Fumikazu Miwakeichi

Theta oscillation in the human anterior cingulate cortex during all-night sleep: An electrocorticographic study

Ten epileptic patients each with subdural electrodes surgically attached to the anterior cingulate cortex (ACC; two cases), the orbitofrontal cortex (OFC; seven cases), or both (one case) were included in this study. We recorded each patients ACC or OFC electrocorticogram (ECoG) during the time period that the patient was awake and naturally asleep. We performed a Fast Fourier Transformation (FFT) power spectral analysis on each ECoG to examine its frequency component. We found that the ACC showed regular and continuous theta oscillation (5-7Hz) during wakefulness and rapid eye movement (REM) sleep, but not during slow wave sleep. Theta waves observed in REM sleep were not as distinct as those found in wakefulness. We also discovered that the orbitofrontal signals represented spectral peaks in the theta band only during wakefulness. This suggests the coexistence of theta oscillation in the ACC. Considering our previous observations of gamma and beta oscillations in the human hippocampus, we hypothesize that the human limbic system manifests two oscillatory activities. The results obtained in this study suggest that electrophysiological activity in the ACC could be related to particular psychological functions in wakefulness and in REM sleep. These results are useful in elucidating the human brain mechanism.

Respiratory calcium fluctuations in low-frequency oscillating astrocytes in the pre-Bötzinger complex

Astrocytes have been found to modulate neuronal activity through calcium-dependent signaling in various brain regions. However, whether astrocytes of the pre-Bötzinger complex (preBötC) exhibit respiratory rhythmic fluctuations is still controversial. Here we evaluated calcium-imaging experiments within preBötC in rhythmically active medullary slices from TgN(hGFAP-EGFP) mice using advanced analyses. 13.8% of EGFP-negative cells, putative neurons, showed rhythmic fluorescent changes that were highly correlated to the respiratory rhythmic fluctuation (cross-correlation coefficient>0.5 and dF/F>0.2%). In contrast, a considerable number of astrocyte somata exhibited synchronized low-frequency (<0.03Hz) calcium oscillations. After band-pass filtering, signals that irregularly preceded the calcium signal of EGFP-negative cells were observed in 10.2% of astrocytes, indicating a functional coupling between astrocytes and neurons in preBötC. A model simulation confirmed that such preinspiratory astrocytic signals can arise from coupled neuronal and astrocytic oscillators, supporting a concept that slow oscillatory changes of astrocytic functions modulate neighboring neuronal activity to add variability in respiratory rhythm.

High frequency activities in the human orbitofrontal cortex in sleep-wake cycle

We recorded human orbitofrontal electrocorticogram during wakefulness and sleep in epileptic patients using subdural electrodes. During wakefulness and rapid eye movement (REM) sleep, we observed beta activity in the raw orbitofrontal signals. Power spectral analysis demonstrated beta enhancement during wakefulness and REM sleep when compared to slow wave sleep (SWS). During the phasic REM periods, the beta power was significantly lower than during the tonic REM periods. Gamma enhancement manifested itself in four out of six subjects during the phasic periods. This study is the first that has focused on electrical activity in the human orbitofrontal cortex. Although the role of the orbitofrontal cortex during sleep still remains unclear, high frequency activities give us important suggestions in elucidating the human sleep mechanism.

A comparison of non-linear non-parametric models for epilepsy data

EEG spike and wave (SW) activity has been described through a non-parametric stochastic model estimated by the Nadaraya-Watson (NW) method. In this paper the performance of the NW, the local linear polynomial regression and support vector machines (SVM) methods were compared. The noise-free realizations obtained by the NW and SVM methods reproduced SW better than as reported in previous works. The tuning parameters had to be estimated manually. Adding dynamical noise, only the NW method was capable of generating SW similar to training data. The standard deviation of the dynamical noise was estimated by means of the correlation dimension.

Basic study for a new assistive system based on brain activity associated with spatial perception task during car driving

Our purpose in this research is to contribute to developing of assistive robot and apparatus. Recently, there is a pressing need to develop a new system which assists and acts for car driving and wheelchair for the elderly as the population grows older. In terms of developing a new system, it is thought that it is important to examine behaviors as well as spatial recognition. So, experiments were performed for an examination of human spatial perceptions, especially right and left ones, during car driving using NIRS. In previous research, it has been documented that there were significant differences at dorsolateral prefrontal cortex at left hemisphere during virtual driving. In addition, significant differences were seen at similar regions during actual driving. In this time, detailed analysis about ocular motions was reported. The final goal of this study is to find a way to apply this result to new systems with functions that are responsive like human.

Decomposing EEG Data into Space-Time-Frequency Components Using Parallel Factor Analysis and Its Relation with Cerebral Blood Flow

Finding the means to efficiently summarize electroencephalographic data has been a long-standing problem in electrophysiology. A popular approach is identification of component modes on the basis of the time-varying spectrum of multichannel EEG recordings--in other words, a space/frequency/time atomic decomposition of the time-varying EEG spectrum. Previous work has been limited to only two of these dimensions. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been used to create space/time decompositions; suffering an inherent lack of uniqueness that is overcome only by imposing constraints of orthogonality or independence of atoms. Conventional frequency/time decompositions ignore the spatial aspects of the EEG. Framing of the data being as a three-way array indexed by channel, frequency, and time allows the application of a unique decomposition that is known as Parallel Factor Analysis (PARAFAC). Each atom is the tri-linear decomposition into a spatial, spectral, and temporal signature. We applied this decomposition to the EEG recordings of five subjects during the resting state and during mental arithmetic. Common to all subjects were two atoms with spectral signatures whose peaks were in the theta and alpha range. These signatures were modulated by physiological state, increasing during the resting stage for alpha and during mental arithmetic for theta. Furthermore, we describe a new method (Source Spectra Imaging or SSI) to estimate the location of electric current sources from the EEG spectrum. The topography of the theta atom is frontal and the maximum of the corresponding SSI solution is in the anterior frontal cortex. The topography of the alpha atom is occipital with maximum of the SSI solution in the visual cortex. We show that the proposed decomposition can be used to search for activity with a given spectral and topographic profile in new recordings, and that the method may be useful for artifact recognition and removal.

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.

Stochastic activation among inspiratory cells in the pre-Bötzinger complex of the rat medulla revealed by Ca(2+) imaging.

In the pre-Bötzinger complex of the ventral medulla (preBötC), a variable pattern of inspiratory neuronal output and synchronous activation of inspiratory cells can be observed. However, it is not well known whether cellular activation patterns among inspiratory cells are variable or fixed. Here, we evaluated the activation sequence of inspiratory cells during individual rhythmic bursts using calcium imaging. Onset timing and peak timing of calcium fluctuations during rhythmic bursts in individual inspiratory cells were used to evaluate the activation sequence. The sequences of both timings changed stochastically in individual rhythmic bursts, although the sequences differed between the two timings even within the same rhythmic burst. The weak correlation between these two timings might indicate that the two parameters reflect different physiological events. Furthermore, a subset of inspiratory cells was found to initially activate in the sequences of successive rhythmic bursts. These results suggest that rhythmic activation of inspiratory cells occurs with a degree of loose regularity but is not invariable with respect to the sequence of either onset or peak timing.

Basic Study for New Assistive Technology Based on Brain Activity During Car Driving

Recently, it is necessary to develop a new system which assists driving car and wheelchair as aged society. The final our purpose in this research is to contribute to developing of assistive robot and related-apparatus. In terms of developing a new system, we thought that it is important to examine behaviors as well as spatial recognition. Therefore, experiments have been performed for an examination of human spatial perceptions, especially right and left recognition, during car driving by using NIRS. In previous research, it has been documented that there were significant differences at dorsolateral prefrontal cortex at left hemisphere during virtual driving task and actual driving. In this paper, we performed measuring the brain activity during car driving by using NIRS. And we performed statistical analysis of the brain activity. The purpose of this paper is discovering the brain region which was involved in decision making when human drive a car and considering between human movement and brain activity during car driving. Keywords—brain information processing during driving task; spatial cognitive task; determining direction; NIRS