Yasushi Naruse

Influence of coherence between multiple cortical columns on alpha rhythm: a computational modeling study.

In electroencephalographic (EEG) and magnetoencephalographic (MEG) signals, stimulus‐induced amplitude increase and decrease in the alpha rhythm, known as event‐related synchronization and desynchronization (ERS/ERD), emerge after a task onset. ERS/ERD is assumed to reflect neural processes relevant to cognitive tasks. Previous studies suggest that several sources of alpha rhythm, each of which can serve as an alpha rhythm generator, exist in the cortex. Since EEG/MEG signals represent spatially summed neural activities, ERS/ERD of the alpha rhythm may reflect the consequence of the interactions between multiple alpha rhythm generators. Two candidates modulate the magnitude of ERS/ERD: (1) coherence between the activities of the alpha rhythm generators and (2) mean amplitude of the activities of the alpha rhythm generators. In this study, we use a computational model of multiple alpha rhythm generators to determine the factor that dominantly causes ERS/ERD. Each alpha rhythm generator is modeled based on local column circuits in the primary visual cortex and made to interact with the neighboring generators through excitatory connections. We observe that the model consistently reproduces spontaneous alpha rhythms, event‐related potentials, phase‐locked alpha rhythms, and ERS/ERD in a specific range of connectivity coefficients. Independent analyses of the coherence and amplitude of multiple alpha rhythm generators reveal that the ERS/ERD in the simulated data is dominantly caused by stimulus‐induced changes in the coherence between multiple alpha rhythm generators. Nonlinear phenomena such as phase‐resetting and entrainment of the alpha rhythm are related to the neural mechanism underlying ERS/ERD. Hum Brain Mapp, 2010.

Phase coherence of auditory steady-state response reflects the amount of cognitive workload in a modified N-back task.

The auditory steady-state response (ASSR) is an oscillatory brain activity evoked by repetitive auditory stimuli. Previous studies have reported that the power and phase locking index (PLI) of ASSR could be modulated by the degree of workload. However, those studies used different physical stimuli for tasks of differing difficulty, and the effect of the internal workload itself has not been clearly understood. In this study, we employed the modified N-back task as a visual working memory task in order to vary the degree of difficulty while keeping the physical stimulus constant. The experiment consisted of four types of tasks: No-Load (NL), 1-back, 2-back, and 3-back tasks. The auditory stimulus was a 40 Hz click sound to induce ASSR. Sixteen healthy subjects participated in the present study and magnetoencephalogram responses were recorded using a 148-channel magnetometer system. The hit rate decreased and the reaction time increased according to the task difficulty. Grand averaged phase coherence activities showed the 40 Hz ASSR reductions accompanying an increase in the task difficulty even with the identical external stimuli. In particular, the phase coherence activities in 3-back task were significantly lower than that in the NL and 1-back tasks. Our results suggest that the ASSR can be a useful indicator for the amount of workload in the brain.

Phase-Interpolated Averaging for Analyzing Electroencephalography and Magnetoencephalography Epochs

Stimulus-locked averaging for electroencephalography and/or megnetoencephalography (EEG/MEG) epochs cancels out ongoing spontaneous activities by treating them as noise. However, such spontaneous activities are the object of interest for EEG/MEG researchers who study phase-related phenomena, e.g., long-distance synchronization, phase-reset, and event-related synchronization/desynchronization (ERD/ERS). We propose a complex-weighted averaging method, called phase-compensated averaging, to investigate phase-related phenomena. In this method, any EEG/MEG channel is used as a trigger for averaging by setting the instantaneous phases at the trigger timings to 0 so that cross-channel averages are obtained. First, we evaluated the fundamental characteristics of this method by performing simulations. The results showed that this method could selectively average ongoing spontaneous activity phase-locked in each channel; that is, it evaluates the directional phase-synchronizing relationship between channels. We then analyzed flash evoked potentials. This method clarified the directional phase-synchronizing relationship from the frontal to occipital channels and recovered another piece of information, perhaps regarding the sequence of experiments, which is lost when using only conventional averaging. This method can also be used to reconstruct EEG/MEG time series to visualize long-distance synchronization and phase-reset directly, and on the basis of the potentials, ERS/ERD can be explained as a side effect of phase-reset.

Statistical Mechanics of Mexican-Hat-Type Horizontal Connection

We propose a multi-hypercolumn model consisting of M hypercolumns. Adjacent hypercolumns interact with each other through horizontal connections. This model consists of inter-hypercolumn and intra-hypercolumn Mexican-hat-type interactions. We analyze our model using statistical–mechanical methods. In this model, we theoretically show that the free energy is equivalent to the posterior distribution in the Bayesian framework in extremely weak inter-hypercolumn interactions. A numerical experiment supports this equivalence. The results of this study reveal the relationship between a microscopic neural structure and a macroscopic computational theory.

Estimation of the reaction times in tasks of varying difficulty from the phase coherence of the auditory steady-state response using the least absolute shrinkage and selection operator analysis

Quantitative estimation of the workload in the brain is an important factor for helping to predict the behavior of humans. The reaction time when performing a difficult task is longer than that when performing an easy task. Thus, the reaction time reflects the workload in the brain. In this study, we employed an N-back task in order to regulate the degree of difficulty of the tasks, and then estimated the reaction times from the brain activity. The brain activity that we used to estimate the reaction time was the auditory steady-state response (ASSR) evoked by a 40-Hz click sound. Fifteen healthy participants participated in the present study and magnetoencephalogram (MEG) responses were recorded using a 148-channel magnetometer system. The least absolute shrinkage and selection operator (LASSO), which is a type of sparse modeling, was employed to estimate the reaction times from the ASSR recorded by MEG. The LASSO showed higher estimation accuracy than the least squares method. This result indicates that LASSO overcame the over-fitting to the learning data. Furthermore, the LASSO selected channels in not only the parietal region, but also in the frontal and occipital regions. Since the ASSR is evoked by auditory stimuli, it is usually large in the parietal region. However, since LASSO also selected channels in regions outside the parietal region, this suggests that workload-related neural activity occurs in many brain regions. In the real world, it is more practical to use a wearable electroencephalography device with a limited number of channels than to use MEG. Therefore, determining which brain areas should be measured is essential. The channels selected by the sparse modeling method are informative for determining which brain areas to measure.

Gap junctions facilitate propagation of synchronous firing in the cortical neural population: A numerical simulation study

This study investigates the effect of gap junctions on firing propagation in a feedforward neural network by a numerical simulation with biologically plausible parameters. Gap junctions are electrical couplings between two cells connected by a binding protein, connexin. Recent electrophysiological studies have reported that a large number of inhibitory neurons in the mammalian cortex are mutually connected by gap junctions, and synchronization of gap junctions, spread over several hundred microns, suggests that these have a strong effect on the dynamics of the cortical network. However, the effect of gap junctions on firing propagation in cortical circuits has not been examined systematically. In this study, we perform numerical simulations using biologically plausible parameters to clarify this effect on population firing in a feedforward neural network. The results suggest that gap junctions switch the temporally uniform firing in a layer to temporally clustered firing in subsequent layers, resulting in an enhancement in the propagation of population firing in the feedforward network. Because gap junctions are often modulated in physiological conditions, we speculate that gap junctions could be related to a gating function of population firing in the brain.

Robust One-dimensional Phase Unwrapping using a Markov Random Field Model

In natural sciences and engineering, observed data are often measured as a wrapped phase. Phase unwrapping is the problem of restoring such discontinuous wrapped phases to the continuous original phase. This is one of the most challenging problems in signal processing because phase unwrapping is an inverse problem and ill-posed, which means the solution contains the arbitrariness of 2πk (\(k = 0, \pm 1, \pm 2, \ldots \)). One of the challenges in phase unwrapping research is how to distinguish whether the wrapping is genuine or fake. To address this, we denote the one-dimensional phase unwrapping problem from a Bayesian perspective using a Markov random field (MRF) model. We clarify that a previous MRF-based work was not robust against fake wrapping and demonstrate through experiments how our proposed method solves this problem.

Logistic Regression of Ligands of Chemotaxis Receptors Offers Clues about Their Recognition by Bacteria

Because of relative simplicity of signal transduction pathway, bacterial chemotaxis sensory systems have been expected to be applied to biosensor. Tar and Tsr receptors mediate chemotaxis of Escherichia coli and have been studied extensively as models of chemoreception by bacterial two-transmembrane receptors. Such studies are typically conducted using two canonical ligands: l-aspartate for Tar and l-serine for Tsr. However, Tar and Tsr also recognize various analogs of aspartate and serine; it remains unknown whether the mechanism by which the canonical ligands are recognized is also common to the analogs. Moreover, in terms of engineering, it is important to know a single species of receptor can recognize various ligands to utilize bacterial receptor as the sensor for wide range of substances. To answer these questions, we tried to extract the features that are common to the recognition of the different analogs by constructing classification models based on machine-learning. We computed 20 physicochemical parameters for each of 38 well-known attractants that act as chemoreception ligands, and 15 known non-attractants. The classification models were generated by utilizing one or more of the seven physicochemical properties as descriptors. From the classification models, we identified the most effective physicochemical parameter for classification: the minimum electron potential. This descriptor that occurred repeatedly in classification models with the highest accuracies, This descriptor used alone could accurately classify 42/53 of compounds. Among the 11 misclassified compounds, eight contained two carboxyl groups, which is analogous to the structure of characteristic of aspartate analog. When considered separately, 16 of the 17 aspartate analogs could be classified accurately based on the distance between their two carboxyl groups. As shown in these results, we succeed to predict the ligands for bacterial chemoreceptors using only a few descriptors; single descriptor for single receptor. This result might be due to the relatively simple topology of bacterial two-transmembrane receptors compared to the G-protein-coupled receptors of seven-transmembrane receptors. Moreover, this distance between carboxyl groups correlated with the receptor binding affinity of the aspartate analogs. In view of this correlation, we propose a common mechanism underlying ligand recognition by Tar of compounds with two carboxyl groups.

Unconscious improvement in foreign language learning using mismatch negativity neurofeedback: A preliminary study

When people learn foreign languages, they find it difficult to perceive speech sounds that are nonexistent in their native language, and extensive training is consequently necessary. Our previous studies have shown that by using neurofeedback based on the mismatch negativity event-related brain potential, participants could unconsciously achieve learning in the auditory discrimination of pure tones that could not be consciously discriminated without the neurofeedback. Here, we examined whether mismatch negativity neurofeedback is effective for helping someone to perceive new speech sounds in foreign language learning. We developed a task for training native Japanese speakers to discriminate between ‘l’ and ‘r’ sounds in English, as they usually cannot discriminate between these two sounds. Without participants attending to auditory stimuli or being aware of the nature of the experiment, neurofeedback training helped them to achieve significant improvement in unconscious auditory discrimination and recognition of the target words ‘light’ and ‘right’. There was also improvement in the recognition of other words containing ‘l’ and ‘r’ (e.g., ‘blight’ and ‘bright’), even though these words had not been presented during training. This method could be used to facilitate foreign language learning and can be extended to other fields of auditory and clinical research and even other senses.

Estimation of Human Workload from the Auditory Steady-State Response Recorded via a Wearable Electroencephalography System during Walking

Workload in the human brain can be a useful marker of internal brain state. However, due to technical limitations, previous workload studies have been unable to record brain activity via conventional electroencephalography (EEG) and magnetoencephalography (MEG) devices in mobile participants. In this study, we used a wearable EEG system to estimate workload while participants walked in a naturalistic environment. Specifically, we used the auditory steady-state response (ASSR) which is an oscillatory brain activity evoked by repetitive auditory stimuli, as an estimation index of workload. Participants performed three types of N-back tasks, which were expected to command different workloads, while walking at a constant speed. We used a binaural 500 Hz pure tone with amplitude modulation at 40 Hz to evoke the ASSR. We found that the phase-locking index (PLI) of ASSR activity was significantly correlated with the degree of task difficulty, even for EEG data from few electrodes. Thus, ASSR appears to be an effective indicator of workload during walking in an ecologically valid environment.