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Measuring Phase Synchrony in Brain Signals
Abstract
This article presents, for the first time, a practical method for the direct quantification of frequency-specific synchronization (i.e., transient phase-locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long-range neural integration during cognitive tasks. The method, called phase-locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time-resolution (<100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration. To validate synchrony values against background fluctuations, PLS uses surrogate data and thus makes no a priori assumptions on the nature of the experimental data. We also apply PLS to investigate intracortical recordings from an epileptic patient performing a visual discrimination task. We find large-scale synchronies in the gamma band (45 Hz), e.g., between hippocampus and frontal gyrus, and local synchronies, within a limbic region, a few cm apart. We argue that whereas long-scale effects do reflect cognitive processing, short-scale synchronies are likely to be due to volume conduction. We discuss ways to separate such conduction effects from true signal synchrony.
... The spatial task was conducted in a virtual reality (VR) environment with an omnidirectional treadmill to simulate real-world navigation. To analyze neural synchronization and coordination during these social interactions and their relationship with dyadic task performance, we used various intra-and inter-brain connectivity measures, namely phase locking value (PLV) [36], corrected imaginary PLV (ciPLV) [37], weighted phase lag index (wPLI) [38], and directed transfer function (dDTF) [39]. Each measure provided a unique perspective on the complex interplay of neural activities between individuals in collaborative settings. ...
... Three measures of functional connectivity, namely PLV [36], ciPLV [37], and wPLI [38], were used to examine intra-and inter-brain connections. In addition, dDTF [39] was used to assess causality within and between brains, providing a comprehensive view of functional and effective connectivity (Fig. 4C). ...
... In addition, dDTF [39] was used to assess causality within and between brains, providing a comprehensive view of functional and effective connectivity (Fig. 4C). These connectivity analyses were conducted across a spectrum of EEG frequency bands, including delta (1-3 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50). Moreover, we determined the relationship between intra-/inter-brain couplings and task performance metrics (follower's post-test scores and completion times). ...
Navigating through a physical environment to reach a desired location involves a complex interplay of cognitive, sensory, and motor functions. When navigating with others, experiencing a degree of behavioral and cognitive synchronization is both natural and ubiquitous. This synchronization facilitates a harmonious effort toward achieving a common goal, reflecting how individuals instinctively align their actions and thoughts in collaborative settings. Collaborative spatial tasks, which are crucial in daily and professional settings, require coordinated navigation and problem-solving skills. This study explores the neural mechanisms underlying such tasks by using hyperscanning electroencephalography (EEG) technology to examine brain dynamics in dyadic route planning within a virtual reality setting. By analyzing intra- and inter-brain couplings across delta, theta, alpha, beta, and gamma EEG bands using both functional and effective connectivity measures, we identified significant neural synchronization patterns associated with collaborative task performance in both leaders and followers. Functional intra-brain connectivity analyses revealed distinct neural engagement across EEG frequency bands, with increased delta couplings observed in both leaders and followers. Theta connectivity was particularly enhanced in followers, whereas the alpha band exhibited divergent patterns that indicate role-specific neural strategies. Inter-brain analysis revealed increased delta causality between interacting members but decreased theta and gamma couplings from followers to leaders. Additionally, inter-brain analysis indicated decreased couplings in faster-performing dyads, especially in theta bands. These insights enhance our understanding of the neural mechanisms driving collaborative spatial navigation and demonstrate the effectiveness of hyperscanning in studying complex brain-to-brain interactions.
... We used all sites instead of just task-modulated sites to prevent a bias in the connectivity analysis towards a pre-specified spectral profile. As our connectivity metric, we assessed the statistical consistency of the phase of oscillatory activity between pairs of sites over time 13 . Following a previously described approach 13 , the pre-cue baseline normalized phase-locking value (PLV) was calculated for both low frequency (3-33 Hz) and high frequency (70-100 Hz) broadband oscillations for the duration of each cue-triggered 'move' and 'wait' epoch. ...
... As our connectivity metric, we assessed the statistical consistency of the phase of oscillatory activity between pairs of sites over time 13 . Following a previously described approach 13 , the pre-cue baseline normalized phase-locking value (PLV) was calculated for both low frequency (3-33 Hz) and high frequency (70-100 Hz) broadband oscillations for the duration of each cue-triggered 'move' and 'wait' epoch. For each subject, using the anatomical locations of each monopolar electrode contact, within-subject pairs were categorized as having both contacts within primary sensorimotor cortex (AA, 549 pairs, Table 1), both contacts within association regions (BB, 6732 pairs, Table 1), or one contact in primary cortex and the other in an association region (AB, 1103 pairs, Table 1). ...
... The precise frequency and anatomical dependency of time-resolved connectivity in the sensorimotor network during movement has not been fully elucidated. The change in phase-locking between two signals induced by a cue or stimulus (such as instructed movement onset) is a statistical measure of induced coordinated activity and argued to be a measure of functional connectivity 13,18,31 . Unlike previous methods of connectivity used in motor network electrocorticography (time-varying dynamic Bayesian networks) 19,20 , phase-based functional connectivity estimates provide a method to investigate the frequency-dependency of the coordinated network activity 13,32 . ...
It is hypothesized that disparate brain regions interact via synchronous activity to control behavior. The nature of these interconnected ensembles remains an area of active investigation, and particularly the role of high frequency synchronous activity in simplistic behavior is not well known. Using intracranial electroencephalography, we explored the spectral dynamics and network connectivity of sensorimotor cortical activity during a simple motor task in seven epilepsy patients. Confirming prior work, we see a “spectral tilt” (increased high-frequency (HF, 70–100 Hz) and decreased low-frequency (LF, 3–33 Hz) broadband oscillatory activity) in motor regions during movement compared to rest, as well as an increase in LF synchrony between these regions using time-resolved phase-locking. We then explored this phenomenon in high frequency and found a robust but opposite effect, where time-resolved HF broadband phase-locking significantly decreased during movement. This “connectivity tilt” (increased LF synchrony and decreased HF synchrony) displayed a graded anatomical dependency, with the most robust pattern occurring in primary sensorimotor cortical interactions and less robust pattern occurring in associative cortical interactions. Connectivity in theta (3–7 Hz) and high beta (23–27 Hz) range had the most prominent low frequency contribution during movement, with theta synchrony building gradually while high beta having the most prominent effect immediately following the cue. There was a relatively sharp, opposite transition point in both the spectral and connectivity tilt at approximately 35 Hz. These findings support the hypothesis that task-relevant high-frequency spectral activity is stochastic and that the decrease in high-frequency synchrony may facilitate enhanced low frequency phase coupling and interregional communication. Thus, the “connectivity tilt” may characterize behaviorally meaningful cortical interactions.
... Importantly, EEG functional connectivity can be constructed using instantaneous phase synchronization, which has been successfully used to distinguish individual changes in functional brain networks, regardless of potential amplitude fluctuations [29,33,34]. The PLV was introduced by Lachaux et al. [35] as a method to compute phase synchronization for EEG signals based on its excellent temporal resolution and distinguished characteristics that could reveal the correlation between different real time series, which has served extensively to investigate EEG functional networks under various conditions [36,37]. The PLV was calculated using the following formula: ...
... This formula represents the mean value of the difference in the instantaneous phase angle for two real signals within a specific time frame, and the significance of calculating the PLV is to quantify the change in phase synchronization across EEG epochs. The PLV range is defined from 0 to 1, if the phase difference approaches 0, the PLV will be approach 1, signifying a near-perfect phase locking between two EEG signals [35,36]. Conversely, a value nearing 0 indicates almost no phase locking. ...
... In the present study, based on the predefined delta (1-3), theta (4-7), alpha (8)(9)(10)(11)(12), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45) bands, the PLV was calculated separately to construct a functional network connectivity map by employing restingstate EEG signals of all channels for boxers and controls. Additionally, before calculating the PLV, the current source density (CSD) was calculated for each resting-state EEG dataset to minimize the interference of volume conduction and eliminate unveracious synchronous connectivity [38]. ...
Background:
Repetitive mild traumatic brain injury (rmTBI) often occurs in individuals engaged in contact sports, particularly boxing. This study aimed to elucidate the effects of rmTBI on phase-locking value (PLV)-based graph theory and functional network architecture in individuals with boxing-related injuries in five frequency bands by employing resting-state electroencephalography (EEG).
Methods:
Twenty-fore professional boxers and 25 matched healthy controls were recruited to perform a resting-state task, and their noninvasive scalp EEG data were collected simultaneously. Based on the construction of PLV matrices for boxers and controls, phase synchronization and graph-theoretic characteristics were identified in each frequency band. The significance of the calculated functional brain networks between the two populations was analyzed using a network-based statistical (NBS) approach.
Results:
Compared to controls, boxers exhibited an increasing trend in PLV synchronization and notable differences in the distribution of functional centers, especially in the gamma frequency band. Additionally, attenuated nodal network parameters and decreased small-world measures were observed in the theta, beta, and gamma bands, suggesting that the functional network efficiency and small-world characteristics were significantly weakened in boxers. NBS analysis revealed that boxers exhibited a significant increase in network connectivity strength compared to controls in the theta, beta, and gamma frequency bands. The functional connectivity of the significance subnetworks exhibited an asymmetric distribution between the bilateral hemispheres, indicating that the optimized organization of information integration and segregation for the resting-state networks was imbalanced and disarranged for boxers.
Conclusions:
This is the first study to investigate the underlying deficits in PLV-based graph-theoretic characteristics and NBS-based functional networks in patients with rmTBI from the perspective of whole-brain resting-state EEG. Joint analyses of distinctive graph-theoretic representations and asymmetrically hyperconnected subnetworks in specific frequency bands may serve as an effective method to assess the underlying deficiencies in resting-state network processing in patients with sports-related rmTBI.
... It helps identify abnormal synchronization patterns, aiding the understanding of underlying mechanisms and contributing to cognitive research and neurological studies. While this paper does not focus on a specific measure, PLV [21] is employed for easier generalization. ...
... To enhance the temporal resolution at lower frequencies and improve the frequency resolution at higher frequencies within the desired frequency range, the number of cycles of wavelets (NCW) is increased gradually, as suggested in the study conducted by [31]. In this study, we utilize PLV as a measure to examine the degree of phase synchronization between electrode pairs [21]. ...
... For a single trial data, phase synchronization measure PLV at a time-frequency instant PLV(t, f ) can be defined as [21], ...
Advancements in network science have facilitated the study of brain communication networks. Existing techniques for identifying event-related brain functional networks (BFNs) often result in fully connected networks. However, determining the optimal and most significant network representation for event-related BFNs is crucial for understanding complex brain networks. The presence of both false and genuine connections in the fully connected network requires network thresholding to eliminate false connections. However, a generalized framework for thresholding in network neuroscience is currently lacking. To address this, we propose four novel methods that leverage network properties, energy, and efficiency to select a generalized threshold level. This threshold serves as the basis for identifying the optimal and most significant event-related BFN. We validate our methods on an openly available emotion dataset and demonstrate their effectiveness in identifying multiple events. Our proposed approach can serve as a versatile thresholding technique to represent the fully connected network as an event-related BFN.
... Recently, Pedersen et al. compared SWPC with a more recently used technique for fMRI data, phase synchrony (Pedersen, Omidvarnia et al. 2018). PS measures the synchronization of neural activities based on the timing of their phase relationships (Lachaux, Rodriguez et al. 1999, Mormann, Lehnertz et al. 2000, Laird, Carew et al. 2001, Varela, Lachaux et al. 2001, Laird, Rogers et al. 2002, Deshmukh, Shivhare et al. 2004, Costa, Rognoni et al. 2006, Pockett, Bold et al. 2009, Fell and Axmacher 2011, Glerean, Salmi et al. 2012, Sun, Hong et al. 2012, Bolt, Nomi et al. 2018, Kumar, Reddy et al. 2018, Pedersen, Omidvarnia et al. 2018, Honari, Choe et al. 2020. PS is typically computed by estimating the phase of the signal and finding the phase difference. ...
... Phase synchrony or synchronization is a phenomenon where the phases of two oscillating signals align over time. This concept has been instrumental in exploring functional connectivity, as evidenced by various studies (Lachaux, Rodriguez et al. 1999, Fell and Axmacher 2011, Glerean, Salmi et al. 2012, Omidvarnia, Pedersen et al. 2016, Bolt, Nomi et al. 2018, Pedersen, Omidvarnia et al. 2018. There are two primary approaches for analyzing phase synchrony: one involves a window-based approach, like SWPC, while the second method involves using the estimated instantaneous phase of signal pairs. ...
Nitazoxanide has an anti-inflammatory effect, we clarified the ameliorative effect of nitazoxanide on asthmatic airway inflammation by conducting in vitro and in vivo experiments. In vitro, we assessed the effect of nitazoxanide on cytokine production by lipopolysaccharide-stimulated RAW 264.7 cells, as well as the diastolic effect of nitazoxanide on isolated rat airways. Nitazoxanide was found to have a diastolic effect on isolated tracheal spasms caused by spasmogenic substances, and to inhibit IL-6 and IL-1β production by RAW 264.7 cells. Meanwhile, nitazoxanide can inhibit the proliferation and migration of human bronchial smooth muscle cells (HBSMCs). In vivo, an ovalbumin (OVA)-induced asthma model was established in mice, and the airway resistance was measured by Whole Body Plethysmography (WBP) after inhalation of acetylcholine in mice, and the levels of IL-4, IL-6, IL-12, and IL-17 were detected in bronchoalveolar lavage fluid (BALF) of mice by ELISA and the inflammatory cells were counted. H&E staining was used to observe the changes in lung histopathology, and the expression of NFkB, MAPK, AMPK, and STAT3 in lung tissues was quantified using Western-blot. Nitazoxanide reduced inflammatory cell infiltration and goblet cell proliferation in the lungs of asthmatic mice. Moreover, the expression of IL-4, IL-5, and IL-6 in BALF was down-regulated in asthmatic mice. In addition, nitazoxanide could inhibit the expression of NFkB, MAPK, STAT 3 proteins and ascend the expression of AMPK in lung tissues. In conclusion, nitazoxanide could diastole airway smooth muscle and ameliorate OVA-induced airway inflammation in asthmatic mice via NFkB/MAPK and AMPK/STAT3 pathways.
... Recently, Pedersen et al. compared SWPC with a more recently used technique for fMRI data, phase synchrony (Pedersen, Omidvarnia et al. 2018). PS measures the synchronization of neural activities based on the timing of their phase relationships (Lachaux, Rodriguez et al. 1999, Mormann, Lehnertz et al. 2000, Laird, Carew et al. 2001, Varela, Lachaux et al. 2001, Laird, Rogers et al. 2002, Deshmukh, Shivhare et al. 2004, Costa, Rognoni et al. 2006, Pockett, Bold et al. 2009, Fell and Axmacher 2011, Glerean, Salmi et al. 2012, Sun, Hong et al. 2012, Bolt, Nomi et al. 2018, Kumar, Reddy et al. 2018, Pedersen, Omidvarnia et al. 2018, Honari, Choe et al. 2020. PS is typically computed by estimating the phase of the signal and finding the phase difference. ...
... Phase synchrony or synchronization is a phenomenon where the phases of two oscillating signals align over time. This concept has been instrumental in exploring functional connectivity, as evidenced by various studies (Lachaux, Rodriguez et al. 1999, Fell and Axmacher 2011, Glerean, Salmi et al. 2012, Omidvarnia, Pedersen et al. 2016, Bolt, Nomi et al. 2018, Pedersen, Omidvarnia et al. 2018. There are two primary approaches for analyzing phase synchrony: one involves a window-based approach, like SWPC, while the second method involves using the estimated instantaneous phase of signal pairs. ...
Time-resolved functional connectivity (trFC) assesses the time-resolved coupling between brain regions using functional magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used to estimate trFC, to investigate their similarities and differences when applied to fMRI data. These techniques are the sliding window Pearson correlation (SWPC), an amplitude-based approach, and phase synchronization (PS), a phase-based technique.
To accomplish our objective, we used resting-state fMRI data from the Human Connectome Project (HCP) with 827 subjects (repetition time: 0.7s) and the Function Biomedical Informatics Research Network (fBIRN) with 311 subjects (repetition time: 2s), which included 151 schizophrenia patients and 160 controls.
Our simulations reveal distinct strengths in two connectivity methods: SWPC captures high-magnitude, low-frequency connectivity, while PS detects low-magnitude, high-frequency connectivity. Stronger correlations between SWPC and PS align with pronounced fMRI oscillations. For fMRI data, higher correlations between SWPC and PS occur with matched frequencies and smaller SWPC window sizes (~30s), but larger windows (~88s) sacrifice clinically relevant information. Both methods identify a schizophrenia-associated brain network state but show different patterns: SWPC highlights low anti-correlations between visual, subcortical, auditory, and sensory-motor networks, while PS shows reduced positive synchronization among these networks.
Our findings underscore the complementary nature of SWPC and PS, elucidating their respective strengths and limitations without implying the superiority of one over the other.
... We reconstructed the source activity at the 1000 Schaefer atlas regions from MEG sensor recordings filtered in the alpha, beta and gamma bands (see Methods). Next, we computed frequency-specific average FCs across subjects using the phase locking value (PLV) 51 a measure of neural synchrony. Similarly, we estimated simulated PLV-FC for our parameter exploration presented above (excluding the delta band due to HCP filter settings). ...
... a Average correlation between empirical MEG and simulated functional connectivity (FC) for different intrinsic frequencies, coupling scalings, and conduction speeds (see Figs. 6 and 7; filter settings of the Human Connectome Project pipeline excluded the delta band from this analysis). The empirical and simulated FCs were estimated using the phase locking value (PLV) 51 . The average empirical PLV-FC was determined from source-reconstructed resting-state MEG activity of 80 subjects that participated in the Human Connectome Project. ...
Traveling waves and neural oscillation frequency gradients are pervasive in the human cortex. While the direction of traveling waves has been linked to brain function and dysfunction, the factors that determine this direction remain elusive. We hypothesized that structural connectivity instrength gradients — defined as the gradually varying sum of incoming connection strengths across the cortex — could shape both traveling wave direction and frequency gradients. We confirm the presence of instrength gradients in the human connectome across diverse cohorts and parcellations. Using a cortical network model, we demonstrate how these instrength gradients direct traveling waves and shape frequency gradients. Our model fits resting-state MEG functional connectivity best in a regime where instrength-directed traveling waves and frequency gradients emerge. We further show how structural subnetworks of the human connectome generate opposing wave directions and frequency gradients observed in the alpha and beta bands. Our findings suggest that structural connectivity instrength gradients affect both traveling wave direction and frequency gradients.
... FC analyses were performed using a custom MATLAB script. The phase-locking value (PLV) metric [24] was employed to evaluate the FC among different brain regions. Notably, the alpha and beta frequency bands, recognized for their physiological relevance to motor function and extensively validated in association with motor recovery in subacute stroke patients. ...
... Notably, the alpha and beta frequency bands, recognized for their physiological relevance to motor function and extensively validated in association with motor recovery in subacute stroke patients. [25][26][27] In line with this, PLVs were computed within these frequency bands: alpha (8-13 Hz) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). [28] The sensorimotor cortex leads (FC3, C3, CP3, P3, FC4, C4, CP4, and P4) within the parietofrontal network were analyzed, focusing on motor-related regions (M1, C3, C4, PMC, FC3, and FC4) and somatosensory-related areas (S1, CP3, CP4, PPC, P3, and P4). ...
Background
Electroacupuncture (EA) is a promising rehabilitation treatment for upper-limb motor recovery in stroke patients. However, the neurophysiological mechanisms underlying its clinical efficacy remain unclear. This study aimed to explore the immediate modulatory effects of EA on brain network functional connectivity and topological properties.
Methods
The randomized, single-blinded, self-controlled two-period crossover trial was conducted among 52 patients with subacute subcortical stroke. These patients were randomly allocated to receive either EA as the initial intervention or sham electroacupuncture (SEA) as the initial intervention. After a washout period of 24 hours, participants underwent the alternate intervention (SEA or EA). Resting state electroencephalography signals were recorded synchronously throughout both phases of the intervention. The functional connectivity (FC) of the parietofrontal network and small-world (SW) property indices of the whole-brain network were compared across the entire course of the two interventions.
Results
The results demonstrated that EA significantly altered ipsilesional parietofrontal network connectivity in the alpha and beta bands (alpha: F = 5.05, P = .011; beta: F = 3.295, P = .047), whereas no significant changes were observed in the SEA group. When comparing between groups, EA significantly downregulated ipsilesional parietofrontal network connectivity in both the alpha and beta bands during stimulation (alpha: t = −1.998, P = .049; beta: t = −2.342, P = .022). Significant differences were also observed in the main effects of time and the group × time interaction for the SW index (time: F = 5.516, P = .026; group × time: F = 6.892, P = .01). In terms of between-group comparisons, the EA group exhibited a significantly higher SW index than the SEA group at the post-stimulation stage ( t = 2.379, P = .018).
Conclusion
These findings suggest that EA downregulates ipsilesional parietofrontal network connectivity and enhances SW properties, providing a potential neurophysiological mechanism for facilitating motor performance in stroke patients.
... After drug delivery, there was a significant increase in average phase locking at low frequencies (1)(2)(3)(4)(5)(6)(7)(8) between the dorsolateral and ventrolateral PFC within each hemisphere, as well as across the two hemispheres (Fig. 2B). Figure 2C shows the change in phase locking caused by the drugs. The increase indicated that the relative phase between pairs of LFPs was becoming more consistent. ...
... We confirmed this effect using another measure of phase locking, coherence. PLV measures phase locking independent of changes in power [8][9][10] . Coherence takes into account both phase and power to measure how linearly predictable one signal is from another. ...
Many different anesthetics cause loss of responsiveness despite having diverse underlying molecular and circuit actions. To explore the convergent effects of these drugs, we examined how ketamine, an N-methyl-D-aspartate (NMDA) receptor antagonist, and dexmedetomidine, an alpha2 adrenergic receptor agonist, affected neural oscillations in the prefrontal cortex of nonhuman primates. Previous work has shown that anesthesia increases phase locking of low-frequency local field potential activity across cortex. We observed similar increases with anesthetic doses of ketamine and dexmedetomidine in the ventrolateral and dorsolateral prefrontal cortex, within and across hemispheres. However, the nature of the phase locking varied between regions. We found that oscillatory activity in different prefrontal subregions within each hemisphere became more anti-phase with both drugs. Local analyses within a region suggested that this finding could be explained by broad cortical distance-based effects, such as a large traveling wave. By contrast, homologous areas across hemispheres increased their phase alignment. Our results suggest that the drugs induce strong patterns of cortical phase alignment that are markedly different from those in the awake state, and that these patterns may be a common feature driving loss of responsiveness from different anesthetic drugs.
... In neurophysiological research, EEG signals are commonly categorized into different frequency bands, including alpha (8-13Hz), beta , and gamma (30-100Hz) [28]. Beta waves are particularly associated with behavior and actions, manifesting during conscious states such as problem-solving and decision-making [29]. ...
... To analyze synchrony patterns in our study, we extracted synchrony features from EEG data, eye tracking data, and action logs. One commonly used method in neuroscience for measuring moment-to-moment synchrony in oscillatory signals such as EEG and fMRI is Instantaneous Phase Synchrony (IPS) [28]. IPS indicates that the peaks and troughs of signals occur at similar time points, resulting in a synchronized oscillation pattern. ...
While current evaluation of maker activities has rarely explored students' learning processes, the multi-perspective and multi-level nature of collaboration adds complexity to learning processes of collaborative maker activities. In terms of group dynamics as an important indicator of collaboration quality, extant studies have shown the benefits of synchrony between learners' actions during collaborative learning processes. However, synchrony of learners' cognitive processes and visual attention in collaborative maker activities remains under-explored. Leveraging the multimodal learning analytics (MMLA) approach, this pilot study examines learners' synchrony patterns from multiple modalities of data in the col-laborative maker activity of virtual reality (VR) content creation. We conducted a user experiment with five pairs of students, and collected and analyzed their electroencephalography (EEG) signals, eye movement and system log data. Results showed that the five pairs of collaborators demonstrated diverse synchrony patterns. We also discovered that, while some groups exhibited synchrony in one modality of data before becoming not synchronized in another modality, other groups started with a lack of synchrony followed by maintaining synchrony. This study is expected to make methodolog-ical and practical contributions to MMLA research and assessment of collaborative maker activities.
... To measure the consistency of stimulation timing relative to alpha phase, we computed the phase locking value (PLV) for each session. The PLV is a measure of variability, such that a PLV value of 1 would indicate all pulses arrived at exactly the same phase, while a PLV near 0 would indicate that pulses arrived at randomly distributed phases 31 . Across all sessions, the median PLV was 0.59004 for pulse onsets and 0.32527 for pulse offsets ( Fig. 4C; Supplementary Fig. 5). ...
Sleep onset insomnia is a pervasive problem that contributes significantly to the poor health outcomes associated with insufficient sleep. Auditory stimuli phase-locked to slow-wave sleep oscillations have been shown to augment deep sleep, but it is unknown whether a similar approach can be used to accelerate sleep onset. The present randomized controlled crossover trial enrolled adults with objectively verified sleep onset latencies (SOLs) greater than 30 min to test the effect of auditory stimuli delivered at specific phases of participants’ alpha oscillations prior to sleep onset. During the intervention week, participants wore an electroencephalogram (EEG)-enabled headband that delivered acoustic pulses timed to arrive anti-phase with alpha for 30 min (Stimulation). During the Sham week, the headband silently recorded EEG. The primary outcome was SOL determined by blinded scoring of EEG records. For the 21 subjects included in the analyses, stimulation had a significant effect on SOL according to a linear mixed effects model (p = 0.0019), and weekly average SOL decreased by 10.5 ± 15.9 min (29.3 ± 44.4%). These data suggest that phase-locked acoustic stimulation can be a viable alternative to pharmaceuticals to accelerate sleep onset in individuals with prolonged sleep onset latencies. Trial Registration: This trial was first registered on clinicaltrials.gov on 24/02/2023 under the name Sounds Locked to ElectroEncephalogram Phase For the Acceleration of Sleep Onset Time (SLEEPFAST), and assigned registry number NCT05743114.
... a minimal architecture that mimicked the operations of classical commons-spatial-pattern filter bank models (Koles 69 et al., 1990). In a recent study, real-valued wavelets were used instead of classical convolutions kernels ( (Lachaux et al., 1999;Varela et al., 2001). Finally, the lack of constraints in 75 these architectures can provoke unsustainable computational scaling laws, hence, requiring massive datasets 76 that are not yet available for EEG (Thompson et al., 2024(Thompson et al., , 2020. ...
Spectral analysis using wavelets is widely used for identifying biomarkers in EEG signals. At the same time, Riemannian geometry enabled theoretically grounded machine learning models with high performance for predicting biomedical outcomes from multichannel EEG recordings. However, these methods often rely on handcrafted rules and sequential optimization. In contrast, deep learning (DL) offers end-to-end trainable models that achieve state-of-the-art performance on various prediction tasks but lack interpretability and interoperability with established neuroscience concepts. We introduce GREEN (Gabor Riemann EEGNet), a lightweight neural network that integrates wavelet transforms and Riemannian geometry for processing raw EEG data. Benchmarking on five prediction tasks (age, sex, eyes-closed detection, dementia diagnosis, EEG pathology) across three datasets (TUAB, CAUEEG, TDBRAIN) with over 5000 participants, GREEN outperformed non-deep state-of-the-art models and performed favorably against large DL models on the CAU benchmark using orders of magnitude fewer parameters. Computational experiments showed that GREEN facilitates learning sparse representations without compromising performance. The modularity of GREEN allows for the computation of classical measures of phase synchrony, such as pairwise phase-locking values, which are found to convey information for dementia diagnosis. The learned wavelets can be interpreted as bandpass filters, enhancing explainability. We illustrate this with the Berger effect, demonstrating the modulation of 8-10 Hz power when closing the eyes. By integrating domain knowledge, GREEN achieves a desirable complexity-performance trade-off and learns interpretable EEG representations. The source code is publicly available.
... , phase-locking value(PLV;Lachaux et al., 1999), various estimators of Granger causality when applied in sensor space (seeBrunner et al., 2016;Van de Steen et al., 2019), phase synchrony(Tass et al., 1998), mutual information (e.g.,Palus, 1997), and their various extensions. Corrected measures, in general, penalize zerolag contributions to various synchrony measures. ...
Electroencephalography (EEG) functional connectivity (FC) estimates are confounded by the volume conduction problem. This effect can be greatly reduced by applying FC measures insensitive to instantaneous, zero‐lag dependencies (corrected measures). However, numerous studies showed that FC measures sensitive to volume conduction (uncorrected measures) exhibit higher reliability and higher subject‐level identifiability. We tested how source reconstruction contributed to the reliability difference of EEG FC measures on a large (n = 201) resting‐state data set testing eight FC measures (including corrected and uncorrected measures). We showed that the high reliability of uncorrected FC measures in resting state partly stems from source reconstruction: idiosyncratic noise patterns define a baseline resting‐state functional network that explains a significant portion of the reliability of uncorrected FC measures. This effect remained valid for template head model‐based, as well as individual head model‐based source reconstruction. Based on our findings we made suggestions how to best use spatial leakage corrected and uncorrected FC measures depending on the main goals of the study.
... PLVs measure instantaneous phase-coupling across different brain regions independent of differences in amplitude, unlike coherence metrics. 71 This makes PLVs more sensitive to detecting weakly coupled oscillators despite differences in amplitude. 72 This coupling of oscillations is thought to indicate event-related communication between electrode contacts. ...
... The phase was computed using the Hilbert transform. the phase locking value using mne_connectivity python function spectral_connectivity_time with the method 223 'plv' based on the phase coherence model developed by Lachaux et al. (1999) . Phase locking was computed for . ...
Our use of language, which is profoundly social in nature, essentially takes place in interactive contexts and is shaped by precise coordination dynamics that interlocutors must observe. Thus language interaction is high demanding on fast adjustment of speech production. Here, we developed a real-time coupled-oscillators virtual partner that allows - by changing the coupling strength parameters - to modulate the ability to synchronise speech with a speaker. Then, we recorded the intracranial brain activity of 16 patients with drug-resistant epilepsy while they performed a verbal coordination task with the virtual partner (VP). More precisely, patients had to repeat short sentences synchronously with the VP. This synchronous speech task is efficient to highlight both the dorsal and ventral language pathways. Importantly, combining time-resolved verbal coordination and neural activity shows more spatially differentiated patterns and different types of neural sensitivity along the dorsal pathway. More precisely, high-frequency activity in secondary auditory regions is highly sensitive to verbal coordinative dynamics, while primary regions are not. Finally, the high-frequency activity of the IFG BA44 seems to specifically index the online coordinative adjustments that are continuously required to compensate deviation from synchronisation. These findings illustrate the possibility and value of using a fully dynamic, adaptive and interactive language task to gather deeper understanding of the subtending neural dynamics involved in speech perception, production as well as their interaction.
... Phase synchrony data were filtered in the theta band (5-11 Hz) and segregated by behavioral epoch. To measure strength of OFC-DMS synchrony, the phase locking index γ was computed by taking the complex value of the average of all points (1/N) where ϕ 1 (t) and ϕ 2 (t) are two phases from the filtered signals, the phase difference θ t j = ϕ 1 t j − ϕ 2 t j , t j are the times of data points, and N is the number of all data points during the given time interval 31,[74][75][76] . Age-group differences were assed using Welch's two sample t-test. ...
Adolescence is characterized by increased impulsive and risk-taking behaviors. To better understand the neural networks that subserves impulsivity in adolescents, we used a reward-guided behavioral model that quantifies age differences in impulsive actions in adult and adolescent rats of both sexes. Using chemogenetics, we identified orbitofrontal cortex (OFC) projections to the dorsomedial striatum (DMS) as a critical pathway for age-related execution of impulsive actions. Simultaneous recording of single units and local field potentials in the OFC and DMS during task performance revealed an overall muted response in adolescents during impulsive actions as well as age-specific differences in theta power and OFC–DMS functional connectivity. Collectively, these data reveal that the OFC–DMS pathway is critical for age-differences in reward-guided impulsive actions and provide a network mechanism to enhance our understanding of how adolescent and adult brains coordinate behavioral inhibition.
... In Basti et al. 20 , by linear transforming the original multivariate time series through a frequency-domain spatial whitening and by applying an averaging process, the authors derived an MD estimator of directionality of frequency-specific dependencies based on the phase slopes of cross-spectral quantities. Bruna and Pereda 21 generalized an index termed as phase-locking value (and based on the mean resultant length of the instantaneous phase difference between time series filtered at the same frequency 22 ) through an eigendecomposition approach. The majority of MD methods are able to catch only dependencies between oscillations at the same frequency. ...
We introduce a blockwise generalisation of the Antisymmetric Cross-Bicoherence (ACB), a statistical method based on bispectral analysis. The Multi-dimensional ACB (MACB) is an approach that aims at detecting quadratic lagged phase-interactions between vector time series in the frequency domain. Such a coupling can be empirically observed in functional neuroimaging data, e.g., in electro/magnetoencephalographic signals. MACB is invariant under orthogonal trasformations of the data, which makes it independent, e.g., on the choice of the physical coordinate system in the neuro-electromagnetic inverse procedure. In extensive synthetic experiments, we prove that MACB performance is significantly better than that obtained by ACB. Specifically, the shorter the data length, or the higher the dimension of the single data space, the larger the difference between the two methods.
... Therefore, the PAC criterion was calculated using the following formula [21]. ...
... PLV is a commonly used measure of phase synchronization in oscillatory activity between two signals. If the phases of the two signals are strongly coupled, then the PLV will approach a value of 1, otherwise it will be close to zero 73 . For this analysis, we first performed a time/frequency decomposition of the entire TMS-EEG epoch (from − 1 to 1 s after TMS) based on a complex Morlet wavelet (cycles = 3; frequency resolution = 1 Hz from 4 to 50 Hz; temporal resolution = 1 ms). ...
The inhibition of action is a fundamental executive mechanism of human behaviour that involve a complex neural network. In spite of the progresses made so far, many questions regarding the brain dynamics occurring during action inhibition are still unsolved. Here, we used a novel approach optimized to investigate real-time effective brain dynamics, which combines transcranial magnetic stimulation (TMS) with simultaneous electroencephalographic (EEG) recordings. 22 healthy volunteers performed a motor Go/NoGo task during TMS of the hand-hotspot of the primary motor cortex (M1) and whole-scalp EEG recordings. We reconstructed source-based real-time spatiotemporal dynamics of cortical activity and cortico-cortical connectivity throughout the task. Our results showed a task-dependent bi-directional change in theta/gamma supplementary motor cortex (SMA) and M1 connectivity that, when participants were instructed to inhibit their response, resulted in an increase of a specific TMS-evoked EEG potential (N100), likely due to a GABA-mediated inhibition. Interestingly, these changes were linearly related to reaction times, when participants were asked to produce a motor response. In addition, TMS perturbation revealed a task-dependent long-lasting modulation of SMA–M1 natural frequencies, i.e. alpha/beta activity. Some of these results are shared by animal models and shed new light on the physiological mechanisms of motor inhibition in humans.
... The interbrain synchrony was characterized by phase locking value (PLV) which measures the consistency of the phase variation over a period of time between two band-pass filtered signals (Lachaux et al., 1999). ...
Mother–child interaction is highly dynamic and reciprocal. Switching roles in these back‐and‐forth interactions serves as a crucial feature of reciprocal behaviors while the underlying neural entrainment is still not well‐studied. Here, we designed a role‐controlled cooperative task with dual EEG recording to explore how differently two brains interact when mothers and children hold different roles. When children were actors and mothers were observers, mother–child interbrain synchrony emerged primarily within the theta oscillations and the frontal lobe, which highly correlated with children's attachment to their mothers (self‐reported by mothers). When their roles were reversed, this synchrony was shifted to the alpha oscillations and the central area and associated with mothers' perception of their relationship with their children. The results suggested an observer‐actor neural alignment within the actor's oscillations, which was related to the actor‐toward‐observer emotional bonding. Our findings contribute to the understanding of how interbrain synchrony is established and dynamically changed during mother–child reciprocal interaction.
... PLV is a method for examining the extent to which the phase of a signal is synchronized between two regions. The PLV equation is as follows [13]: (1) where N is the total number of trials, and θ(t, N) is the phase difference [φ 1 (t, n)−φ 2 (t, n)] between two signals at time t. If the phase difference between two signals is constant, the PLV will be close to 1. ...
In many memory impairment cases, memory failure is due to impaired retrieval and not loss of memory information. Studies on memory retrieval using electroencephalography have mainly focused on changes in power and connectivity in the gamma band, which is a high-frequency region (> 30 Hz). However, previous research has not focused in detail on network activity during memory retrieval. To clarify this, we quantitatively compared retrieval and non-retrieval conditions using network analysis for time-varying functional connectivity. This study analyzed memory retrieval using a paired associative learning task. Consequently, this research found gamma band responses similar to those observed in previous time-frequency analysis in high gamma band. Furthermore, the high-gamma characteristic path length in the target condition was significantly higher than that in the distractor condition. The network becomes more efficient in the non-retrieval condition in the high-gamma band (50-80 Hz). We considered that this was due to the high workload, resulting in distracted memory retrieval. In non-retrieval conditions, participants must focus only on the next stimuli, which may increase network efficiency. We believe that this study shows the potential of a time-varying network analysis for revealing complex brain network activity.
... To measure a causal relation with fine temporal resolution we defined magnitudes that describe the similarity in phase and amplitude across multiple channels at each time point t'. For this purpose, we replaced the standard pairwise windowbased approach of connectivity (1,3,4,12) with a multichannel measure of similarity across channels for each time t'. For phase, similarity is measured by the phase consistency (PC), defined in equation (1) (see Methods), which is a function of the instant phase ! ( ) of channel k at time t and N, the total number of channels. ...
Phase coherence and amplitude correlations across brain regions are two main mechanisms of connectivity that govern brain dynamics at multiple scales. However, despite the increasing evidence that associates these mechanisms with brain functions and cognitive processes, the relationship between these different coupling modes is not well understood. Here, we study the causal relation between both types of functional coupling across multiple cortical areas. While most of the studies adopt a definition based on pairs of electrodes or regions of interest, we here employ a multichannel approach that provides us with a time-resolved definition of phase and amplitude coupling parameters. Using data recorded with a multichannel μECoG array from the ferret brain, we found that the transmission of information between both modes can be unidirectional or bidirectional, depending on the frequency band of the underlying signal. These results were reproduced in magnetoencephalography (MEG) data recorded during resting from the human brain. We show that this transmission of information occurs in a model of coupled oscillators and may represent a generic feature of a dynamical system. Together, our findings open the possibility of a general mechanism that may govern multi-scale interactions in brain dynamics.
... First, we would like to emphasize that the interpretation of connectivity measures is still controversial [54][55][56] . Here, we adhere to the following notions: an increase in the PLV is suggestive of inter-network communication, as two communicating network nodes presumably result in transient, frequency-specific phase synchronization 57 . For the DTF, if DTF m→n is greater than DTFn→m, then the dominant direction of information flow is from network node m to network node n 58 ; hence, node m exerts a larger causal influence on node n. ...
Antagonistic activity of brain networks likely plays a fundamental role in how the brain optimizes its performance by efficient allocation of computational resources. A prominent example involves externally/internally oriented attention tasks, implicating two anticorrelated, intrinsic brain networks: the default mode network (DMN) and the dorsal attention network (DAN). To elucidate electrophysiological underpinnings and causal interplay during attention switching, we recorded intracranial EEG (iEEG) from 25 epilepsy patients with electrode contacts localized in the DMN and DAN. We show antagonistic network dynamics of activation-related changes in high-frequency (> 50 Hz) and low-frequency (< 30 Hz) power. The temporal profile of information flow between the networks estimated by effective connectivity suggests that the activated network inhibits the other one, gating its activity by increasing the amplitude of the low-frequency oscillations. Insights about inter-network communication may have profound implications for various brain disorders in which these dynamics are compromised.
... Refs. 37,38 ). The PLF provides a measure of between-trial phase synchronization of oscillatory activity independently of the signal's amplitude. ...
It has been demonstrated that during motor responses, the activation of the motor cortical regions emerges in close association with the activation of the medial frontal cortex implicated with performance monitoring and cognitive control. The present study explored the oscillatory neurodynamics of response-related potentials during correct and error responses to test the hypothesis that such continuous communication would modify the characteristics of motor potentials during performance errors. Electroencephalogram (EEG) was recorded at 64 electrodes in a four-choice reaction task and response-related potentials (RRPs) of correct and error responses were analysed. Oscillatory RRP components at extended motor areas were analysed in the theta (3.5–7 Hz) and delta (1–3 Hz) frequency bands with respect to power, temporal synchronization (phase-locking factor, PLF), and spatial synchronization (phase-locking value, PLV). Major results demonstrated that motor oscillations differed between correct and error responses. Error-related changes (1) were frequency-specific, engaging delta and theta frequency bands, (2) emerged already before response production, and (3) had specific regional topographies at posterior sensorimotor and anterior (premotor and medial frontal) areas. Specifically, the connectedness of motor and sensorimotor areas contra-lateral to the response supported by delta networks was substantially reduced during errors. Also, there was an error-related suppression of the phase stability of delta and theta oscillations at these areas. This synchronization reduction was accompanied by increased temporal synchronization of motor theta oscillations at bi-lateral premotor regions and by two distinctive error-related effects at medial frontal regions: (1) a focused fronto-central enhancement of theta power and (2) a separable enhancement of the temporal synchronization of delta oscillations with a localized medial frontal focus. Together, these observations indicate that the electrophysiological signatures of performance errors are not limited to the medial frontal signals, but they also involve the dynamics of oscillatory motor networks at extended cortical regions generating the movement. Also, they provide a more detailed picture of the medial frontal processes activated in relation to error processing.
... Several analytical methods are used to estimate the covariance or directional neural coupling of the time series produced by two interacting individuals on EEG and MEG. Common analyses of IBS in electrophysiological studies use intra-brain estimators such as the Phase Locking Value (PLV) [56], Inter-brain Phase Coherence (IPC) [40], and Partial Directed Coherence (PDC) [44]. While PLV and IPC measure the phase synchrony between neural signals, PDC is useful for determining the direction of synchrony between neural signals in a dyad [33,34]. ...
Our actions and decisions in everyday life are heavily influenced by social interactions, which are dynamic feedback loops involving actions, reactions, and internal cognitive processes between individual agents. Social interactions induce interpersonal synchrony, which occurs at different biobehavioral levels and comprises behavioral, physiological, and neurological activities. Hyperscanning—a neuroimaging technique that simultaneously measures the activity of multiple brain regions—has provided a powerful second-person neuroscience tool for investigating the phase alignment of neural processes during interactive social behavior. Neural synchronization, revealed by hyperscanning, is a phenomenon called inter-brain synchrony- a process that purportedly facilitates social interactions by prompting appropriate anticipation of and responses to each other's social behaviors during ongoing shared interactions. In this review, I explored the therapeutic dual-brain approach using noninvasive brain stimulation to target inter-brain synchrony based on second-person neuroscience to modulate social interaction. Artificially inducing synchrony between the brains is a potential adjunct technique to physiotherapy, psychotherapy, and pain treatment- which are strongly influenced by the social interaction between the therapist and patient. Dual-brain approaches to personalize stimulation parameters must consider temporal, spatial, and oscillatory factors. Multiple data fusion analysis, the assessment of inter-brain plasticity, a closed-loop system, and a brain-to-brain interface can support personalized stimulation.
... Coherence does not separate the phase and amplitude components, whereas phase locking value (PLV) uses only the phase component to explore neural synchronization (Lachaux et al., 1999). ...
The assessment of consciousness states, especially distinguishing minimally conscious states (MCS) from unresponsive wakefulness states (UWS), constitutes a pivotal role in clinical therapies. Despite that numerous neural signatures of consciousness have been proposed, the effectiveness and reliability of such signatures for clinical consciousness assessment still remains an intense debate. Through a comprehensive review of the literature, inconsistent findings are observed about the effectiveness of diverse neural signatures. Notably, the majority of existing studies have evaluated neural signatures on a limited number of subjects (usually below 30), which may result in uncertain conclusions due to small data bias. This study presents a systematic evaluation of neural signatures with large‐scale clinical resting‐state electroencephalography (EEG) signals containing 99 UWS, 129 MCS, 36 emergence from the minimally conscious state, and 32 healthy subjects (296 total) collected over 3 years. A total of 380 EEG‐based metrics for consciousness detection, including spectrum features, nonlinear measures, functional connectivity, and graph‐based measures, are summarized and evaluated. To further mitigate the effect of data bias, the evaluation is performed with bootstrap sampling so that reliable measures can be obtained. The results of this study suggest that relative power in alpha and delta serve as dependable indicators of consciousness. With the MCS group, there is a notable increase in the phase lag index‐related connectivity measures and enhanced functional connectivity between brain regions in comparison to the UWS group. A combination of features enables the development of an automatic detector of conscious states.
... Thus, it is reasonable that stranger pairs were more densely connected to each other in the neural network than acquaintance pairs since stranger pairs were more attentive to the mutual prediction of their behavior than acquaintance pairs. Djalovski et al. showed that in social tasks, inter-brain EEG [alpha (8)(9)(10)(11)(12), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (31-48 Hz) bands] synchronization is higher in stranger pairs than in friends or couples 18 . In addition, Kikuchi et al. argued that stranger pairs may show higher inter-brain fNIRS synchronization than acquaintance pairs in an economic exchange task 64 . ...
The strategies for social interaction between strangers differ from those between acquaintances, whereas the differences in neural basis of social interaction have not been fully elucidated. In this study, we examined the geometrical properties of interpersonal neural networks in pairs of strangers and acquaintances during antiphase joint tapping. Dual electroencephalogram (EEG) of 29 channels per participant was measured from 14 strangers and 13 acquaintance pairs.Intra-brain synchronizations were calculated using the weighted phase lag index (wPLI) for intra-brain electrode combinations, and inter-brain synchronizations were calculated using the phase locking value (PLV) for inter-brain electrode combinations in the theta, alpha, and beta frequency bands. For each participant pair, electrode combinations with larger wPLI/PLV than their surrogates were defined as the edges of the neural networks. We calculated global efficiency, local efficiency, and modularity derived from graph theory for the combined intra- and inter-brain networks of each pair. In the theta band networks, stranger pairs showed larger local efficiency than acquaintance pairs, indicating that the two brains of stranger pairs were more densely connected. Hence, weak social ties require extensive social interactions and result in high efficiency of information transfer between neighbors in neural network.
Visceral rhythms orchestrate the physiological states underlying human emotion. Chronic aberrations in these brain-body interactions are implicated in a broad spectrum of mental health disorders. However, the specific contributions of the gastric-brain coupling to affective symptoms remain poorly understood. We investigated the relationship between this novel interoceptive axis and mental health symptoms in 243 participants, using a cross validated machine learning approach. We find that frontal parietal brain coupling to the gastric rhythm indexes a dimensional signature of mental health spanning anxiety, depression, stress, and well-being. Control analyses confirm the specificity of these interactions to the gastric-brain axis. Our study establishes coupling between the stomach and brain as a factor in the pathology of mental health, and offers new targets for interventions remediating aberrant brain-body coupling.
In his book 'A Beautiful Question' [1], physicist Frank Wilczek argues that symmetry is 'nature's deep design,' governing the behavior of the universe, from the smallest particles to the largest structures [1-4]. While symmetry is a cornerstone of physics, it has not yet been found widespread applicability to describe biological systems [5], particularly the human brain. In this context, we study the human brain network engaged in language and explore the relationship between the structural connectivity (connectome or structural network) and the emergent synchronization of the mesoscopic regions of interest (functional network). We explain this relationship through a different kind of symmetry than physical symmetry, derived from the categorical notion of Grothendieck fibrations [6]. This introduces a new understanding of the human brain by proposing a local symmetry theory of the connectome, which accounts for how the structure of the brain's network determines its coherent activity. Among the allowed patterns of structural connectivity, synchronization elicits different symmetry subsets according to the functional engagement of the brain. We show that the resting state is a particular realization of the cerebral synchronization pattern characterized by a fibration symmetry that is broken [7] in the transition from rest to language. Our findings suggest that the brain's network symmetry at the local level determines its coherent function, and we can understand this relationship from theoretical principles.
Ketamine exerts rapid, long-lasting antidepressant effects after a single administration and, thus, overcomes the limitations of classic drugs but also induces psychotic effects. It is, therefore, essential to pinpoint the biomarkers of each effect to develop new fast-acting antidepressants.
With this purpose, we examined, in male mice, the temporal evolution of the antidepressant and psychotic-like effects of 5 and 30 mg/kg of ketamine, and the electrical activity and the expression of the plasticity-related molecules in both the ventromedial prefrontal cortex and dorsal hippocampus were analyzed.
Ketamine induced immediate psychotic-like effects. They were milder and shorter at the 5 mg/kg dose, with an equivalent antidepressant-like effect of both doses, at 2 and 24 h. Both doses evoked a short-lasting electrical pattern that was dose-dependent, characterized mainly by increased synchronized gamma, excitatory/inhibitory balance, synchronized theta, phase-amplitude coupling, and decreased mutual information in slow (SW), beta, and theta waves. The higher dose led to longer-lasting changes. The most significant were decreased SW and beta and increased gamma and communication in theta and beta. Both doses altered sleep architecture at 24 h and the expression of AKT, pAKT, pAKT/AKT, pERK/ER, and pmTOR/mTOR at 2 and 24 h.
Given their temporal association, the decreased SW and beta mutual information, changes in hyperexcitability and gamma and theta activity may be biomarkers of ketamine’s psychotic effect. However, changes in sleep architecture and in the expression of plasticity proteins, together with delayed increased raw information, gamma and excitability, among others, are likely associated with its antidepressant effect.
Voltage imaging with cellular specificity has been made possible by advances in genetically encoded voltage indicators. However, the kilohertz rates required for voltage imaging lead to weak signals. Moreover, out-of-focus fluorescence and tissue scattering produce background that both undermines the signal-to-noise ratio and induces crosstalk between cells, making reliable in vivo imaging in densely labeled tissue highly challenging. We describe a microscope that combines the distinct advantages of targeted illumination and confocal gating while also maximizing signal detection efficiency. The resulting benefits in signal-to-noise ratio and crosstalk reduction are quantified experimentally and theoretically. Our microscope provides a versatile solution for enabling high-fidelity in vivo voltage imaging at large scales and penetration depths, which we demonstrate across a wide range of imaging conditions and different genetically encoded voltage indicator classes.
Time discrimination, a critical aspect of auditory perception, is influenced by numerous factors. Previous research has suggested that musical experience can restructure the brain, thereby enhancing time discrimination. However, this phenomenon remains underexplored. In this study, we seek to elucidate the enhancing effect of musical experience on time discrimination, utilizing both behavioral and electroencephalogram methodologies. Additionally, we aim to explore, through brain connectivity analysis, the role of increased connectivity in brain regions associated with auditory perception as a potential contributory factor to time discrimination induced by musical experience. The results show that the music‐experienced group demonstrated higher behavioral accuracy, shorter reaction time, and shorter P3 and mismatch response latencies as compared to the control group. Furthermore, the music‐experienced group had higher connectivity in the left temporal lobe. In summary, our research underscores the positive impact of musical experience on time discrimination and suggests that enhanced connectivity in brain regions linked to auditory perception may be responsible for this enhancement.
Neurological pathologies as e.g. Alzheimer’s Disease or Multiple Sclerosis are often associated to neurodegenerative processes affecting the strength and the transmission speed of long-range inter-regional fiber tracts. Such degradation of Structural Connectivity impacts on large-scale brain dynamics and the associated Functional Connectivity, eventually perturbing network computations and cognitive performance. Functional Connectivity however is not bound to merely mirror Structural Connectivity, but rather reflects the complex coordinated dynamics of many regions. Here, using analytical characterizations of toy models and computational simulations connectome-base whole-brain models, we predict that suitable modulations of regional dynamics could precisely compensate for the effects of structural degradation, as if the original Structural Connectivity strengths and speeds of conduction were effectively restored. The required dynamical changes are widespread and aspecific (i.e. they do not need to be restricted to specific regions) so that they could be potentially implemented via neuromodulation or pharmacological therapy, globally shifting regional excitability and/or excitation/inhibition balance. Computational modelling and theory thus suggest that, in the future therapeutic interventions could be designed to “repair brain dynamics” rather than structure to boost functional connectivity without having to block or revert neurodegenerative processes.
AUTHOR SUMMARY
Neurological disorders affect Structural Connectivity, i.e. the wiring infrastructure interlinking distributed brain regions. Here we propose that the resulting disruptions in Functional Connectivity, i.e. inter-regional coordination and information sharing, could be compensated by modifying local dynamics so to effectively emulate the restoration of Structural Connectivity (but through a suitable “software patch” rather than by repairing the “hardware”). For simple toy models involving a few regions we can achieve an analytical understanding of how structural and dynamical changes jointly control Functional Connectivity. We then show that the concept of “effective connectome change” via modulation of dynamics robustly extend also to simulation of large-scale models embedding realistic whole-brain connectivity. We thus forecast that novel therapeutic strategies could be devised, targeting dynamics rather than neurodegenerative mechanisms.
The third trimester is a critical period for the development of functional networks that support the lifelong neurocognitive performance, yet the emergence of neuronal coupling in these networks is poorly understood. Here, we used longitudinal high-density electroencephalographic recordings from preterm infants during the period from 33 to 45 weeks of conceptional age (CA) to characterize early spatiotemporal patterns in the development of local cortical function and the intrinsic coupling modes [ICMs; phase–phase (PPCs), amplitude–amplitude (AACs), and phase–amplitude correlations (PACs)]. Absolute local power showed a robust increase with CA across the full frequency spectrum, while local PACs showed sleep state-specific, biphasic development that peaked a few weeks before normal birth. AACs and distant PACs decreased globally at nearly all frequencies. In contrast, the PPCs showed frequency- and region-selective development, with an increase of coupling strength with CA between frontal, central, and occipital regions at low-delta and alpha frequencies together with a wider-spread decrease at other frequencies. Our findings together present the spectrally and spatially differential development of the distinct ICMs during the neonatal period and provide their developmental templates for future basic and clinical research.
Interpersonal touch plays a crucial role in human communication, development, and wellness. Mediated interpersonal touch (MIT), a technology to distance or virtually simulated interpersonal touch, has received significant attention to counteract the negative consequences of touch deprivation. Studies investigating the effectiveness of MIT have primarily focused on self-reporting or behavioral correlates. It is largely unknown how MIT affects neural processes such as interbrain functional connectivity during human interactions. Given how users exchange haptic information simultaneously during interpersonal touch, interbrain functional connectivity provides a more ecologically valid way of studying the neural correlates associated with MIT. In this study, a palm squeeze task is designed to examine interbrain synchrony associated with MIT using EEG-based hyperscanning methodology. The phase locking value (PLV) index is used to measure interbrain synchrony. Results demonstrate that MIT elicits a significant increase in alpha interbrain synchronization between participants’ brains. Especially, there was a significant difference in the alpha PLV indices between no MIT and MIT conditions in the early stage (130–470 ms) of the interaction period (t-test, p < 0.05). Given the role that alpha interbrain synchrony plays during social interaction, a significant increase in PLV index during MIT interaction seems to indicate an effect of social coordination. The findings and limitations of this study are further discussed, and perspectives on future research are provided.
Cooperation and competition are two of the most important social behaviours in human society. Investigating human social behavior in cooperative and competitive contexts, involving the insula and inferior frontal gyrus (IFG) related to executive function and mentalizing, is crucial for comprehending our social nature. However, the detailed study of their neural oscillations is limited by the deep location of the insula and the time resolution constraints of existing imaging techniques. This study aims to employ advanced hyperscanning and stereo-electroencephalography (SEEG), with high temporal and spatial resolution, to examine intra- and inter-brain dynamics within the insula and IFG during cooperative and competitive tasks. We identified distinct high-gamma responses and observed varying connectivity patterns, with the IFG modulating insula activity during competition, and balanced interactions between the insula and IFG occurring during cooperation. Notably, we observed enhanced inter-brain insula synchronization in competition and greater IFG synchronization in cooperation, elucidating the gamma band's role in social interactions. These results enhance our understanding of the neural bases of cooperation and competition, emphasizing the critical roles of both insula and IFG in social cognition.
The brain consists of a vastly interconnected network of regions, the connectome. By estimating the statistical interdependence of neurophysiological time series, we can measure the functional connectivity (FC) of this connectome. Pearson’s correlation ( r P ) is a common metric of coupling in FC studies. Yet r P does not account properly for the non-stationarity of the signals recorded in neuroimaging. In this study, we introduced a novel estimator of coupled dynamics termed multiscale detrended cross-correlation coefficient (MDC 3 ). Firstly, we showed that MDC 3 had higher accuracy compared to r P using simulated time series with known coupling, as well as simulated functional magnetic resonance imaging (fMRI) signals with known underlying structural connectivity. Next, we computed functional brain networks based on empirical magnetoencephalography (MEG) and fMRI. We found that by using MDC 3 we could construct networks of healthy populations with significantly different properties compared to r P networks. Based on our results, we believe that MDC 3 is a valid alternative to r P that should be incorporated in future FC studies.
Author Summary
The brain consists of a vastly interconnected network of regions. To estimate the connection strength of such networks the coupling between different brain regions should be calculated. This can be achieved by using a series of statistical methods that capture the connection strength between signals originating across the brain, one of them being Pearson’s correlation ( r P ). Despite its benefits, r P is not suitable for realistic estimation of brain network architecture. In this study, we introduced a novel estimator called multiscale detrended cross-correlation coefficient (MDC 3 ). Firstly, we showed that MDC 3 was more accurate than r P using simulated signals with known connection strength, as well as simulated brain activity emerging from realistic brain simulations. Next, we constructed brain networks based on real-life brain activity, recorded using two different methodologies. We found that by using MDC 3 we could construct networks of healthy populations with significantly different properties compared to r P networks. Based on our results, we believe that MDC 3 is a valid alternative to r P that should be incorporated in future studies of brain networks.
We investigated the differences in functional connectivity based on the source-level electroencephalography (EEG) analysis between stroke patients with and without post-stroke epilepsy (PSE). Thirty stroke patients with PSE and 35 stroke patients without PSE were enrolled. EEG was conducted during a resting state period. We used a Brainstorm program for source estimation and the connectivity matrix. Data were processed according to EEG frequency bands. We used a BRAPH program to apply a graph theoretical analysis. In the beta band, radius and diameter were increased in patients with PSE than in those without PSE (2.699 vs. 2.579, adjusted p = 0.03; 2.261 vs. 2.171, adjusted p = 0.03). In the low gamma band, radius was increased in patients with PSE than in those without PSE (2.808 vs. 2.617, adjusted p = 0.03). In the high gamma band, the radius, diameter, average eccentricity, and characteristic path length were increased (1.828 vs. 1.559, adjusted p < 0.01; 2.653 vs. 2.306, adjusted p = 0.01; 2.212 vs. 1.913, adjusted p < 0.01; 1.425 vs. 1.286, adjusted p = 0.01), whereas average strength, mean clustering coefficient, and transitivity were decreased in patients with PSE than in those without PSE (49.955 vs. 55.055, adjusted p < 0.01; 0.727 vs. 0.810, adjusted p < 0.01; 1.091 vs. 1.215, adjusted p < 0.01). However, in the delta, theta, and alpha bands, none of the functional connectivity measures were different between groups. We demonstrated significant alterations of functional connectivity in patients with PSE, who have decreased segregation and integration in brain network, compared to those without PSE.
Modulating brain oscillations has strong therapeutic potential. Interventions that both non-invasively modulate deep brain structures and are practical for chronic daily home use are desirable for a variety of therapeutic applications. Repetitive audio-visual stimulation, or sensory flicker, is an accessible approach that modulates hippocampus in mice, but its effects in humans are poorly defined. We therefore quantified the neurophysiological effects of flicker with high spatiotemporal resolution in patients with focal epilepsy who underwent intracranial seizure monitoring. In this interventional trial (NCT04188834) with a cross-over design, subjects underwent different frequencies of flicker stimulation in the same recording session with the effect of sensory flicker exposure on local field potential (LFP) power and interictal epileptiform discharges (IEDs) as primary and secondary outcomes, respectively. Flicker focally modulated local field potentials in expected canonical sensory cortices but also in the medial temporal lobe and prefrontal cortex, likely via resonance of stimulated long-range circuits. Moreover, flicker decreased interictal epileptiform discharges, a pathological biomarker of epilepsy and degenerative diseases, most strongly in regions where potentials were flicker-modulated, especially the visual cortex and medial temporal lobe. This trial met the scientific goal and is now closed. Our findings reveal how multi-sensory stimulation may modulate cortical structures to mitigate pathological activity in humans.
Capturing how the Caenorhabditis elegans connectome structure gives rise to its neuron functionality remains unclear. It is through fiber symmetries found in its neuronal connectivity that synchronization of a group of neurons can be determined. To understand these we investigate graph symmetries and search for such in the symmetrized versions of the forward and backward locomotive sub-networks of the Caenorhabditi elegans worm neuron network. The use of ordinarily differential equations simulations admissible to these graphs are used to validate the predictions of these fiber symmetries and are compared to the more restrictive orbit symmetries. Additionally fibration symmetries are used to decompose these graphs into their fundamental building blocks which reveal units formed by nested loops or multilayered fibers. It is found that fiber symmetries of the connectome can accurately predict neuronal synchronization even under not idealized connectivity as long as the dynamics are within stable regimes of simulations.
Developments in cognitive neuroscience have led to the emergence of hyperscanning, the simultaneous measurement of brain activity from multiple people. Hyperscanning is useful for investigating social cognition, including joint action, because of its ability to capture neural processes that occur within and between people as they coordinate actions toward a shared goal. Here, we provide a practical guide for researchers considering using hyperscanning to study joint action and seeking to avoid frequently raised concerns from hyperscanning skeptics. We focus specifically on EEG hyperscanning, which is widely available and optimally suited for capturing fine-grained temporal dynamics of action coordination. Our guidelines cover questions that are likely to arise when planning a hyperscanning project, ranging from whether hyperscanning is appropriate for answering one’s research questions to considerations for study design, dependent variable selection, data analysis, and visualization. By following clear guidelines that facilitate careful consideration of the theoretical implications of research design choices and other methodological decisions, joint action researchers can mitigate interpretability issues and maximize the benefits of hyperscanning paradigms.
The accumulation of amyloid- β ( Aβ ) and hyperphosphorylated-tau (hp-tau) are two classical histopathological biomarkers in Alzheimer’s disease (AD). However, their detailed interactions with the electrophysiological changes at the meso- and macroscale are not yet fully understood. We developed a mechanistic multiscale model of AD progression, linking proteinopathy to its effects on neural activity and vice-versa. We integrated a heterodimer model of prion-like protein propagation and a brain network model of Jansen–Rit neural masses derived from human neuroimaging data whose parameters varied due to neurotoxicity. Results showed that changes in inhibition guided the electrophysiological alterations found in AD, and these changes were mainly attributed to Aβ effects. Additionally, we found a causal disconnection between cellular hyperactivity and interregional hypersynchrony contrary to previous beliefs. Finally, we demonstrated that early Aβ and hp-tau depositions’ location determine the spatiotemporal profile of the proteinopathy. The presented model combines the molecular effects of both Aβ and hp-tau together with a mechanistic protein propagation model and network effects within a closed-loop model. This holds the potential to enlighten the interplay between AD mechanisms on various scales, aiming to develop and test novel hypotheses on the contribution of different AD-related variables to the disease evolution.
Based on increasing incidents of mental ill-health associated with living in dense urban environments, the field of Neurourbanism developed rapidly, aiming at identifying and improving urban factors that impact the health of city dwellers. Neurourbanism and the closely related field of Neuro-Architecture have seen a surge in studies using mobile electroencephalography (EEG) to investigate the impact of the built and natural environment on human brain activity moving from the laboratory into the real world. This trend predominantly arises from the ready availability of affordable and portable consumer hardware, which not only guarantees operational simplicity but also frequently incorporates automated data analysis functions. This significantly streamlines the process of EEG data acquisition, analysis, and interpretation, seemingly challenging the necessity of specialized expertise in the method of EEG or neurosciences in general. As a consequence, numerous studies in the field of Neurourbanism have used such off-the-shelf systems in laboratory and real-world experimental protocols including active movement of participants through the environment. However, the recording and analysis of EEG data entails numerous requisites, the disregard of which may culminate in errors during data acquisition, processing, and subsequent interpretation, potentially compromising the scientific validity of the outcomes. The often relatively low number of electrodes offered by affordable and portable consumer EEG systems further restricts specific analyses approaches to the low-dimensional EEG data. Crucially, a large part of Neurourbanism studies used black-box analyses provided by such consumer systems or incorrectly applied complex data-driven analyses methods that are incompatible with the recorded low-dimensional data. The current manuscript delineates the prerequisites concerning EEG hardware and analytical methodologies applicable to stationary and mobile EEG protocols, whether conducted within a controlled laboratory environment or in real-world settings. It conducts a comprehensive review of EEG studies within the domain of Neurourbanism and Neuro-Architecture, assessing their adherence to these prerequisites. The findings reveal severe deficiencies in the utilization of hardware and data processing methods, thereby rendering these studies unsuitable for scientific scrutiny. Consequently, the present paper provides guidelines for the selection of EEG hardware and analytical strategies for researchers engaged in mobile EEG recordings, be it within a laboratory or real-world context, aimed at steering future investigations in the field of Neurourbanism and Neuro-Architecture.
Hyperscanning approaches to human neuroscience aim to uncover the neural mechanisms of social interaction. They have been largely guided by the expectation that increased levels of engagement between two persons will be supported by higher levels of inter-brain synchrony (IBS). A common approach to measuring IBS is phase synchrony in the context of EEG hyperscanning. Yet the growing number of experimental findings does not yield a straightforward interpretation, which has prompted critical reflections about the field’s theoretical and methodological principles. In this perspective piece, we make a conceptual contribution to this debate by considering the role of a possibly overlooked effect of inter-brain desynchronization (IBD), as for example measured by decreased phase synchrony. A principled reason to expect this role comes from the recent proposal of irruption theory, which operationalizes the efficacy of a person’s subjective involvement in behavior generation in terms of increased neural entropy. Accordingly, IBD is predicted to increase with one or more participant’s socially motivated subjective involvement in interaction, because of the associated increase in their neural entropy. Additionally, the relative prominence of IBD compared to IBS is expected to vary in time, as well as across frequency bands, depending on the extent that subjective involvement is elicited by the task and/or desired by the person. If irruption theory is on the right track, it could thereby help to explain the notable variability of IBS in social interaction in terms of a countertendency from another factor: IBD due to subjective involvement.
Objective
We aimed to investigate the brain network activity during seizures in patients with untreated juvenile absence epilepsy.
Methods
Thirty-six juvenile absence epilepsy (JAE) patients with a current high frequency of seizures (more than five seizures during a 2 h EEG examination) were included. Each participant underwent a 2 h video EEG examination. Five 10 s EEG epochs for inter-ictal, pre-ictal, and post-ictal, and five 5 s EEG epochs for ictal states were extracted. Five 10 s resting-state EEG epochs for each participant from a sex- and age-matched healthy control (HC) were enrolled. The topological parameters of the brain networks were calculated using a graph theory analysis.
Results
Compared with the resting state of the HC group, the global efficiency, local efficiency, and clustering coefficients of the JAE group decreased in the inter-ictal state. In addition, the ictal state showed significantly increased global and local efficiency and clustering coefficients (p < 0.05) and a decreased small-world index and the shortest path length (p < 0.05) in the theta and alpha bands, compared to the remaining states within the JAE group. Moreover, subgroup analysis revealed that those JAE patients with typical 3 Hz discharges had upgraded global efficiency, local efficiency, and clustering coefficients in both delta and beta1 bands, compared to those JAE patients with non-3 Hz discharges during seizures.
Conclusion
The present study supported the idea that the changes in the EEG brain networks in JAE patients are characterized by decreased global and local efficiency and clustering coefficient in the alpha band. Moreover, the onset of seizures is accompanied by excessively enhanced network efficiency. JAE patients with different ictal discharge patterns may have different functional network oscillations.
When engaged in a conversation, one receives auditory information from the other’s speech but also from their own speech. However, this information is processed differently by an effect called Speech-Induced Suppression. Here, we studied brain representation of acoustic properties of speech in natural unscripted dialogues, using electroencephalography (EEG) and high-quality speech recordings from both participants. Using encoding techniques, we were able to reproduce a broad range of previous findings on listening to another’s speech, and achieving even better performances when predicting EEG signal in this complex scenario. Furthermore, we found no response when listening to oneself, using different acoustic features (spectrogram, envelope, etc.) and frequency bands, evidencing a strong effect of SIS. The present work shows that this mechanism is present, and even stronger, during natural dialogues. Moreover, the methodology presented here opens the possibility of a deeper understanding of the related mechanisms in a wider range of contexts.
Brain structure and function are intimately linked, however this association remains poorly understood and the complexity of this relationship has remained understudied. Healthy aging is characterised by heterogenous levels of structural integrity changes that influence functional network dynamics. Here, we use the multilayer brain network analysis on structural (diffusion weighted imaging) and functional (magnetoencephalography) data from the Cam-CAN database. We found that the level of similarity of connectivity patterns between brain structure and function in the parietal and temporal regions (alpha frequency band) is associated with cognitive performance in healthy older individuals. These results highlight the impact of structural connectivity changes on the reorganisation of functional connectivity associated with the preservation of cognitive function, and provide a mechanistic understanding of the concepts of brain maintenance and compensation with aging. Investigation of the link between structure and function could thus represent a new marker of individual variability, and of pathological changes.
When people surf online, they share audio-visual materials and may have similar emotional reactions. We carried out this study to explore the neural mechanism of emotional inter-brain synchronization (IBS). Multilateral interaction is more practical in IBS research than the interaction between two people. We propose a novel inspection method for emotional IBS based on brain connectivity analysis and manifold measurement. The method is expected to deal with multilateral interaction IBS and the problem of heterogeneous data brought by the data collection paradigm. Specifically, the algorithm was constructed based on causal brain connectivity analysis and Riemannian metrics (SPD). Our results suggest that participants have an emotional IBS effect in the context of audio-visual material sharing. The proposed IBS method is more adept at dealing with multiple interaction IBS and effectively reduces algorithm complexity.
The basal forebrain (BF) is critical for the motivational recruitment of attention in response to reward-related cues. This finding is consistent with a role for the BF in encoding and transmitting motivational salience and readying prefrontal circuits for further attentional processing. We recorded local field potentials to determine connectivity between prelimbic cortex (PrL) and BF during the modulation of attention by reward-related cues. We find that theta and gamma power are robustly associated with behavior. Power in both bands is significantly lower during trials in which an incorrect behavioral response is made. We find strong coherence during responses that are significantly stronger when a correct response is made. We show that information flow is largely monodirectional from BF to and is strongest when correct responses are made. These experiments demonstrate that connectivity between BF and the PrL increases during periods of increased motivational recruitment of attentional resources.
One of the aims of wavelet transforms is to provide an easily interpretable visual representation of signals. This is a prerequisite for applications such as selective modifications of signals or pattern recognition.
Magnetoencephalography (MEG) is a noninvasive technique for investigating neuronal activity in the living human brain. The time resolution of the method is better than 1 ms and the spatial discrimination is, under favorable circumstances, 2-3 mm for sources in the cerebral cortex. In MEG studies, the weak 10 fT-1 pT magnetic fields produced by electric currents flowing in neurons are measured with multichannel SQUID (superconducting quantum interference device) gradiometers. The sites in the cerebral cortex that are activated by a stimulus can be found from the detected magnetic-field distribution, provided that appropriate assumptions about the source render the solution of the inverse problem unique. Many interesting properties of the working human brain can be studied, including spontaneous activity and signal processing following external stimuli. For clinical purposes, determination of the locations of epileptic foci is of interest. The authors begin with a general introduction and a short discussion of the neural basis of MEG. The mathematical theory of the method is then explained in detail, followed by a thorough description of MEG instrumentation, data analysis, and practical construction of multi-SQUID devices. Finally, several MEG experiments performed in the authors' laboratory are described, covering studies of evoked responses and of spontaneous activity in both healthy and diseased brains. Many MEG studies by other groups are discussed briefly as well.
A fundamental step in visual pattern recognition is the establishment of relations between spatially separate features. Recently, we have shown that neurons in the cat visual cortex have oscillatory responses in the range 40-60 Hz (refs 1, 2) which occur in synchrony for cells in a functional column and are tightly correlated with a local oscillatory field potential. This led us to hypothesize that the synchronization of oscillatory responses of spatially distributed, feature selective cells might be a way to establish relations between features in different parts of the visual field. In support of this hypothesis, we demonstrate here that neurons in spatially separate columns can synchronize their oscillatory responses. The synchronization has, on average, no phase difference, depends on the spatial separation and the orientation preference of the cells and is influenced by global stimulus properties.
Recent theoretical studies have suggested that oscillatory firing patterns with frequencies in the gamma band (30-70 Hz) may be instrumental for the establishment of synchrony among widely distributed neurons if synchrony is to be achieved by reciprocal connections. We have now investigated the relationship between synchrony and oscillations in cat visual cortex. Our results show that when synchronization of neuronal activity occurs over distances of > 2 mm in primary visual cortex, or occurs between the two hemispheres, it is almost always associated with oscillatory firing patterns, whereas synchronization over short distances occurs also in the absence of oscillations. Furthermore, our results indicate that short-range interactions affect both the firing rate of the respective neurons and the timing of their discharges, whereas only the latter is influenced by long-range interactions. These data support the hypothesis that oscillatory activity can contribute to the establishment of long-range synchrony in a network of reciprocally coupled neurons.
The way in which the brain integrates fragmentary neural events at multiple locations to produce unified perceptual experience and behaviour is called the binding problem. Binding has been proposed to involve correlated activity at different cortical sites during perceptuomotor behaviour, particularly by synchronization of narrow-band oscillations in the gamma-frequency range (30-80 Hz). In the rabbit olfactory system, inhalation induces increased gamma-correlation between sites in olfactory bulb and cortex. In the cat visual system, coherent visual stimuli increase gamma-correlation between sites in both the same and different visual cortical areas. In monkeys, some groups have found that gamma-oscillations transiently synchronize within striate cortex, superior temporal sulcus and somatosensorimotor cortex. Others have reported that visual stimuli produce increased broad-band power, but not gamma-oscillations, in several visual cortical areas. But the absence of narrow-band oscillations in itself does not disprove interregional synchronization, which may be a broad-band phenomenon. We now describe episodes of increased broad-band coherence among local field potentials from sensory, motor and higher-order cortical sites of macaque monkeys performing a visual discrimination task. Widely distributed sites become coherent without involving other intervening sites. Spatially selective multiregional cortical binding, in the form of broad-band synchronization, may thus play a role in primate perceptuomotor behaviour.
Considerable interest has been raised by non-phase-locked episodes of synchronization in the gamma-band (30–60 Hz). One of their putative roles in the visual modality is feature-binding. We tested the stimulus specificity of high-frequency oscillations in humans using three types of visual stimuli: two coherent stimuli (a Kanizsa and a real triangle) and a noncoherent stimulus (“no-triangle stimulus”). The task of the subject was to count the occurrences of a curved illusory triangle. A time–frequency analysis of single-trial EEG data recorded from eight human subjects was performed to characterize phase-locked as well as non-phase-locked high-frequency activities.
We found an early phase-locked 40 Hz component, maximal at electrodes Cz–C4, which does not vary with stimulation type. We describe a second 40 Hz component, appearing around 280 msec, that is not phase-locked to stimulus onset. This component is stronger in response to a coherent triangle, whether real or illusory: it could reflect, therefore, a mechanism of feature binding based on high-frequency synchronization. Because both the illusory and the real triangle are more target-like, it could also correspond to an oscillatory mechanism for testing the match between stimulus and target. At the same latencies, the low-frequency evoked response components phase-locked to stimulus onset behave differently, suggesting that low- and high-frequency activities have different functional roles.
Multiunit activity was recorded in the optic tectum of awake pigeons with two electrodes at sites varying in depth and separated by 0.3 to 3.0 mm. Autocorrelation and cross-correlation functions were computed from the recorded spike trains to determine temporal relationships in the neuronal firing patterns. Cross-correlation analysis revealed that spatially separate groups of cells in the tectum show synchronous responses to a visual stimulus. Strong synchronization occurred in both superficial and deep layers of the tectum, in general with zero-phase shift. The response synchronization in the avian optic tectum resembles that observed in the mammalian cortex, suggesting that it may subserve common functions in visual processing.
The coherent representation of an object in the visual system has been suggested to be achieved by the synchronization in the gamma-band (30-70 Hz) of a distributed neuronal assembly. Here we measure variations of high-frequency activity on the human scalp. The experiment is designed to allow the comparison of two different perceptions of the same picture. In the first condition, an apparently meaningless picture that contained a hidden Dalmatian, a neutral stimulus, and a target stimulus (twirled blobs) are presented. After the subject has been trained to perceive the hidden dog and its mirror image, the second part of the recordings is performed (condition 2). The same neutral stimulus is presented, intermixed with the picture of the dog and its mirror image (target stimulus). Early (95 msec) phase-locked (or stimulus-locked) gamma-band oscillations do not vary with stimulus type but can be subdivided into an anterior component (38 Hz) and a posterior component (35 Hz). Nonphase-locked gamma-band oscillations appear with a latency jitter around 280 msec after stimulus onset and disappear in averaged data. They increase in amplitude in response to both target stimuli. They also globally increase in the second condition compared with the first one. It is suggested that this gamma-band energy increase reflects both bottom-up (binding of elementary features) and top-down (search for the hidden dog) activation of the same neural assembly coding for the Dalmatian. The relationships between high- and low-frequency components of the response are discussed, and a possible functional role of each component is suggested.
Information processing in the cerebral cortex invariably involves the activation of millions of neurons that are widely distributed over its various areas. These distributed activity patterns need to be integrated into coherent representational states. A candidate mechanism for the integration and coordination of neuronal activity between different brain regions is synchronization on a fine temporal scale. In the visual cortex, synchronization occurs selectively between the responses of neurons that represent related features and that need to be integrated for the generation of coherent percepts; neurons in other areas of the cerebral cortex also synchronize their discharges. However, little is known about the patterns and the behavioural correlates of synchrony among widely separated cortical regions. Here we report that synchronization occurs between areas of the visual and parietal cortex, and between areas of the parietal and motor cortex, in the awake cat. When cats responded to a sudden change of a visual pattern, neuronal activity in cortical areas exhibited synchrony without time lags; this synchrony was particularly strong between areas subserving related functions. During reward and inter-trial episodes, zero-time-lag synchrony was lost and replaced by interactions exhibiting large and unsystematic time lags.
Conventional approaches to understanding consciousness are generally concerned with the contribution of specific brain areas or groups of neurons. By contrast, it is considered here what kinds of neural processes can account for key properties of conscious experience. Applying measures of neural integration and complexity, together with an analysis of extensive neurological data, leads to a testable proposal-the dynamic core hypothesis-about the properties of the neural substrate of consciousness.
Transient periods of synchronization of oscillating neuronal discharges in the frequency range 30-80 Hz (gamma oscillations) have been proposed to act as an integrative mechanism that may bring a widely distributed set of neurons together into a coherent ensemble that underlies a cognitive act. Results of several experiments in animals provide support for this idea. In humans, gamma oscillations have been described both on the scalp (measured by electroencephalography and magnetoencephalography) and in intracortical recordings, but no direct participation of synchrony in a cognitive task has been demonstrated so far. Here we record electrical brain activity from subjects who are viewing ambiguous visual stimuli (perceived either as faces or as meaningless shapes). We show for the first time, to our knowledge, that only face perception induces a long-distance pattern of synchronization, corresponding to the moment of perception itself and to the ensuing motor response. A period of strong desynchronization marks the transition between the moment of perception and the motor response. We suggest that this desynchronization reflects a process of active uncoupling of the underlying neural ensembles that is necessary to proceed from one cognitive state to another.
As one test of the idea that compound field potentials in higher centers have a fine structure, the horizontal extent of coherence (C) was studied on the brain surface, with many closely spaced semimicroelectrodes in rabbits and rats. On the average C tends to fall with distance (D) in the 0.5–10 mm range; apart from driven rhythms, C usually falls to noise level at D > 10 nun. A useful measure is D (mm) where C has fallen to 0.5 (DC−0.5); for most F bands within the range 1–50 Hz this is usually 2.5–5 mm, averaging over the neocortex in both species.
Preface; 1. The purpose of the book; 2. Survey of contents; 3. How to
use the book; 4. Notation, terminology and conventions; 5.
Acknowledgements; Part I. Introduction: Part II. Descriptive Methods:
2.1. Introduction; 2.2. Data display; 2.3. Simple summary quantities;
2.4. Modifications for axial data; Part III. Models: 3.1. Introduction;
3.2. Notation; trigonometric moments; 3.3. Probability distributions on
the circle; Part IV. Analysis of a Single Sample of Data: 4.1.
Introduction; 4.2. Exploratory analysis; 4.3. Testing a sample of unit
vectors for uniformity; 4.4. Nonparametric methods for unimodal data;
4.5. Statistical analysis of a random sample of unit vectors from a von
Mises distribution; 4.6. Statistical analysis of a random sample of unit
vectors from a multimodal distribution; 4.7. Other topics; Part V.
Analysis of Two or More Samples, and of Other Experimental Layouts: 5.1.
Introduction; 5.2. Exploratory analysis; 5.3. Nonparametric methods for
analysing two or more samples of unimodal data; 5.4. Analysis of two or
more samples from von Mises distributions; 5.5. Analysis of data from
more complicated experimental designs; Part VI. Correlation and
Regression: 6.1. Introduction; 6.2. Linear-circular association and
circular-linear association; 6.3. Circular-circular association; 6.4.
Regression models for a circular response variable; Part VII. Analysis
of Data with Temporal or Spatial Structure: 7.1. Introduction; 7.2.
Analysis of temporal data; 7.3. Spatial analysis; Part VIII. Some Modern
Statistical Techniques for Testing and Estimation: 8.1. Introduction;
8.2. Bootstrap methods for confidence intervals and hypothesis tests:
general description; 8.3. Bootstrap methods for circular data:
confidence regions for the mean direction; 8.4. Bootstrap methods for
circular data: hypothesis tests for mean directions; 8.5. Randomisation,
or permutation, tests; Appendix A. Tables; Appendix B. Data sets;
References; Index.
Several methodological issues which impact experimental design and physiological interpretations in EEG coherence studies are considered, including reference electrode and volume conduction contributions to erroneous coherence estimates. A new measure, `reduced coherency', is introduced as the difference between measured coherency and the coherency expected from uncorrelated neocortical sources, based on simulations and analytic-statistical studies with a volume conductor model. The concept of reduced coherency is shown to be in semi-quantitative agreement with experimental EEG data. The impact of volume conduction on statistical confidence intervals for coherence estimates is discussed. Conventional reference, average reference, bipolar, Laplacian, and cortical image coherencies are shown to be partly independent measures of neocortical dynamic function at different spatial scales, due to each method's unique spatial filtering of intracranial source activity.
The question of the presence and detection of non-linear dynamics and possibly low-dimensional chaos in the brain is still an open question, with recent results indicating that initial claims for low dimensionality were faulted by incomplete statistical testing. To make some progress on this question, our approach was to use stringent data analysis of precisely controlled and behaviorally significant neuroelectric data. There are strong indications that functional brain activity is correlated with synchronous local field potentials. We examine here such synchronous episodes in data recorded from the visual system of behaving cats and pigeons. Our purpose was to examine under these ideal conditions whether the time series showed any evidence of non-linearity concommitantly with the arising of synchrony. To test for non-linearity we have used surrogate sets for non-linear forecasting, the false nearest strands method, and an examination of deterministic vs stochastic modeling. Our results indicate that the time series under examination do show evidence for traces of non-linear dynamics but weakly, since they are not robust under changes of parameters. We conclude that low-dimensional chaos is unlikely to be found in the brain, and that a robust detection and characterization of higher-dimensional non-linear dynamics is beyond the reach of current analytical tools.
Several methodological issues which impact experimental design and physiological interpretations in EEG coherence studies are considered, including reference electrode and volume conduction contributions to erroneous coherence estimates. A new measure, `reduced coherency', is introduced as the difference between measured coherency and the coherency expected from uncorrelated neocortical sources, based on simulations and analytic-statistical studies with a volume conductor model. The concept of reduced coherency is shown to be in semi-quantitative agreement with experimental EEG data. The impact of volume conduction on statistical confidence intervals for coherence estimates is discussed. Conventional reference, average reference, bipolar, Laplacian, and cortical image coherencies are shown to be partly independent measures of neocortical dynamic function at different spatial scales, due to each method's unique spatial filtering of intracranial source activity.
We describe a statistical approach for identifying nonlinearity in time series. The method first specifies some linear process as a null hypothesis, then generates surrogate data sets which are consistent with this null hypothesis, and finally computes a discriminating statistic for the original and for each of the surrogate data sets. If the value computed for the original data is significantly different than the ensemble of values computed for the surrogate data, then the null hypothesis is rejected and nonlinearity is detected. We discuss various null hypotheses and discriminating statistics. The method is demonstrated for numerical data generated by known chaotic systems, and applied to a number of experimental time series which arise in the measurement of superfluids, brain waves, and sunspots; we evaluate the statistical significance of the evidence for nonlinear structure in each case, and illustrate aspects of the data which this approach identifies.
This work represents an attempt to bring together two important themes in neuronal dynamics. The first is the characterization of dynamic correlations in multiunit recordings of spike activity using joint-peri-stimulus time histograms (J-PSTHs) [Aertsen and Preissl, 1991: Non Linear Dynamics and Neural Networks]. The second is transient phase-locking at high (gamma) frequencies, either in terms of spiking in separable spike trains [e.g., Eckhorn et al., 1988: Biol Cybern 60:121-130, Gray and Singer, 1989 Proc Natl Acad Sci USA 86:1698-1702], or using continuous electrical or biomagnetic signals [e.g., Desmedt and Tomberg, 1994 Neurosci Lett 168:126-129]. In this paper we suggest that transient phase-locking is necessary for frequency-specific, dynamic event-related correlations. This point is demonstrated using the gamma-frequency (36 Hz) component of neuromagnetic signals measured in the prefrontal and partial regions of a subject during self-paced movements. A J-PSTH analysis revealed dynamic changes in prefronto-parietal correlations in relation to movement onset. These frequency-specific dynamic correlations were associated with changes in the degree of phase-locking, of the sort reported by Desmedt and Tomberg [1994 Neurosci Lett 168:126-129]. Hum. Brain Mapping 5:48-57, 1997. (c) 1997 Wiley-Liss, Inc.
We report here on a first attempt to settle the methodological controversy between advocates of two alternative reconstruction approaches for temporal dynamics in brain signals: the single-channel method (using data from one recording site and reconstructing by time-lags), and the multiple-channel method (using data from a spatially distributed set of recordings sites and reconstructing by means of spatial position). For the purpose of a proper comparison of these two techniques, we computed a series of EEG-like measures on the basis of well-known dynamical systems placed inside a spherical model of the head. For each of the simulations, the correlation dimension estimates obtained by both methods were calculated and compared, when possible, with the known (or estimated) dimension of the underlying dynamical system. We show that the single-channel method fails to reliably quantify spatially extended dynamics, while the multichannel method performs better. It follows that the latter is preferable, given the known spatially distributed nature of brain processes. Hum. Brain Mapping 5:26-47, 1997. (c) 1997 Wiley-Liss, Inc.
The physical meaning of scalp current density (SCD) is presented and its properties are described using simulations of brain generators by dipolar models inside an inhomogeneous sphere. Its properties are compared with those of potentials and magnetic fields.
As one test of the idea that compound field potentials in higher centers have a fine structure, the horizontal extent of coherence (C) was studied on the brain surface, with many closely spaced semimicroelectrodes in rabbits and rats. On the average C tends to fall with distance (D) in the 0.5–10 mm range; apart from driven rhythms, C usually falls to noise level at D > 10 mm. A useful measure is D (mm) where C has fallen to 0.5 (DC = 0.5); for most F bands within the range 1–50 Hz this is usually 2.5–5 mm, averaging over the neocortex in both species.
Interchannel coherence is a measure of spatial extent of and timing relationships among cerebral electroencephalogram (EEG) generators. Interchannel coherence of referentially recorded potentials includes components due to volume conduction and reference site activity. The laplacian of the potential is reference independent and decreases the contribution of volume conduction. Interchannel coherences of the laplacian should, therefore, be less than those of referentially recorded potentials. However, methods used to compute the laplacian involve forming linear combinations of multiple recorded potentials, which may inflate interchannel coherences. WE compared 3 methods of computing the laplacian: (1) modified Hjorth (4 equidistant neighbors to each electrode), (2) Taylor's series (4 nonequidistant neighbors), and (3) spherical harmonic expansion (SHE). Average interchannel coherence introduced by computing the laplacian was less for nearest-neighbor methods (0.0207 +/- 0.0766) but still acceptable for the SHE method (0.0337 +/- 0.0865). Average interchannel coherence for simulated EEG (random data plus a common 10 Hz signal) was less for laplacian than for referential data because of removal of the common referential signal. Interchannel coherences of background EEG and partial seizure activity were less with the laplacian (any method) than with referential recordings. Laplacians calculated from the SHE do not demonstrate excessively large interchannel coherences, as have been reported for laplacians from spherical splines.
Electrical potential oscillations in the range of 35-45 Hz (gamma waves) have recently been shown to occur rather ubiquitously in the brain of awake humans. During selective somatic attention, we demonstrate a transient phase-locking of the gamma waves generated in the contralateral prefrontal and parietal cortical areas that we had previously shown to be involved in such selective attention tasks. In line with other microphysiological evidence obtained on mammalian visual cortex, this selective functional synchronization between critical human brain areas (as far as about 9 cm apart) is proposed to reflect the transient 'binding' of discrete cognitive features that are processed in distributed neuronal assemblies of the brain whereby the conscious perception of an object or event can be achieved. On this basis we emphasize that the conscious function of the brain is neither epiphenomenal nor delayed, but operates transiently to integrate relevant perceptual features at the time of target object identification and of conscious behavioural decision.
A mathematical procedure, which we call "Deblurring," was developed to reduce spatial blur distortion of scalp-recorded brain potentials due to transmission through the skull and other tissues. Deblurring estimates potentials at the superficial cerebral cortical surface from EEG's recorded at the scalp using a finite element model of each subject's scalp, skull and cortical surface constructed from their magnetic resonance images (MRI's). Simulations indicate that Deblurring is numerically stable, while a comparison of deblurred data with a direct cortical recording from a neurosurgery patient suggests that the procedure is valid. Application of Deblurring to somatosensory evoked potential data recorded at 124 scalp sites suggests that the method produces a dramatic improvement in spatial detail, and merits further development.
Animal electrocorticogram (ECoG) studies have shown that spatial patterns in the gamma band (> 20 Hz) reflect perceptual categorization. Spatio-temporal correlations were investigated in the 20–50 Hz range in search for similar phenomena in human ECoG. ECoGs were recorded in a somatosensory discrimination task from 64-electrode subdural grid arrays, with inter-electrode spacing of 1 cm, overlying somatosensory, motor and superior temporal cortices in 2 patients with intractable epilepsy. Bootstrap techniques were devised to analyze the spatial and temporal characteristics of the correlations.
This paper presents a novel reading of ideas on temporal binding as a key for cognitive operations by means of fast (gamma band) phase synchrony. We advocate a view of binding of widely distributed cell assemblies transiently locked in a neural hypergraph which serves as a reference point to incorporate or interpret other less coherent concurrent neural events. The paper traces in some detail the empirical evidence concerning the gamma binding process and presents some implications for the constitution of a unified cognitive-mental space.
We studied subdural recordings from a patient with an unusually focal and stable occipito-temporal epileptic discharge under four experimental conditions. The series of time intervals between successive spike discharges displayed a few (3-5) clusters of periodic values representing statistically significant short-term periodicities when tested against surrogate data. This short-term predictability was modulated during the different experimental conditions by periodicity shifts of the order of 15-30 ms. Correspondingly, there was an increased gamma-band (30-70 Hz) coherence between the epileptic focus and surrounding recording sites. We conclude that the focal epileptic activity is part of an extended network of neural activities which exert a fast modulation reflected in changes of transiently periodic activities.
This paper investigates methods for passive estimation of the bearing to a slowly moving acoustically radiating source. The mathematics for the solution to such a problem is analogous to estimating the time delay (or group delay) between two time series. Since the estimation of time delay is intimately related to the coherence between two time series, a summary of the properties of coherence is presented
The maximum likelihood (ML) estimate of time delay (under jointly stationary Gaussian assumptions) is presented. The explicit dependence of time delay estimates on coherence is evident in the estimator realization in which the two time series are prefiltered (to accentuate frequency bands according to the strength of the coherence) and subsequently crosscorrelated. The hypothesized delay at which the generalized crosscorrelation (GCC) function peaks is the time delay estimate. The variance of the time delay estimate is presented and discussed.
Measuring coherence between brain signals: A new method for single-trial analysis Interelectrode coherences from nearest-neighbor and spherical harmonic expansion computation of laplacian of scalp potential
- J-P Lachaux
- Jm Hurtado
- K Sigvart
- Fj Varla
- Cm Gray
- Td Lagerlund
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