Enhanced Correlation between Arousal and Infra-Slow Brain Activity in Experienced Meditators.

BACKGROUND/OBJECTIVES: Meditation induces changes in the nervous system, which presumably underpin positive psychological and physiological effects. Such neural changes include alterations in the arousal fluctuation, as well as in infraslow brain activity (ISA, <0.1 Hz). Furthermore, it is known that fluctuations of arousal over time correlate with the oscillatory phase of ISA. However, whether this arousal-ISA correlation changes after meditation practices remains unanswered.; Methods: The present study aims to address this question by analyzing a publicly available electroencephalogram (EEG) dataset recorded during meditation sessions in the groups of experienced meditators and novices. The arousal fluctuation is measured by galvanic skin responses (GSR), and arousal-ISA correlations are measured by phase synchronization between GSR and EEG ISAs.; Results: While both groups exhibit arousal-ISA correlations, experienced meditators display higher correlations than novices. These increased arousal-ISA correlations in experienced meditators manifest more clearly when oscillatory phase differences between GSR and EEG ISAs are either 0 or π radians. As such, we further investigate the characteristics of these phase differences with respect to spatial distribution over the brain. We found that brain regions with the phase difference of either 0 or π radians form distinct spatial clusters, and that these clusters are spatially correlated with functional organization estimated by the principal gradient, based on functional connectivity.; Conclusions: Since increased arousal-ISA correlations reflect enhanced global organization of the central and autonomic nervous systems, our findings imply that the positive effects of meditation might be mediated by enhanced global organization of the nervous system.

Effects of Continuous Theta Burst Stimulation on Contralateral Primary Motor Cortex: A Concurrent TMS-EEG Study.

Continuous theta burst stimulation (cTBS) is a non-invasive brain stimulation technique. cTBS modulation is an effective treatment for motor dysfunction rehabilitation in post-stroke patients. However, there's currently a lack of research on the effects of cTBS stimulation on the contralesional hemisphere. To better understand the role of cTBS in motor rehabilitation, we investigated the neuroregulatory mechanisms of cTBS in the contralateral cortex using transcranial magnetic stimulation-evoked electroencephalography (TMS-EEG). In this randomized, sham-controlled, single-blind study, 18 healthy subjects received two separate stimulation conditions:cTBS or sham stimulation applied to the left M1. TMS-EEG measurements were taken before and immediately after stimulation. We investigated the TMS-evoked potentials (TEPs), evoked oscillatory responses (EOR), and phase synchronization index(PSI) of TMS-EEG. The effects of cTBS were analyzed using two-way repeated measures analysis of variance (RMANOVA). There was a significant "cTBS condition×time" interaction effect on the theta and gamma bands of EOR, as well as on inter-hemisphere PSI (inter-PSI) and global PSI in both cTBS stimulation conditions. (theta:F=4.526,p=0.041;gamma:F=5.574,p=0.024;inter-PSI:F=5.028,p=0.032;global PSI:F=5.129,p=0.030).After real cTBS modulation, the energy in the theta and gamma frequency bands was significantly higher than before (theta: F=5.747, p=0.022; gamma: F=5.545, p=0.024). The inter-PSI and global PSI significantly increased after real cTBS modulation (inter-PSI: F=6.209, p=0.018; global PSI: F=6.530, p=0.015). cTBS modulation significantly increased EOR and PSI in contralateral brain regions, thereby enhancing cortical excitability and cortical functional connectivity throughout the brain. This provides a theoretical basis for cTBS neuromodulation in stroke patients.

Oscillatory mechanisms of intrinsic human brain networks.

Non-invasive neuroimaging has revealed specific network-based resting-state dynamics in the human brain, yet the underlying neurophysiological mechanism remains unclear. We employed intracranial electroencephalography to characterize local field potentials within the default mode network (DMN), frontoparietal network (FPN), and salience network (SN) in 42 participants. We identified stronger within-network phase coherence at low frequencies (θ and α band) within the DMN, and at high frequencies (γ band) within the FPN. Hidden Markov modeling indicated that the DMN exhibited preferential low frequency phase coupling. Phase-amplitude coupling (PAC) analysis revealed that the low-frequency phase in the DMN modulated the high-frequency amplitude envelopes of the FPN, suggesting frequency-dependent characterizations of intrinsic brain networks at rest. These findings provide intracranial electrophysiological evidence in support of the network model for intrinsic organization of human brain and shed light on the way brain networks communicate at rest.

Hyper-brain hyper-frequency network topology dynamics when playing guitar in quartet.

Ensemble music performance is a highly coordinated form of social behavior requiring not only precise motor actions but also synchronization of different neural processes both within and between the brains of ensemble players. In previous analyses, which were restricted to within-frequency coupling (WFC), we showed that different frequencies participate in intra- and inter-brain coordination, exhibiting distinct network topology dynamics that underlie coordinated actions and interactions. However, many of the couplings both within and between brains are likely to operate across frequencies. Hence, to obtain a more complete picture of hyper-brain interaction when musicians play the guitar in a quartet, cross-frequency coupling (CFC) has to be considered as well. Furthermore, WFC and CFC can be used to construct hyper-brain hyper-frequency networks (HB-HFNs) integrating all the information flows between different oscillation frequencies, providing important details about ensemble interaction in terms of network topology dynamics (NTD). Here, we reanalyzed EEG (electroencephalogram) data obtained from four guitarists playing together in quartet to explore changes in HB-HFN topology dynamics and their relation to acoustic signals of the music. Our findings demonstrate that low-frequency oscillations (e.g., delta, theta, and alpha) play an integrative or pacemaker role in such complex networks and that HFN topology dynamics are specifically related to the guitar quartet playing dynamics assessed by sound properties. Simulations by link removal showed that the HB-HFN is relatively robust against loss of connections, especially when the strongest connections are preserved and when the loss of connections only affects the brain of one guitarist. We conclude that HB-HFNs capture neural mechanisms that support interpersonally coordinated action and behavioral synchrony.

Phase synchronization between culture and climate forcing.

Over the history of humankind, cultural innovations have helped improve survival and adaptation to environmental stress. This has led to an overall increase in human population size, which in turn further contributed to cumulative cultural learning. During the Anthropocene, or arguably even earlier, this positive sociodemographic feedback has caused a strong decline in important resources that, coupled with projected future transgression of planetary boundaries, may potentially reverse the long-term trend in population growth. Here, we present a simple consumer/resource model that captures the coupled dynamics of stochastic cultural learning and transmission, population growth and resource depletion in a changing environment. The idealized stochastic mathematical model simulates boom/bust cycles between low-population subsistence, high-density resource exploitation and subsequent population decline. For slow resource recovery time scales and in the absence of climate forcing, the model predicts a long-term global population collapse. Including a simplified periodic climate forcing, we find that cultural innovation and population growth can couple with climatic forcing via nonlinear phase synchronization. We discuss the relevance of this finding in the context of cultural innovation, the anthropological record and long-term future resilience of our own predatory species.

Decreased Beta Power and OFC-STN Phase Synchronization during Reactive Stopping in Freely Behaving Rats.

During natural behavior, an action often needs to be suddenly stopped in response to an unexpected sensory input-referred to as reactive stopping. Reactive stopping has been mostly investigated in humans, which led to hypotheses about the involvement of different brain structures, in particular the hyperdirect pathway. Here, we directly investigate the contribution and interaction of two key regions of the hyperdirect pathway, the orbitofrontal cortex (OFC) and subthalamic nucleus (STN), using dual-area, multielectrode recordings in male rats performing a stop-signal task. In this task, rats have to initiate movement to a go-signal, and occasionally stop their movement to the go-signal side after a stop-signal, presented at various stop-signal delays. Both the OFC and STN show near-simultaneous field potential reductions in the beta frequency range (12-30 Hz) compared with the period preceding the go-signal and the movement period. These transient reductions (∼200 ms) only happen during reactive stopping, which is when the stop-signal was received after action initiation, and are well timed after stop-signal onset and before the estimated time of stopping. Phase synchronization analysis also showed a transient attenuation of synchronization between the OFC and STN in the beta range during reactive stopping. The present results provide the first direct quantification of local neural oscillatory activity in the OFC and STN and interareal synchronization specifically timed during reactive stopping.

Effects of transcranial direct current stimulation on brain activity and cortical functional connectivity in children with autism spectrum disorders.

Introduction: Transcranial direct current stimulation (tDCS) has emerged as a therapeutic option to mitigate symptoms in individuals with autism spectrum disorder (ASD). Our study investigated the effects of a two-week regimen of tDCS targeting the left dorsolateral prefrontal cortex (DLPFC) in children with ASD, examining changes in rhythmic brain activity and alterations in functional connectivity within key neural networks: the default mode network (DMN), sensorimotor network (SMN), and dorsal attention network (DAN). Methods: We enrolled twenty-six children with ASD and assigned them randomly to either an active stimulation group (n=13) or a sham stimulation group (n=13). The active group received tDCS at an intensity of 1mA to the left DLPFC for a combined duration of 10 days. Differences in electrical brain activity were pinpointed using standardized low-resolution brain electromagnetic tomography (sLORETA), while functional connectivity was assessed via lagged phase synchronization. Results: Compared to the typically developing children, children with ASD exhibited lower current source density across all frequency bands. Post-treatment, the active stimulation group demonstrated a significant increase in both current source density and resting state network connectivity. Such changes were not observed in the sham stimulation group. Conclusion: tDCS targeting the DLPFC may bolster brain functional connectivity in patients with ASD, offering a substantive groundwork for potential clinical applications.

Review on Phase Synchronization Methods for Spaceborne Multistatic Synthetic Aperture Radar.

Multistatic synthetic aperture radar (SAR) is a special mode of SAR system. The radar transmitter and receiver are located on different satellites, which brings many advantages, such as flexible baseline configuration, diverse receiving modes, and more detailed ground object classification information. The multistatic SAR has been widely used in interferometry, moving target detection, three-dimensional imaging, and other fields. The frequency offset between different oscillators will cause a modulation phase error in the signal. Therefore, phase synchronization is one of the most critical problems to be addressed in distributed SAR systems. This article reviews phase synchronization techniques, which are mainly divided into two methods: synchronization by direct microwave link and synchronization by a data-based estimation algorithm. Furthermore, the future development of synchronization technology is anticipated.

Phenylephrine alters phase synchronization between cerebral blood velocity and blood pressure in ME/CFS with orthostatic intolerance.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) with orthostatic intolerance (OI) is characterized by neurocognitive deficits perhaps related to upright hypocapnia and loss of cerebral autoregulation (CA). We performed N-back neurocognition testing and calculated the phase synchronization index (PhSI) between arterial pressure (AP) and cerebral blood velocity (CBV) as a time-dependent measurement of cerebral autoregulation in 11 control (mean age = 24.1 yr) and 15 patients with ME/CFS (mean age = 21.8 yr). All patients with ME/CFS had postural tachycardia syndrome (POTS). A 10-min 60° head-up tilt (HUT) significantly increased heart rate (109.4 ± 3.9 vs. 77.2 ± 1.6 beats/min, P < 0.05) and respiratory rate (20.9 ± 1.7 vs. 14.2 ± 1.2 breaths/min, P < 0.05) and decreased end-tidal CO2 (ETCO2; 33.9 ± 1.1 vs. 42.8 ± 1.2 Torr, P < 0.05) in ME/CFS versus control. In ME/CFS, HUT significantly decreased CBV compared with control (-22.5% vs. -8.7%, P < 0.005). To mitigate the orthostatic CBV reduction, we administered supplemental CO2, phenylephrine, and acetazolamide and performed N-back testing supine and during HUT. Only phenylephrine corrected the orthostatic decrease in neurocognition by reverting % correct n = 4 N-back during HUT in ME/CFS similar to control (ME/CFS = 38.5 ± 5.5 vs. ME/CFS + PE= 65.6 ± 5.7 vs. Control 56.9 ± 7.5). HUT in ME/CFS resulted in increased PhSI values indicating decreased CA. Although CO2 and acetazolamide had no effect on PhSI in ME/CFS, phenylephrine caused a significant reduction in PhSI (ME/CFS = 0.80 ± 0.03 vs. ME/CFS + PE= 0.69 ± 0.04, P < 0.05) and improved cerebral autoregulation. Thus, PE improved neurocognitive function in patients with ME/CFS, perhaps related to improved neurovascular coupling, cerebral autoregulation, and maintenance of CBV.NEW & NOTEWORTHY We evaluated cognitive function before and after CO2, acetazolamide, and phenylephrine, which mitigate orthostatic reductions in cerebral blood velocity. Neither CO2 nor acetazolamide affected N-back testing (% correct answers) during an orthostatic challenge. Only phenylephrine improved upright N-back performance in ME/CFS, as it both blocked hyperventilation and increased CO2 significantly compared with those untreated. And only phenylephrine resulted in improved PSI values in both ME/CFS and control while upright, suggesting improved cerebral autoregulation.

Neural Oscillations and Functional Significances for Prioritizing Dual-Task Walking in Parkinson's Disease.

Background: Task prioritization involves allocating brain resources in a dual-task scenario, but the mechanistic details of how prioritization strategies affect dual-task walking performance for Parkinson's disease (PD) are little understood. Objective: We investigated the performance benefits and corresponding neural signatures for people with PD during dual-task walking, using gait-prioritization (GP) and manual-prioritization (MP) strategies. Methods: Participants (N = 34) were asked to hold two inter-locking rings while walking and to prioritize either taking big steps (GP strategy) or separating the two rings (MP strategy). Gait parameters and ring-touch time were measured, and scalp electroencephalograph was performed. Results: Compared with the MP strategy, the GP strategy yielded faster walking speed and longer step length, whereas ring-touch time did not significantly differ between the two strategies. The MP strategy led to higher alpha (8-12 Hz) power in the posterior cortex and beta (13-35 Hz) power in the left frontal-temporal area, but the GP strategy was associated with stronger network connectivity in the beta band. Changes in walking speed and step length because of prioritization negatively correlated with changes in alpha power. Prioritization-related changes in ring-touch time correlated negatively with changes in beta power but positively with changes in beta network connectivity. Conclusions: A GP strategy in dual-task walking for PD can enhance walking speed and step length without compromising performance in a secondary manual task. This strategy augments attentional focus and facilitates compensatory reinforcement of inter-regional information exchange.

Unsupervised ensembling of multiple software sensors with phase synchronization: a robust approach for electrocardiogram-derived respiration.

Objective.We aimed to fuse the outputs of different electrocardiogram-derived respiration (EDR) algorithms to create one higher quality EDR signal.Methods.We viewed each EDR algorithm as a software sensor that recorded breathing activity from a different vantage point, identified high-quality software sensors based on the respiratory signal quality index, aligned the highest-quality EDRs with a phase synchronization technique based on the graph connection Laplacian, and finally fused those aligned, high-quality EDRs. We refer to the output as the sync-ensembled EDR signal. The proposed algorithm was evaluated on two large-scale databases of whole-night polysomnograms. We evaluated the performance of the proposed algorithm using three respiratory signals recorded from different hardware sensors, and compared it with other existing EDR algorithms. A sensitivity analysis was carried out for a total of five cases: fusion by taking the mean of EDR signals, and the four cases of EDR signal alignment without and with synchronization and without and with signal quality selection.Results.The sync-ensembled EDR algorithm outperforms existing EDR algorithms when evaluated by the synchronized correlation (γ-score), optimal transport (OT) distance, and estimated average respiratory rate score, all with statistical significance. The sensitivity analysis shows that the signal quality selection and EDR signal alignment are both critical for the performance, both with statistical significance.Conclusion.The sync-ensembled EDR provides robust respiratory information from electrocardiogram.Significance.Phase synchronization is not only theoretically rigorous but also practical to design a robust EDR.

Phase synchronization during the processing of taxonomic and thematic relations.

Semantic relations include "taxonomic" relations based on shared features and "thematic" relations based on co-occurrence in events. The "dual-hub" account proposes that the anterior temporal lobe (ATL) is functionally specialized for taxonomic relations and the inferior parietal lobule (IPL) for thematic relations. This study examined this claim by analyzing the intra- and inter-region phase synchronization of intracranial EEG data from electrodes in the ATL, IPL, and two subregions of the semantic control network: left inferior frontal gyrus (IFG) and posterior middle temporal gyrus (pMTG). Ten participants with epilepsy completed a semantic relatedness judgment task during intracranial EEG recording and had electrodes in at least one hub and at least one semantic control region. Theta band phase synchronization was partially consistent with the dual-hub account: synchronization between the ATL and IFG/pMTG increased when processing taxonomic relations, and synchronization within the IPL and between IPL and pMTG increased when processing thematic relations.

Dynamics in interbrain synchronization while playing a piano duet.

Humans interact with each other through actions that are implemented by sensory and motor processes. To investigate the role of interbrain synchronization emerging during interpersonal action coordination, electroencephalography data from 13 pairs of pianists were recorded simultaneously while they performed a duet together. The study aimed to investigate whether interbrain phase couplings can be reduced to similar bottom-up driven processes during synchronous play, or rather represent cognitive top-down control required during periods of higher coordination demands. To induce such periods, one of the musicians acted as a confederate who deliberately desynchronized the play. As intended, on the behavioral level, the perturbation caused a breakdown in the synchronization of the musicians' play and in its stability across trials. On the brain level, interbrain synchrony, as measured by the interbrain phase coherence (IPC), increased in the delta and theta frequency bands during perturbation as compared to non-perturbed trials. Interestingly, this increase in IPC in the delta band was accompanied by the shift of the phase difference angle from in-phase toward anti-phase synchrony. In conclusion, the current study demonstrates that interbrain synchronization is based on the interpersonal temporal alignment of different brain mechanisms and is not simply reducible to similar sensory or motor responses.

Measuring multivariate phase synchronization with symbolization and permutation.

Phase synchronization is an important mechanism for the information processing of neurons in the brain. Most of the current phase synchronization measures are bivariate and focus on the synchronization between pairs of time series. However, these methods do not provide a full picture of global interactions in neural systems. Considering the prevalence and importance of multivariate neural signal analysis, there is an urgent need to quantify global phase synchronization (GPS) in neural networks. Therefore, we propose a new measure named symbolic phase difference and permutation entropy (SPDPE), which symbolizes the phase difference in multivariate neural signals and estimates GPS according to the permutation patterns of the symbolic sequences. The performance of SPDPE was evaluated using simulated data generated by Kuramoto and Rössler model. The results demonstrate that SPDPE exhibits low sensitivity to data length and outperforms existing methods in accurately characterizing GPS and effectively resisting noise. Moreover, to validate the method with real data, it was applied to classify seizures and non-seizures by calculating the GPS of stereoelectroencephalography (SEEG) data recorded from the onset zones of ten epilepsy patients. We believe that SPDPE will improve the estimation of GPS in many applications, such as EEG-based brain-computer interfaces, brain modeling, and simultaneous EEG-fMRI analysis.

Potential biomarker for early detection of ADHD using phase-based brain connectivity and graph theory.

This research investigates an efficient strategy for early detection and intervention of attention-deficit hyperactivity disorder (ADHD) in children. ADHD is a neurodevelopmental condition characterized by inattention and hyperactivity/impulsivity symptoms, which can significantly impact a child's daily life. This study employed two distinct brain functional connectivity measurements to assess our approach across various local graph features. Six common classifiers are employed to distinguish between children with ADHD and healthy control. Based on the phase-based analysis, the study proposes two biomarkers that differentiate children with ADHD from healthy control, with a remarkable accuracy of 99.174%. Our findings suggest that subgraph centrality of phase-lag index brain connectivity within the beta and delta frequency bands could be a promising biomarker for ADHD diagnosis. Additionally, we identify node betweenness centrality of inter-site phase clustering connectivity within the delta and theta bands as another potential biomarker that warrants further exploration. These biomarkers were validated using a t-statistical test and yielded a p-value of under 0.05, which approved their significant difference in these two groups. Suggested biomarkers have the potential to improve the accuracy of ADHD diagnosis and could help identify effective intervention strategies for children with the condition.

The influence of inter-regional delays in generating large-scale brain networks of phase synchronization.

Large-scale networks of phase synchronization are considered to regulate the communication between brain regions fundamental to cognitive function, but the mapping to their structural substrates, i.e., the structure-function relationship, remains poorly understood. Biophysical Network Models (BNMs) have demonstrated the influences of local oscillatory activity and inter-regional anatomical connections in generating alpha-band (8-12 Hz) networks of phase synchronization observed with Electroencephalography (EEG) and Magnetoencephalography (MEG). Yet, the influence of inter-regional conduction delays remains unknown. In this study, we compared a BNM with standard "distance-dependent delays", which assumes constant conduction velocity, to BNMs with delays specified by two alternative methods accounting for spatially varying conduction velocities, "isochronous delays" and "mixed delays". We followed the Approximate Bayesian Computation (ABC) workflow, i) specifying neurophysiologically informed prior distributions of BNM parameters, ii) verifying the suitability of the prior distributions with Prior Predictive Checks, iii) fitting each of the three BNMs to alpha-band MEG resting-state data (N = 75) with Bayesian optimization for Likelihood-Free Inference (BOLFI), and iv) choosing between the fitted BNMs with ABC model comparison on a separate MEG dataset (N = 30). Prior Predictive Checks revealed the range of dynamics generated by each of the BNMs to encompass those seen in the MEG data, suggesting the suitability of the prior distributions. Fitting the models to MEG data yielded reliable posterior distributions of the parameters of each of the BNMs. Finally, model comparison revealed the BNM with "distance-dependent delays", as the most probable to describe the generation of alpha-band networks of phase synchronization seen in MEG. These findings suggest that distance-dependent delays might contribute to the neocortical architecture of human alpha-band networks of phase synchronization. Hence, our study illuminates the role of inter-regional delays in generating the large-scale networks of phase synchronization that might subserve the communication between regions vital to cognition.

The influence of synaptic pathways on the synchronization patterns of regularly structured mChialvo map network.

Synchronization of interconnecting units is one of the hottest topics many researchers are interested in. In addition, this emerging phenomenon is responsible for many biological processes, and thus, the synchronization of interacting neurons is an important field of study in neuroscience. Employing the memristive Chialvo (mChialvo) neuron map, this paper investigates the effect of electrical, inner-linking, chemical, and hybrid coupling functions on the synchronization state of a neuronal network with regular structure. Master stability function (MSF) analysis is performed to obtain the necessary conditions for synchronizing the built networks. Afterward, the MSF-based results are confirmed by calculating the synchronization error. Besides, the dynamics of the synchronous neurons are discussed based on the bifurcation analysis. Our results suggest that, compared to the electrical and inner-linking functions, chemical synapses facilitate mChialvo neurons' synchronization since the neurons can achieve synchrony with a negligible chemical coupling strength. Further studies reveal that based on the active synapses, coupled mChialvo neurons can reach cluster synchronization, chimera state, sine-like synchronization, phase synchronization, and cluster phase synchronization.

A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network.

Background: The convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as Alzheimer's disease (AD). AD is a degenerative disease difficult to clinical diagnosis due to its unclear underlying pathological mechanism. Previous studies have primarily focused on investigating structural abnormalities in the brain's functional networks related to the AD or proposing different deep learning approaches for AD classification. Objective: The aim of this study is to leverage the advantages of combining brain topological features extracted from functional network exploration and deep features extracted by the CNN. We establish a novel fMRI-based classification framework that utilizes Resting-state functional magnetic resonance imaging (rs-fMRI) with the phase synchronization index (PSI) and 2D-CNN to detect abnormal brain functional connectivity in AD. Methods: First, PSI was applied to construct the brain network by region of interest (ROI) signals obtained from data preprocessing stage, and eight topological features were extracted. Subsequently, the 2D-CNN was applied to the PSI matrix to explore the local and global patterns of the network connectivity by extracting eight deep features from the 2D-CNN convolutional layer. Results: Finally, classification analysis was carried out on the combined PSI and 2D-CNN methods to recognize AD by using support vector machine (SVM) with 5-fold cross-validation strategy. It was found that the classification accuracy of combined method achieved 98.869%. Conclusion: These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional connections, and characterize brain functional abnormalities, which could effectively detect AD anomalies by the extracted features that may provide new insights into exploring the underlying pathogenesis of AD.

Intracortical functional connectivity dynamics induced by reflex seizures.

Functional connectivity analysis is gaining more interest due to its promising clinical applications. To study network mechanisms underlying seizure termination and postictal depression, we explore dynamics of interhemispheric functional connectivity near the offset of focal and bilateral seizures in the experimental model of reflex audiogenic epilepsy. In the model, seizures and spreading depression are induced by sound stimulation of genetically predisposed rodents. We characterize temporal evolution of seizure-associated coupling dynamics in the frontoparietal cortex during late ictal, immediate postictal and interictal resting states, using two measures applied to local field potentials recorded in awake epileptic rats. Signals were analyzed with mean phase coherence index in delta (1-4 Hz), theta (4-10 Hz) beta (10-25 Hz) and gamma (25-50 Hz) frequency bands and mutual information function. The study shows that reflex seizures elicit highly dynamic changes in interhemispheric functional coupling with seizure-, region- and frequency-specific patterns of increased and decreased connectivity during late ictal and immediate postictal periods. Also, secondary generalization of recurrent seizures (kindling) is associated with pronounced alterations in resting-state functional connectivity - an early wideband decrease and a subsequent beta-gamma increase. The findings show that intracortical functional connectivity is dynamically modified in response to seizures on short and long timescales, suggesting the existence of activity-dependent plastic network alterations that may promote or prevent seizure propagation within the cortex and underlie postictal behavioral impairments.

Frontal-occipital phase synchronization predicts occipital alpha power in perceptual decision-making.

Numerous studies of perceptual decision-making have shown that lower prestimulus alpha power leads to a higher hit rate in visual detection, which is believed to correlate with the top-down control. However, whether frontal-occipital phase synchronization underlying the top-down control could impact the occipital alpha power that directly affects the perceptual performance remains unclear. In this study, we used analyses of the general linear mixed model (GLMM) and event-related potentials (ERPs) to show that the prestimulus alpha power over the occipital area directly affected visual perception. Using both the univariate and multivariate methods, we found that low-frequency (4-30 Hz) frontal-occipital phase synchronization predicted the prestimulus alpha power over the occipital area. Overall, our results suggested that frontal-occipital phase synchronization could predict occipital alpha power that directly affects perceptual decision-making. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09862-7.