaptic inputs are targeted to specific regions within a dendritic tree and whether dendritic tree geometry and subdendritic synapse distributions might be optimized to support proper neuronal input-output computations. This study uses an insect model where structure and function of an individually identifiable neuron, motoneuron 5 (MN5), are changed while it develops from a slow larval crawling into a fast adult flight motoneuron during metamorphosis. This allows for relating postembryonic dendritic remodeling of an individual motoneuron to developmental changes in behavioral function. Dendritic architecture of MN5 is analyzed by three-dimensional geometric reconstructions and quantitative co-localization analysis to address the distribution of synaptic terminals. Postembryonic development of MN5 comprises distinct changes in dendritic shape and in the subdendritic distribution of GABAergic input synapses onto MN5. Subdendritic synapse targeting is not a consequence of neuropil structure but must rely on specific subdendritic recognition mechanisms. Passive multicompartment simulations indicate that postembryonic changes in dendritic architecture and in subdendritic input synapse distributions may tune the passive computational properties of MN5 toward stage-specific behavioral requirements.

Simulation of Ca2+ Movements within the Sarcomere of Fast-Twitch Mouse Fibers Stimulated by Action Potentials

Ca2+ release from the sarcoplasmic reticulum (SR) of skeletal muscle takes place at the triadic junctions; following release, Ca2+ spreads within the sarcomere by diffusion. Here, we report multicompartment simulations of changes in sarcomeric Ca2+ evoked by action potentials (APs) in fast-twitch fibers of adult mice. The simulations include Ca2+ complexation reactions with ATP, troponin, parvalbumin, and the SR Ca2+ pump, as well as Ca2+ transport by the pump. Results are compared with spatially averaged Ca2+ transients measured in mouse fibers with furaptra, a low-affinity, rapidly responding Ca2+ indicator. The furaptra ΔfCaD signal (change in the fraction of the indicator in the Ca2+-bound form) evoked by one AP is well simulated under the assumption that SR Ca2+ release has a peak of 200–225 μM/ms and a FDHM of ∼1.6 ms (16°C). ΔfCaD elicited by a five-shock, 67-Hz train of APs is well simulated under the assumption that in response to APs 2–5, Ca2+ release decreases progressively from 0.25 to 0.15 times that elicited by the first AP, a reduction likely due to Ca2+ inactivation of Ca2+ release. Recovery from inactivation was studied with a two-AP protocol; the amplitude of the second release recovered to >0.9 times that of the first with a rate constant of 7 s−1. An obvious feature of ΔfCaD during a five-shock train is a progressive decline in the rate of decay from the individual peaks of ΔfCaD. According to the simulations, this decline is due to a reduction in available Ca2+ binding sites on troponin and parvalbumin. The effects of sarcomere length, the location of the triadic junctions, resting [Ca2+], the parvalbumin concentration, and possible uptake of Ca2+ by mitochondria were also investigated. Overall, the simulations indicate that this reaction-diffusion model, which was originally developed for Ca2+ sparks in frog fibers, works well when adapted to mouse fast-twitch fibers stimulated by APs.

On theorists and data in computational neuroscience

A diversity of activities in neuroscience are labeled 'theory'. Developing Bayesian spike sorting algorithms, making a theory of consciousness, attractor neural network dynamics, constructing multi-compartment simulations of neurons, these and many other activities have a theoretical component. So of course there is a role for theory in neuroscience.