Phase Synchronization Measures Research Articles

Measuring multivariate phase synchronization with symbolization and permutation

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

A 16-Channel Low-Power Neural Connectivity Extraction and Phase-Locked Deep Brain Stimulation SoC.

Growing evidence suggests that phase-locked deep brain stimulation (DBS) can effectively regulate abnormal brain connectivity in neurological and psychiatric disorders. This letter therefore presents a low-power SoC with both neural connectivity extraction and phase-locked DBS capabilities. A 16-channel low-noise analog front-end (AFE) records local field potentials (LFPs) from multiple brain regions with precise gain matching. A novel low-complexity phase estimator and neural connectivity processor subsequently enable energy-efficient, yet accurate measurement of the instantaneous phase and cross-regional synchrony measures. Through flexible combination of neural biomarkers such as phase synchrony and spectral energy, a four-channel charge-balanced neurostimulator is triggered to treat various pathological brain conditions. Fabricated in 65-nm CMOS, the SoC occupies a silicon area of 2.24 mm2 and consumes 60 μW, achieving over 60% power saving in neural connectivity extraction compared to the state-of-the-art. Extensive in-vivo measurements demonstrate multi-channel LFP recording, real-time extraction of phase and neural connectivity measures, and phase-locked stimulation in rats.

Synchronization of Belousov-Zhabotinsky oscillators with electrochemical coupling in a spontaneous process.

A passive electrochemical coupling approach is proposed to induce spontaneous synchronization between chemical oscillators. The coupling exploits the potential difference between a catalyst redox couple in the Belousov-Zhabotinsky (BZ) reaction, without external feedback, to induce surface reactions that impact the kinetics of the bulk system. The effect of coupling in BZ oscillators under batch condition is characterized using phase synchronization measures. Although the frequency of the oscillators decreases nonlinearly over time, by a factor of 2 or more within 100 cycles, the coupling is strong enough to maintain synchronization. In such a highly drifting system, the Gibbs-Shannon entropy of the cyclic phase difference distribution can be used to quantify the coupling effect. We extend the Oregonator BZ model to account for the drifting natural frequencies in batch condition and for electrochemical coupling, and numerical simulations of the effect of acid concentration on synchronization patterns are in agreement with the experiments. Because of the passive nature of coupling, the proposed coupling scheme can open avenues for designing pattern recognition and neuromorphic computation systems using chemical reactions in a spontaneous process.

Brain Fingerprinting and Lie Detection: A Study of Dynamic Functional Connectivity Patterns of Deception Using EEG Phase Synchrony Analysis.

This study investigated the brain functional connectivity (FC) patterns related to lie detection (LD) tasks with the purpose of analyzing the underlying cognitive processes and mechanisms in deception. Using the guilty knowledge test protocol, 30 subjects were divided randomly into guilty and innocent groups, and their electroencephalogram (EEG) signals were recorded on 32 electrodes. Phase synchrony of EEG was analyzed between different brain regions. A few-trials-based relative phase synchrony (FTRPS) measure was proposed to avoid the false synchronization that occurs due to volume conduction. FTRPS values with a significantly statistical difference between two groups were employed to construct FC patterns of deception, and the FTRPS values from the FC networks were extracted as the features for the training and testing of the support vector machine. Finally, four more intuitive brain fingerprinting graphs (BFG) on delta, theta, alpha and beta bands were respectively proposed. The experimental results reveal that deceptive responses elicited greater oscillatory synchronization than truthful responses between different brain regions, which plays an important role in executing lying tasks. The functional connectivity in the BFG is mainly implicated in the visuo-spatial imagery, bottom-top attention and memory systems, work memory and episodic encoding, and top-down attention and inhibition processing. These may, in part, underlie the mechanism of communication between different brain cortices during lying. High classification accuracy demonstrates the validation of BFG to identify deception behavior, and suggests that the proposed FTRPS could be a sensitive measure for LD in the real application.

EEG Phase Synchrony Reflects SNR Levels During Continuous Speech-in-Noise Tasks.

Comprehension of speech in noise is a challenge for hearing-impaired (HI) individuals. Electroencephalography (EEG) provides a tool to investigate the effect of different levels of signal-to-noise ratio (SNR) of the speech. Most studies with EEG have focused on spectral power in well-defined frequency bands such as alpha band. In this study, we investigate how local functional connectivity, i.e. functional connectivity within a localized region of the brain, is affected by two levels of SNR. Twenty-two HI participants performed a continuous speech in noise task at two different SNRs (+3 dB and +8 dB). The local connectivity within eight regions of interest was computed by using a multivariate phase synchrony measure on EEG data. The results showed that phase synchrony increased in the parietal and frontal area as a response to increasing SNR. We contend that local connectivity measures can be used to discriminate between speech-evoked EEG responses at different SNRs.

Detection of large vessel occlusion stroke with electroencephalography in the emergency room: first results of the ELECTRA-STROKE study

BackgroundPrehospital detection of large vessel occlusion stroke of the anterior circulation (LVO-a) would enable direct transportation of these patients to an endovascular thrombectomy (EVT) capable hospital. The ongoing ELECTRA-STROKE study investigates the diagnostic accuracy of dry electrode electroencephalography (EEG) for LVO-a stroke in the prehospital setting. To determine which EEG features are most useful for this purpose and assess EEG data quality, EEG recordings are also performed in the emergency room (ER). Here, we report data of the first 100 patients included in the ER.MethodsPatients presented to the ER with a suspected stroke or known LVO-a stroke underwent a single EEG prior to EVT. Diagnostic accuracy for LVO-a stroke of frequency band power, brain symmetry and phase synchronization measures were evaluated by calculating receiver operating characteristic curves. Optimal cut-offs were determined as the highest sensitivity at a specificity of ≥ 80%.ResultsEEG data were of sufficient quality for analysis in 65/100 included patients. Of these, 35/65 (54%) had an acute ischemic stroke, of whom 9/65 (14%) had an LVO-a stroke. Median onset-to-EEG-time was 266 min (IQR 121–655) and median EEG-recording-time was 3 min (IQR 3–5). The EEG feature with the highest diagnostic accuracy for LVO-a stroke was theta–alpha ratio (AUC 0.83; sensitivity 75%; specificity 81%). Combined, weighted phase lag index and relative theta power best identified LVO-a stroke (sensitivity 100%; specificity 84%).ConclusionDry electrode EEG is a promising tool for LVO-a stroke detection, but data quality needs to be improved and validation in the prehospital setting is necessary. (TRN: NCT03699397, registered October 9 2018).

Применение анализа совместных рекуррентностей к оценке фазовой синхронизации физиологических сигналов

Based on the analysis of joint recurrences, differences in phase synchronization between rhythmic photostimulation and brain responses were revealed in individuals with atrial fibrillation of paroxysmal and persistent types. As a measure of phase synchronization between two signals, the cross-correlation coefficient between the probabilities of recurrences of the corresponding phase trajectories is considered. With a lengthening of the lifetime of atrial fibrillation and an increase in the degree of decline in cognitive functions, the value of this coefficient increases for brain responses to theta-range frequencies.

Transient loss of interhemispheric functional connectivity following unilateral cortical spreading depression in awake rats.

Growing evidence shows a critical role of network disturbances in the pathogenesis of migraine. Unilateral pattern of neurological symptoms of aura suggests disruption of interhemispheric interactions during the early phase of a migraine attack. Using local field potentials data from the visual and motor cortices, this study explored effects of unilateral cortical spreading depression, the likely pathophysiological mechanism of migraine aura, on interhemispheric functional connectivity in freely behaving rats. Temporal evolution of the functional connectivity was evaluated using mutual information and phase synchronization measures applied to local field potentials recordings obtained in homotopic points of the motor and visual cortices of the two hemispheres in freely behaving rats after induction of a single unilateral cortical spreading depression in the somatosensory S1 cortex and sham cortical stimulation. Cortical spreading depression was followed by a dramatic broadband loss of interhemispheric functional connectivity in the visual and motor regions of the cortex. The hemispheric disconnection started after the end of the depolarization phase of cortical spreading depression, progressed gradually, and terminated by 5 min after initiation of cortical spreading depression. The network impairment had region- and frequency-specific characteristics and was more pronounced in the visual cortex than in the motor cortex. The period of impaired neural synchrony coincided with post-cortical spreading depression electrographic aberrant activation of the ipsilateral cortex and abnormal behavior. The study provides the first evidence that unilateral cortical spreading depression induces a reversible loss of functional hemispheric connectivity in the cortex of awake animals. Given a critical role of long-distance cortical synchronization in sensory processing and sensorimotor integration, the post-cortical spreading depression breakdown of functional connectivity may contribute to neuropathological mechanisms of aura generation.

Prediction of Depression Severity Scores Based on Functional Connectivity and Complexity of the EEG Signal.

Depression is one of the most common mental disorders and the leading cause of functional disabilities. This study aims to specify whether functional connectivity and complexity of brain activity can predict the severity of depression (Beck Depression Inventory-II scores). Resting-state, eyes-closed EEG data were recorded from 60 depressed patients. A phase synchronization measure was used to estimate functional connectivity between all pairs of the EEG channels in the delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequency bands. To quantify the local value of functional connectivity, 2 graph theory metrics, degree, and clustering coefficient (CC), were measured. Moreover, Lempel-Ziv complexity (LZC) and fuzzy entropy (FuzzyEn) were used to measure the complexity of the EEG signal. Through correlation analysis, a significant negative relationship was found between graph metrics and depression severity in the alpha band. This association was strongly positive for the complexity measures in alpha and delta bands. Also, the linear regression model represented a substantial performance of depression severity prediction based on EEG features of the alpha band (r = 0.839; P < .0001, root mean square error score of 7.69). We found that the brain activity of patients with depression was related to depression severity. Abnormal brain activity reflects an increase in the severity of depression. The presented regression model provides a quantitative depression severity prediction, which can inform the development of EEG state and exhibit potential desirable application for the medical treatment of the depressive disorder.

Comparison of Phase Synchronization Measures for Identifying Stimulus-Induced Functional Connectivity in Human Magnetoencephalographic and Simulated Data.

Phase synchronization measures are widely used for investigating inter-regional functional connectivity (FC) of brain oscillations, but which phase synchronization measure should be chosen for a given experiment remains unclear. Using neuromagnetic brain signals recorded from healthy participants during somatosensory stimuli, we compared the performance of four phase synchronization measures, imaginary part of phase-locking value, imaginary part of coherency (ImCoh), phase lag index and weighted phase lag index (wPLI), for detecting stimulus-induced FCs between the contralateral primary and ipsilateral secondary somatosensory cortices. The analyses revealed that ImCoh exhibited the best performance for detecting stimulus-induced FCs, followed by the wPLI. We found that amplitude weighting, which is related to computing both ImCoh and wPLI, effectively attenuated the influence of noise contamination. A simulation study modeling noise-contaminated periodograms replicated these findings. The present results suggest that the amplitude-dependent measures, ImCoh followed by wPLI, may have the advantage in detecting stimulus-induced FCs.

Altered Connectivity During a False-Belief Task in Adults With Autism Spectrum Disorder

BackgroundDeficits in social communication are one of the main features of autism spectrum disorder (ASD). Adults with ASD show atypical brain activity during false-belief understanding, an aspect of social communication involving the ability to infer that an individual can have an incorrect belief about a situation. Our study is the first to investigate whether adults with ASD exhibit differences in frequency-specific functional connectivity patterns during false-belief reasoning. MethodsWe used magnetoencephalography to contrast functional connectivity underlying false-belief understanding between 40 adults with ASD and 39 control adults. We examined whole-brain phase synchrony measures during a false-belief task in 3 frequency bands: theta (4–7 Hz), alpha (8–14 Hz), and beta (15–30 Hz). ResultsAdults with ASD demonstrated reduced theta-band connectivity compared with control adults between several right-lateralized and midline regions such as the medial prefrontal cortex, right temporoparietal junction, right inferior frontal gyrus, and right superior temporal gyrus. During false-belief trials, they also recruited a network in the beta band that included primary visual regions such as the bilateral inferior occipital gyri and the left anterior temporoparietal junction. ConclusionsReduced theta-band synchrony between areas associated with mentalizing, inhibition, and visual processing implies some difficulty in communication among these functions in ASD. This impairment in top-down control in the theta band may be counterbalanced by their engagement of a beta-band network because both the left anterior temporoparietal junction and beta-band oscillations are associated with attentional processes. Thus, adults with ASD demonstrate alternative neural mechanisms for successful false-belief reasoning.

Detecting chimeras by eigenvalue decomposition of the bivariate local order parameter

It has been shown that the eigenvalue decomposition of the matrix of the bivariate phase synchronization measure can be used for the detection of cluster synchronization. It has also been shown that other measures, such as the strength of incoherence and various local order parameters, can be used to quantitatively characterize chimeras, or chimera states. Here we bridge these two domains by showing that the eigenvalue decomposition method can also be used for the detection of chimeras. We compute the local order parameter for all oscillator pairs and apply the eigenvalue decomposition on the bivariate matrix. We show that, in contrast to cluster synchronization, there are more eigenvalues above one than the number of synchronized clusters in the network. The corresponding eigenvectors correspond to synchronized groups, while the oscillators that are not represented by the eigenvectors form the chimeras. We demonstrate our approach on coupled Liénard equations and FitzHugh-Nagumo neurons.

Altered beta-band functional connectivity may be related to ‘performance slowing’ in good outcome aneurysmal subarachnoid patients

Recent evidence suggests that good neurological outcome in subarachnoid hemorrhage (SAH) does not equate to good neuropsychological and cognitive outcome. These individuals continue to face cognitive difficulties in tasks involving mental flexibility, short-term memory and attention, resulting in decreased independence in daily living and reduced ability to return to work. In the current study, we examined the functional connectivity profiles using magnetoencephalography (MEG) in SAH patients, versus controls, during a visual short-term memory, 1-back, task.Our results found that a global measure of MEG-based phase synchrony in the beta band (15–30 Hz), derived from a time window during correct recognition, significantly differentiated the controls from the patients. During correct recognition, the connectivity patterns in the controls were characterized by inter-hemispheric parieto-frontal connections, involving the posterior parietal cortex, while patients appeared to recruit an entirely different network of regions, involving the anterior frontal and temporal regions. Reduced beta-band synchrony during recognition was associated with overall poorer performance, demonstrated as lower accuracy and slower reaction times in patients, but not in controls. This differentiation between groups suggests an important and distinct role of beta-band phase synchronization, perhaps for memory retrieval, associated with good performance. Performance slowing, short-term memory and attention deficits in these patients may be attributed to the impaired beta-band connectivity among prefrontal regions and the posterior parietal cortex.

Using Phase Synchrony Index for Improved Assessment of Consciousness in Ischemic Stroke Patients

Accurate behavioral assessments of consciousness are crucial in guiding management as they provide diagnostic and prognostic information, but they are challenging since different clinical scales lead to varying results for the same patient. This paper aimed to investigate the phase synchrony index in differentiating the states of consciousness (wakefulness, somnolence, stupor, light coma, middle coma, and deep coma) in ischemic stroke. We evaluated whether the quantitative electroencephalography (EEG) measure, phase synchrony index of the left and right hemispheres, can facilitate the assessment of consciousness in stroke patients. This paper included 82 patients with ischemic stroke admitted for inpatient rehabilitation. The phase synchrony index of the left and right hemispheres was computed in the alpha band (8–12 Hz) and beta band (13–30 Hz), respectively. The associations between the phase synchrony index of the left and right hemispheres in two frequency bands with the clinical states, including the level of consciousness and the National Institutes of Health Stroke Scale score were analyzed. For further assessments of phase synchrony of the left and right hemispheres (PSI-LR), four local phase synchrony indexes in the beta band were also evaluated. The experiments result showed that PSI-LRs in the beta band correlated significantly with the level of consciousness of ischemic stroke patient and the National Institutes of Health Stroke Scale score, and they can identify the impaired consciousness in ischemic stroke patients with an accuracy of 84.15%. Compared with local phase synchrony, the phase synchrony index of channel FP1 and channel FP2 exhibited a more significant correlation with the level of consciousness than the other three local phase synchrony measures. These results suggest that the novel quantitative EEG measure, PSI-LR provides a new objective way to assess the level of consciousness in ischemic stroke patients.

A novel multivariate phase synchrony measure: Application to multichannel newborn EEG analysis

Phase synchrony assessment across non-stationary multivariate signals is a useful way to characterize the dynamical behavior of their underlying systems. Traditionally, phase synchrony of a multivariate signal has been quantified by first assessing all pair-wise phase relationships between different channels and then, averaging their phase coupling. This approach, however, may not necessarily provide a full picture of multiple phase ratios within non-stationary signals with time-varying statistical properties. Several attempts have been made to generalize pair-wise phase synchrony concept to multivariate signals. In this paper, we introduce a new measure of generalized phase synchrony based on the concept of circular statistics. The performance of the measure is evaluated with simulations using the Kuramoto and Rössler models and compared with that of three existing generalized phase synchrony measures based respectively on 1) the concept of co-integration, 2) S-estimator and 3) hyper-dimensional geometry. The simulation results represent the correct degree of synchronization between channels with negligible mean of squared error, i.e. below 4.2e−4. We then use the proposed measure to assess inter-hemispheric phase synchrony in two abnormal multichannel newborn EEG datasets with manually marked seizure/non-seizure and burst–suppression signatures. The EEG results suggest that the proposed measure is able to detect inter-hemispheric phase synchrony changes with higher accuracy than the other existing measures, i.e. 2% for seizure/non-seizure database and 11% for burst/suppression database than the best performing existing multivariate phase synchrony measure.

Time-frequency approaches to investigating changes in feedback processing during childhood and adolescence.

Processing feedback from the environment is an essential function during development to adapt behavior in advantageous ways. One measure of feedback processing, the feedback negativity (FN), is an ERP observed following the presentation of feedback. Findings detailing developmental changes in the FN have been mixed, possibly due to limitations in traditional ERP measurement methods. Recent work shows that both theta and delta frequency activity contribute to the FN; utilizing time-frequency methods to measure change in power and phase in these frequency bands may provide more accurate representation of feedback processing development in childhood and adolescence. We employ time-frequency power and intertrial phase synchrony measures, in addition to conventional time-domain ERP methods, to examine the development of feedback processing in the theta (4-7 Hz) and delta (.1-3 Hz) bands throughout adolescence. A sample of 54 female participants (8-17 years old) completed a gambling task while EEG was recorded. As expected, time-domain ERP amplitudes showed no association with age. In contrast, significant effects were observed for the time-frequency measures, with theta power decreasing with age and delta power increasing with age. For intertrial phase synchrony, delta synchrony increased with age, while age-related changes in theta synchrony differed for gains and losses. Collectively, these findings highlight the importance of considering time-frequency dynamics when exploring how the processing of feedback develops through late childhood and adolescence. In particular, the role of delta band activity and theta synchrony appear central to understanding age-related changes in the neural response to feedback.

Whole-brain MEG connectivity-based analyses reveals critical hubs in childhood absence epilepsy

Absence seizures are thought to be linked to abnormal interplays between regions of a thalamocortical network. However, the complexity of this widespread network makes characterizing the functional interactions among various brain regions challenging. Using whole-brain functional connectivity and network analysis of magnetoencephalography (MEG) data, we explored pre-treatment brain hubs (“highly connected nodes”) of patients aged 6 to 12 years with childhood absence epilepsy. We analyzed ictal MEG data of 74 seizures from 16 patients. We employed a time-domain beamformer technique to estimate MEG sources in broadband (1–40 Hz) where the greatest power changes between ictal and preictal periods were identified. A phase synchrony measure, phase locking value, and a graph theory metric, eigenvector centrality (EVC), were utilized to quantify voxel-level connectivity and network hubs of ictal > preictal periods, respectively. A volumetric atlas containing 116 regions of interests (ROIs) was utilized to summarize the network measures. ROIs with EVC (z-score) > 1.96 were reported as critical hubs. ROIs analysis revealed functional-anatomical hubs in a widespread network containing bilateral precuneus (right/left, z = 2.39, 2.18), left thalamus (z = 2.28), and three anterior cerebellar subunits of lobule “IV-V” (z = 3.9), vermis “IV-V” (z = 3.57), and lobule “III” (z = 2.03). Findings suggest that highly connected brain areas or hubs are present in focal cortical, subcortical, and cerebellar regions during absence seizures. Hubs in thalami, precuneus and cingulate cortex generally support a theory of rapidly engaging and bilaterally distributed networks of cortical and subcortical regions responsible for seizures generation, whereas hubs in anterior cerebellar regions may be linked to terminating motor automatisms frequently seen during typical absence seizures. Whole-brain network connectivity is a powerful analytic tool to reveal focal components of absence seizures in MEG. Our investigations can lead to a better understanding of the pathophysiology of CAE.

Utilizing time-frequency amplitude and phase synchrony measure to assess feedback processing in a gambling task.

The neurophysiological mechanisms involved in the evaluation of performance feedback have been widely studied in the ERP literature over the past twenty years, but understanding has been limited by the use of traditional time-domain amplitude analytic approaches. Gambling outcome valence has been identified as an important factor modulating event-related potential (ERP) components, most notably the feedback negativity (FN). Recent work employing time-frequency analysis has shown that processes indexed by the FN are confounded in the time-domain and can be better represented as separable feedback-related processes in the theta (3-7 Hz) and delta (0-3 Hz) frequency bands. In addition to time-frequency amplitude analysis, phase synchrony measures have begun to further our understanding of performance evaluation by revealing how feedback information is processed within and between various brain regions. The current study aimed to provide an integrative assessment of time-frequency amplitude, inter-trial phase synchrony, and inter-channel phase synchrony changes following monetary feedback in a gambling task. Results revealed that time-frequency amplitude activity explained separable loss and gain processes confounded in the time-domain. Furthermore, phase synchrony measures explained unique variance above and beyond amplitude measures and demonstrated enhanced functional integration between medial prefrontal and bilateral frontal, motor, and occipital regions for loss relative to gain feedback. These findings demonstrate the utility of assessing time-frequency amplitude, inter-trial phase synchrony, and inter-channel phase synchrony together to better elucidate the neurophysiology of feedback processing.

Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation.

Most EEG phase synchrony measures are of bivariate nature. Those that are multivariate focus on producing global indices of the synchronization state of the system. Thus, better descriptions of spatial and temporal local interactions are still in demand. A framework for characterization of phase synchrony relationships between multivariate neural time series is presented, applied either in a single epoch or over an intertrial assessment, relying on a proposed clustering algorithm, termed Multivariate Time Series Clustering by Phase Synchrony, which generates fuzzy clusters for each multivalued time sample and thereupon obtains hard clusters according to a circular variance threshold; such cluster modes are then depicted in Time-Frequency-Topography representations of synchrony state beyond mere global indices. EEG signals from P300 Speller sessions of four subjects were analyzed, obtaining useful insights of synchrony patterns related to the ERP and even revealing steady-state artifacts at 7.6 Hz. Further, contrast maps of Levenshtein Distance highlight synchrony differences between ERP and no-ERP epochs, mainly at delta and theta bands. The framework, which is not limited to one synchrony measure, allows observing dynamics of phase changes and interactions among channels and can be applied to analyze other cognitive states rather than ERP versus no ERP.

Quantifying team cooperation through intrinsic multi-scale measures: respiratory and cardiac synchronization in choir singers and surgical teams.

A highly localized data-association measure, termed intrinsic synchrosqueezing transform (ISC), is proposed for the analysis of coupled nonlinear and non-stationary multivariate signals. This is achieved based on a combination of noise-assisted multivariate empirical mode decomposition and short-time Fourier transform-based univariate and multivariate synchrosqueezing transforms. It is shown that the ISC outperforms six other combinations of algorithms in estimating degrees of synchrony in synthetic linear and nonlinear bivariate signals. Its advantage is further illustrated in the precise identification of the synchronized respiratory and heart rate variability frequencies among a subset of bass singers of a professional choir, where it distinctly exhibits better performance than the continuous wavelet transform-based ISC. We also introduce an extension to the intrinsic phase synchrony (IPS) measure, referred to as nested intrinsic phase synchrony (N-IPS), for the empirical quantification of physically meaningful and straightforward-to-interpret trends in phase synchrony. The N-IPS is employed to reveal physically meaningful variations in the levels of cooperation in choir singing and performing a surgical procedure. Both the proposed techniques successfully reveal degrees of synchronization of the physiological signals in two different aspects: (i) precise localization of synchrony in time and frequency (ISC), and (ii) large-scale analysis for the empirical quantification of physically meaningful trends in synchrony (N-IPS).