Multivariate sharp-wave ripples in schizophrenia during awake state.

AIMS: Schizophrenia (SZ) is a brain disorder characterized by psychotic symptoms and cognitive dysfunction. Recently, irregularities in sharp-wave ripples (SPW-Rs) have been reported in SZ. As SPW-Rs play a critical role in memory, their irregularities can cause psychotic symptoms and cognitive dysfunction in patients with SZ. In this study, we investigated the SPW-Rs in human SZ. METHODS: We measured whole-brain activity using magnetoencephalography (MEG) in patients with SZ (n = 20) and sex- and age-matched healthy participants (n = 20) during open-eye rest. We identified SPW-Rs and analyzed their occurrence and time-frequency traits. Furthermore, we developed a novel multivariate analysis method, termed "ripple-gedMEG" to extract the global features of SPW-Rs. We also examined the association between SPW-Rs and brain state transitions. The outcomes of these analyses were modeled to predict the positive and negative syndrome scale (PANSS) scores of SZ. RESULTS: We found that SPW-Rs in the SZ (1) occurred more frequently, (2) the delay of the coupling phase (3) appeared in different brain areas, (4) consisted of a less organized spatiotemporal pattern, and (5) were less involved in brain state transitions. Finally, some of the neural features associated with the SPW-Rs were found to be PANSS-positive, a pathological indicator of SZ. These results suggest that widespread but disorganized SPW-Rs underlies the symptoms of SZ. CONCLUSION: We identified irregularities in SPW-Rs in SZ and confirmed that their alternations were strongly associated with SZ neuropathology. These results suggest a new direction for human SZ research.

Disrupted local beta band networks in schizophrenia revealed through graph analysis: A magnetoencephalography study.

AIMS: Schizophrenia (SZ) is characterized by psychotic symptoms and cognitive impairment, and is hypothesized to be a 'dysconnection' syndrome due to abnormal neural network formation. Although numerous studies have helped elucidate the pathophysiology of SZ, many aspects of the mechanism underlying psychotic symptoms remain unknown. This study used graph theory analysis to evaluate the characteristics of the resting-state network (RSN) in terms of microscale and macroscale indices, and to identify candidates as potential biomarkers of SZ. Specifically, we discriminated topological characteristics in the frequency domain and investigated them in the context of psychotic symptoms in patients with SZ. METHODS: We performed graph theory analysis of electrophysiological RSN data using magnetoencephalography to compare topological characteristics represented by microscale (degree centrality and clustering coefficient) and macroscale (global efficiency, local efficiency, and small-worldness) indices in 29 patients with SZ and 38 healthy controls. In addition, we investigated the aberrant topological characteristics of the RSN in patients with SZ and their relationship with SZ symptoms. RESULTS: SZ was associated with a decreased clustering coefficient, local efficiency, and small-worldness, especially in the high beta band. In addition, macroscale changes in the low beta band are closely associated with negative symptoms. CONCLUSIONS: The local networks of patients with SZ may disintegrate at both the microscale and macroscale levels, mainly in the beta band. Adopting an electrophysiological perspective of SZ as a failure to form local networks in the beta band will provide deeper insights into the pathophysiology of SZ as a 'dysconnection' syndrome.

Neural mechanisms of mental schema: a triplet of delta, low beta/spindle and ripple oscillations.

Schemas are higher-level knowledge structures that integrate and organise lower-level representations. As internal templates, schemas are formed according to how events are perceived, interpreted and remembered. Although these higher-level units are assumed to play a fundamental role in our daily life from an early age, the neuronal basis and mechanisms of schema formation and use remain largely unknown. It is important to elucidate how the brain constructs and maintains these higher-level units. In order to examine the possible neural underpinnings of schema, we recapitulate previous work and discuss their findings related to schemas as the brain template. We specifically focused on low beta/spindle oscillations, which are assumed to be the key components of schemas, and propose that the brain template is implemented with a triplet of neural oscillations, that is delta, low beta/spindle and ripple oscillations.

Efficient, continual, and generalized learning in the brain - neural mechanism of Mental Schema 2.0.

There has been tremendous progress in artificial neural networks (ANNs) over the past decade; however, the gap between ANNs and the biological brain as a learning device remains large. With the goal of closing this gap, this paper reviews learning mechanisms in the brain by focusing on three important issues in ANN research: efficiency, continuity, and generalization. We first discuss the method by which the brain utilizes a variety of self-organizing mechanisms to maximize learning efficiency, with a focus on the role of spontaneous activity of the brain in shaping synaptic connections to facilitate spatiotemporal learning and numerical processing. Then, we examined the neuronal mechanisms that enable lifelong continual learning, with a focus on memory replay during sleep and its implementation in brain-inspired ANNs. Finally, we explored the method by which the brain generalizes learned knowledge in new situations, particularly from the mathematical generalization perspective of topology. Besides a systematic comparison in learning mechanisms between the brain and ANNs, we propose "Mental Schema 2.0," a new computational property underlying the brain's unique learning ability that can be implemented in ANNs.

Measuring phase-amplitude coupling between neural oscillations of different frequencies via the Wasserstein distance.

BACKGROUND: Phase-amplitude coupling (PAC) is a key neuronal mechanism. Here, a novel method for quantifying PAC via the Wasserstein distance is presented. NEW METHOD: The Wasserstein distance is an optimization algorithm for minimizing transportation cost and distance. For the first time, the author has applied this distance function to quantify PAC and named the Wasserstein Modulation Index (wMI). As the wMI accommodates the product of the amplitude value in each phase position and the coupling phase position, it allows for extraction of more detailed PAC features from the data. RESULTS: The validity of the wMI calculations was examined using various simulation data, including sinusoidal and non-sinusoidal waves and empirical data sets. The current findings showed that the wMI is a more robust and stable index for quantifying PAC under various measuring conditions. Specifically, it can better reflect the timing of coupling and distinguish the shape of the coupling distribution than other measurements, both of which are the most significant parameters related to the functionality of PAC. Furthermore, the wMI is also suitable for many applications, such as more data-driven approaches and direct comparisons. COMPARISON WITH EXISTING METHOD(S): Compared with Euler-based PAC methods and the Modulation Index (MI), the wMI is not easily affected by the non-sinusoidal nature of neural oscillation and the short data length and enables better reflection of the natures of PAC, such as the timing of coupling and the amplitude distribution in the phase plane, than the MI. CONCLUSION: The wMI is expected to extract more detailed PAC characteristics, which could considerably contribute to the neuroscience field.

Timing of phase-amplitude coupling is essential for neuronal and functional maturation of audiovisual integration in adolescents.

OBJECTIVE: The ability to integrate audiovisual information matures late in adolescents, but its neuronal mechanism is still unknown. Recent studies showed that phase-amplitude coupling (PAC) of neuronal oscillations, which is defined as the modulation of high-frequency amplitude by low-frequency phase, is associated with audiovisual integration in adults. Thus, we investigated how PAC develops in adolescents and whether it is related to the functional maturation of audiovisual integration. In particular, we focused on the timing of PAC (or the coupling phase), which is defined as the low-frequency phase with maximum high-frequency amplitude. METHODS: Using magnetoencephalography (MEG) on 15 adults and 14 adolescents while they performed an audiovisual speech integration task, we examined PAC in association cortexes with a trial-by-trial analysis. RESULTS: Whereas delta-beta coupling was consistently observed in both adults and adolescents, we found that the timing of delta-beta PAC was delayed by 20-40 milliseconds in adolescents compared with adults. In addition, a logistic regression analysis revealed that the task performance improves as the timing of delta-beta PAC in the right temporal pole (TP) got closer to the trough position (180 degrees). CONCLUSION: These results suggest that the timing of PAC is essential for binding audiovisual information and underlies the developmental process in adolescents.

Frequency-Specific Resting Connectome in Bipolar Disorder: An MEG Study.

BACKGROUND: Bipolar disorder (BD) is a serious psychiatric disorder that is associated with a high suicide rate, and for which no clinical biomarker has yet been identified. To address this issue, we investigated the use of magnetoencephalography (MEG) as a new prospective tool. MEG has been used to evaluate frequency-specific connectivity between brain regions; however, no previous study has investigated the frequency-specific resting-state connectome in patients with BD. This resting-state MEG study explored the oscillatory representations of clinical symptoms of BD via graph analysis. METHODS: In this prospective case-control study, 17 patients with BD and 22 healthy controls (HCs) underwent resting-state MEG and evaluations for depressive and manic symptoms. After estimating the source current distribution, orthogonalized envelope correlations between multiple brain regions were evaluated for each frequency band. We separated regions-of-interest into seven left and right network modules, including the frontoparietal network (FPN), limbic network (LM), salience network (SAL), and default mode network (DMN), to compare the intra- and inter-community edges between the two groups. RESULTS: In the BD group, we found significantly increased inter-community edges of the right LM-right DMN at the gamma band, and decreased inter-community edges of the right SAL-right FPN at the delta band and the left SAL-right SAL at the theta band. Intra-community edges in the left LM at the high beta band were significantly higher in the BD group than in the HC group. The number of connections in the left LM at the high beta band showed positive correlations with the subjective and objective depressive symptoms in the BD group. CONCLUSION: We introduced graph theory into resting-state MEG studies to investigate the functional connectivity in patients with BD. To the best of our knowledge, this is a novel approach that may be beneficial in the diagnosis of BD. This study describes the spontaneous oscillatory brain networks that compensate for the time-domain issues associated with functional magnetic resonance imaging. These findings suggest that the connectivity of the LM at the beta band may be a good objective biological biomarker of the depressive symptoms associated with BD.

Neural oscillations in the temporal pole for a temporally congruent audio-visual speech detection task.

Though recent studies have elucidated the earliest mechanisms of processing in multisensory integration, our understanding of how multisensory integration of more sustained and complicated stimuli is implemented in higher-level association cortices is lacking. In this study, we used magnetoencephalography (MEG) to determine how neural oscillations alter local and global connectivity during multisensory integration processing. We acquired MEG data from 15 healthy volunteers performing an audio-visual speech matching task. We selected regions of interest (ROIs) using whole brain time-frequency analyses (power spectrum density and wavelet transform), then applied phase amplitude coupling (PAC) and imaginary coherence measurements to them. We identified prominent delta band power in the temporal pole (TP), and a remarkable PAC between delta band phase and beta band amplitude. Furthermore, imaginary coherence analysis demonstrated that the temporal pole and well-known multisensory areas (e.g., posterior parietal cortex and post-central areas) are coordinated through delta-phase coherence. Thus, our results suggest that modulation of connectivity within the local network, and of that between the local and global network, is important for audio-visual speech integration. In short, these neural oscillatory mechanisms within and between higher-level association cortices provide new insights into the brain mechanism underlying audio-visual integration.