Long-lasting increases in GABAB receptor subunit levels in hippocampal dentate gyrus of mice with a single systemic injection of trimethyltin.

We have recently shown delayed increases in GABAB receptor (GABABR) subunit protein levels in the hippocampal dentate gyrus (DG), but not in the pyramidal CA1 and CA3 regions, at 15-30 days after the systemic single administration of trimethyltin (TMT) in mice. An attempt was thus made to determine whether the delayed increases return to the control levels found in naive mice afterward. In the DG on hippocampal slices obtained at 90 days after the administration, however, marked increases were still seen in protein levels of both GABABR1 and GABABR2 subunits without significant changes in calbindin and glial fibrillary acidic protein (GFAP) levels on immunoblotting analysis. Fluoro-Jade B staining clearly revealed the absence of degenerated neurons from the DG at 90 days after the administration. Although co-localization was invariably detected between GABABR2 subunit and GFAP in the DG at 30 days on immunohistochemical analysis, GABABR2-positive cells did not merge well with GFAP-positive cells in the DG at 90 days. These results suggest that both GABABR1 and GABABR2 subunits would be tardily and sustainably up-regulated by cells other than neurons and astrocytes in the murine DG at 90 days after a systemic single injection of TMT.

Dimension-wise Sequential Update for Learning a Multidimensional Environment in Humans.

When confronted with multidimensional environment problems, humans may need to jointly update multiple state-action-outcome associations across various dimensions. Computational modeling of human behavior and neural activities suggests that such updates are implemented based upon Bayesian update principle. However, it is unclear whether humans perform these updates individually or sequentially. If the update occurs sequentially, the order in which the associations are updated matters and can influence the updated results. To address this question, we tested a few computational models with different update orders using both human behavior and EEG data. Our results indicated that a model undertaking dimension-wise sequential updates was the best fit to human behavior. In this model, ordering the dimensions was decided using entropy, which indexed the uncertainty of associations. Simultaneously collected EEG data revealed evoked potentials that were correlated to the proposed timing of this model. These findings provide new insights into the temporal processes underlying Bayesian update in multidimensional environments.

Delayed Expression of Both GABABR1 and GABABR2 Subunits in Murine Hippocampal Dentate Gyrus After a Single Systemic Injection of Trimethyltin.

Trimethyltin (TMT) has been used as a cytotoxin to neurons rather than glial cells in the mammalian hippocampus. The systemic administration of TMT led to declined fluorescence of ZnAF-2 DA staining as a marker of intact mossy fibers and increased fluorescence of Fluoro-Jade B staining as a marker of degenerated neurons during the initial 2 to 5 days after the administration with later ameliorations within 30 days in the hippocampal dentate gyrus (DG) and CA3 region in mice. On immunoblotting analysis, both GABABR1 and GABABR2 subunit levels increased during 15 to 30 days after TMT along with significant decreases in glutamatergic GluA1 and GluA2/3 receptor subunit levels during 2 to 7 days in the DG, but not in other hippocampal regions such as CA1 and CA3 regions. Immunohistochemical analysis revealed the constitutive and inducible expression of GABABR2 subunit in cells immunoreactive to an astrocytic marker as well as neuronal markers in the DG with the absence of neither GABABR1a nor GABABR1b subunit from cells positive to an astrocytic marker. These results suggest that both GABABR1 and GABABR2 subunits may be up-regulated in cells other than neurons and astroglia in the DG at a late stage of TMT intoxication in mice.

Universality and superiority in preference for chromatic composition of art paintings.

Color composition in paintings is a critical factor affecting observers' aesthetic judgments. We examined observers' preferences for the color composition of Japanese and Occidental paintings when their color gamut was rotated. In the experiment, observers were asked to select their preferred image from original and three hue-rotated images in a four-alternative forced choice paradigm. Despite observers' being unfamiliar with the presented artwork, the original paintings (0 degrees) were preferred more frequently than the hue-rotated ones. Furthermore, the original paintings' superiority was observed when the images were divided into small square pieces and their positions randomized (Scrambled condition), and when the images were composed of square pieces collected from different art paintings and composed as patchwork images (Patchwork condition). Therefore, the original paintings' superiority regarding preference was quite robust, and the specific objects in the paintings associated with a particular color played only a limited role. Rather, the original paintings' general trend in color statistics influenced hue-angle preference. Art paintings likely share common statistical regulations in color distributions, which may be the basis for the universality and superiority of the preference for original paintings.

The Optimization of the Light-Source Spectrum Utilizing Neural Networks for Detecting Oral Lesions.

Any change in the light-source spectrum modifies the color information of an object. The spectral distribution of the light source can be optimized to enhance specific details of the obtained images; thus, using information-enhanced images is expected to improve the image recognition performance via machine vision. However, no studies have applied light spectrum optimization to reduce the training loss in modern machine vision using deep learning. Therefore, we propose a method for optimizing the light-source spectrum to reduce the training loss using neural networks. A two-class classification of one-vs-rest among the classes, including enamel as a healthy condition and dental lesions, was performed to validate the proposed method. The proposed convolutional neural network-based model, which accepts a 5 × 5 small patch image, was compared with an alternating optimization scheme using a linear-support vector machine that optimizes classification weights and lighting weights separately. Furthermore, it was compared with the proposed neural network-based algorithm, which inputs a pixel and consists of fully connected layers. The results of the five-fold cross-validation revealed that, compared to the previous method, the proposed method improved the F1-score and was superior to the models that were using the immutable standard illuminant D65.

Apelin/Apelin Receptor System: Molecular Characteristics, Physiological Roles, and Prospects as a Target for Disease Prevention and Pharmacotherapy.

Among the various orphan G protein-coupled receptors, apelin receptor (APJ), originally identified in the human genome as an orphan G-protein-coupled receptor, was deorphanised in 1998 with the discovery of its endogenous ligand, apelin. Apelin and APJ mRNA are widely expressed in peripheral tissues and the central nervous system in mammals. In this review, we discuss the characteristics, pharmacology, physiology, and pathology of the apelin/APJ system in mammals. Several physiological roles of the apelin/APJ system have been reported, including in homeostasis, cardiovascular maintenance, angiogenesis, and neuroprotection. In cellular signaling, apelin has been shown to drive the PI3K/Akt, MAPK, and PKA signaling pathways, leading to cell proliferation and protection from excitotoxicity. Apelin is also found in breast milk; therefore, apelin is believed to contribute to the establishment of the infant immune system. Furthermore, activation of the apelin/APJ system is reported to restore muscular weakness associated with aging. Thus, the apelin/APJ system represents a novel target for the prevention of several important cardiovascular and neurodegenerative diseases and the maintenance of health during old age.

Asymmetrical characteristics of emotional responses to pictures and sounds: Evidence from pupillometry.

In daily life, our emotions are often elicited by a multimodal environment, mainly visual and auditory stimuli. Therefore, it is crucial to investigate the symmetrical characteristics of emotional responses to pictures and sounds. In this study, we aimed to elucidate the relationship of attentional states to emotional unimodal stimuli (pictures or sounds) and emotional responses by measuring the pupil diameter, which reflects the emotional arousal associated with increased sympathetic activity. Our hypothesis was that the emotional responses to both the image and sound stimuli are symmetrical: emotion might be suppressed when attentional resources are allocated to another stimulus of the same modality as the emotional stimulus-such as a dot presented at the same time as an emotional image, and a beep sound presented at the same time as an emotional sound. In our two experiments, data for 24 participants were analyzed for a pupillary response. In experiment 1, we investigated the relationship of the attentional state with emotional visual stimuli (International Affective Picture System) and emotional responses by using pupillometry. We set four task conditions to modulate the attentional state (emotional task, no task, visual detection task, and auditory detection task). We observed that the velocity of pupillary dilation was faster during the presentation of emotionally arousing pictures compared to that of neutral ones, regardless of the valence of the pictures. Importantly, this effect was not dependent on the task condition. In experiment 2, we investigated the relationship of the attentional state with emotional auditory sounds (International Affective Digitized Sounds) and emotional responses. We observed a trend towards a significant interaction between the stimulus and the task conditions with regard to the velocity of pupillary dilation. In the emotional and auditory detection tasks, the velocity of pupillary dilation was faster with positive and neutral sounds than negative sounds. However, there were no significant differences between the no task and visual detection task conditions. Taken together, the current data reveal that different pupillary responses were elicited to emotional visual and auditory stimuli, at least in the point that there is no attentional effect to emotional responses to visual stimuli, despite both experiments being sufficiently controlled to be of symmetrical experimental design.

EEG-based personal identification method using unsupervised feature extraction and its robustness against intra-subject variability.

OBJECTIVE: Brain activity signals are possible biomarkers for personal authentication. However, they are inherently variable due to measurement-environment factors and subject-dependent factors; electroencephalography (EEG) signals could be different in days even for the same task, subject, and experimental settings. This variability could cause loss of consistency of the signals across multiple measurements of a single subject, and hence decrease the performance of EEG-based personal identification. In this study, we evaluated the influence of the variability on personal EEG features by using our original EEG dataset. APPROACH: We collected EEG signals in twenty subjects across four rounds (morning and afternoon daily for two days). At each round, we reinstalled an EEG cap on the subjects' scalps. To extract personal EEG features that were invariant across the sessions, we proposed unsupervised learning methods; common dictionary learning and t-distributed stochastic neighbor embedding. To assess the performance of personal identification, we compared two different experimental settings; test data recorded in the same round as the training data (Setting SR) and test data recorded in different rounds (Setting DR). MAIN RESULTS: The performance in SR was better than that in DR, suggesting that features dependent on the rounds were dominant. However, the 40% accuracy rate in DR, which is significantly higher than the chance level, suggests that our proposed method robustly extracted the personal features against the variability, in most cases. Furthermore, we also evaluated the performance of a problem, which involved detecting individuals who were not registered in the authentication system. In this problem, we obtained a similar result that the variability for the rounds influenced the performance. However, we obtained a good performance in the detection of some unknown subjects even in DR. SIGNIFICANCE: We found the variability in EEG data actually affected the personal features that were used for personal identification. Even considering the variability in EEG data, however, we found our proposed method is applicable in personal authentication scenarios, i.e. personal identification and unknown detection.

Regulatory effects associated with changes in intracellular potassium level in susceptibility to mitochondrial depolarization and excitotoxicity.

Excitotoxicity has been believed to be one of the causes of neurodegenerative diseases such as Alzheimer's disease and Huntington's disease. So far, much research has been done to suppress the neuronal excessive excitations, however, we still have not achieved full control, which may be due to the lack of some factors. As a matter of course, there is an urgent need to clarify all mechanisms that inhibit the onset and progression of neurodegenerative diseases. We found that potassium ion level regulation may be important in the sense that it suppresses mitochondrial depolarization rather than hyperpolarization of cell membrane potential. Minoxidil, an opener of ATP-activated potassium (KATP) channels decreased injury with middle cerebral artery occlusion in vivo experiment using TTC staining. In the primary cortical neurons, N-methyl-D-aspartate (NMDA)-induced mitochondrial depolarization was suppressed by minoxidil treatment. Minoxidil inhibited the increase in levels of cleaved caspase 3 and the release of cytochrome c into the cytosol, further reducing potassium ion levels. It was observed decreased potassium levels in neurons by the treatment of minoxidil. Those effects of minoxidil were blocked by glibenclamide. Therefore, it was suggested that minoxidil, via opening of KATP channels, reduced intracellular potassium ion level that contribute to mitochondrial depolarization, and suppressed subsequent NMDA-induced mitochondrial depolarization. Our findings suggest that the control of ion levels in neurons could dominate the onset and progression of neurodegenerative diseases.

Cooperative update of beliefs and state-transition functions in human reinforcement learning.

It is widely known that reinforcement learning systems in the brain contribute to learning via interactions with the environment. These systems are capable of solving multidimensional problems, in which some dimensions are relevant to a reward, while others are not. To solve these problems, computational models use Bayesian learning, a strategy supported by behavioral and neural evidence in human. Bayesian learning takes into account beliefs, which represent a learner's confidence in a particular dimension being relevant to the reward. Beliefs are given as a posterior probability of the state-transition (reward) function that maps the optimal actions to the states in each dimension. However, when it comes to implementing this learning strategy, the order in which beliefs and state-transition functions update remains unclear. The present study investigates this update order using a trial-by-trial analysis of human behavior and electroencephalography signals during a task in which learners have to identify the reward-relevant dimension. Our behavioral and neural results reveal a cooperative update-within 300 ms after the outcome feedback, the state-transition functions are updated, followed by the beliefs for each dimension.

Dynamic Visual Cues for Differentiating Mirror and Glass.

Mirror materials (perfect specular surfaces such as polished metal) and glass materials (transparent and refraction media) are quite commonly encountered in everyday life. The human visual system can discriminate these complex distorted images formed by reflection or transmission of the surrounding environment even though they do not intrinsically possess surface colour. In this study, we determined the cues that aid mirror and glass discrimination. From video analysis, we found that glass objects have more opposite motion components relative to the direction of object rotation. Then, we hypothesised a model developed using motion transparency because motion information is not only present on the front side, but also on the rear side of the object surface in the glass material object. In materials judging experiments, we found that human performance with rotating video stimuli is higher than that with static stimuli (simple images). Subsequently, we compared the developed model derived from motion coherency to human rating performance for transparency and specular reflection. The model sufficiently identified the different materials using dynamic information. These results suggest that the visual system relies on dynamic cues that indicate the difference between mirror and glass.

Alleviation by GABAB Receptors of Neurotoxicity Mediated by Mitochondrial Permeability Transition Pore in Cultured Murine Cortical Neurons Exposed to N-Methyl-D-aspartate.

Mitochondrial permeability transition pore (PTP) is supposed to at least in part participate in molecular mechanisms underlying the neurotoxicity seen after overactivation of N-methyl-D-aspartate (NMDA) receptor (NMDAR) in neurons. In this study, we have evaluated whether activation of GABAB receptor (GABABR), which is linked to membrane G protein-coupled inwardly-rectifying K+ ion channels (GIRKs), leads to protection of the NMDA-induced neurotoxicity in a manner relevant to mitochondrial membrane depolarization in cultured embryonic mouse cortical neurons. The cationic fluorescent dye 3,3'-dipropylthiacarbocyanine was used for determination of mitochondrial membrane potential. The PTP opener salicylic acid induced a fluorescence increase with a vitality decrease in a manner sensitive to the PTP inhibitor ciclosporin, while ciclosporin alone was effective in significantly preventing both fluorescence increase and viability decrease by NMDA as seen with an NMDAR antagonist. The NMDA-induced fluorescence increase and viability decrease were similarly prevented by pretreatment with the GABABR agonist baclofen, but not by the GABAAR agonist muscimol, in a fashion sensitive to a GABABR antagonist. Moreover, the GIRK inhibitor tertiapin canceled the inhibition by baclofen of the NMDA-induced fluorescence increase. These results suggest that GABABR rather than GABAAR is protective against the NMDA-induced neurotoxicity mediated by mitochondrial PTP through a mechanism relevant to opening of membrane GIRKs in neurons.

Variation in Event-Related Potentials by State Transitions.

The probability of an event's occurrence affects event-related potentials (ERPs) on electroencephalograms. The relation between probability and potentials has been discussed by using a quantity called surprise that represents the self-information that humans receive from the event. Previous studies have estimated surprise based on the probability distribution in a stationary state. Our hypothesis is that state transitions also play an important role in the estimation of surprise. In this study, we compare the effects of surprise on the ERPs based on two models that generate an event sequence: a model of a stationary state and a model with state transitions. To compare these effects, we generate the event sequences with Markov chains to avoid a situation that the state transition probability converges with the stationary probability by the accumulation of the event observations. Our trial-by-trial model-based analysis showed that the stationary probability better explains the P3b component and the state transition probability better explains the P3a component. The effect on P3a suggests that the internal model, which is constantly and automatically generated by the human brain to estimate the probability distribution of the events, approximates the model with state transitions because Bayesian surprise, which represents the degree of updating of the internal model, is highly reflected in P3a. The global effect reflected in P3b, however, may not be related to the internal model because P3b depends on the stationary probability distribution. The results suggest that an internal model can represent state transitions and the global effect is generated by a different mechanism than the one for forming the internal model.

Spatial smoothing of canonical correlation analysis for steady state visual evoked potential based brain computer interfaces.

Brain computer interface (BCI) is a system for communication between people and computers via brain activity. Steady-state visual evoked potentials (SSVEPs), a brain response observed in EEG, are evoked by flickering stimuli. SSVEP is one of the promising paradigms for BCI. Canonical correlation analysis (CCA) is widely used for EEG signal processing in SSVEP-based BCIs. However, the classification accuracy of CCA with short signal length is low. In order to solve the problem, we propose a regularization which works in such a way that the CCA spatial filter becomes spatially smooth to give robustness in short signal length condition. The spatial filter is designed in a parameter space spanned by a spatially smooth basis which are given by a graph Fourier transform of three dimensional electrode coordinates. We compared the classification accuracy of the proposed regularized CCA with the standard CCA. The result shows that the proposed CCA outperforms the standard CCA in short signal length condition.

Active data selection for motor imagery EEG classification.

Rejecting or selecting data from multiple trials of electroencephalography (EEG) recordings is crucial. We propose a sparsity-aware method to data selection from a set of multiple EEG recordings during motor-imagery tasks, aiming at brain machine interfaces (BMIs). Instead of empirical averaging over sample covariance matrices for multiple trials including low-quality data, which can lead to poor performance in BMI classification, we introduce weighted averaging with weight coefficients that can reject such trials. The weight coefficients are determined by the l1-minimization problem that lead to sparse weights such that almost zero-values are allocated to low-quality trials. The proposed method was successfully applied for estimating covariance matrices for the so-called common spatial pattern (CSP) method, which is widely used for feature extraction from EEG in the two-class classification. Classification of EEG signals during motor imagery was examined to support the proposed method. It should be noted that the proposed data selection method can be applied to a number of variants of the original CSP method.

Common spatio-time-frequency patterns for motor imagery-based brain machine interfaces.

For efficient decoding of brain activities in analyzing brain function with an application to brain machine interfacing (BMI), we address a problem of how to determine spatial weights (spatial patterns), bandpass filters (frequency patterns), and time windows (time patterns) by utilizing electroencephalogram (EEG) recordings. To find these parameters, we develop a data-driven criterion that is a natural extension of the so-called common spatial patterns (CSP) that are known to be effective features in BMI. We show that the proposed criterion can be optimized by an alternating procedure to achieve fast convergence. Experiments demonstrate that the proposed method can effectively extract discriminative features for a motor imagery-based BMI.

Regularization using similarities of signals observed in nearby sensors for feature extraction of brain signals.

In order to solve uncertainty of spatial weights learned with small amount of training samples for feature extraction from brain signals, a regularization using similarity of signals observed in sensors that are located near each other is proposed. Deriving the regularization is begun defining a distance between the sensors. Under the distance, the proposed regularization works so that the spatial weights extracts similar signals in the nearby sensors. The proposed regularization is applied to the well known common spatial pattern (CSP) method that finds spatial weights for EEG based brain machine interface. In the classification experiment using a dataset of EEG signals during motor imagery, the proposed method achieved maximum improvement by 28% in the classification accuracy over the standard CSP in a setting of even when only five samples are used.

[A case of rectal cancer which showed complete response to chemotherapy for para-aortic lymph node metastases remaining after surgery, for which resection of Virchow's lymph node recurrence led to long-term survival].

A 55-year-old man with a positive fecal occult blood test visited our department, and after a thorough medical evaluation, was diagnosed with Stage IV Rs rectal cancer with marked para-aortic lymph node metastasis. In December 2007, the patient underwent low anterior rectal resection with D3 lymph node dissection, but the para-aortic lymph nodes were left. The metastatic lymph nodes showed a complete response(CR)to post-operative chemotherapy with FOLFOX, FOLFIRI, IRIS, and irinotecan+cetuximab, and the complete response was sustained for 18 months after surgery. Later, he developed Virchow's lymph node metastasis, which was also resected. At present, 5 years after the first surgery, the patient, whose chemotherapy has been discontinued, is alive without recurrence. It appears that using key drugs, such as 5-fluorouracil, leukovorin, oxaliplatin, irinotecan, and cetuximab, and performing aggressive salvage surgery for Virchow's lymph node recurrence, led to long-term recurrence-free survival.

SSVEP-based brain-computer interfaces using FSK-modulated visual stimuli.

A brain-computer interface (BCI) based on steady-state visual-evoked potentials (SSVEP) has two difficulties: limitation of the number of commands and uneven probabilities of command execution. To address these problems, the present paper proposes a paradigm of BCI using frequency-modulated visual stimuli. The commands are translated into code words consisting of binary digits, to which visual stimuli with distinct frequencies are assigned. Frequencies of SSVEP are recognized to detect bits, and a command to be executed is determined from the sequence of detected bits. Experimental results show that the proposed paradigm achieves a reliable BCI with higher accuracies and balanced command executing probabilities.

Simultaneous design of FIR filter banks and spatial patterns for EEG signal classification.

The spatial weights for electrodes called common spatial pattern (CSP) are known to be effective in EEG signal classification for motor imagery-based brain-computer interface (MI-BCI). To achieve accurate classification in CSP, it is necessary to find frequency bands that relate to brain activities associated with BCI tasks. Several methods that determine such a set of frequency bands have been proposed. However, the existing methods cannot find the multiple frequency bands by using only learning data. To address this problem, we propose discriminative filter bank CSP (DFBCSP) that designs finite impulse response filters and the associated spatial weights by optimizing an objective function which is a natural extension of that of CSP. The optimization is conducted by sequentially and alternatively solving subproblems into which the original problem is divided. By experiments, it is shown that DFBCSP can effectively extract discriminative features for MI-BCI. Moreover, experimental results exhibit that DFBCSP can detect and extract the bands related to brain activities of motor imagery.