Yoshiyasu Tamura

Respiratory and metabolic acidosis differentially affect the respiratory neuronal network in the ventral medulla of neonatal rats

Two respiratory‐related areas, the para‐facial respiratory group/retrotrapezoid nucleus (pFRG/RTN) and the pre‐Bötzinger complex/ventral respiratory group (preBötC/VRG), are thought to play key roles in respiratory rhythm. Because respiratory output patterns in response to respiratory and metabolic acidosis differ, we hypothesized that the responses of the medullary respiratory neuronal network to respiratory and metabolic acidosis are different. To test these hypotheses, we analysed respiratory‐related activity in the pFRG/RTN and preBötC/VRG of the neonatal rat brainstem–spinal cord in vitro by optical imaging using a voltage‐sensitive dye, and compared the effects of respiratory and metabolic acidosis on these two populations. We found that the spatiotemporal responses of respiratory‐related regional activities to respiratory and metabolic acidosis are fundamentally different, although both acidosis similarly augmented respiratory output by increasing respiratory frequency. PreBötC/VRG activity, which is mainly inspiratory, was augmented by respiratory acidosis. Respiratory‐modulated pixels increased in the preBötC/VRG area in response to respiratory acidosis. Metabolic acidosis shifted the respiratory phase in the pFRG/RTN; the pre‐inspiratory dominant pattern shifted to inspiratory dominant. The responses of the pFRG/RTN activity to respiratory and metabolic acidosis are complex, and involve either augmentation or reduction in the size of respiratory‐related areas. Furthermore, the activation pattern in the pFRG/RTN switched bi‐directionally between pre‐inspiratory/inspiratory and post‐inspiratory. Electrophysiological study supported the results of our optical imaging study. We conclude that respiratory and metabolic acidosis differentially affect activities of the pFRG/RTN and preBötC/VRG, inducing switching and shifts of the respiratory phase. We suggest that they differently influence the coupling states between the pFRG/RTN and preBötC/VRG.

A novel statistical analysis of voltage-imaging data by structural time series modeling and its application to the respiratory neuronal network.

The respiratory neuronal network activity can be optically recorded from the ventral medulla of the in vitro brainstem-spinal cord preparation using a voltage-sensitive dye. To assess the synchronicity between respiratory-related neurons and the breath-by-breath variability of respiratory neuronal activity from optical signals, we developed a novel method by which we are able to analyze respiratory-related optical signals without cycle-triggered averaging. The model, called the sigmoid and transfer function model, assumes a respiratory motor activity as the output and optical signals of each pixel as the input, and activity patterns of respiratory-related regions are characterized by estimated model parameter values. We found that rats intermittently showing multi-peaked respiratory motor activities had a relatively low appearance frequency of respiratory-related pixels. Further, correlations between respiratory-related pixels in rats with such unstable respiratory motor activities were poor. The poor correlations were caused by respiratory neurons recruited in the late inspiratory phase. These results suggest that poor synchronicity between respiratory neurons, which are recruited at various timings of inspiration, causes intermittent multi-peaked respiratory motor output. In conclusion, analyses of respiratory-related optical signals without cycle-triggered averaging are feasible by using the proposed method. This approach can be widely applied to the analysis of event-related optical signals.

Emergent Network Topology within the Respiratory Rhythm-Generating Kernel Evolved In Silico

We hypothesize that the network topology within the pre-Bötzinger Complex (preBötC), the mammalian respiratory rhythm generating kernel, is not random, but is optimized in the course of ontogeny/phylogeny so that the network produces respiratory rhythm efficiently and robustly. In the present study, we attempted to identify topology of synaptic connections among constituent neurons of the preBötC based on this hypothesis. To do this, we first developed an effective evolutionary algorithm for optimizing network topology of a neuronal network to exhibit a ‘desired characteristic’. Using this evolutionary algorithm, we iteratively evolved an in silico preBötC ‘model’ network with initial random connectivity to a network exhibiting optimized synchronous population bursts. The evolved ‘idealized’ network was then analyzed to gain insight into: (1) optimal network connectivity among different kinds of neurons—excitatory as well as inhibitory pacemakers, non-pacemakers and tonic neurons—within the preBötC, and (2) possible functional roles of inhibitory neurons within the preBötC in rhythm generation. Obtained results indicate that (1) synaptic distribution within excitatory subnetwork of the evolved model network illustrates skewed/heavy-tailed degree distribution, and (2) inhibitory subnetwork influences excitatory subnetwork primarily through non-tonic pacemaker inhibitory neurons. Further, since small-world (SW) network is generally associated with network synchronization phenomena and is suggested as a possible network structure within the preBötC, we compared the performance of SW network with that of the evolved model network. Results show that evolved network is better than SW network at exhibiting synchronous bursts.

Parametric Modeling Analysis of Optical Imaging Data on Neuronal Activities in the Brain

An optical imaging technique using a voltage-sensitive dye (voltage imaging) has been widely applied to the analyses of various brain functions. Because optical signals in voltage imaging are small and require several kinds of preprocessing, researchers who use voltage imaging often conduct signal averaging of multiple trials and correction of signals by cutting the noise near the baseline in order to improve the apparent signal–noise ratio. However, a noise cutting threshold level that is usually set arbitrarily largely affects the analyzed results. Therefore, we aimed to develop a new method to objectively evaluate optical imaging data on neuronal activities. We constructed a parametric model to analyze optical time series data. We have chosen the respiratory neuronal network in the brainstem as a representative system to test our method. In our parametric model we assumed an optical signal of each pixel as the input and the inspiratory motor nerve activity of the spinal cord as the output. The model consisted of a threshold function and a delay transfer function. Although it was a simple nonlinear dynamic model, it could provide precise estimation of the respiratory motor output. By classifying each pixel into five types based on our model parameter values and the estimation error ratio, we obtained detailed classification of neuronal activities. The parametric modeling approach can be effectively employed for the evaluation of voltage-imaging data and thus for the analysis of the brain function.

A standardization method of voltage-imaging data that enables to average and compare spatio-temporal neuronal activities obtained from different samples

O1-5-2-3 A standardization method of voltage-imaging data that enables to average and compare spatio-temporal neuronal activities obtained from different samples Yasuhisa Fujiki 1 , Shigefumi Yokota 2, Yasumasa Okada 3, Yoshitaka Oku 4, Fumikazu Miwakeichi 1,5, Yoshiyasu Tamura 1,5, Makio Ishiguro 1,5 1 The Graduate University for Advanced Studies 2 Shimane University 3 Keio University 4 Hyogo College of Medicine 5 The Institute of Statistical Mathematics

Standardization of Size, Shape and Internal Structure of Spinal Cord Images: Comparison of Three Transformation Methods

Functional fluorescence imaging has been widely applied to analyze spatio-temporal patterns of cellular dynamics in the brain and spinal cord. However, it is difficult to integrate spatial information obtained from imaging data in specific regions of interest across multiple samples, due to large variability in the size, shape and internal structure of samples. To solve this problem, we attempted to standardize transversely sectioned spinal cord images focusing on the laminar structure in the gray matter. We employed three standardization methods, the affine transformation (AT), the angle-dependent transformation (ADT) and the combination of these two methods (AT+ADT). The ADT is a novel non-linear transformation method developed in this study to adjust an individual image onto the template image in the polar coordinate system. We next compared the accuracy of these three standardization methods. We evaluated two indices, i.e., the spatial distribution of pixels that are not categorized to any layer and the error ratio by the leave-one-out cross validation method. In this study, we used neuron-specific marker (NeuN)-stained histological images of transversely sectioned cervical spinal cord slices (21 images obtained from 4 rats) to create the standard atlas and also to serve for benchmark tests. We found that the AT+ADT outperformed other two methods, though the accuracy of each method varied depending on the layer. This novel image standardization technique would be applicable to optical recording such as voltage-sensitive dye imaging, and will enable statistical evaluations of neural activation across multiple samples.

A comparison of standardization methods of voltage-imaging data that enable to integrate the spatial information from different samples

The silicon neuron is an electronic circuit that mimics the electrophysiological functions of a neuron. In the neurophysiological studies, it has been used for the hybrid system, in which silicon neurons and living neurons are connected to each other, to elucidate the neuronal behaviors. The hybrid system makes it easy to analyze the changes in the behavior of the neural network when properties of the specific neurons are altered because behaviors of silicon neurons are configurable. To reproduce behaviors of a neuron precisely, silicon neurons based on the ionic-conductance neuron models are mainly used for the hybrid system. However, their circuit and the number of bias voltages to be applied tend to be quite large, which strictly limits the number of silicon neurons that can be incorporated. The silicon neural network, in which silicon neurons are connected via silicon synapses that mimics the functions of synapses, has been developed to realize various types of neuromorphic hardware, such as silicon cochlea, vision sensor, and pattern recognition system. Simple silicon neurons based on the leaky integrate-and-fire model are mainly used to construct the large scale silicon neural network. However, such silicon neuron can reproduce limited number of neuronal behaviors, which limits the dynamics of the neural network. We propose a silicon neuron circuit based on the Izhikevich model to overcome those limitations. It reproduces the same dynamics as the Izhikevich model, a mathematical neuron model that can reproduce rich dynamics in cortical neurons by a two-variable differential equation, with simple circuits by using a mathematical-structure-based method. The circuitry is composed of MOSFETs that are operated in the subthreshold region, and its power consumption is estimated by HSpice simulation to be under 20 nW. This power consumption is significantly lower than the conductance-based silicon neurons, which is a great advantage for biomedical applications.