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Abstract: A PAssive Dosimeter for Life-science Experiments in Space (PADLES) has been developed for measuring total absorbed dose and dose equivalents in the radiation environments of the International Space Station (ISS) where the Linear Energy Transfer (LET) of radiation ranges from 0.2 (ionization minimum) to 103 keVμm−1 or more. PADLES consists of two types of passive and integrating radiation detectors: MSO-S (Mg2SiO4:Tb) ThermoLuminescence Dosimeters (TLDs) and antioxidant-doped CR-39 plastic nuclear track detectors. In this paper, we first describe a method to obtain a water-equivalent absorbed dose by combining data from these two types of detector. In order to increase the reliability of PADLES for ISS space radiation dosimetry, we investigated the following characteristics of MSO-S TLDs: calibration of our ThermoLuminescence (TL) readout system for high-energy protons and gamma rays from 60Co and 137Cs sources; dose responses for high-energy heavy ions (He, C, Si, Ar, Fe); response variation of different manufacture batches; directional response for the high-energy protons; the initial variations and long-term fading effects of the TL response for high-energy protons and heavy ions at temperatures from −80 °C to 60 °C; and LET response. [Copyright &y& Elsevier]

Neuronal ensemble activity entrained by local field potential (LFP) patterns underlies a variety of brain functions, including emotion, cognition, and pain perception. Recent advances in machine learning approaches may enable more effective methods for analyzing LFP patterns across multiple brain areas than conventional time-frequency analysis. In this study, we tested the performance of two machine learning algorithms, AlexNet and the Transformer models, to classify LFP patterns in eight pain-related brain regions before and during acetic acid-induced visceral pain behaviors. Over short time windows lasting several seconds, applying AlexNet to LFP power datasets, but not to raw time-series LFP traces from multiple brain areas, successfully achieved superior classification performance compared with simple LFP power analysis. Furthermore, applying the Transformer directly to the raw LFP traces achieved significantly superior classification performance than AlexNet when using LFP power datasets. These results demonstrate the utility of the Transformer in the analysis of neurophysiological signals, and pave the way for its future applications in the decoding of more complex neuronal activity patterns., (© 2024. The Author(s).)

Understanding the mechanism by which the brain achieves relatively consistent information processing contrary to its inherent inconsistency in activity is one of the major challenges in neuroscience. Recently, it has been reported that the consistency of neural responses to stimuli that are presented repeatedly is enhanced implicitly in an unsupervised way, and results in improved perceptual consistency. Here, we propose the term "selective consistency" to describe this input-dependent consistency and hypothesize that it will be acquired in a self-organizing manner by plasticity within the neural system. To test this, we investigated whether a reservoir-based plastic model could acquire selective consistency to repeated stimuli. We used white noise sequences randomly generated in each trial and referenced white noise sequences presented multiple times. The results showed that the plastic network was capable of acquiring selective consistency rapidly, with as little as five exposures to stimuli, even for white noise. The acquisition of selective consistency could occur independently of performance optimization, as the network's time-series prediction accuracy for referenced stimuli did not improve with repeated exposure and optimization. Furthermore, the network could only achieve selective consistency when in the region between order and chaos. These findings suggest that the neural system can acquire selective consistency in a self-organizing manner and that this may serve as a mechanism for certain types of learning., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Goto, Kitajo. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Hypoxic-ischemic (HI) insult in infants induces brain injury and results in motor coordination impairments associated with cerebral palsy; however, preventive measures for HI brain injury in preterm infants remain unclear. We investigated the impact of progesterone (P4) in a rat HI insult model that mimics HI brain injury in preterm infants., Methods: Neonatal male rats with their right common carotid artery coagulated were exposed to a 1-h hypoxia (6% oxygen) on postnatal day (PND) 3. P4 (0.2 mg) was subcutaneously administered daily from PND4-12. Motor coordination function and muscular strength were evaluated on PND50 using rotarod and grip strength tests, respectively. Brain histology was evaluated via immunohistochemistry using anti-NeuN, anti-Olig2, and anti-IbaI antibodies on PND15 and PND50., Results: In male rats, P4 significantly improved the latency-to-fall off on the rotarod test in the insult rats to the levels of the sham-operation rats. Neither the insult nor P4 administration impacted the grip strength results. No significant differences were observed in the number of neurons, oligodendrocyte progenitor cells (OPCs), and microglia in the motor and somatosensory area of the cortex between the insult and insult followed by P4-administered rats on PND50. The number of OPCs in the corpus callosum was significantly increased in the ipsilateral side compared with the contralateral side of the insult in the P4-administered rats, indicating that P4 facilitates recruitment of OPCs to the corpus callosum. HI insult accelerated neuronal differentiation in rats on PND15, which was abrogated in the P4-administerd group, suggesting that P4 suppresses transient neuronal differentiation caused by the insult., Conclusion: P4 administration restored motor coordination impairments caused by postnatal HI insult in male rats. The insult timing corresponds to that of human preterm infants, indicating P4's potential for protecting HI brain injury in preterm male infants., Competing Interests: Declaration of competing interest All authors have nothing to disclose., (Copyright © 2024 Taiwan Pediatric Association. Published by Elsevier B.V. All rights reserved.)

Visual stimulation can generate illusory self-motion perception (vection) and cause motion sickness among susceptible people, but the underlying neural mechanism is not fully understood. In this study, SSVEP responses to visual stimuli presented in different parts of the visual field are examined in individuals with different susceptibilities to motion sickness to identify correlates of motion sickness. Alpha band SSVEP data were collected from fifteen university students when they were watching roll-vection-inducing visual stimulation containing: (1) an achromatic checkerboard flickering at 8.6 Hz in the central visual field (CVF) and (2) rotating dots pattern flickering at 12 Hz in the peripheral visual field. Rotating visual stimuli provoked explicit roll-vection perception in all participants. The motion sickness resistant participants showed reduced SSVEP response to CVF checkerboard during vection, while the motion sickness susceptible participants showed increased SSVEP response. The changes of SSVEP in the presence of vection significantly correlated with individual motion sickness susceptibility and rated scores on simulator sickness symptoms. Discussion on how the findings can support the sensory conflict theory is presented. Results offer a new perspective on vection and motion sickness susceptibility., Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-023-09991-7., (© The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Z-DNA binding protein 1 (ZBP1) is a cytosolic nucleic acid sensor, functioning as a critical mediator of inflammation and cell death pathways. Since neuroinflammation could occur in response to damage-associated molecular patterns (DAMPs), ZBP1 might be involved in neuroinflammation after stroke. However, the spatiotemporal expression profile of ZBP1 in the post-stroke brain remains to be elucidated. The aim of this study is to demonstrate the spatiotemporal expression patterns of ZBP1 in the post-stroke brain using a mouse photothrombotic stroke model. Real-time PCR assays showed that ZBP1 is induced on days 3-14 post stroke. ZBP1 immunoreactivity was observed in Iba1-positive microglia/macrophages in peri-infarct regions by immunohistochemistry. ZBP1-positive cells were spread in layers surrounding the infarct core by 7-14 days post stroke. Interestingly, ZBP1 immunoreactivity was also detected in CD206-positive border-associated macrophages (BAMs) in the meninges. Furthermore, ZBP1-expressing cells were positive for antibodies against inflammatory mediators such as Toll-like receptor 4 (TLR4), Toll/IL-1R domain-containing adaptor-inducing IFN-β (TRIF), and receptor-interacting serine/threonine-protein kinase 1 (RIPK1). Morphological analysis with confocal microscopy showed that the co-localization signals of ZBP1 and its adaptor, TRIF, are increased by glucose oxidase (GOx) treatment, which has been reported to induce mitochondrial DNA (mtDNA) release. These results suggest that ZBP1 is induced in peri-infarct microglia/macrophages and may be involved in DAMPs-mediated neuroinflammation involving mtDNA in the post-infarct brain., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial or personal relationships that could be viewed as influencing the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

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., (© 2023. The Author(s).)

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., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2021 Elsevier B.V. and Japan Neuroscience Society. All rights reserved.)

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., (Creative Commons Attribution license.)

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., Competing Interests: Declaration of Competing Interest None., (Copyright © 2021 Elsevier B.V. and Japan Neuroscience Society. All rights reserved.)

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., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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.

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., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: KK was supported by a research grant from TOYOTA Motor Corporation.

Background: Chronic pain is a highly refractory and complicated condition that persists even without nociception. Several genome-wide gene expression analyses have shown that the immune response and inflammatory cytokines affect chronic pain establishment in the acute pain phase. However, compared with the acute phase, the chronic phase has a poorly elucidated gene expression profile. This study aimed to determine the gene expression profile in the spinal cord of a neuropathic pain mouse model in the chronic phase to elucidate the chronic pain characteristics., Methods: We established a sciatic nerve cuff mouse model as a neuropathic pain model by placing a 2-mm section of a split PE-20 polyethylene tube around the sciatic nerve. The spinal cord was harvested at the L4-6 level at 28 postoperative days. Next, we examined differentially expressed genes (DEGs) through RNA sequencing (RNA-seq) compared with the sham group; moreover, we conducted enrichment analyses of the expressed genes. To reveal the chronic pain characteristics, we compared the gene expression profiles of the spinal cord between the acute and chronic phases in the neuropathic pain model. Among the chronic pain-related genes categorized in the dendrites, we focused on cyclin-dependent kinase-like 5 (CDKL5). We analyzed CDKL5 expression and function using real-time polymerase chain reaction (PCR), immunohistochemistry, and neurite extension assay in Neuro 2a (N2a) cells. We used three types of CDKL5 plasmids: wild type, nuclear localization signal-attached, and K42R kinase-dead CDKL5., Results: We identified 403 DEGs, including 104 upregulated and 43 downregulated genes (false discovery rate < 0.01). Rather than inflammation or immune response, the most enriched terms in the chronic phase were "regulation of plasma membrane-bounded cell projection organization" and "dendrite." Real-time PCR assay confirmed increased CDKL5 expression in the ipsilateral dorsal horn. CDKL5 was broadly expressed in the ipsilateral dorsal horn across all layers. The neurite extension assay revealed that the cytoplasmic kinase function of CDKL5 was necessary for neurite outgrowth in N2a cells., Conclusion: RNA-seq of the spinal cord revealed that the most enriched genes during the chronic pain phase were involved in regulating axon and dendrite morphogenesis, including CDKL5. Our findings suggest that neural remodeling affects chronic pain establishment. Since patients with CDKL5 mutations have shown reduced pain perception, our findings suggest that CDKL5 in the spinal cord could result in neural remodeling during the chronic pain phase through cytoplasmic kinase activity., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Okazaki, Nakagawa, Mizuno, Hanakawa and Kitajo.)

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.

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.

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., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

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., (Copyright © 2020 Elsevier B.V. and Japan Neuroscience Society. All rights reserved.)

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., (Copyright © 2020 Elsevier B.V. and Japan Neuroscience Society. All rights reserved.)

The neuroinflammation in the ischemic brain could occur as sterile inflammation in response to damage-associated molecular patterns (DAMPs). However, its long-term dynamic transcriptional changes remain poorly understood. It is also unknown whether this neuroinflammation contributes to the recovery or just deteriorates the outcome. The purpose of this study is to characterize the temporal transcriptional changes in the post-stroke brain focusing on DAMPs-related genes by RNA-sequencing during the period of 28 days. We conducted the RNA-sequencing on day 1, 3, 7, 14, 28 post-stroke in the mouse photothrombosis model. The gross morphological observation showed the ischemic lesion on the ipsilateral cortex turned into a scar with the clearance of cellular debris by day 28. The transcriptome analyses indicated that post-stroke period of 28 days was classified into four categories (I Baseline, II Acute, III Sub-acute-#1, IV Sub-acute-#2 phase). During this period, the well-known genes for DAMPs, receptors, downstream cascades, pro-inflammatory cytokines, and phagocytosis were transcriptionally increased. The gene ontology (GO) analysis of biological process indicated that differentially expressed genes (DEGs) are genetically programmed to achieve immune and inflammatory pathways. Interestingly, we found the biphasic induction of various genes, including DAMPs and pro-inflammatory factors, peaking at acute and sub-acute phases. At the sub-acute phase, we also observed the induction of genes for phagocytosis as well as regulatory and growth factors. Further, we found the activation of CREB (cAMP-response element binding protein), one of the key players for neuronal plasticity, in peri-ischemic neurons by immunohistochemistry at this phase. Taken together, these findings raise the possibility the recurrent inflammation occurs at the sub-acute phase in the post-stroke brain, which could be involved in the debris clearance as well as neural reorganization.

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.

The vertical occipital fasciculus (VOF) is an association fiber tract coursing vertically at the posterolateral corner of the brain. It is re-evaluated as a major fiber tract to link the dorsal and ventral visual stream. Although previous tractography studies showed the VOF's cortical projections fall in the dorsal and ventral visual areas, the post-mortem dissection study for the validation remains limited. First, to validate the previous tractography data, we here performed the white matter dissection in post-mortem brains and demonstrated the VOF's fiber bundles coursing between the V3A/B areas and the posterior fusiform gyrus. Secondly, we analyzed the VOF's structural connectivity with diffusion tractography to link vision-associated cortical areas of the HCP MMP1.0 atlas, an updated map of the human cerebral cortex. Based on the criteria the VOF courses laterally to the inferior longitudinal fasciculus (ILF) and craniocaudally at the posterolateral corner of the brain, we reconstructed the VOF's fiber tracts and found the widespread projections to the visual cortex. These findings could suggest a crucial role of VOF in integrating visual information to link the broad visual cortex as well as in connecting the dual visual stream.

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., (Copyright © 2019. Published by Elsevier Inc.)

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.

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.

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., (Copyright © 2018. Published by Elsevier Ltd.)

Autism is characterized by two primary characteristics: deficits in social interaction and repetitive behavioral patterns. Because interpersonal communication is extremely complicated, its underlying brain mechanisms remain unclear. Here we showed that both characteristics can be explained by a unifying underlying mechanism related to difficulties with irregularities. To address the issues, we measured electroencephalographm during a cooperative tapping task, which required participants to tap a key alternately and synchronously with constant rhythmic a PC program, a variable rhythmic PC program, or a human partner. We found that people with autism had great difficulty synchronizing tapping behavior with others, and exhibited greater than normal theta-wave (6 Hz) activity in the frontal cortex during the task, especially when their partner behaved somewhat irregularly (i.e. a variable rhythmic PC program or a human partner). Importantly, the higher theta-wave activity was related to the severity of autism, not the performance on the task. This indicates that people with autism need to use intense cognition when trying to adapt to irregular behavior and can easily become overtaxed. Difficulty adapting to irregular behavior in others is likely related to their own tendencies for repetitive and regular behaviors. Thus, while the two characteristics of autism have been comprehended separately, our unifying theory makes understanding the condition and developing therapeutic strategies more tractable.

Blind source separation (BSS) algorithms extract neural signals from electroencephalography (EEG) data. However, it is difficult to quantify source separation performance because there is no criterion to dissociate neural signals and noise in EEG signals. This study develops a method for evaluating BSS performance. The idea is neural signals in EEG can be estimated by comparison with simultaneously measured electrocorticography (ECoG). Because the ECoG electrodes cover the majority of the lateral cortical surface and should capture most of the original neural sources in the EEG signals. We measured real EEG and ECoG data and developed an algorithm for evaluating BSS performance. First, EEG signals are separated into EEG components using the BSS algorithm. Second, the EEG components are ranked using the correlation coefficients of the ECoG regression and the components are grouped into subsets based on their ranks. Third, canonical correlation analysis estimates how much information is shared between the subsets of the EEG components and the ECoG signals. We used our algorithm to compare the performance of BSS algorithms (PCA, AMUSE, SOBI, JADE, fastICA) via the EEG and ECoG data of anesthetized nonhuman primates. The results (Best case >JADE = fastICA >AMUSE = SOBI ≥ PCA >random separation) were common to the two subjects. To encourage the further development of better BSS algorithms, our EEG and ECoG data are available on our Web site (http://neurotycho.org/) as a common testing platform., (Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.)