Phase-amplitude Coupling Analysis Research Articles

Dynamic evolution of frontal-temporal network connectivity in temporal lobe epilepsy: A magnetoencephalography study.

Temporal lobe epilepsy (TLE) frequently involves an intricate, extensive epileptic frontal-temporal network. This study aimed to investigate the interactions between temporal and frontal regions and the dynamic patterns of the frontal-temporal network in TLE patients with different disease durations. The magnetoencephalography data of 36 postoperative seizure-free patients with long-term follow-up of at least 1 year, and 21 age- and sex-matched healthy subjects were included in this study. Patients were initially divided into LONG-TERM (n = 18, DURATION >10 years) and SHORT-TERM (n = 18, DURATION ≤10 years) groups based on 10-year disease duration. For reliability, supplementary analyses were conducted with alternative cutoffs, creating three groups: 0 < DURATION ≤7 years (n = 11), 7 < DURATION ≤14 years (n = 11), and DURATION >14 years (n = 14). This study examined the intraregional phase-amplitude coupling (PAC) between theta phase and alpha amplitude across the whole brain. The interregional directed phase transfer entropy (dPTE) between frontal and temporal regions in the alpha and theta bands, and the interregional cross-frequency directionality (CFD) between temporal and frontal regions from the theta phase to the alpha amplitude were further computed and compared among groups. Partial correlation analysis was conducted to investigate correlations between intraregional PAC, interregional dPTE connectivity, interregional CFD, and disease duration. Whole-brain intraregional PAC analyses revealed enhanced theta phase-alpha amplitude coupling within the ipsilateral temporal and frontal regions in TLE patients, and the ipsilateral temporal PAC was positively correlated with disease duration (r = 0.38, p <.05). Interregional dPTE analyses demonstrated a gradual increase in frontal-to-temporal connectivity within the alpha band, while the direction of theta-band connectivity reversed from frontal-to-temporal to temporal-to-frontal as the disease duration increased. Interregional CFD analyses revealed that the inhibitory effect of frontal regions on temporal regions gradually increased with prolonged disease duration (r = -0.36, p <.05). This study clarified the intrinsic reciprocal connectivity between temporal and frontal regions with TLE duration. We propose a dynamically reorganized triple-stage network that transitions from balanced networks to constrained networks and further develops into imbalanced networks as the disease duration increases.

Phase-amplitude coupling detection and analysis of human 2-dimensional neural cultures in multi-well microelectrode array in vitro

BackgroundHuman induced pluripotent stem cell (hiPSC)- derived neurons offer the possibility of studying human-specific neuronal behaviors in physiologic and pathologic states in vitro. It is unclear whether cultured neurons can achieve the fundamental network behaviors required to process information in the brain. Investigating neuronal oscillations and their interactions, as occurs in cross-frequency coupling (CFC), addresses this question. New methodsWe examined whether networks of two-dimensional (2D) cultured hiPSC-derived cortical neurons grown with hiPSC-derived astrocytes on microelectrode array plates recapitulate the CFC that is present in vivo. We employed the modulation index method for detecting phase-amplitude coupling (PAC) and used offline spike sorting to analyze the contribution of single neuron spiking to network behavior. ResultsWe found that PAC is present, the degree of PAC is specific to network structure, and it is modulated by external stimulation with bicuculline administration. Modulation of PAC is not driven by single neurons, but by network-level interactions. Comparison with existing methodsPAC has been demonstrated in multiple regions of the human cortex as well as in organoids. This is the first report of analysis demonstrating the presence of coupling in 2D cultures. ConclusionCFC in the form of PAC analysis explores communication and integration between groups of neurons and dynamical changes across networks. In vitro PAC analysis has the potential to elucidate the underlying mechanisms as well as capture the effects of chemical, electrical, or ultrasound stimulation; providing insight into modulation of neural networks to treat nervous system disorders in vivo.

Neural Oscillation Disorder in the Hippocampal CA1 Region of Different Alzheimer's Disease Mice.

Alzheimer's disease (AD) is a well-known neurodegenerative disease that gradually induces neural network dysfunction and progressive memory deficits. Neural network activity is represented by rhythmic oscillations that influence local field potentials (LFPs). However, changes in hippocampal neural rhythmic oscillations in the early stage of AD remain largely unexplored. This study investigated neural rhythmic oscillations in 3-month-old APP/PS1 and 5x- FAD mice to assess early neural connectivity in AD. LFPs were recorded from the hippocampal CA1 region with implanted microelectrode arrays while the mice were in the awake resting stage. Welch fast Fourier transforms, continuous wavelet transforms, and phase-amplitude coupling analyses were used to compute the power density of different frequency bands and phase-amplitude modulation indices in the LFPs. Our results showed impaired theta, low gamma, and high gamma frequency band power in APP/PS1 and 5xFAD mice during the awake resting stage. AD mice also showed decreased delta, alpha, and beta frequency band power. Impaired theta-low gamma and theta-high gamma phaseamplitude coupling were observed in 5xFAD mice. This study revealed neural network activity differences in oscillation power and cross-frequency coupling in the early stage of AD, providing a new perspective for developing biomarkers for early AD diagnosis.

Music-emotion EEG coupling effects based on representational similarity

BackgroundMusic can evoke intense emotions and music emotion is a complex cognitive process. However, we know little about the cognitive mechanisms underlying these processes, and there are significant individual differences in the emotional responses to the same musical stimuli. New MethodWe used the inter-subject representational similarity analysis (IS-RSA) method to investigate the shared music emotion responses across multiple participants. In addition, we extended IS-RSA to estimate the group cross-frequency coupling effects of music emotion. Based on the cross-frequency coupling IS-RSA, we analyzed the differences in cross-frequency coupling patterns under different music emotions using MI. Comparison of existing methods: most current IS-RSA analyses focus on within-frequency band analysis. However, the cognitive processing of music emotion involves not only activation and brain network connections differences within frequency bands but also information communication between frequency bands. ResultsThe results of the within-frequency band IS-RSA analysis showed that the theta and gamma frequency bands play important roles in the inter-participant consistency of music emotion. The inter-frequency band IS-RSA analysis showed that the theta-beta coupling pattern exhibited stronger inter-participant consistency compared to the theta-gamma coupling pattern, and the theta-beta coupling had significant consistent representation across various music conditions. Through the significant regions of cross-frequency coupling representation similarity analysis, we performed phase-amplitude coupling analysis on FC4-C6 and FC4-Pz connections. For the theta-beta coupling pattern, we found that the MI of these two connections exhibited different coupling patterns under different music conditions, and they showed a significant decrease compared to the baseline period.

Epileptic seizures detection and the analysis of optimal seizure prediction horizon based on frequency and phase analysis.

Changes in the frequency composition of the human electroencephalogram are associated with the transitions to epileptic seizures. Cross-frequency coupling (CFC) is a measure of neural oscillations in different frequency bands and brain areas, and specifically phase-amplitude coupling (PAC), a form of CFC, can be used to characterize these dynamic transitions. In this study, we propose a method for seizure detection and prediction based on frequency domain analysis and PAC combined with machine learning. We analyzed two databases, the Siena Scalp EEG database and the CHB-MIT database, and used the frequency features and modulation index (MI) for time-dependent quantification. The extracted features were fed to a random forest classifier for classification and prediction. The seizure prediction horizon (SPH) was also analyzed based on the highest-performing band to maximize the time for intervention and treatment while ensuring the accuracy of the prediction. Under comprehensive consideration, the results demonstrate that better performance could be achieved at an interval length of 5 min with an average accuracy of 85.71% and 95.87% for the Siena Scalp EEG database and the CHB-MIT database, respectively. As for the adult database, the combination of PAC analysis and classification can be of significant help for seizure detection and prediction. It suggests that the rarely used SPH also has a major impact on seizure detection and prediction and further explorations for the application of PAC are needed.

Chronological changes in phase-amplitude coupling during epileptic seizures in temporal lobe epilepsy

ObjectiveTo analyze chronological changes in phase-amplitude coupling (PAC) and verify whether PAC analysis can diagnose epileptogenic zones during seizures. MethodsWe analyzed 30 seizures in 10 patients with mesial temporal lobe epilepsy who had ictal discharges with preictal spiking followed by low-voltage fast activity patterns on intracranial electroencephalography. We used the amplitude of two high-frequency bands (ripples: 80–200 Hz, fast ripples: 200–300 Hz) and the phase of three slow wave bands (0.5–1 Hz, 3–4 Hz, and 4–8 Hz) for modulation index (MI) calculation from 2 minutes before seizure onset to seizure termination. We evaluated the accuracy of epileptogenic zone detection by MI, in which a combination of MI was better for diagnosis and analyzed patterns of chronological changes in MI during seizures. ResultsMIRipples/3–4 Hz and MIRipples/4–8 Hz in the hippocampus were significantly higher than those in the peripheral regions from seizure onset. Corresponding to the phase on intracranial electroencephalography, MIRipples/3–4 Hz decreased once and subsequently increased again. MIRipples/4–8 Hz showed continuously high values. ConclusionsContinuous measurement of MIRipples/3–4 Hz and MIRipples/4–8 Hz could help identify epileptogenic zones. SignificancePAC analysis of ictal epileptic discharges can help epileptogenic zone identification.

Large-scale frontoparietal theta, alpha, and beta phase synchronization: A set of EEG differential characteristics for freezing of gait in Parkinson’s disease?

Freezing of gait (FOG) is a complex gait disturbance in Parkinson's disease (PD), during which the patient is not able to effectively initiate gait or continue walking. The mystery of the FOG phenomenon is still unsolved. Recent studies have revealed abnormalities in cortical activities associated with FOG, which highlights the importance of cortical and cortical-subcortical network dysfunction in PD patients with FOG. In this paper, phase-locking value (PLV) of eight frequency sub-bands between 0.05 Hz and 35 Hz over frontal, motor, and parietal areas [during an ankle dorsiflexion (ADF) task] is used to investigate EEG phase synchronization. PLV was investigated over both superficial and deeper networks by analyzing EEG signals preprocessed with and without Surface Laplacian (SL) spatial filter. Four groups of participants were included: PD patients with severe FOG (N = 5, 5 males), PD patients with mild FOG (N = 7, 6 males), PD patients without FOG (N = 14, 13 males), and healthy age-matched controls (N = 13, 10 males). Fifteen trials were recorded from each participant. At superficial layers, frontoparietal theta phase synchrony was a unique feature present in PD with FOG groups. At deeper networks, significant dominance of interhemispheric frontoparietal alpha phase synchrony in PD with FOG, in contrast to beta phase synchrony in PD without FOG, was identified. Alpha phase synchrony was more distributed in PD with severe FOG, with higher levels of frontoparietal alpha phase synchrony. In addition to FOG-related abnormalities in PLV analysis, phase-amplitude coupling (PAC) analysis was also performed on frequency bands with PLV abnormalities. PAC analysis revealed abnormal coupling between theta and low beta frequency bands in PD with severe FOG at the superficial layers over frontal areas. At deeper networks, theta and alpha frequency bands show high PAC over parietal areas in PD with severe FOG. Alpha and low beta also presented PAC over frontal areas in PD groups with FOG. The results introduced significant phase synchrony differences between PD with and without FOG and provided important insight into a possible unified underlying mechanism for FOG. These results thus suggest that PLV and PAC can potentially be used as EEG-based biomarkers for FOG.

State space methods for phase amplitude coupling analysis

Phase amplitude coupling (PAC) is thought to play a fundamental role in the dynamic coordination of brain circuits and systems. There are however growing concerns that existing methods for PAC analysis are prone to error and misinterpretation. Improper frequency band selection can render true PAC undetectable, while non-linearities or abrupt changes in the signal can produce spurious PAC. Current methods require large amounts of data and lack formal statistical inference tools. We describe here a novel approach for PAC analysis that substantially addresses these problems. We use a state space model to estimate the component oscillations, avoiding problems with frequency band selection, nonlinearities, and sharp signal transitions. We represent cross-frequency coupling in parametric and time-varying forms to further improve statistical efficiency and estimate the posterior distribution of the coupling parameters to derive their credible intervals. We demonstrate the method using simulated data, rat local field potentials (LFP) data, and human EEG data.

Intrinsic phase–amplitude coupling on multiple spatial scales during the loss and recovery of consciousness

Background:Recent studies have demonstrated that changes in brain information processing during anesthetic-induced loss of consciousness (LOC) might be influenced by phase–amplitude coupling (PAC) in electroencephalogram (EEG). However, most anesthesia research on PAC typically focuses on delta and alpha oscillations. Studies of spatial-frequency characteristics by PAC for EEG may yield additional insights into understanding the impaired information processing under anesthesia unconsciousness and provide potential improvements in anesthesia monitoring. Objective:Considering different frequency bands of EEG represent neural activities on different spatial scales, we hypothesized that functional coupling simultaneously appears in multiple frequency bands and specific brain regions during anesthesia unconsciousness. In this paper, PAC analysis on whole-brain EEG besides delta and alpha oscillations was investigated to understand the influence of multiple cross-frequency coordination coupling on information processing during the loss and recovery of consciousness. Method:EEG data from fifteen patients without cognitive diseases (7 males/8 females, aged 43.8 ± 13.4 years, weighing 63.3 ± 14.9 kilograms) undergoing lower limb surgery and sevoflurane anesthesia was recorded. To investigate the spatial-frequency characteristics of EEG source signals during loss and recovery of consciousness, the time-resolved PAC (tPAC) was calculated to reflect cross-frequency coordination in different frequency bands (delta, theta, alpha, beta, gamma) and different functional regions (Visual, Limbic, Dorsal attention, Ventral attention, Default, Somatomotor, Control, Salience networks). Furthermore, different patterns (peak-max and trough-max) of PAC were examined by constructing phase–amplitude histograms using phase bins to investigate the different information processing during LOC. The multivariate analysis of variance (MANOVA) and trend analysis were used for statistical analysis. Results:Theta-alpha and alpha-beta PAC were observed during sevoflurane-induced LOC, which significantly changed during loss and recovery of consciousness (F4,70 = 16.553, p < 0.001 for theta-alpha PAC and F4,70 = 12.446, p < 0.001 for alpha-beta PAC, MANOVA test). Simultaneously, PAC was distributed in specific functional regions, i.e., Visual, Limbic, Default, Somatomotor, etc. Furthermore, peak-max patterns of theta-alpha PAC were observed while alpha-beta PAC showed trough-max patterns and vice versa. Conclusion:Theta-alpha and alpha-beta PAC observed in specific brain regions represent information processing on multiple spatial scales, and the opposite patterns of PAC indicate opposite information processing on multiple spatial scales during LOC. Our study demonstrates the regulation of local–global information processing during sevoflurane-induced LOC. It suggests the utility of evaluating the balance of functional integration and segregation in monitoring anesthetized states.

Phase amplitude coupling analysis of local field potentials in working memory of rats affected by transcranial magneto-acoustic-electrical stimulation

Transcranial magneto-acoustic-electrical stimulation is a new non-invasive neuromodulation technology, in which the induced electric field generated by the coupling effect of ultrasound and static magnetic field are used to regulate the neural rhythm oscillation activity in the corresponding brain region. The purpose of this paper is to investigate the effects of transcranial magneto-acoustic-electrical stimulation on the information transfer and communication in neuronal clusters during memory. In the experiment, twenty healthy adult Wistar rats were randomly divided into a control group (five rats) and stimulation groups (fifteen rats). Transcranial magneto-acoustic-electrical stimulation of 0.05~0.15 T and 2.66~13.33 W/cm 2 was applied to the rats in stimulation groups, and no stimulation was applied to the rats in the control group. The local field potentials signals in the prefrontal cortex of rats during the T-maze working memory tasks were acquired. Then the coupling differences between delta rhythm phase, theta rhythm phase and gamma rhythm amplitude of rats in different parameter stimulation groups and control group were compared. The experimental results showed that the coupling intensity of delta and gamma rhythm in stimulation groups was significantly lower than that in the control group ( P<0.05), while the coupling intensity of theta and gamma rhythm was significantly higher than that in the control group ( P<0.05). With the increase of stimulation parameters, the degree of coupling between delta and gamma rhythm showed a decreasing trend, while the degree of coupling between theta and gamma rhythm tended to increase. The preliminary results of this paper indicated that transcranial magneto-acoustic-electrical stimulation inhibited delta rhythmic neuronal activity and enhanced the oscillation of theta and gamma rhythm in the prefrontal cortex, thus promoted the exchange and transmission of information between neuronal clusters in different spatial scales. This lays the foundation for further exploring the mechanism of transcranial magneto-acoustic-electrical stimulation in regulating brain memory function.

Toward the Identification of Neurophysiological Biomarkers for Alzheimer's Disease in Down Syndrome: A Potential Role for Cross-Frequency Phase-Amplitude Coupling Analysis.

Cross-frequency coupling (CFC) mechanisms play a central role in brain activity. Pathophysiological mechanisms leading to many brain disorders, such as Alzheimer's disease (AD), may produce unique patterns of brain activity detectable by electroencephalography (EEG). Identifying biomarkers for AD diagnosis is also an ambition among research teams working in Down syndrome (DS), given the increased susceptibility of people with DS to develop early-onset AD (DS-AD). Here, we review accumulating evidence that altered theta-gamma phase-amplitude coupling (PAC) may be one of the earliest EEG signatures of AD, and therefore may serve as an adjuvant tool for detecting cognitive decline in DS-AD. We suggest that this field of research could potentially provide clues to the biophysical mechanisms underlying cognitive dysfunction in DS-AD and generate opportunities for identifying EEG-based biomarkers with diagnostic and prognostic utility in DS-AD.

Automated preprocessing and phase-amplitude coupling analysis of scalp EEG discriminates infantile spasms from controls during wakefulness

ObjectiveDelta-gamma phase-amplitude coupling in EEG is useful for localizing epileptic sources and to evaluate severity in children with infantile spasms. We (1) develop an automated EEG preprocessing pipeline to clean data using artifact subspace reconstruction (ASR) and independent component (IC) analysis (ICA) and (2) evaluate delta-gamma modulation index (MI) as a method to distinguish children with epileptic spasms (cases) from normal controls during sleep and awake. MethodsUsing 400 scalp EEG datasets (200 sleep, 200 awake) from 100 subjects, we calculated MI after applying high-pass and line-noise filters (Clean 0), and after ASR followed by either conservative (Clean 1) or stringent (Clean 2) artifactual IC rejection. Classification of cases and controls using MI was evaluated with Receiver Operating Characteristics (ROC) to obtain area under curve (AUC). ResultsThe artifact rejection algorithm reduced raw signal variance by 29–45% and 38–60% for Clean 1 and Clean 2, respectively. MI derived from sleep data, with or without preprocessing, robustly classified the groups (all AUC > 0.98). In contrast, group classification using MI derived from awake data was successful only after Clean 2 (AUC = 0.85). ConclusionsWe have developed an automated EEG preprocessing pipeline to perform artifact rejection and quantify delta-gamma modulation index.

P 40. β-γ phase-amplitude coupling during movements in Parkinson's disease

Question. Enhanced phase amplitude coupling (PAC) between β (13–30 Hz) and broadband γ (50–150 Hz) activities has been found in surface EEG recordings from Parkinson patients at rest [1]. However, the dynamics of PAC during voluntary movement and its relationship with motor impairment of Parkinson“s disease (PD) remain unclear. Methods. Experimental design : We recorded 64-channel EEG signals from 19 PD patients and 20 sex- and age-matched healthy controls at rest, during a repetitive pressing task and during repetitive index finger tapping performed at the patients“ own pace and at their fastest pace. Source analysis : We obtained source signals by projecting EEG sensor signals to the individual cortical surfaces. Independent component analysis (ICA) was applied for separating temporally independent subnetworks of each task in each of the 4 brain regions: premotor cortex, primary motor cortex, primary somatosensory cortex and primary somatosensory complex. PAC analysis: Pairwise transient PAC values were calculated among ICA components of each region by zscore-normalized mean vector length [2] in 300ms windows shifted with 50ms time steps in 3s epochs which included an interval of 1s before, and 2s after the onset of the movement. Statistics : We applied a non-parametric three-way ANOVA [3] to test the effects of the disease state, tasks and different brain regions on PAC. For post-hoc testing, a two tailed Wilcoxon ranksum test was used between groups and Wilcoxon signrank test within groups. Results. As expected, maximum tapping rate was lower, and fastest movements were executed more irregularly in patients compared to controls. ANOVA performed on the β-γ PAC values averaged over the entire 3s-epoch revealed an interaction of disease state and tasks ( p < 0.001). Post-hoc testing showed enhanced PAC in patients compared with controls in the resting state ( p = 0.018), in agreement with previous findings, but not in any of the tapping tasks. However, PAC during the pressing task remained elevated in the patients compared to controls ( p = 0.020). Analysis of the temporal dynamics revealed reduction of PAC after the onset of the movement (0–0.25 s) compared with that of the pre-onset period (−0.5 to −0.25 s). The reduction of PAC relative to pre-onset period correlated with the desynchronization in the β power. However, the PAC reduction relative to the resting state was not correlated with the β power reduction. Discussion. Persistence of PAC differences during the pressing task on one hand, and disappearance of PAC differences during the tapping tasks on the other hand, suggest that exaggerated β-broadband γ PAC, as detected in the resting EEG, is not necessarily directly related to impaired motor behavior in PD patients. As our previous findings [1] have shown that abnormal PAC at rest is unrelated to the β band power, partial renormalization of PAC by movement-related β band desynchronization may suggest that mechanisms modulating β activity during movement may selectively affect β activity involved in abnormal coupling.

Locomotor activity and resting local field potential oscillatory rhythms of 6-OHDA mouse model of Parkinson’s disease in response to acute and repeated treatments with L-dopa

Phase-amplitude coupling (PAC) of local field potential (LFP) has been recognized as higher-order representation of brain states. Neuronal loss in the striatum leads to Parkinson’s disease (PD) symptoms and modifies LFP oscillation. However, PAC in the striatum of PD mouse model induced by 6-hydroxydopamne (6-OHDA) remained to be investigated. Male Swiss albino ICR mice were implanted with intracranial electrode and injected with 6-OHDA to the left striatum. Levodopa (L-dopa) (10 mg/kg, oral) was used for treatment once a day from day 15–19. Locomotor activity and resting LFP signals were selectively analyzed on day 15 and 19. One-way ANOVA revealed significant decreases in travelled distance induced by 6-OHDA on both days (p ≤ 0.05). However, the decreased travelled distances were significantly reversed by L-dopa. On day 15, LFP powers of theta, alpha, beta and low gamma waves were significantly increased by 6-OHDA injection and the powers of beta and low gamma were significantly reversed to control level by treatment with L-dopa. On day 19, LFP powers of delta, theta, alpha, beta and low gamma waves were significantly increased by 6-OHDA injection and the powers of low gamma were significantly reversed to control level by treatment with L-dopa. Theta-gamma PAC analyses also confirmed significant increase in modulation index (MI) induced by 6-OHDA on day 19. However, L-dopa failed to significantly reverse the MI to control level. These findings indicated theta-gamma coupling in the striatum of PD mouse model. Taken together, change in striatal theta-gamma PAC might be one of biomarkers in addition to hypokinesia and increased LFP powers that reflect disrupted neural mechanisms in PD mouse model.

What Can Local Transfer Entropy Tell Us about Phase-Amplitude Coupling in Electrophysiological Signals?

Modulation of the amplitude of high-frequency cortical field activity locked to changes in the phase of a slower brain rhythm is known as phase-amplitude coupling (PAC). The study of this phenomenon has been gaining traction in neuroscience because of several reports on its appearance in normal and pathological brain processes in humans as well as across different mammalian species. This has led to the suggestion that PAC may be an intrinsic brain process that facilitates brain inter-area communication across different spatiotemporal scales. Several methods have been proposed to measure the PAC process, but few of these enable detailed study of its time course. It appears that no studies have reported details of PAC dynamics including its possible directional delay characteristic. Here, we study and characterize the use of a novel information theoretic measure that may address this limitation: local transfer entropy. We use both simulated and actual intracranial electroencephalographic data. In both cases, we observe initial indications that local transfer entropy can be used to detect the onset and offset of modulation process periods revealed by mutual information estimated phase-amplitude coupling (MIPAC). We review our results in the context of current theories about PAC in brain electrical activity, and discuss technical issues that must be addressed to see local transfer entropy more widely applied to PAC analysis. The current work sets the foundations for further use of local transfer entropy for estimating PAC process dynamics, and extends and complements our previous work on using local mutual information to compute PAC (MIPAC).

Tensorpac: An open-source Python toolbox for tensor-based phase-amplitude coupling measurement in electrophysiological brain signals.

Despite being the focus of a thriving field of research, the biological mechanisms that underlie information integration in the brain are not yet fully understood. A theory that has gained a lot of traction in recent years suggests that multi-scale integration is regulated by a hierarchy of mutually interacting neural oscillations. In particular, there is accumulating evidence that phase-amplitude coupling (PAC), a specific form of cross-frequency interaction, plays a key role in numerous cognitive processes. Current research in the field is not only hampered by the absence of a gold standard for PAC analysis, but also by the computational costs of running exhaustive computations on large and high-dimensional electrophysiological brain signals. In addition, various signal properties and analyses parameters can lead to spurious PAC. Here, we present Tensorpac, an open-source Python toolbox dedicated to PAC analysis of neurophysiological data. The advantages of Tensorpac include (1) higher computational efficiency thanks to software design that combines tensor computations and parallel computing, (2) the implementation of all most widely used PAC methods in one package, (3) the statistical analysis of PAC measures, and (4) extended PAC visualization capabilities. Tensorpac is distributed under a BSD-3-Clause license and can be launched on any operating system (Linux, OSX and Windows). It can be installed directly via pip or downloaded from Github (https://github.com/EtienneCmb/tensorpac). By making Tensorpac available, we aim to enhance the reproducibility and quality of PAC research, and provide open tools that will accelerate future method development in neuroscience.

Addressing Pitfalls in Phase-Amplitude Coupling Analysis with an Extended Modulation Index Toolbox

Phase-amplitude coupling (PAC) is proposed to play an essential role in coordinating the processing of information on local and global scales. In recent years, the methods able to reveal trustworthy PAC has gained considerable interest. However, the intrinsic features of some signals can lead to the identification of spurious or waveform-dependent coupling. This prompted us to develop an easily accessible tool that could be used to differentiate spurious from authentic PAC. Here, we propose a new tool for more reliable detection of PAC named the Extended Modulation Index (eMI) based on the classical Modulation Index measure of coupling. eMI is suitable both for continuous and epoched data and allows estimation of the statistical significance of each pair of frequencies for phase and for amplitude in the whole comodulogram in the framework of extreme value statistics. We compared eMI with the reference PAC measures—direct PAC estimator (a modification of Mean Vector Length) and standard Modulation Index. All three methods were tested using computer-simulated data and actual local field potential recordings from freely moving rats. All methods exhibited similar properties in terms of sensitivity and specificity of PAC detection. eMI proved to be more selective in the dimension of frequency for phase. One of the novelty’s offered by eMI is a heuristic algorithm for classification of PAC as Reliable or Ambiguous. It relies on analysis of the relation between the spectral properties of the signal and the detected coupling. Moreover, eMI generates visualizations that support further evaluation of the coupling properties. It also introduces the concept of the polar phase-histogram to study phase relations of coupled slow and fast oscillations. We discuss the extent to which eMI addresses the known problems of interpreting PAC. The Matlab® toolbox implementing eMI framework, and the two reference PAC estimators is freely available as EEGLAB plugin at https://github.com/GabrielaJurkiewicz/ePAC.

The intimate relationship between coalescent generators in very premature human newborn brains: Quantifying the coupling of nested endogenous oscillations.

Temporal theta slow‐wave activity (TTA‐SW) in premature infants is a specific neurobiomarker of the early neurodevelopment of perisylvian networks observed as early as 24 weeks of gestational age (wGA). It is present at the turning point between non‐sensory driven spontaneous networks and cortical network functioning. Despite its clinical importance, the underlying mechanisms responsible for this spontaneous nested activity and its functional role have not yet been determined. The coupling between neural oscillations at different timescales is a key feature of ongoing neural activity, the characteristics of which are determined by the network structure and dynamics. The underlying mechanisms of cross‐frequency coupling (CFC) are associated with several putative functions in adults. In order to show that this generic mechanism is already in place early in the course of development, we analyzed electroencephalography recordings from sleeping preterm newborns (24–27 wGA). Employing cross‐frequency phase–amplitude coupling analyses, we found that TTAs were orchestrated by the SWs defined by a precise temporal relationship. Notably, TTAs were synchronized to the SW trough, and were suppressed during the SW peak. Spontaneous endogenous TTA‐SWs constitute one of the very early signatures of the developing temporal neural networks with key functions, such as language and communication. The presence of a fine‐tuned relationship between the slow activity and the TTA in premature neonates emphasizes the complexity and relative maturity of the intimate mechanisms that shape the CFC, the disruption of which can have severe neurodevelopmental consequences.

Distinct Oscillatory Dynamics Underlie Different Components of Hierarchical Cognitive Control.

Hierarchical cognitive control enables us to execute actions guided by abstract goals. Previous research has suggested that neuronal oscillations at different frequency bands are associated with top-down cognitive control; however, whether distinct neural oscillations have similar or different functions for cognitive control is not well understood. The aim of the current study was to investigate the oscillatory neuronal mechanisms underlying two distinct components of hierarchical cognitive control: the level of abstraction of a rule, and the number of rules that must be maintained (set-size). We collected EEG data in 31 men and women who performed a hierarchical cognitive control task that varied in levels of abstraction and set-size. Results from time-frequency analysis in frontal electrodes showed an increase in theta amplitude for increased set-size, whereas an increase in δ was associated with increased abstraction. Both theta and δ amplitude correlated with behavioral performance in the tasks but in an opposite manner: theta correlated with response time slowing when the number of rules increased, whereas δ correlated with response time when rules became more abstract. Phase-amplitude coupling analysis revealed that δ phase-coupled with β amplitude during conditions with a higher level of abstraction, whereby beta band may potentially represent motor output that was guided by the δ phase. These results suggest that distinct neural oscillatory mechanisms underlie different components of hierarchical cognitive control.SIGNIFICANCE STATEMENT Cognitive control allows us to perform immediate actions while maintaining more abstract, overarching goals in mind and to choose between competing actions. We found distinct oscillatory signatures that correspond to two different components of hierarchical control: the level of abstraction of a rule and the number of rules in competition. An increase in the level of abstraction was associated with δ oscillations, whereas theta oscillations were observed when the number of rules increased. Oscillatory amplitude correlated with behavioral performance in the task. Finally, the expression of β amplitude was coordinated via the phase of δ oscillations, and theta phase-coupled with γ amplitude. These results suggest that distinct neural oscillatory mechanisms underlie different components of hierarchical cognitive control.

Role of joint language control during cross-language communication: evidence from cross-frequency coupling.

How do bilingual interlocutors inhibit interference from the non-target language to achieve brain-to-brain information exchange in a task to simulate a bilingual speaker-listener interaction. In the current study, two electroencephalogram devices were employed to record pairs of participants' performances in a joint language switching task. Twenty-eight (14 pairs) unbalanced Chinese-English bilinguals (L1 Chinese) were instructed to name pictures in the appropriate language according to the cue. The phase-amplitude coupling analysis was employed to reveal the large-scale brain network responsible for joint language control between interlocutors. We found that (1) speakers and listeners coordinately suppressed cross-language interference through cross-frequency coupling, as shown in the increased delta/theta phase-amplitude and delta/alpha phase-amplitude coupling when switching to L2 than switching to L1; (2) speakers and listeners were both able to simultaneously inhibit cross-person item-level interference which was demonstrated by stronger cross-frequency coupling in the cross person condition compared to the within person condition. These results indicate that current bilingual models (e.g., the inhibitory control model) should incorporate mechanisms that address inhibiting interference sourced in both language and person (i.e., cross-language and cross-person item-level interference) synchronously through joint language control in dynamic cross-language communication.