Amplitude-amplitude Coupling Research Articles

Cross-frequency modulation of postural fluctuations and scalp EEG in older adults: error amplification feedback for rapid balance adjustments.

Virtual error amplification (VEA) in visual feedback enhances attentive control over postural stability, although the neural mechanisms are still debated. This study investigated the distinct cortical control of unsteady stance in older adults using VEA through cross-frequency modulation of postural fluctuations and scalp EEG. Thirty-seven community-dwelling older adults (68.1 ± 3.6years) maintained an upright stance on a stabilometer while receiving either VEA or real error feedback. Along with postural fluctuation dynamics, phase-amplitude coupling (PAC) and amplitude-amplitude coupling (AAC) were analyzed for postural fluctuations under 2Hz and EEG sub-bands (theta, alpha, and beta). The results revealed a higher mean frequency of the postural fluctuation phase (p = .005) and a greater root mean square of the postural fluctuation amplitude (p = .003) with VEA compared to the control condition. VEA also reduced PAC between the postural fluctuation phase and beta-band EEG in the left frontal (p = .009), sensorimotor (p = .002), and occipital (p = .018) areas. Conversely, VEA increased the AAC of posture fluctuation amplitude and beta-band EEG in FP2 (p = .027). Neither theta nor alpha band PAC or AAC were affected by VEA. VEA optimizes postural strategies in older adults during stabilometer stance by enhancing visuospatial attentive control of postural responses and facilitating the transition of motor states against postural perturbations through a disinhibitory process. Incorporating VEA into virtual reality technology is advocated as a valuable strategy for optimizing therapeutic interventions in postural therapy, particularly to mitigate the risk of falls among older adults.

Multilayer Connector Hub Mapping Reveals Key Brain Regions Supporting Expressive Language.

Introduction: How components of the distributed brain networks that support cognition participate in typical functioning remains a largely unanswered question. An important subgroup of regions in the larger network are connector hubs, which are areas that are highly connected to several other functionally specialized sets of regions, and are likely important for sensorimotor integration. The present study attempts to characterize connector hubs involved in typical expressive language functioning using a data-driven, multimodal, full multilayer magnetoencephalography (MEG) connectivity-based pipeline.Methods: Twelve adolescents, 16–18 years of age (five males), participated in this study. Participants underwent MEG scanning during a verb generation task. MEG and structural connectivity were calculated at the whole-brain level. Amplitude/amplitude coupling (AAC) was used to compute functional connections both within and between discrete frequency bins. AAC values were then multiplied by a binary structural connectivity matrix, and then entered into full multilayer network analysis. Initially, hubs were defined based on multilayer versatility and subsequently reranked by a novel measure called delta centrality on interconnectedness (DCI). DCI is defined as the percent change in interfrequency interconnectedness after removal of a hub.Results: We resolved regions that are important for between-frequency communication among other areas during expressive language, with several potential theoretical and clinical applications that can be generalized to other cognitive domains.Conclusion: Our multilayer, data-driven framework captures nonlinear connections that span across scales that are often missed in conventional analyses. The present study suggests that crucial hubs may be conduits for interfrequency communication between action and perception systems that are crucial for typical functioning.Impact statementWe present methodology to characterize regions supporting cross-frequency communication in the distributed language network. There are 3 key innovations: (1) incorporation of a structural connectivity constraint based on diffusion magnetic resonance imaging (MRI), (2) use of a full multilayer framework that captures both within- and between-frequency connections, and (3) introduction of a new metric, delta centrality on interconnectedness (DCI), that quantifies the importance of a region for cross-frequency coupling.

Predicting Grating Orientations With Cross-Frequency Coupling and Least Absolute Shrinkage and Selection Operator in V1 and V4 of Rhesus Monkeys.

Orientation selectivity, as an emergent property of neurons in the visual cortex, is of critical importance in the processing of visual information. Characterizing the orientation selectivity based on neuronal firing activities or local field potentials (LFPs) is a hot topic of current research. In this paper, we used cross-frequency coupling and least absolute shrinkage and selection operator (LASSO) to predict the grating orientations in V1 and V4 of two rhesus monkeys. The experimental data were recorded by utilizing two chronically implanted multi-electrode arrays, which were placed, respectively, in V1 and V4 of two rhesus monkeys performing a selective visual attention task. The phase–amplitude coupling (PAC) and amplitude–amplitude coupling (AAC) were employed to characterize the cross-frequency coupling of LFPs under sinusoidal grating stimuli with different orientations. Then, a LASSO logistic regression model was constructed to predict the grating orientation based on the strength of PAC and AAC. Moreover, the cross-validation method was used to evaluate the performance of the model. It was found that the average accuracy of the prediction based on the combination of PAC and AAC was 73.9%, which was higher than the predicting accuracy with PAC or AAC separately. In conclusion, a LASSO logistic regression model was introduced in this study, which can predict the grating orientations with relatively high accuracy by using PAC and AAC together. Our results suggest that the principle behind the LASSO model is probably an alternative direction to explore the mechanism for generating orientation selectivity.

Dissociating harmonic and non-harmonic phase-amplitude coupling in the human brain

Phase-amplitude coupling (PAC) has been hypothesized to coordinate cross-frequency interactions of neuronal activity in the brain. However, little is known about the distribution of PAC across the human brain and the frequencies involved. Furthermore, it remains unclear to what extent PAC may reflect spurious cross-frequency coupling induced by physiological artifacts or rhythmic non-sinusoidal signals with higher harmonics. Here, we combined MEG, source-reconstruction and different measures of cross-frequency coupling to systematically characterize local PAC across the resting human brain. We show that cross-frequency measures of phase-amplitude, phase-phase, and amplitude-amplitude coupling are all sensitive to signals with higher harmonics. In conjunction, these measures allow to distinguish harmonic and non-harmonic PAC. Based on these insights, we found no evidence for non-harmonic local PAC in resting-state MEG. Instead, we found cortically and spectrally wide-spread PAC driven by harmonic signals. Furthermore, we show how physiological artifacts and spectral leakage cause spurious PAC across wide frequency ranges. Our results clarify how different measures of cross-frequency interactions can be combined to characterize PAC, and cast doubt on the presence of prominent non-harmonic phase-amplitude coupling in human resting-state MEG.

Frequency–amplitude coupling: a new approach for decoding of attended features in covert visual attention task

A method that has recently been mentioned as information encoding brain is cross-frequency coupling (CFC). It is generally assumed that CFC can play a crucial role in perception, memory, and attention. In this study, two new indices for evaluating frequency–amplitude coupling (FAC) through generalized linear model (GLM) and linear regression method were introduced and investigated along with other CFC indices. Electroencephalogram (EEG) signals were recorded during covert visual attention tasks to find out the CFC index capability so as to distinguish different states in the mentioned tasks. To this end, machine learning algorithms were used and four various types of CFC, phase–amplitude coupling (PAC), phase–phase coupling (PPC), amplitude–amplitude coupling (AAC), and frequency–amplitude coupling (FAC) in recorded signals were considered as inputs for classifiers. The results demonstrated that the proposed method used for evaluating FAC through linear regression can provide more information about the different states in two covert attention tasks using quadratic discriminant analysis (QDA) by classification performance of 94.21% and 90.54% in color and direction tasks, respectively. Also, FAC that used a GLM model and PAC had a higher performance compared with PPC and AAC in color task (90.74 and 92.24% against 83.21 and 86.22). We can conclude that CFC can encompass useful information about semantic category of stimuli in covert attention tasks and can be used as an acceptable alternative for the time–frequency features of brain signals.

Hippocampus-nidopallium caudolaterale interactions exist in the goal-directed behavior of pigeon

Avian hippocampus (Hp) and nidopallium caudolaterale (NCL) are believed to play key roles in goal-directed behavior. However, it is still unclear whether there are interactions between the two brain regions in the goal-directed behavior of pigeons. To investigate the interactions between the Hp and the NCL in the goal-directed behavior, we recorded local field potential (LFP) signals from the two regions simultaneously when the pigeons performed a goal-directed decision-making task. Amplitude-amplitude coupling analysis revealed that the coupling value between the LFP recorded from the Hp and that from the NCL increased significantly (P < 0.05) in slow gamma-band (40–60 Hz) during the turning area. In addition, the LFP functional network analysis demonstrated the LFP functional connections between the Hp and the NCL increased significantly (P < 0.05) in the turning area. The result of partial directed coherence (PDC) analysis showed that the predominant direction of information flow is thought to be from the Hp to the NCL. These findings suggest that there are causal functional interactions between the Hp and the NCL by which information is transmitted between the two regions relevant to goal-directed behavior.

Signature Patterns for Top-Down and Bottom-Up Information Processing via Cross-Frequency Coupling in Macaque Auditory Cortex.

Predictive coding is a theoretical framework that provides a functional interpretation of top-down and bottom-up interactions in sensory processing. The theory suggests there are differences in message passing up versus down the cortical hierarchy. These differences result from the linear feedforward of prediction errors, and the nonlinear feedback of predictions. This implies that cross-frequency interactions should predominate top-down. But it remains unknown whether these differences are expressed in cross-frequency interactions in the brain. Here we examined bidirectional cross-frequency coupling across four sectors of the auditory hierarchy in the macaque. We computed two measures of cross-frequency coupling, phase–amplitude coupling (PAC) and amplitude–amplitude coupling (AAC). Our findings revealed distinct patterns for bottom-up and top-down information processing among cross-frequency interactions. Both top-down and bottom-up interactions made prominent use of low frequencies: low-to-low-frequency (theta, alpha, beta) and low-frequency-to-high- gamma couplings were predominant top-down, while low-frequency-to-low-gamma couplings were predominant bottom-up. These patterns were largely preserved across coupling types (PAC and AAC) and across stimulus types (natural and synthetic auditory stimuli), suggesting that they are a general feature of information processing in auditory cortex. Our findings suggest the modulatory effect of low frequencies on gamma-rhythms in distant regions is important for bidirectional information transfer. The finding of low-frequency-to-low-gamma interactions in the bottom-up direction suggest that nonlinearities may also play a role in feedforward message passing. Altogether, the patterns of cross-frequency interaction we observed across the auditory hierarchy are largely consistent with the predictive coding framework.

Delta-gamma coupling as a potential neurophysiological mechanism of fluid intelligence

Electrophysiological investigations have pointed out significant yet weak correlations between intelligence and oscillatory brain activity, including the spectral power, frequency, complexity, synchrony, and coherence of electroencephalographic (EEG) signals, however neglecting the interplay between fast and slow neuronal oscillations underlying information transfer in the brain. We found that fluid intelligence level (gf) depends on the precise synchronization of fast oscillations to a specific time window of slow brain rhythms. Specifically, by examining EEG recordings of 50 people in resting state as well as during performance at two gf tasks we found converging evidence that high-gf participants display stronger Phase-Amplitude Coupling (PAC) related to an increased concentration of gamma (~36Hz) spectral power at the descending phase of delta oscillations (~3Hz). Delta-gamma PAC strength explained 35% variance in scores on multiple gf tests, outperforming six alternative EEG-based predictors including spectral power, complexity and amplitude-amplitude coupling. Present results suggest PAC as a neurophysiological substrate of gf in humans, offering novel insight about the role of slow and fast brain rhytms in high-order cognition, as well as a potential new target for neuromodulatory interventions in the healthy and pathological brain.

Neural synchronization deficits linked to cortical hyper-excitability and auditory hypersensitivity in fragile X syndrome

BackgroundStudies in the fmr1 KO mouse demonstrate hyper-excitability and increased high-frequency neuronal activity in sensory cortex. These abnormalities may contribute to prominent and distressing sensory hypersensitivities in patients with fragile X syndrome (FXS). The current study investigated functional properties of auditory cortex using a sensory entrainment task in FXS.MethodsEEG recordings were obtained from 17 adolescents and adults with FXS and 17 age- and sex-matched healthy controls. Participants heard an auditory chirp stimulus generated using a 1000-Hz tone that was amplitude modulated by a sinusoid linearly increasing in frequency from 0–100 Hz over 2 s.ResultsSingle trial time-frequency analyses revealed decreased gamma band phase-locking to the chirp stimulus in FXS, which was strongly coupled with broadband increases in gamma power. Abnormalities in gamma phase-locking and power were also associated with theta-gamma amplitude-amplitude coupling during the pre-stimulus period and with parent reports of heightened sensory sensitivities and social communication deficits.ConclusionsThis represents the first demonstration of neural entrainment alterations in FXS patients and suggests that fast-spiking interneurons regulating synchronous high-frequency neural activity have reduced functionality. This reduced ability to synchronize high-frequency neural activity was related to the total power of background gamma band activity. These observations extend findings from fmr1 KO models of FXS, characterize a core pathophysiological aspect of FXS, and may provide a translational biomarker strategy for evaluating promising therapeutics.

Topology, Cross-Frequency, and Same-Frequency Band Interactions Shape the Generation of Phase-Amplitude Coupling in a Neural Mass Model of a Cortical Column.

Phase-amplitude coupling (PAC), a type of cross-frequency coupling (CFC) where the phase of a low-frequency rhythm modulates the amplitude of a higher frequency, is becoming an important indicator of information transmission in the brain. However, the neurobiological mechanisms underlying its generation remain undetermined. A realistic, yet tractable computational model of the phenomenon is thus needed. Here we analyze a neural mass model of a cortical column, comprising fourteen neuronal populations distributed across four layers (L2/3, L4, L5 and L6). A control analysis showed that the conditional transfer entropy (cTE) measure is able to correctly estimate the flow of information between neuronal populations. Then, we computed cTE from the phases to the amplitudes of the oscillations generated in the cortical column. This approach provides information regarding directionality by distinguishing PAC from APC (amplitude-phase coupling), i.e. the information transfer from amplitudes to phases, and can be used to estimate other types of CFC such as amplitude-amplitude coupling (AAC) and phase-phase coupling (PPC). While experiments often only focus on one or two PAC combinations (e.g., theta-gamma or alpha-gamma), we found that a cortical column can simultaneously generate almost all possible PAC combinations, depending on connectivity parameters, time constants, and external inputs. PAC interactions with and without an anatomical equivalent (direct and indirect interactions, respectively) were analyzed. We found that the strength of PAC between two populations was strongly correlated with the strength of the effective connections between the populations and, on average, did not depend on whether the PAC connection was direct or indirect. When considering a cortical column circuit as a complex network, we found that neuronal populations making indirect PAC connections had, on average, higher local clustering coefficient, efficiency, and betweenness centrality than populations making direct connections and populations not involved in PAC connections. This suggests that their interactions were more effective when transmitting information. Since approximately 60% of the obtained interactions represented indirect connections, our results highlight the importance of the topology of cortical circuits for the generation of the PAC phenomenon. Finally, our results demonstrated that indirect PAC interactions can be explained by a cascade of direct CFC and same-frequency band interactions, suggesting that PAC analysis of experimental data should be accompanied by the estimation of other types of frequency interactions for an integrative understanding of the phenomenon.

Spurious cross-frequency amplitude–amplitude coupling in nonstationary, nonlinear signals

Recent studies of brain activities show that cross-frequency coupling (CFC) plays an important role in memory and learning. Many measures have been proposed to investigate the CFC phenomenon, including the correlation between the amplitude envelopes of two brain waves at different frequencies — cross-frequency amplitude–amplitude coupling (AAC). In this short communication, we describe how nonstationary, nonlinear oscillatory signals may produce spurious cross-frequency AAC. Utilizing the empirical mode decomposition, we also propose a new method for assessment of AAC that can potentially reduce the effects of nonlinearity and nonstationarity and, thus, help to avoid the detection of artificial AACs. We compare the performances of this new method and the traditional Fourier-based AAC method. We also discuss the strategies to identify potential spurious AACs.

Decoding of object categories from brain signals using cross frequency coupling methods

Abstract The procedure by which information about conceptual category of visual objects is encoded in brain signals, is the topic of many studies in the field of visual system. A phenomenon that is recently mentioned as one of information encoding strategies in the brain is cross frequency coupling (CFC), means the interactions between different frequency bands in physiological signals such as EEG. This interaction can occur between various components of such signals including amplitude or power, phase and frequency. Different types of CFC have been reported in various cognitive tasks and within or between different brain regions. However the role of CFC in encoding of information about the category of visual objects is greatly unknown and here we attempted to investigate it. So in this paper, we used machine learning algorithms to find out whether CFC contains such information. To this end, we recorded EEG from 10 participants while they were observing stimuli from 12 different visual object categories. Then amplitude–amplitude coupling (AAC), phase–amplitude coupling (PAC) and phase–phase coupling (PPC) within each electrode were calculated for the recorded signals in order to use them as input to SVM classifier. The results show that phase-phase coupling can provide more information about the category of objects compared to other types of CFC, by classification performance of 92.33% against 70.28% and 60.52% for phase–amplitude and amplitude–amplitude coupling, respectively. In addition, this feature was more informative when occurred between alpha and gamma frequency bands. The performance of classification by means of CFC features was then compared with the result of classification by wavelet transform on the same data. We observed that PAC can improve the performance of categorical-based classification relative to wavelet coefficients (with the performance of 70.73%). So we can conclude that cross frequency coupling encompass useful information about semantic category of stimuli which is not available in time-frequency components of the signal obtained by wavelet transform.

An epileptic focus location method based on ECoG.

Epilepsy is a neurological disorder characterized by the sudden abnormal discharging of brain neurons that can lead to encephalographic (EEG) abnormalities. In this study, data was obtained from epileptic patients with intracranial depth electrodes and analyzed using wavelet entropy algorithms in order to locate the epileptic foci. Significant increases in the wavelet entropy of the epileptic signals were identified during multiple episodes of clinical seizures. The results indicated that the algorithm was capable of identifying entropy changes in the epileptic sources. Furthermore, the correlations among the electrocorticogram (ECoG) signals of different channels determined using the amplitude-amplitude coupling method verified that the epileptic foci exhibited significantly higher coupling strengths. Thus, cross frequency coupling (CFC) could be an inspiration to energy and signal transitive mode of seizure and, thereby, improve diagnostic processes.

Parametric estimation of cross-frequency coupling

BackgroundGrowing experimental evidence suggests an important role for cross-frequency coupling in neural processing, in particular for phase-amplitude coupling (PAC). Although the details of methods to detect PAC may vary, a common procedure to estimate the significance level is the comparison of observed values to those of at least 100 surrogate time series. When scanning large parts of the frequency spectrum and multiple recording sites, this could amount to very large computation times. New methodWe demonstrate that the general linear model (GLM) allows for a parametric estimation of significant PAC. Continuous recordings are split into epochs, of a few seconds duration, on which an F-test can be performed. We compared its performance against traditional non-parametric permutation tests in both simulated and experimental data. ResultsOur method was able to reproduce findings of phase-amplitude coupling in local field potential recordings obtained from the subthalamic nucleus in patients with Parkinson's disease. We also show that PAC may be detected between the subthalamic nucleus and cortical motor areas. Comparison with existing method(s)Although the GLM slightly underestimated significance compared to permutation tests in the simulations, for experimental data the two methods produced highly similar results. Computation times were drastically lower for the GLM. Furthermore, we demonstrate that the GLM can be easily extended by including additional predictors such as low-frequency amplitude to test for amplitude-amplitude coupling. ConclusionsThe GLM forms an adequate and computationally efficient approach for detecting cross-frequency coupling with the flexibility to add other explanatory variables of interest.

Source-Space Cross-Frequency Amplitude-Amplitude Coupling in Tinnitus.

The thalamocortical dysrhythmia (TCD) model has been influential in the development of theoretical explanations for the neurological mechanisms of tinnitus. It asserts that thalamocortical oscillations lock a region in the auditory cortex into an ectopic slow-wave theta rhythm (4–8 Hz). The cortical area surrounding this region is hypothesized to generate abnormal gamma (>30 Hz) oscillations (“edge effect”) giving rise to the tinnitus percept. Consequently, the model predicts enhanced cross-frequency coherence in a broad range between theta and gamma. In this magnetoencephalography study involving tinnitus and control cohorts, we investigated this prediction. Using beamforming, cross-frequency amplitude-amplitude coupling (AAC) was computed within the auditory cortices for frequencies (f 1, f 2) between 2 and 80 Hz. We find the AAC signal to decompose into two distinct components at low (f 1, f 2 < 30 Hz) and high (f 1, f 2 > 30 Hz) frequencies, respectively. Studying the correlation of AAC with several key covariates (age, hearing level (HL), tinnitus handicap and duration, and HL at tinnitus frequency), we observe a statistically significant association between age and low-frequency AAC. Contrary to the TCD predictions, however, we do not find any indication of statistical differences in AAC between tinnitus and controls and thus no evidence for the predicted enhancement of cross-frequency coupling in tinnitus.

Cross-frequency coupling of brain oscillations in studying motivation and emotion

Research has shown that brain functions are realized by simultaneous oscillations in various frequency bands. In addition to examining oscillations in pre-specified bands, interactions and relations between the different frequency bandwidths is another important aspect that needs to be considered in unraveling the workings of the human brain and its functions. In this review we provide evidence that studying interdependencies between brain oscillations may be a valuable approach to study the electrophysiological processes associated with motivation and emotional states. Studies will be presented showing that amplitude-amplitude coupling between delta-alpha and delta-beta oscillations varies as a function of state anxiety and approach-avoidance-related motivation, and that changes in the association between delta-beta oscillations can be observed following successful psychotherapy. Together these studies suggest that cross-frequency coupling of brain oscillations may contribute to expanding our understanding of the neural processes underlying motivation and emotion.