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.

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.

Scaling of seismic memory with earthquake size.

It has been observed that discrete earthquake events possess memory, i.e., that events occurring in a particular location are dependent on the history of that location. We conduct an analysis to see whether continuous real-time data also display a similar memory and, if so, whether such autocorrelations depend on the size of earthquakes within close spatiotemporal proximity. We analyze the seismic wave form database recorded by 64 stations in Japan, including the 2011 "Great East Japan Earthquake," one of the five most powerful earthquakes ever recorded, which resulted in a tsunami and devastating nuclear accidents. We explore the question of seismic memory through use of mean conditional intervals and detrended fluctuation analysis (DFA). We find that the wave form sign series show power-law anticorrelations while the interval series show power-law correlations. We find size dependence in earthquake autocorrelations: as the earthquake size increases, both of these correlation behaviors strengthen. We also find that the DFA scaling exponent α has no dependence on the earthquake hypocenter depth or epicentral distance.

Detection and visualization method of dynamic state transition for biological spatio-temporal imaging data.

In the statistical analysis of functional brain imaging data, regression analysis and cross correlation analysis between time series data on each grid point have been widely used. The results can be graphically represented as an activation map on an anatomical image, but only activation signal, whose temporal pattern resembles the predefined reference function, can be detected. In the present study, we propose a fusion method comprising innovation approach in time series analysis and statistical test. Autoregressive (AR) models were fitted to time series data of each pixel for the range sufficiently before or after the state transition. Then, the remaining time series data were filtered using these AR parameters to obtain its innovation (filter output). The proposed method could extract brain neural activation as a phase transition of dynamics in the system without employing external information such as the reference function. The activation could be detected as temporal transitions of statistical test values. We evaluated this method by applying to optical imaging data obtained from the mammalian brain and the cardiac sino-atrial node (SAN), and demonstrated that our method can precisely detect spatio-temporal activation profiles in the brain or SAN.

Dual oscillator model of the respiratory neuronal network generating quantal slowing of respiratory rhythm.

We developed a dual oscillator model to facilitate the understanding of dynamic interactions between the parafacial respiratory group (pFRG) and the preBötzinger complex (preBötC) neurons in the respiratory rhythm generation. Both neuronal groups were modeled as groups of 81 interconnected pacemaker neurons; the bursting cell model described by Butera and others [model 1 in Butera et al. (J Neurophysiol 81:382-397, 1999a)] were used to model the pacemaker neurons. We assumed (1) both pFRG and preBötC networks are rhythm generators, (2) preBötC receives excitatory inputs from pFRG, and pFRG receives inhibitory inputs from preBötC, and (3) persistent Na(+) current conductance and synaptic current conductances are randomly distributed within each population. Our model could reproduce 1:1 coupling of bursting rhythms between pFRG and preBötC with the characteristic biphasic firing pattern of pFRG neurons, i.e., firings during pre-inspiratory and post-inspiratory phases. Compatible with experimental results, the model predicted the changes in firing pattern of pFRG neurons from biphasic expiratory to monophasic inspiratory, synchronous with preBötC neurons. Quantal slowing, a phenomena of prolonged respiratory period that jumps non-deterministically to integer multiples of the control period, was observed when the excitability of preBötC network decreased while strengths of synaptic connections between the two groups remained unchanged, suggesting that, in contrast to the earlier suggestions (Mellen et al., Neuron 37:821-826, 2003; Wittmeier et al., Proc Natl Acad Sci USA 105(46):18000-18005, 2008), quantal slowing could occur without suppressed or stochastic excitatory synaptic transmission. With a reduced excitability of preBötC network, the breakdown of synchronous bursting of preBötC neurons was predicted by simulation. We suggest that quantal slowing could result from a breakdown of synchronized bursting within the preBötC.

What does the multi-peaked respiratory output pattern tell us about the respiratory pattern generating 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 spatiotemporal dynamics of respiratory-related regions of the ventral medulla, we developed a novel non-linear response model called the sigmoid and transfer function model. It regards the respiratory motor activity recorded from the fourth cervical ventral root (C4VR) as the response to optical signals from pixels within respiratory-related regions. When the C4VR activity had less than three peaks, optical time series of a single suitably chosen pixel could precisely estimate the activity. However, it was difficult to find a single explanatory pixel for multi-peaked C4VR activity. In this paper, we show that the multi-input single-output (MISO) STF model that takes a few different pixels as inputs greatly improves the precision of the estimation. We interpret this result that multi-peaked respiratory output patterns are caused by "migration of recruited area". Here the term "migration" denotes the phenomenon that the transition of respiratory-recruited subareas on the ventral medulla is observed within a single breath. In conclusion, the STF model is useful for analyzing spatiotemporal dynamics of optically recorded respiratory neuronal activities.

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.

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.

Differential diagnosis between dentigerous cyst and benign tumor with an embedded tooth.

It has been generally recognized that the radiological appearances of cysts and tumors related to an embedded tooth are similar. However, based on their clinical experience, Abrams et al. pointed out that there was a difference between the two lesions at the attachment point to the embedded tooth. To investigate this difference, we conducted a study employing the radiographs of patients who visited Nihon University Dental Hospital at Matsudo and were pathologically defined as having a cyst or tumor. Using radiographs of these patients, we investigated the attachment point to the embedded tooth, and expressed the results as the proportion of the attachment point to the embedded tooth root length. The study was carried out in 100 patients with cysts (87 dentigerous cysts and 13 odontogenic keratocysts), and 27 patients with benign tumors (24 ameloblastomas and three adenomatoid odontogenic tumors). Prior to treatment based on the numerical results, the distribution of the results was examined. Thus, we evaluated several methods of examining the distributions, and found the best method to be discriminant analysis. The results showed that the discriminated boundary value (from the cemento-enamel junction) was 0.38 for the embedded tooth root length. The cases showing a boundary value of less than 0.4 for the cemento-enamel junction were judged to be cysts, and those showing a value of 0.4 or more were judged to be benign tumors. The rate of misjudgement was 28% in the cyst group and 33.3% in the benign tumor group.