Spatiotemporal consistency of neural responses to repeatedly presented video stimuli accounts for population preferences.

Population preferences for video advertisements vary across short video clips. What underlies these differences? Repeatedly watching a video clip may produce a consistent spatiotemporal pattern of neural activity that is dependent on the individual and the stimulus. Moreover, such consistency may be associated with the degree of engagement and memory of individual viewers. Since the population preferences are associated with the engagement and memory of the individual viewers, the consistency observed in a smaller group of viewers can be a predictor of population preferences. To test the hypothesis, we measured the degree of inter-trial consistency in participants' electroencephalographic (EEG) responses to repeatedly presented television commercials. We observed consistency in the neural activity patterns across repetitive views and found that the similarity in the spatiotemporal patterns of neural responses while viewing popular television commercials predicts population preferences obtained from a large audience. Moreover, a regression model that used two datasets, including two separate groups of participants viewing different stimulus sets, showed good predictive performance in a leave-one-out cross-validation. These findings suggest that universal spatiotemporal patterns in EEG responses can account for population-level human behaviours.

Detecting changes in dynamical structures in synchronous neural oscillations using probabilistic inference.

Recent neuroscience studies have suggested that cognitive functions and learning capacity are reflected in the time-evolving dynamics of brain networks. However, an efficient method to detect changes in dynamical brain structures using neural data has yet to be established. To address this issue, we developed a new model-based approach to detect change points in dynamical network structures by combining the model-based network estimation with a phase-coupled oscillator model and sequential Bayesian inference. By giving the model parameter as the prior distribution, applying Bayesian inference allows the extent of temporal changes in dynamic brain networks to be quantified by comparing the prior distribution with the posterior distribution using information theoretical criteria. For this, we used the Kullback-Leibler divergence as an index of such changes. To validate our method, we applied it to numerical data and electroencephalography data. As a result, we confirmed that the Kullback-Leibler divergence only increased when changes in dynamical network structures occurred. Our proposed method successfully estimated both directed network couplings and change points of dynamical structures in the numerical and electroencephalography data. These results suggest that our proposed method can reveal the neural basis of dynamic brain networks.

Rat call-evoked electrocorticographic responses and intercortical phase synchrony impaired in a cytokine-induced animal model for schizophrenia.

Patients with schizophrenia exhibit impaired performance in tone-matching or voice discrimination tests. However, there is no animal model recapitulating these pathophysiological traits. Here, we tested the representation of auditory recognition deficits in an animal model of schizophrenia. We established a rat model for schizophrenia using a perinatal challenge of epidermal growth factor (EGF), exposed adult rats to 55 kHz sine tones, rat calls (50-60 kHz), or reversely played calls, analyzed electrocorticography (ECoG) of the auditory and frontal cortices. Grand averages of event-related responses (ERPs) in the auditory cortex showed between-group size differences in the P1 component, whereas the P2 component differed among sound stimulus types. In EGF model rats, gamma band amplitudes were decreased in the auditory cortex and were enhanced in the frontal cortex with sine stimulus. The model rats also exhibited a reduction in rat call-triggered intercortical phase synchrony in the beta range. Risperidone administration restored normal phase synchrony. These findings suggest that perinatal exposure to the cytokine impairs tone/call recognition processes in these neocortices. In conjunction with previous studies using this model, our findings indicate that perturbations in ErbB/EGF signaling during development exert a multiscale impact on auditory functions at the cellular, circuit, and cognitive levels.

A state-informed stimulation approach with real-time estimation of the instantaneous phase of neural oscillations by a Kalman filter.

Objective.We propose a novel method to estimate the instantaneous oscillatory phase to implement a real-time system for state-informed sensory stimulation in electroencephalography (EEG) experiments.Approach.The method uses Kalman filter-based prediction to estimate current and future EEG signals. We tested the performance of our method in a real-time situation.Main results.Our method showed higher accuracy in predicting the EEG phase than the conventional autoregressive (AR) model-based method.Significance.A Kalman filter allows us to easily estimate the instantaneous phase of EEG oscillations based on the automatically estimated AR model implemented in a real-time signal processing machine. The proposed method has a potential for versatile applications targeting the modulation of EEG phase dynamics and the plasticity of brain networks in relation to perceptual or cognitive functions.

Association between aphasia severity and brain network alterations after stroke assessed using the electroencephalographic phase synchrony index.

Electroencephalographic synchrony can help assess brain network status; however, its usefulness has not yet been fully proven. We developed a clinically feasible method that combines the phase synchrony index (PSI) with resting-state 19-channel electroencephalography (EEG) to evaluate post-stroke motor impairment. In this study, we investigated whether our method could be applied to aphasia, a common post-stroke cognitive impairment. This study included 31 patients with subacute aphasia and 24 healthy controls. We assessed the expressive function of patients and calculated the PSIs of three motor language-related regions: frontofrontal, left frontotemporal, and right frontotemporal. Then, we evaluated post-stroke network alterations by comparing PSIs of the patients and controls and by analyzing the correlations between PSIs and aphasia scores. The frontofrontal PSI (beta band) was lower in patients than in controls and positively correlated with aphasia scores, whereas the right frontotemporal PSI (delta band) was higher in patients than in controls and negatively correlated with aphasia scores. Evaluation of artifacts suggests that this association is attributed to true synchrony rather than spurious synchrony. These findings suggest that post-stroke aphasia is associated with alternations of two different networks and point to the usefulness of EEG PSI in understanding the pathophysiology of aphasia.

Behavioral and electrophysiological investigations of effects of temporal regularity on illusory-figure processing.

The allocation of limited processing resources at an appropriate timing should be critical for selecting incoming signals. On the other hand, perceptual organization, which relatively automatically integrates fragmentary elements into coherent objects, should also be critical to decrease the processing load. By indexing behavioral measures and event-related potentials (ERPs), this study examined the effects of temporal regularity, which makes it possible to predict the time at which stimuli occur, on task-unrelated early processing of perceptual organization. Twenty-six volunteers participated in a task to discriminate central targets that were simultaneously but infrequently presented inside a Kanizsa-type illusory figure (KF) or a control stimulus (CS) without perception of an illusory figure. Inter-stimulus intervals were fixed or varied in different blocks. Both temporal regularity and the illusory figure accelerated behavioral responses and enlarged negative ERP amplitudes at 120-160 ms and 280-320 ms post-stimulus over posterior electrode sites. However, importantly, there was no evidence indicating that temporal regularity modulates illusory-figure processing. The finding may suggest that temporal expectation operates in parallel with implicit perceptual organization, although further examinations that involve spatial attention or individual differences are required.

Frequency-dependent effects of EEG phase resetting on reaction time.

There is trial-to-trial variability in the reaction time to stimulus presentation. Since this variability exists even in an identical stimulus condition, it reflects the internal neural dynamics of the brain. To understand the neural dynamics that influence the reaction time, we conducted an electroencephalogram (EEG) experiment in which participants were asked to press a response button as quickly as possible when a stimulus was visually presented. Phase-locking factor analysis revealed that phase resetting in two frequency bands, which appeared 0.2 s after the stimulus presentation, characterized the reaction time. The combination of the theta band phase resetting in the left parietal region and the delta band phase resetting mainly in the posterior region was associated with the fastest reaction time, whereas delta band phase resetting without theta band phase resetting was associated with the faster reaction time. The results indicated that there were frequency-dependent effects in the relationships between the EEG phase resetting and reaction time.

The metastable brain associated with autistic-like traits of typically developing individuals.

Metastability in the brain is thought to be a mechanism involved in the dynamic organization of cognitive and behavioral functions across multiple spatiotemporal scales. However, it is not clear how such organization is realized in underlying neural oscillations in a high-dimensional state space. It was shown that macroscopic oscillations often form phase-phase coupling (PPC) and phase-amplitude coupling (PAC), which result in synchronization and amplitude modulation, respectively, even without external stimuli. These oscillations can also make spontaneous transitions across synchronous states at rest. Using resting-state electroencephalographic signals and the autism-spectrum quotient scores acquired from healthy humans, we show experimental evidence that the PAC combined with PPC allows amplitude modulation to be transient, and that the metastable dynamics with this transient modulation is associated with autistic-like traits. In individuals with a longer attention span, such dynamics tended to show fewer transitions between states by forming delta-alpha PAC. We identified these states as two-dimensional metastable states that could share consistent patterns across individuals. Our findings suggest that the human brain dynamically organizes inter-individual differences in a hierarchy of macroscopic oscillations with multiple timescales by utilizing metastability.

Frequency- and Area-Specific Phase Entrainment of Intrinsic Cortical Oscillations by Repetitive Transcranial Magnetic Stimulation.

Synchronous oscillations are ubiquitous throughout the cortex, but the frequency of oscillations differs from area to area. To elucidate the mechanistic architectures underlying various rhythmic activities, we tested whether spontaneous neural oscillations in different local cortical areas and large-scale networks can be phase-entrained by direct perturbation with distinct frequencies of repetitive transcranial magnetic stimulation (rTMS). While recording the electroencephalogram (EEG), we applied single-pulse TMS (sp-TMS) and rTMS at 5, 11, and 23 Hz over the motor or visual cortex. We assessed local and global modulation of phase dynamics using the phase-locking factor (PLF). sp-TMS to the motor and the visual cortex triggered a transient increase in PLF in distinct frequencies that peaked at 21 and 8 Hz, respectively. rTMS at 23 Hz over the motor cortex and 11 Hz over the visual cortex induced a prominent and progressive increase in PLF that lasted for a few cycles after the termination of rTMS. Moreover, the local increase in PLF propagated to other cortical areas. These results suggest that distinct cortical areas have area-specific oscillatory frequencies, and the manipulation of oscillations in local areas impacts other areas through the large-scale oscillatory network with the corresponding frequency specificity. We speculate that rTMS that is close to area-specific frequencies (natural frequencies) enables direct manipulation of brain dynamics and is thus useful for investigating the causal roles of synchronous neural oscillations. Moreover, this technique could be used to treat clinical symptoms associated with impaired oscillations and synchrony.

Real-Time Implementation of EEG Oscillatory Phase-Informed Visual Stimulation Using a Least Mean Square-Based AR Model.

It is a technically challenging problem to assess the instantaneous brain state using electroencephalography (EEG) in a real-time closed-loop setup because the prediction of future signals is required to define the current state, such as the instantaneous phase and amplitude. To accomplish this in real-time, a conventional Yule-Walker (YW)-based autoregressive (AR) model has been used. However, the brain state-dependent real-time implementation of a closed-loop system employing an adaptive method has not yet been explored. Our primary purpose was to investigate whether time-series forward prediction using an adaptive least mean square (LMS)-based AR model would be implementable in a real-time closed-loop system or not. EEG state-dependent triggers synchronized with the EEG peaks and troughs of alpha oscillations in both an open-eyes resting state and a visual task. For the resting and visual conditions, statistical results showed that the proposed method succeeded in giving triggers at a specific phase of EEG oscillations for all participants. These individual results showed that the LMS-based AR model was successfully implemented in a real-time closed-loop system targeting specific phases of alpha oscillations and can be used as an adaptive alternative to the conventional and machine-learning approaches with a low computational load.

Electroencephalographic Phase Synchrony Index as a Biomarker of Poststroke Motor Impairment and Recovery.

Background. Motor recovery after stroke is of great clinical interest. Besides magnetic resonance imaging functional connectivity, electroencephalographic synchrony is also an available biomarker. However, the clinical relevance of electroencephalographic synchrony in hemiparesis has not been fully understood. Objective. We aimed to demonstrate the usefulness of the phase synchrony index (PSI) by showing associations between the PSI and poststroke outcome in patients with hemiparesis. Methods. This observational study included 40 participants with cortical ischemic stroke (aged 69.8 ± 13.8 years) and 22 healthy controls (aged 66.9 ± 6.5 years). Nineteen-channel electroencephalography was recorded at 36.9 ± 11.8 days poststroke. Upper extremity Fugl-Meyer scores were assessed at the time of admission/before discharge (FM-UE1/FM-UE2; 32.6 ± 12.3/121.0 ± 44.7 days poststroke). Then, correlations between the PSIs and FM-UE1 as well as impairment reduction after rehabilitation (FM-UEgain) were analyzed. Results. The interhemispheric PSI (alpha band) between the primary motor areas (M1s) was lower in patients than in controls and was selectively correlated with FM-UE1 (P = .001). In contrast, the PSI (theta band) centered on the contralesional M1 was higher in patients than in controls and was selectively correlated with FM-UEgain (P = .003). These correlations remained significant after adjusting for confounding factors (age, time poststroke, National Institute of Health Stroke Scale, and lesion volume). Furthermore, the latter correlation was significant in severely impaired patients (FM-UE1 ≤ 10). Conclusions. This study showed that the PSIs were selectively correlated with motor impairment and recovery. Therefore, the PSIs may be potential biomarkers in persons with a hemispheric infarction.

Mathematical mechanism of state-dependent phase resetting properties of alpha rhythm in the human brain.

It is well-known that 10-Hz alpha oscillations in humans observed by electroencephalogram (EEG) are enhanced when the eyes are closed. Toward explaining this, a previous experimental study using manipulation by transcranial magnetic stimulation (TMS) revealed more global propagation of phase resetting in the eyes-open condition than in the eyes-closed condition in the alpha band. Those results indicate a significant increase of directed information flow across brain networks from the stimulated area to the rest of the brain when the eyes are open, suggesting that sensitivity to environmental changes and external stimuli is adaptively controlled by changing the dynamics of the alpha rhythm. However, the mathematical mechanism mediating the changes in the sensitivity has not been well elucidated. In this study, we propose a qualitative mathematical model that describes the characteristic behavior of the EEG phase dynamics. Numerically, we find that the propagation properties of the phase resetting qualitatively depend on whether the population of oscillators at the stimulated area are synchronized. These results support the hypothesis that the dynamics of the alpha oscillations controls sensitivity to external stimuli.

Probing dynamical cortical gating of attention with concurrent TMS-EEG.

Attention facilitates the gating of information from the sending brain area to the receiving areas, with this being achieved by dynamical changes in effective connectivity, which refers to the directional influences between cortical areas. To probe the effective connectivity and cortical excitability modulated by covertly shifted attention, transcranial magnetic stimulation (TMS) was used to directly perturb the right retinotopic visual cortex with respect to attended and unattended locations, and the impact of this was tracked from the stimulated area to other areas by concurrent use of electroencephalography (EEG). TMS to the contralateral visual hemisphere led to a stronger evoked potential than stimulation to the ipsilateral hemisphere. Moreover, stronger beta- and gamma-band effective connectivities assessed as time-delayed phase synchronizations between stimulated areas and other areas were observed when TMS was delivered to the contralateral hemisphere. These effects were more enhanced when they preceded more prominent alpha lateralization, which is known to be associated with attentional gating. Our results indicate that attention-regulated cortical feedforward effective connectivity can be probed by TMS-EEG with direct cortical stimulation, thereby bypassing thalamic gating. These results suggest that cortical gating of the feedforward input is achieved by regulating the effective connectivity in the phase dynamics between cortical areas.

A manifold learning approach to mapping individuality of human brain oscillations through beta-divergence.

This paper proposes an approach for visualizing individuality and inter-individual variations of human brain oscillations measured as multichannel electroencephalographic (EEG) signals in a low-dimensional space based on manifold learning. Using a unified divergence measure between spectral densities termed the "beta-divergence", we introduce an appropriate dissimilarity measure between multichannel EEG signals. Then, t-distributed stochastic neighbor embedding (t-SNE; a state-of-the-art algorithm for manifold learning) together with the beta-divergence based distance was applied to resting state EEG signals recorded from 100 healthy subjects. We were able to obtain a fine low-dimensional visualization that enabled each subject to be identified as an isolated point cloud and that represented inter-individual variations as the relationships between such point clouds. Furthermore, we also discuss how the performance of the low-dimensional visualization depends on the beta-divergence parameter and the t-SNE hyper parameter. Finally, borrowing from the concept of locally linear embedding (LLE), we propose a method for projecting the test sample to the t-SNE space obtained from the training samples and investigate that availability.

Information-theoretic approach to detect directional information flow in EEG signals induced by TMS.

Effective connectivity analysis has been widely applied to noninvasive recordings such as functional magnetic resonance imaging and electroencephalograms (EEGs). Previous studies have aimed to extract the causal relations between brain regions, but the validity of the derived connectivity has not yet been fully determined. This is because it is generally difficult to identify causality in the usual experimental framework based on observations alone. Transcranial magnetic stimulation (TMS) provides a framework in which a controllable perturbation is applied to a local brain region and the effect is examined by comparing the neural activity with and without this stimulation. This study evaluates two methods for effective connectivity analysis, symbolic transfer entropy (STE) and vector autoregression (VAR), by applying them to TMS-EEG data. In terms of the consistency of results from different experimental sessions, STE is found to yield robust results irrespective of sessions, whereas VAR produces less correlation between sessions. Furthermore, STE preferentially detects the directional information flow from the TMS target. Taken together, our results suggest that STE is a reliable method for detecting the effect of TMS, implying that it would also be useful for identifying neural activity during cognitive tasks and resting states.

Motion sickness-susceptible participants exposed to coherent rotating dot patterns show excessive N2 amplitudes and impaired theta-band phase synchronization.

Visually induced motion sickness (VIMS) can occur via prolonged exposure to visual stimulation that generates the illusion of self-motion (vection). Not everyone is susceptible to VIMS and the neural mechanism underlying susceptibility is unclear. This study explored the differences of electroencephalographic (EEG) signatures between VIMS-susceptible and VIMS-resistant groups. Thirty-two-channel EEG data were recorded from 12 VIMS-susceptible and 15 VIMS-resistant university students while they were watching two patterns of moving dots: (1) a coherent rotation pattern (vection-inducing and potentially VIMS-provoking pattern), and (2) a random movement pattern (non-VIMS-provoking control). The VIMS-susceptible group exhibited a significantly larger increase in the parietal N2 response when exposed to the coherent rotating pattern than when exposed to control patterns. In members of the VIMS-resistant group, before vection onset, global connectivity from all other EEG electrodes to the right-temporal-parietal and to the right-central areas increased, whereas after vection onset the global connectivity to the right-frontal area reduced. Such changes were not observed in the susceptible group. Further, the increases in N2 amplitude and the identified phase synchronization index were significantly correlated with individual motion sickness susceptibility. Results suggest that VIMS susceptibility is associated with systematic impairment of dynamic cortical coordination as captured by the phase synchronization of cortical activities. Analyses of dynamic EEG signatures could be a means to unlock the neural mechanism of VIMS.

Phase-Amplitude Coupling of Neural Oscillations Can Be Effectively Probed with Concurrent TMS-EEG.

Despite the widespread use of transcranial magnetic stimulation (TMS), knowledge of its neurophysiological mode of action is still incomplete. Recently, TMS has been proposed to synchronise neural oscillators and to thereby increase the detectability of corresponding oscillations at the population level. As oscillations in the human brain are known to interact within nested hierarchies via phase-amplitude coupling, TMS might also be able to increase the macroscopic detectability of such coupling. In a concurrent TMS-electroencephalography study, we therefore examined the technique's influence on theta-gamma, alpha-gamma, and beta-gamma phase-amplitude coupling by delivering single-pulse TMS (sTMS) and repetitive TMS (rTMS) over the left motor cortex and right visual cortex of healthy participants. The rTMS pulse trains were of 5 Hz, 11 Hz, and 23 Hz for the three coupling variations, respectively. Relative to sham stimulation, all conditions showed transient but significant increases in phase-amplitude coupling at the stimulation site. In addition, we observed enhanced coupling over various other cortical sites, with a more extensive propagation during rTMS than during sTMS. By indicating that scalp-recorded phase-amplitude coupling can be effectively probed with TMS, these findings open the door to the technique's application in manipulative dissections of such coupling during human cognition and behaviour in healthy and pathological conditions.

Neural network model for path-finding problems with the self-recovery property.

The large-scale synchronization of neural oscillations is crucial in the functional integration of brain modules, but the combination of modules changes depending on the task. A mathematical description of this flexibility is a key to elucidating the mechanism of such spontaneous neural activity. We present a model that finds the loop structure of a network whose nodes are connected by unidirectional links. Using this model, we propose a path-finding system that spontaneously finds a path connecting two specified nodes. The solution path is represented by phase-synchronized oscillatory solutions. The model has the self-recovery property: that is, it is a system with the ability to find a new path when one of the connections in the existing path is suddenly removed. We show that the model construction procedure is applicable to a wide class of nonlinear systems arising in chemical reactions and neural networks.

Sensory-motor synchronization in the brain corresponds to behavioral synchronization between individuals.

Behavioral rhythms between individuals are known to spontaneously synchronize through social interactions; however, it remains unclear whether inter-brain synchronization emerges with this behavioral synchronization in the case of anti-phase coordination with other's behavior (e.g. turn-taking). In this study, we simultaneously recorded electroencephalograms (EEGs) we simultaneously recorded electroencephalograms (EEGs) from 2 participants as 1 pair (in total, 34 right-handed participants as 17 pairs) during an alternate tapping task in which pairs of participants alternated tapping a key with their right finger. Participants sat facing computer displays and were asked to match their partners' tapping intervals using visual feedback that was presented on the displays. Based on their ability to synchronize, we divided participants into Good performance and Poor performance groups. In both groups, wavelet analyses of EEG data revealed alpha-(approximately 12 Hz) and beta-(approximately 20 Hz) amplitude modulation in the left motor areas. Interestingly, both alpha and beta amplitudes were correlated between individuals from the Good group, but not from the Poor group. Moreover, while the Good group showed intra-brain and inter-brain alpha-phase synchronization (about 12 Hz) within the posterior brain areas (i.e., visual areas) and the central brain areas (i.e., motor areas), the Poor group did not. These results suggest that inter-brain synchronization may play an important role in coordinating one's behavioral rhythms with those of others.