Formation of visual memories controlled by gamma power phase-locked to alpha oscillations

Errors elicit frontoparietal theta-gamma coupling that is modulated by endogenous estradiol levels

Cognitive control-related error monitoring is intimately involved in behavioral adaptation, learning, and individual differences in a variety of psychological traits and disorders. Accumulating evidence suggests that a focus on women's health and ovarian hormones is critical to the study of such cognitive brain functions. Here we sought to identify a novel index of error monitoring using a time-frequency based phase amplitude coupling (t-f PAC) measure and examine its modulation by endogenous levels of estradiol in females. Forty-three healthy, naturally cycling young adult females completed a flanker task while continuous electroencephalogram was recorded on four occasions across the menstrual cycle. Results revealed significant error-related t-f PAC between theta phase generated in fronto-central areas and gamma amplitude generated in parietal-occipital areas. Moreover, this error-related theta-gamma coupling was enhanced by endogenous levels of estradiol both within females across the cycle as well as between females with higher levels of average circulating estradiol. While the role of frontal midline theta in error processing is well documented, this paper extends the extant literature by illustrating that error monitoring involves the coordination between multiple distributed systems with the slow midline theta activity modulating the power of gamma-band oscillatory activity in parietal regions. They further show enhancement of inter-regional coupling by endogenous estradiol levels, consistent with research indicating modulation of cognitive control neural functions by the endocrine system in females. Together, this work identifies a novel neurophysiological marker of cognitive control-related error monitoring in females that has implications for neuroscience and women's health.

The applied principles of EEG analysis methods in neuroscience and clinical neurology

Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and the application of relevant methods in the neuroscience field and for diagnosing neurological diseases. First, three types of EEG signals, including time-invariant EEG, accurate event-related EEG, and random event-related EEG, are introduced. Second, five main directions for the methods of EEG analysis, including power spectrum analysis, time-frequency analysis, connectivity analysis, source localization methods, and machine learning methods, are described in the main section, along with different sub-methods and effect evaluations for solving the same problem. Finally, the application scenarios of different EEG analysis methods are emphasized, and the advantages and disadvantages of similar methods are distinguished. This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives, provide references for subsequent research, and summarize current issues and prospects for the future.

Distinct interacting cortical networks for stimulus-response and repetition-suppression

Non-invasive studies consider the initial neural stimulus response (SR) and repetition suppression (RS) - the decreased response to repeated sensory stimuli - as engaging the same neurons. That is, RS is a suppression of the SR. We challenge this conjecture using electrocorticographic (ECoG) recordings with high spatial resolution in ten patients listening to task-irrelevant trains of auditory stimuli. SR and RS were indexed by high-frequency activity (HFA) across temporal, parietal, and frontal cortices. HFASR and HFARS were temporally and spatially distinct, with HFARS emerging later than HFASR and showing only a limited spatial intersection with HFASR: most HFASR sites did not demonstrate HFARS, and HFARS was found where no HFASR could be recorded. β activity was enhanced in HFARS compared to HFASR cortical sites. θ activity was enhanced in HFASR compared to HFARS sites. Furthermore, HFASR sites propagated information to HFARS sites via transient θ:β phase-phase coupling. In contrast to predictive coding (PC) accounts our results indicate that HFASR and HFARS are functionally linked but have minimal spatial overlap. HFASR might enable stable and rapid perception of environmental stimuli across extended temporal intervals. In contrast HFARS might support efficient generation of an internal model based on stimulus history.

Interictal SEEG Resting-State Connectivity Localizes the Seizure Onset Zone and Predicts Seizure Outcome

Localization of epileptogenic zone currently requires prolonged intracranial recordings to capture seizure, which may take days to weeks. The authors developed a novel method to identify the seizure onset zone (SOZ) and predict seizure outcome using short-time resting-state stereotacticelectroencephalography (SEEG) data. In a cohort of 27 drug-resistant epilepsy patients, the authors estimated the information flow via directional connectivity and inferred the excitation-inhibition ratio from the 1/f power slope. They hypothesized that the antagonism of information flow at multiple frequencies between SOZ and non-SOZ underlying the relatively stable epilepsy resting state could be related to the disrupted excitation-inhibition balance. They found flatter 1/f power slope in non-SOZ regions compared to the SOZ, with dominant information flow from non-SOZ to SOZ regions. Greater differences in resting-state information flow between SOZ and non-SOZ regions are associated with favorable seizure outcome. By integrating a balanced random forest model with resting-state connectivity, their method localized the SOZ with an accuracy of 88% and predicted the seizure outcome with an accuracy of 92% using clinically determined SOZ. Overall, this study suggests that brief resting-state SEEG data can significantly facilitate the identification of SOZ and may eventually predict seizure outcomes without requiring long-term ictal recordings.

Working Memory and Cross-Frequency Coupling of Neuronal Oscillations

Working memory (WM) is the active retention and processing of information over a few seconds and is considered an essential component of cognitive function. The reduced WM capacity is a common feature in many diseases, such as schizophrenia, attention deficit hyperactivity disorder (ADHD), mild cognitive impairment (MCI), and Alzheimer's disease (AD). The theta-gamma neural code is an essential component of memory representations in the multi-item WM. A large body of studies have examined the association between cross-frequency coupling (CFC) across the cerebral cortices and WM performance; electrophysiological data together with the behavioral results showed the associations between CFC and WM performance. The oscillatory entrainment (sensory, non-invasive electrical/magnetic, and invasive electrical) remains the key method to investigate the causal relationship between CFC and WM. The frequency-tuned non-invasive brain stimulation is a promising way to improve WM performance in healthy and non-healthy patients with cognitive impairment. The WM performance is sensitive to the phase and rhythm of externally applied stimulations. CFC-transcranial-alternating current stimulation (CFC-tACS) is a recent approach in neuroscience that could alter cognitive outcomes. The studies that investigated (1) the association between CFC and WM and (2) the brain stimulation protocols that enhanced WM through modulating CFC by the means of the non-invasive brain stimulation techniques have been included in this review. In principle, this review can guide the researchers to identify the most prominent form of CFC associated with WM processing (e.g., theta/gamma phase-amplitude coupling), and to define the previously published studies that manipulate endogenous CFC externally to improve WM. This in turn will pave the path for future studies aimed at investigating the CFC-tACS effect on WM. The CFC-tACS protocols need to be thoroughly studied before they can be considered as therapeutic tools in patients with WM deficits.

High-Frequency Visual Stimulation Primes Gamma Oscillations for Visually Evoked Phase Reset and Enhances Spatial Acuity

The temporal frequency of sensory stimulation is a decisive factor in the plasticity of perceptual detection thresholds. However, surprisingly little is known about how distinct temporal parameters of sensory input differentially recruit activity of neuronal circuits in sensory cortices. Here we demonstrate that brief repetitive visual stimulation induces long-term plasticity of visual responses revealed 24 h after stimulation and that the location and generalization of visual response plasticity is determined by the temporal frequency of the visual stimulation. Brief repetitive low-frequency stimulation (2 Hz) is sufficient to induce a visual response potentiation that is expressed exclusively in visual cortex layer 4 and in response to a familiar stimulus. In contrast, brief, repetitive high-frequency stimulation (HFS, 20 Hz) is sufficient to induce a visual response potentiation that is expressed in all cortical layers and transfers to novel stimuli. HFS induces a long-term suppression of the activity of fast-spiking interneurons and primes ongoing gamma oscillatory rhythms for phase reset by subsequent visual stimulation. This novel form of generalized visual response enhancement induced by HFS is paralleled by an increase in visual acuity, measured as improved performance in a visual detection task.

Frontolimbic alpha activity tracks intentional rest BCI control improvement through mindfulness meditation

Brain-computer interfaces (BCIs) are capable of translating human intentions into signals controlling an external device to assist patients with severe neuromuscular disorders. Prior work has demonstrated that participants with mindfulness meditation experience evince improved BCI performance, but the underlying neural mechanisms remain unclear. Here, we conducted a large-scale longitudinal intervention study by training participants in mindfulness-based stress reduction (MBSR; a standardized mind-body awareness training intervention), and investigated whether and how short-term MBSR affected sensorimotor rhythm (SMR)-based BCI performance. We hypothesize that MBSR training improves BCI performance by reducing mind wandering and enhancing self-awareness during the intentional rest BCI control, which would mainly be reflected by modulations of default-mode network and limbic network activity. We found that MBSR training significantly improved BCI performance compared to controls and these behavioral enhancements were accompanied by increased frontolimbic alpha activity (9-15 Hz) and decreased alpha connectivity among limbic network, frontoparietal network, and default-mode network. Furthermore, the modulations of frontolimbic alpha activity were positively correlated with the duration of meditation experience and the extent of BCI performance improvement. Overall, these data suggest that mindfulness allows participant to reach a state where they can modulate frontolimbic alpha power and improve BCI performance for SMR-based BCI control.

Magnetoencephalography resting-state spectral fingerprints distinguish bipolar depression and unipolar depression

OBJECTIVES

In clinical practice, bipolar depression (BD) and unipolar depression (UD) appear to have similar symptoms, causing BD being frequently misdiagnosed as UD, leading to improper treatment decision and outcome. Therefore, it is in urgent need of distinguishing BD from UD based on clinical objective biomarkers as early as possible. Here, we aimed to integrate brain neuroimaging data and an advanced machine learning technique to predict different types of mood disorder patients at the individual level.

METHODS

Eyes closed resting-state magnetoencephalography (MEG) data were collected from 23 BD, 30 UD, and 31 healthy controls (HC). Individual power spectra were estimated by Fourier transform, and statistic spectral differences were assessed via a cluster permutation test. A support vector machine classifier was further applied to predict different mood disorder types based on discriminative oscillatory power.

RESULTS

Both BD and UD showed decreased frontal-central gamma/beta ratios comparing to HC, in which gamma power (30-75 Hz) was decreased in BD while beta power (14-30 Hz) was increased in UD vs HC. The support vector machine model obtained significant high classification accuracies distinguishing three groups based on mean gamma and beta power (BD: 79.9%, UD: 81.1%, HC: 76.3%, P < .01).

CONCLUSIONS

In combination with resting-state MEG data and machine learning technique, it is possible to make an individual and objective prediction for mode disorder types, which in turn has implications for diagnosis precision and treatment decision of mood disorder patients.

Spectral fingerprints of facial affect processing bias in major depression disorder

In major depressive disorder (MDD), processing of facial affect is thought to reflect a perceptual bias (toward negative emotion, away from positive emotion, and interpretation of neutral as emotional). However, it is unclear to what extent and which specific perceptual bias is represented in MDD at the behavior and neuronal level. The present report examined 48 medication naive MDD patients and 41 healthy controls (HCs) performing a facial affect judgment task while magnetoencephalography was recorded. MDD patients were characterized by overall slower response times and lower perceptual judgment accuracies. In comparison with HC, MDD patients exhibited less somatosensory beta activity (20–30 Hz) suppression, more visual gamma activity (40–80 Hz) modulation and somatosensory beta and visual gamma interaction deficit. Moreover, frontal gamma activity during positive facial expression judgment was found to be negatively correlated with depression severity. Present findings suggest that perceptual bias in MDD is associated with distinct spatio-spectral manifestations on the neural level, which potentially establishes aberrant pathways during facial emotion processing and contributes to MDD pathology.

Genuine cross-frequency coupling networks in human resting-state electrophysiological recordings

Phase synchronization of neuronal oscillations in specific frequency bands coordinates anatomically distributed neuronal processing and communication. Typically, oscillations and synchronization take place concurrently in many distinct frequencies, which serve separate computational roles in cognitive functions. While within-frequency phase synchronization has been studied extensively, less is known about the mechanisms that govern neuronal processing distributed across frequencies and brain regions. Such integration of processing between frequencies could be achieved via cross-frequency coupling (CFC), either by phase–amplitude coupling (PAC) or by n:m-cross–frequency phase synchrony (CFS). So far, studies have mostly focused on local CFC in individual brain regions, whereas the presence and functional organization of CFC between brain areas have remained largely unknown. We posit that interareal CFC may be essential for large-scale coordination of neuronal activity and investigate here whether genuine CFC networks are present in human resting-state (RS) brain activity. To assess the functional organization of CFC networks, we identified brain-wide CFC networks at mesoscale resolution from stereoelectroencephalography (SEEG) and at macroscale resolution from source-reconstructed magnetoencephalography (MEG) data. We developed a novel, to our knowledge, graph-theoretical method to distinguish genuine CFC from spurious CFC that may arise from nonsinusoidal signals ubiquitous in neuronal activity. We show that genuine interareal CFC is present in human RS activity in both SEEG and MEG data. Both CFS and PAC networks coupled theta and alpha oscillations with higher frequencies in large-scale networks connecting anterior and posterior brain regions. CFS and PAC networks had distinct spectral patterns and opposing distribution of low- and high-frequency network hubs, implying that they constitute distinct CFC mechanisms. The strength of CFS networks was also predictive of cognitive performance in a separate neuropsychological assessment. In conclusion, these results provide evidence for interareal CFS and PAC being 2 distinct mechanisms for coupling oscillations across frequencies in large-scale brain networks.

Genuine interareal cross-frequency coupling (CFC) can be identified from human resting state activity using magnetoencephalography, stereoelectroencephalography, and novel network approaches. CFC couples slow theta and alpha oscillations to faster oscillations across brain regions.

Multiple Oscillatory Push-Pull Antagonisms Constrain Seizure Propagation

OBJECTIVE

Drug-resistant focal epilepsy is widely recognized as a network disease in which epileptic seizure propagation is likely coordinated by different neuronal oscillations such as low-frequency activity (LFA), high-frequency activity (HFA), or low-to-high cross-frequency coupling. However, the mechanism by which different oscillatory networks constrain the propagation of focal seizures remains unclear.

METHODS

We studied focal epilepsy patients with invasive electrocorticography (ECoG) recordings and compared multilayer directional network interactions between focal seizures either with or without secondary generalization. Within-frequency and cross-frequency directional connectivity were estimated by an adaptive directed transfer function and cross-frequency directionality, respectively.

RESULTS

In the within-frequency epileptic network, we found that the seizure onset zone (SOZ) always sent stronger information flow to the surrounding regions, and secondary generalization was accompanied by weaker information flow in the LFA from the surrounding regions to SOZ. In the cross-frequency epileptic network, secondary generalization was associated with either decreased information flow from surrounding regions' HFA to SOZ's LFA or increased information flow from SOZ's LFA to surrounding regions' HFA.

INTERPRETATION

Our results suggest that the secondary generalization of focal seizures is regulated by numerous within- and cross-frequency push-pull dynamics, potentially reflecting impaired excitation-inhibition interactions of the epileptic network. ANN NEUROL 2019;86:683-694.

Neuromodulators and Long-Term Synaptic Plasticity in Learning and Memory: A Steered-Glutamatergic Perspective

The molecular pathways underlying the induction and maintenance of long-term synaptic plasticity have been extensively investigated revealing various mechanisms by which neurons control their synaptic strength. The dynamic nature of neuronal connections combined with plasticity-mediated long-lasting structural and functional alterations provide valuable insights into neuronal encoding processes as molecular substrates of not only learning and memory but potentially other sensory, motor and behavioural functions that reflect previous experience. However, one key element receiving little attention in the study of synaptic plasticity is the role of neuromodulators, which are known to orchestrate neuronal activity on brain-wide, network and synaptic scales. We aim to review current evidence on the mechanisms by which certain modulators, namely dopamine, acetylcholine, noradrenaline and serotonin, control synaptic plasticity induction through corresponding metabotropic receptors in a pathway-specific manner. Lastly, we propose that neuromodulators control plasticity outcomes through steering glutamatergic transmission, thereby gating its induction and maintenance.

Hyperactive frontolimbic and frontocentral resting-state gamma connectivity in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a system-level disorder affecting multiple functionally integrated cerebral networks. Nevertheless, their temporospatial organization and potential disturbance remain mostly unknown. The present report tested the hypothesis that deficient temporospatial network organization separates MDD and healthy controls (HC), and is linked to symptom severity of the disorder.

METHODS

Eyes-closed resting-state magnetoencephalographic (MEG) recordings were obtained from twenty-two MDD and twenty-two HC subjects. Beamforming source localization and functional connectivity analysis were applied to identify frequency-specific network interactions. Then, a novel virtual cortical resection approach was used to pinpoint putatively critical network controllers, accounting for aberrant cerebral connectivity patterns in MDD.

RESULTS

We found significantly elevated frontolimbic and frontocentral connectivity mediated by gamma (30-48 Hz) activity in MDD versus HC, and the right amygdala was the key differential network controller accounting for aberrant cerebral connectivity patterns in MDD. Furthermore, this frontolimbic and frontocentral gamma-band hyper-connectivity was positively correlated with depression severity.

LIMITATIONS

The overall sample size was small, and we found significant effects in the deep limbic regions with resting-state MEG, the reliability of which was difficult to corroborate further.

CONCLUSIONS

Overall, these findings support a notion that the right amygdala critically controls the exaggerated gamma-band frontolimbic and frontocentral connectivity in MDD during the resting-state condition, which potentially constitutes pre-established aberrant pathways during task processing and contributes to MDD pathology.

Reward Expectation Modulates Local Field Potentials, Spiking Activity and Spike-Field Coherence in the Primary Motor Cortex

Reward modulation (M1) could be exploited in developing an autonomously updating brain-computer interface (BCI) based on a reinforcement learning (RL) architecture. For an autonomously updating RL-based BCI system, we would need a reward prediction error, or a state-value representation from the user's neural activity, which the RL-BCI agent could use to update its BCI decoder. In order to understand the multifaceted effects of reward on M1 activity, we investigated how neural spiking, oscillatory activities and their functional interactions are modulated by conditioned stimuli related reward expectation. To do so, local field potentials (LFPs) and single/multi-unit activities were recorded simultaneously and bilaterally from M1 cortices while four non-human primates (NHPs) performed cued center-out reaching or grip force tasks either manually using their right arm/hand or observed passively. We found that reward expectation influenced the strength of α (8-14 Hz) power, α-γ comodulation, α spike-field coherence (SFC), and firing rates (FRs) in general in M1. Furthermore, we found that an increase in α-band power was correlated with a decrease in neural spiking activity, that FRs were highest at the trough of the α-band cycle and lowest at the peak of its cycle. These findings imply that α oscillations modulated by reward expectation have an influence on spike FR and spike timing during both reaching and grasping tasks in M1. These LFP, spike, and spike-field interactions could be used to follow the M1 neural state in order to enhance BCI decoding (An et al., 2018; Zhao et al., 2018).

Lingering Sound: Event-Related Phase-Amplitude Coupling and Phase-Locking in Fronto-Temporo-Parietal Functional Networks During Memory Retrieval of Music Melodies

Brain oscillations and connectivity have emerged as promising measures of evaluating memory processes, including encoding, maintenance, and retrieval, as well as the related executive function. Although many studies have addressed the neural mechanisms underlying working memory, most of these studies have focused on the visual modality. Neurodynamics and functional connectivity related to auditory working memory are yet to be established. In this study, we explored the dynamic of high density (128-channel) electroencephalography (EEG) in a musical delayed match-to-sample task (DMST), in which 36 participants were recruited and were instructed to recognize and distinguish the target melodies from similar distractors. Event-related spectral perturbations (ERSPs), event-related phase-amplitude couplings (ERPACs), and phase-locking values (PLVs) were used to determine the corresponding brain oscillations and connectivity. First, we observed that low-frequency oscillations in the frontal, temporal, and parietal regions were increased during the processing of both target and distracting melodies. Second, the cross-frequency coupling between low-frequency phases and high-frequency amplitudes was elevated in the frontal and parietal regions when the participants were distinguishing between the target from distractor, suggesting that the phase-amplitude coupling could be an indicator of neural mechanisms underlying memory retrieval. Finally, phase-locking, an index evaluating brain functional connectivity, revealed that there was fronto-temporal phase-locking in the theta band and fronto-parietal phase-locking in the alpha band during the recognition of the two stimuli. These findings suggest the existence of functional connectivity and the phase-amplitude coupling in the neocortex during musical memory retrieval, and provide a highly resolved timeline to evaluate brain dynamics. Furthermore, the inter-regional phase-locking and phase-amplitude coupling among the frontal, temporal and parietal regions occurred at the very beginning of musical memory retrieval, which might reflect the precise timing when cognitive resources were involved in the retrieval of targets and the rejection of similar distractors. To the best of our knowledge, this is the first EEG study employing a naturalistic task to study auditory memory processes and functional connectivity during memory retrieval, results of which can shed light on the use of natural stimuli in studies that are closer to the real-life applications of cognitive evaluations, mental treatments, and brain-computer interface.

The Strength of Alpha-Beta Oscillatory Coupling Predicts Motor Timing Precision

Precise timing makes the difference between harmony and cacophony, but how the brain achieves precision during timing is unknown. In this study, human participants (7 females, 5 males) generated a time interval while being recorded with magnetoencephalography. Building on the proposal that the coupling of neural oscillations provides a temporal code for information processing in the brain, we tested whether the strength of oscillatory coupling was sensitive to self-generated temporal precision. On a per individual basis, we show the presence of alpha-beta phase-amplitude coupling whose strength was associated with the temporal precision of self-generated time intervals, not with their absolute duration. Our results provide evidence that active oscillatory coupling engages α oscillations in maintaining the precision of an endogenous temporal motor goal encoded in β power; the when of self-timed actions. We propose that oscillatory coupling indexes the variance of neuronal computations, which translates into the precision of an individual's behavioral performance.SIGNIFICANCE STATEMENT Which neural mechanisms enable precise volitional timing in the brain is unknown, yet accurate and precise timing is essential in every realm of life. In this study, we build on the hypothesis that neural oscillations, and their coupling across time scales, are essential for the coding and for the transmission of information in the brain. We show the presence of alpha-beta phase-amplitude coupling (α-β PAC) whose strength was associated with the temporal precision of self-generated time intervals, not with their absolute duration. α-β PAC indexes the temporal precision with which information is represented in an individual's brain. Our results link large-scale neuronal variability on the one hand, and individuals' timing precision, on the other.

Generalized Cross-Frequency Decomposition: A Method for the Extraction of Neuronal Components Coupled at Different Frequencies

Perceptual, motor and cognitive processes are based on rich interactions between remote regions in the human brain. Such interactions can be carried out through phase synchronization of oscillatory signals. Neuronal synchronization has been primarily studied within the same frequency range, e.g., within alpha or beta frequency bands. Yet, recent research shows that neuronal populations can also demonstrate phase synchronization between different frequency ranges. An extraction of such cross-frequency interactions in EEG/MEG recordings remains, however, methodologically challenging. Here we present a new method for the robust extraction of cross-frequency phase-to-phase synchronized components. Generalized Cross-Frequency Decomposition (GCFD) reconstructs the time courses of synchronized neuronal components, their spatial filters and patterns. Our method extends the previous state of the art, Cross-Frequency Decomposition (CFD), to the whole range of frequencies: it works for any f₁ and f₂ whenever f₁:f₂ is a rational number. GCFD gives a compact description of non-linearly interacting neuronal sources on the basis of their cross-frequency phase coupling. We successfully validated the new method in simulations and tested it with real EEG recordings including resting state data and steady state visually evoked potentials (SSVEP).

Gating by induced Α-Γ asynchrony in selective attention

Visual selective attention operates through top-down mechanisms of signal enhancement and suppression, mediated by α-band oscillations. The effects of such top-down signals on local processing in primary visual cortex (V1) remain poorly understood. In this work, we characterize the interplay between large-scale interactions and local activity changes in V1 that orchestrates selective attention, using Granger-causality and phase-amplitude coupling (PAC) analysis of EEG source signals. The task required participants to either attend to or ignore oriented gratings. Results from time-varying, directed connectivity analysis revealed frequency-specific effects of attentional selection: bottom-up γ-band influences from visual areas increased rapidly in response to attended stimuli while distributed top-down α-band influences originated from parietal cortex in response to ignored stimuli. Importantly, the results revealed a critical interplay between top-down parietal signals and α-γ PAC in visual areas. Parietal α-band influences disrupted the α-γ coupling in visual cortex, which in turn reduced the amount of γ-band outflow from visual areas. Our results are a first demonstration of how directed interactions affect cross-frequency coupling in downstream areas depending on task demands. These findings suggest that parietal cortex realizes selective attention by disrupting cross-frequency coupling at target regions, which prevents them from propagating task-irrelevant information.

Low-frequency alternating current stimulation rhythmically suppresses gamma-band oscillations and impairs perceptual performance

Low frequency oscillations such as alpha (8-12 Hz) are hypothesized to rhythmically gate sensory processing, reflected by 40-100 Hz gamma band activity, via the mechanism of pulsed inhibition. We applied transcranial alternating current stimulation (TACS) at individual alpha frequency (IAF) and flanking frequencies (IAF-4 Hz, IAF+4 Hz) to the occipital cortex of healthy human volunteers during concurrent magnetoencephalography (MEG), while participants performed a visual detection task inducing strong gamma-band responses. Occipital (but not retinal) TACS phasically suppressed stimulus-induced gamma oscillations in the visual cortex and impaired target detection, with stronger phase-to-amplitude coupling predicting behavioral impairments. Retinal control TACS ruled out retino-thalamo-cortical entrainment resulting from (subthreshold) retinal stimulation. All TACS frequencies tested were effective, suggesting that visual gamma-band responses can be modulated by a range of low frequency oscillations. We propose that TACS-induced membrane potential modulations mimic the rhythmic change in cortical excitability by which spontaneous low frequency oscillations may eventually exert their impact when gating sensory processing via pulsed inhibition.

Dorsal and ventral cortices are coupled by cross-frequency interactions during working memory

Oscillatory activity in the alpha and gamma bands is considered key in shaping functional brain architecture. Power increases in the high-frequency gamma band are typically reported in parallel to decreases in the low-frequency alpha band. However, their functional significance and in particular their interactions are not well understood. The present study shows that, in the context of an N-back working memory task, alpha power decreases in the dorsal visual stream are related to gamma power increases in early visual areas. Granger causality analysis revealed directed interregional interactions from dorsal to ventral stream areas, in accordance with task demands. Present results reveal a robust, behaviorally relevant, and architectonically decisive power-to-power relationship between alpha and gamma activity. This relationship suggests that anatomically distant power fluctuations in oscillatory activity can link cerebral network dynamics on trial-by-trial basis during cognitive operations such as working memory.

Enhanced Gamma Activity and Cross-Frequency Interaction of Resting-State Electroencephalographic Oscillations in Patients with Alzheimer's Disease

Cognitive impairment, functional decline and behavioral symptoms that characterize Alzheimer’s disease (AD) are associated with perturbations of the neuronal network. The typical electroencephalographic (EEG) features in AD patients are increased delta or theta rhythm and decreased alpha or beta rhythm activities. However, considering the role of cross-frequency couplings in cognition, the alternation of cross-frequency couplings in AD patients is still obscure. This study aims to explore the interaction dynamics between different EEG oscillations in AD patients. We recorded the resting eye-closed EEG signals in 8 AD patients and 12 healthy volunteers. By analyzing the wavelet power spectrum and bicoherence of EEG, we found enhanced gamma rhythm power in AD patients in addition to the increased delta and decreased alpha power. Furthermore, an enhancement of the cross-frequency coupling strength between the beta/gamma and low-frequency bands was observed in AD patients compared to healthy controls (HCs). We propose that the pathological increase of ongoing gamma-band power might result from the disruption of the GABAergic interneuron network in AD patients. Furthermore, the cross-frequency overcouplings, which reflect the enhanced synchronization, might indicate the attenuated complexity of the neuronal network, and AD patients have to use more neural resources to maintain the resting brain state. Overall, our findings provide new evidence of the disturbance of the brain oscillation network in AD and further deepen our understanding of the central mechanisms of AD.

Discriminating Valid from Spurious Indices of Phase-Amplitude Coupling

Recently there has been a strong interest in cross-frequency coupling, the interaction between neuronal oscillations in different frequency bands. In particular, measures quantifying the coupling between the phase of slow oscillations and the amplitude of fast oscillations have been applied to a wide range of data recorded from animals and humans. Some of the measures applied to detect phase-amplitude coupling have been criticized for being sensitive to nonsinusoidal properties of the oscillations and thus spuriously indicate the presence of coupling. While such instances of spurious identification of coupling have been observed, in this commentary we give concrete examples illustrating cases when the identification of cross-frequency coupling can be trusted. These examples are based on control analyses and empirical observations rather than signal-processing tools. Finally, we provide concrete advice on how to determine when measures of phase-amplitude coupling can be considered trustworthy.

Cross-frequency synchronization connects networks of fast and slow oscillations during visual working memory maintenance

Neuronal activity in sensory and fronto-parietal (FP) areas underlies the representation and attentional control, respectively, of sensory information maintained in visual working memory (VWM). Within these regions, beta/gamma phase-synchronization supports the integration of sensory functions, while synchronization in theta/alpha bands supports the regulation of attentional functions. A key challenge is to understand which mechanisms integrate neuronal processing across these distinct frequencies and thereby the sensory and attentional functions. We investigated whether such integration could be achieved by cross-frequency phase synchrony (CFS). Using concurrent magneto- and electroencephalography, we found that CFS was load-dependently enhanced between theta and alpha–gamma and between alpha and beta-gamma oscillations during VWM maintenance among visual, FP, and dorsal attention (DA) systems. CFS also connected the hubs of within-frequency-synchronized networks and its strength predicted individual VWM capacity. We propose that CFS integrates processing among synchronized neuronal networks from theta to gamma frequencies to link sensory and attentional functions.

DOI:http://dx.doi.org/10.7554/eLife.13451.001