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

The classification of absence seizures using power-to-power cross-frequency coupling analysis with a deep learning network

High frequency oscillations are important novel biomarkers of epileptic tissue. The interaction of oscillations across different time scales is revealed as cross-frequency coupling (CFC) representing a high-order structure in the functional organization of brain rhythms. Power-to-power coupling (PPC) is one form of coupling with significant research attesting to its neurobiological significance as well as its computational efficiency, yet has been hitherto unexplored within seizure classification literature. New artificial intelligence methods such as deep learning neural networks can provide powerful tools for automated analysis of EEG. Here we present a Stacked Sparse Autoencoder (SSAE) trained to classify absence seizure activity based on this important form of cross-frequency patterns within scalp EEG. The analysis is done on the EEG records from the Temple University Hospital database. Absence seizures (n = 94) from 12 patients were taken into analysis along with segments of background activity. Power-to-power coupling was calculated between all frequencies 2-120 Hz pairwise using the EEGLAB toolbox. The resulting CFC matrices were used as training or testing inputs to the autoencoder. The trained network was able to recognize background and seizure segments (not used in training) with a sensitivity of 93.1%, specificity of 99.5% and overall accuracy of 96.8%. The results provide evidence both for (1) the relevance of PPC for seizure classification, as well as (2) the efficacy of an approach combining PPC with SSAE neural networks for automated classification of absence seizures within scalp EEG.

The role of brain oscillations in feature integration

Our sensory system is able to build a unified perception of the world, which although rich, is limited and inaccurate. Sometimes, features from different objects are erroneously combined. At the neural level, the role of the parietal cortex in feature integration is well-known. However, the brain dynamics underlying correct and incorrect feature integration are less clear. To explore the temporal dynamics of feature integration, we studied the modulation of different frequency bands in trials in which feature integration was correct or incorrect. Participants responded to the color of a shape target, surrounded by distractors. A calibration procedure ensured that accuracy was around 70% in each participant. To explore the role of expectancy in feature integration, we introduced an unexpected feature to the target in the last blocks of trials. Results demonstrated the contribution of several frequency bands to feature integration. Alpha and beta power was reduced for hits compared to illusions. Moreover, gamma power was overall larger during the experiment for participants who were aware of the unexpected target presented during the last blocks of trials (as compared to unaware participants). These results demonstrate that feature integration is a complex process that can go wrong at different stages of information processing and is influenced by top-down expectancies.

GABAergic inhibition shapes behavior and neural dynamics in human visual working memory

Neuronal inhibition, primarily mediated by GABAergic neurotransmission, is crucial for brain development and healthy cognition. Gamma-aminobutyric acid concentration levels in sensory areas have been shown to correlate with hemodynamic and oscillatory neuronal responses. How these measures relate to one another during working memory, a higher-order cognitive process, is still poorly understood. We address this gap by collecting magnetoencephalography, functional magnetic resonance imaging, and Flumazenil positron emission tomography data within the same subject cohort using an n-back working-memory paradigm. By probing the relationship between GABAA receptor distribution, neural oscillations, and Blood Oxygen Level Dependent (BOLD) modulations, we found that GABAA receptor density in higher-order cortical areas predicted the reaction times on the working-memory task and correlated positively with the peak frequency of gamma power modulations and negatively with BOLD amplitude. These findings support and extend theories linking gamma oscillations and hemodynamic responses to gamma-aminobutyric acid neurotransmission and to the excitation-inhibition balance and cognitive performance in humans. Considering the small sample size of the study, future studies should test whether these findings also hold for other, larger cohorts as well as to examine in detail how the GABAergic system and neural fluctuations jointly support working-memory task performance.

Beta- and gamma-band cortico-cortical interactions support naturalistic reading of continuous text

Large-scale integration of information across cortical structures, building on neural connectivity, has been proposed to be a key element in supporting human cognitive processing. In electrophysiological neuroimaging studies of reading, quantification of neural interactions has been limited to the level of isolated words or sentences due to artefacts induced by eye movements. Here, we combined magnetoencephalography recording with advanced artefact rejection tools to investigate both cortico-cortical coherence and directed neural interactions during naturalistic reading of full-page texts. Our results show that reading versus visual scanning of text was associated with wide-spread increases of cortico-cortical coherence in the beta and gamma bands. We further show that the reading task was linked to increased directed neural interactions compared to the scanning task across a sparse set of connections within a wide range of frequencies. Together, the results demonstrate that neural connectivity flexibly builds on different frequency bands to support continuous natural reading.

Brain criticality predicts individual levels of inter-areal synchronization in human electrophysiological data

Neuronal oscillations and their synchronization between brain areas are fundamental for healthy brain function. Yet, synchronization levels exhibit large inter-individual variability that is associated with behavioral variability. We test whether individual synchronization levels are predicted by individual brain states along an extended regime of critical-like dynamics - the Griffiths phase (GP). We use computational modelling to assess how synchronization is dependent on brain criticality indexed by long-range temporal correlations (LRTCs). We analyze LRTCs and synchronization of oscillations from resting-state magnetoencephalography and stereo-electroencephalography data. Synchronization and LRTCs are both positively linearly and quadratically correlated among healthy subjects, while in epileptogenic areas they are negatively linearly correlated. These results show that variability in synchronization levels is explained by the individual position along the GP with healthy brain areas operating in its subcritical and epileptogenic areas in its supercritical side. We suggest that the GP is fundamental for brain function allowing individual variability while retaining functional advantages of criticality.

Measurable fields-to-spike causality and its dependence on cortical layer and area

Distinct dynamics in different cortical layers are apparent in neuronal and local field potential (LFP) patterns, yet their associations in the context of laminar processing have been sparingly analyzed. Here, we study the laminar organization of spike-field causal flow within and across visual (V4) and frontal areas (PFC) of monkeys performing a visual task. Using an event-based quantification of LFPs and a directed information estimator, we found area and frequency specificity in the laminar organization of spike-field causal connectivity. Gamma bursts (40-80 Hz) in the superficial layers of V4 largely drove intralaminar spiking. These gamma influences also fed forward up the cortical hierarchy to modulate laminar spiking in PFC. In PFC, the direction of intralaminar information flow was from spikes → fields where these influences dually controlled top-down and bottom-up processing. Our results, enabled by innovative methodologies, emphasize the complexities of spike-field causal interactions amongst multiple brain areas and behavior.

Evaluating the Alterations Induced by Virtual Reality in Cerebral Small-World Networks Using Graph Theory Analysis with Electroencephalography

Virtual reality (VR), a rapidly evolving technology that simulates three-dimensional virtual environments for users, has been proven to activate brain functions. However, the continuous alteration pattern of the functional small-world network in response to comprehensive three-dimensional stimulation rather than realistic two-dimensional media stimuli requires further exploration. Here, we aimed to validate the effect of VR on the pathways and network parameters of a small-world organization and interpret its mechanism of action. Fourteen healthy volunteers were selected to complete missions in an immersive VR game. The changes in the functional network in six different frequency categories were analyzed using graph theory with electroencephalography data measured during the pre-, VR, and post-VR stages. The mutual information matrix revealed that interactions between the frontal and posterior areas and those within the frontal and occipital lobes were strengthened. Subsequently, the betweenness centrality (BC) analysis indicated more robust and extensive pathways among hubs. Furthermore, a specific lateralized channel (O1 or O2) increment in the BC was observed. Moreover, the network parameters improved simultaneously in local segregation, global segregation, and global integration. The overall topological improvements of small-world organizations were in high-frequency bands and exhibited some degree of sustainability.

Working Memory and Transcranial-Alternating Current Stimulation-State of the Art: Findings, Missing, and Challenges

Working memory (WM) is a cognitive process that involves maintaining and manipulating information for a short period of time. WM is central to many cognitive processes and declines rapidly with age. Deficits in WM are seen in older adults and in patients with dementia, schizophrenia, major depression, mild cognitive impairment, Alzheimer's disease, etc. The frontal, parietal, and occipital cortices are significantly involved in WM processing and all brain oscillations are implicated in tackling WM tasks, particularly theta and gamma bands. The theta/gamma neural code hypothesis assumes that retained memory items are recorded via theta-nested gamma cycles. Neuronal oscillations can be manipulated by sensory, invasive- and non-invasive brain stimulations. Transcranial alternating-current stimulation (tACS) and repetitive transcranial magnetic stimulation (rTMS) are frequency-tuned non-invasive brain stimulation (NIBS) techniques that have been used to entrain endogenous oscillations in a frequency-specific manner. Compared to rTMS, tACS demonstrates superior cost, tolerability, portability, and safety profile, making it an attractive potential tool for improving cognitive performance. Although cognitive research with tACS is still in its infancy compared to rTMS, a number of studies have shown a promising WM enhancement effect, especially in the elderly and patients with cognitive deficits. This review focuses on the various methods and outcomes of tACS on WM in healthy and unhealthy human adults and highlights the established findings, unknowns, challenges, and perspectives important for translating laboratory tACS into realistic clinical settings. This will allow researchers to identify gaps in the literature and develop frequency-tuned tACS protocols with promising safety and efficacy outcomes. Therefore, research efforts in this direction should help to consider frequency-tuned tACS as a non-pharmacological tool of cognitive rehabilitation in physiological aging and patients with cognitive deficits.

The Human Connectome Project: A retrospective

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The "HCP-style" neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium.

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.

Alterations in the default mode-salience network circuit provide a potential mechanism supporting negativity bias in depression

Aberrant effective connectivity between default mode (DMN) and salience (SAL) networks may support the tendency of depressed individuals to find it difficult to disengage from self-focused, negatively-biased thinking and may contribute to the onset and maintenance of depression. Assessment of effective connectivity, which can statistically characterize the direction of influence between regions within neural circuits, may provide new insights into the nature of DMN-SAL connectivity disruptions in depression. Functional magnetic resonance imaging (fMRI) was collected from 38 individuals with a history of major depression and 50 healthy comparison participants during completion of an emotion-word Stroop task. Activation within DMN and SAL networks and effective connectivity between DMN and SAL, assessed via Granger causality, were examined. Individuals with a history of depression exhibited greater overall network activation, greater directed connectivity from DMN to SAL, and less directed connectivity from SAL to DMN than healthy comparison participants during negative-word trials. Among individuals with a history of depression, greater DMN-to-SAL connectivity was associated with lower overall network activation and worse task performance during positive-word trials; this pattern was not observed among healthy participants. Present findings indicate that greater network activation and, specifically, influence of DMN on SAL, support negativity bias among previously depressed individuals.

Measuring Phase-Amplitude Coupling Based on the Jensen-Shannon Divergence and Correlation Matrix

Phase-amplitude coupling (PAC) measures the relationship between the phase of low-frequency oscillations (LFO) and the amplitude of high-frequency oscillations (HFO). It plays an important functional role in neural information processing and cognition. Thus, we propose a novel method based on the Jensen-Shannon (JS) divergence and correlation matrix. The method takes the amplitude distributions of the HFO located in the corresponding phase bins of the LFO as multichannel inputs to construct a correlation matrix, where the elements are calculated based on the JS divergence between pairs of amplitude distributions. Then, the omega complexity extracted from the correlation matrix is used to estimate the PAC strength. The simulation results demonstrate that the proposed method can effectively reflect the PAC strength and slightly vary with the data length. Moreover, it outperforms five frequently used algorithms in the performance against additive white Gaussian noise and spike noise and the ability of detecting PAC in wide frequency ranges. To validate our proposed method with real data, it was applied to analyze the local field potential recorded from the dorsomedial striatum in a male Sprague-Dawley rat. It can replicate previous results obtained with other PAC metrics. In conclusion, these results suggest that our proposed method is a powerful tool for measuring the PAC between neural oscillations.

Neural oscillations underlying selective attention follow sexually divergent developmental trajectories during adolescence

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A cohort of 9- to 16-year-olds completed a classic flanker task during MEG.

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There were developmentally-sensitive interference effects in key attention regions.

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Youth showed sexually-divergent patterns of age-related interference activity.

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Maturational differences among males supported improved task behavior.

Selective attention processes are critical to everyday functioning and are known to develop through at least young adulthood. Although numerous investigations have studied the maturation of attention systems in the brain, these studies have largely focused on the spatial configuration of these systems; there is a paucity of research on the neural oscillatory dynamics serving selective attention, particularly among youth. Herein, we examined the developmental trajectory of neural oscillatory activity serving selective attention in 53 typically developing youth age 9-to-16 years-old. Participants completed the classic arrow-based flanker task during magnetoencephalography, and the resulting data were imaged in the time-frequency domain. Flanker interference significantly modulated theta and alpha/beta oscillations within prefrontal, mid-cingulate, cuneus, and occipital regions. Interference-related neural activity also increased with age in the temporoparietal junction and the rostral anterior cingulate. Sex-specific effects indicated that females had greater theta interference activity in the anterior insula, whereas males showed differential effects in theta and alpha/beta oscillations across frontoparietal regions. Finally, males showed age-related changes in alpha/beta interference in the cuneus and middle frontal gyrus, which predicted improved behavioral performance. Taken together, these data suggest sexually-divergent developmental trajectories underlying selective attention in youth.

Oscillatory connectivity as a mechanism of auditory sensory gating and its disruption in schizophrenia

Although innumerable studies using an auditory sensory gating paradigm have confirmed that individuals with schizophrenia (SZ) show less reduction in brain response to the second in a pair of clicks, this large literature has not yielded consensus on the circuit(s) responsible for gating nor for the gating difference in SZ. Clinically stable adult inpatients (N = 157) and matched community participants (N = 90) participated in a standard auditory sensory gating protocol. Responses to paired clicks were quantified as peak-to-peak amplitude from a response at approximately 50 ms to a response at approximately 100 ms in MEG-derived source waveforms. For bilateral sources in each of four regions near Heschl's gyrus, the gating ratio was computed as the response to the second stimulus divided by the response to the first stimulus. Spectrally resolved Granger causality quantified effective connectivity among regions manifested in alpha-band oscillatory coupling before and during stimulation. Poorer sensory gating localized to A1 in SZ than in controls confirmed previous results, here found in adjacent brain regions as well. Spontaneous, stimulus-independent effective connectivity within the hemisphere from angular gyrus to portions of the superior temporal gyrus was lower in SZ and correlated with gating ratio. Significant involvement of frontal and subcortical brain regions previously proposed as contributing to the auditory gating abnormality was not found. Findings point to endogenous connectivity evident in a sequence of activity from angular gyrus to portions of superior temporal gyrus as a mechanism contributing to normal and abnormal gating in SZ and potentially to sensory and cognitive symptoms.

Cross-Frequency Power-Power Coupling Analysis: A Useful Cross-Frequency Measure to Classify ICA-Decomposed EEG

Magneto-/Electro-encephalography (M/EEG) commonly uses (fast) Fourier transformation to compute power spectral density (PSD). However, the resulting PSD plot lacks temporal information, making interpretation sometimes equivocal. For example, consider two different PSDs: a central parietal EEG PSD with twin peaks at 10 Hz and 20 Hz and a central parietal PSD with twin peaks at 10 Hz and 50 Hz. We can assume the first PSD shows a mu rhythm and the second harmonic; however, the latter PSD likely shows an alpha peak and an independent line noise. Without prior knowledge, however, the PSD alone cannot distinguish between the two cases. To address this limitation of PSD, we propose using cross-frequency power-power coupling (PPC) as a post-processing of independent component (IC) analysis (ICA) to distinguish brain components from muscle and environmental artifact sources. We conclude that post-ICA PPC analysis could serve as a new data-driven EEG classifier in M/EEG studies. For the reader's convenience, we offer a brief literature overview on the disparate use of PPC. The proposed cross-frequency power-power coupling analysis toolbox (PowPowCAT) is a free, open-source toolbox, which works as an EEGLAB extension.

Investigating the effects of pre-stimulus cortical oscillatory activity on behavior

Rhythmic brain activity may reflect a functional mechanism that facilitates cortical processing and dynamic interareal interactions and thereby give rise to complex behavior. Using magnetoencephalography (MEG), we investigated rhythmic brain activity in a brain-wide network and their relation to behavior, while human subjects executed a variant of the Simon task, a simple stimulus-response task with well-studied behavioral effects. We hypothesized that the faster reaction times (RT) on stimulus-response congruent versus incongruent trials are associated with oscillatory power changes, reflecting a change in local cortical activation. Additionally, we hypothesized that the faster reaction times for trials following instances with the same stimulus-response contingency (the so-called Gratton effect) is related to contingency-induced changes in the state of the network, as measured by differences in local spectral power and interareal phase coherence. This would be achieved by temporarily upregulating the connectivity strength between behaviorally relevant network nodes. We identified regions-of-interest that differed in local synchrony during the response phase of the Simon task. Within this network, spectral power in none of the nodes in either of the studied frequencies was significantly different in the pre-cue window of the subsequent trial. Nor was there a significant difference in coherence between the task-relevant nodes that could explain the superior behavioral performance after compatible consecutive trials.

Personalizing neuromodulation

In the era of "big data", we are gaining rich person-specific information about neuroanatomy, neural function, and cognitive functions. However, the optimal ways to create precise approaches to optimize individuals' mental functions in health and disease are unclear. Multimodal analysis and modeling approaches can guide neuromodulation by combining anatomical networks, functional signal analysis, and cognitive neuroscience paradigms in single subjects. Our progress could be improved by progressing from statistical fits to mechanistic models. Using transcranial magnetic stimulation as an example, we discuss how integrating methods with a focus on mechanisms could improve our predictions TMS effects within individuals, refine our models of health and disease, and improve our treatments.

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.

Discovering dynamic task-modulated functional networks with specific spectral modes using MEG

Efficient neuronal communication between brain regions through oscillatory synchronization at certain frequencies is necessary for cognition. Such synchronized networks are transient and dynamic, established on the timescale of milliseconds in order to support ongoing cognitive operations. However, few studies characterizing dynamic electrophysiological brain networks have simultaneously accounted for temporal non-stationarity, spectral structure, and spatial properties. Here, we propose an analysis framework for characterizing the large-scale phase-coupling network dynamics during task performance using magnetoencephalography (MEG). We exploit the high spatiotemporal resolution of MEG to measure time-frequency dynamics of connectivity between parcellated brain regions, yielding data in tensor format. We then use a tensor component analysis (TCA)-based procedure to identify the spatio-temporal-spectral modes of covariation among separate regions in the human brain. We validate our pipeline using MEG data recorded during a hand movement task, extracting a transient motor network with beta-dominant spectral mode, which is significantly modulated by the movement task. Next, we apply the proposed pipeline to explore brain networks that support cognitive operations during a working memory task. The derived results demonstrate the temporal formation and dissolution of multiple phase-coupled networks with specific spectral modes, which are associated with face recognition, vision, and movement. The proposed pipeline can characterize the spectro-temporal dynamics of functional connectivity in the brain on the subsecond timescale, commensurate with that of cognitive performance.

A statistical framework to assess cross-frequency coupling while accounting for confounding analysis effects

Cross frequency coupling (CFC) is emerging as a fundamental feature of brain activity, correlated with brain function and dysfunction. Many different types of CFC have been identified through application of numerous data analysis methods, each developed to characterize a specific CFC type. Choosing an inappropriate method weakens statistical power and introduces opportunities for confounding effects. To address this, we propose a statistical modeling framework to estimate high frequency amplitude as a function of both the low frequency amplitude and low frequency phase; the result is a measure of phase-amplitude coupling that accounts for changes in the low frequency amplitude. We show in simulations that the proposed method successfully detects CFC between the low frequency phase or amplitude and the high frequency amplitude, and outperforms an existing method in biologically-motivated examples. Applying the method to in vivo data, we illustrate examples of CFC during a seizure and in response to electrical stimuli.

Inhibitory control of emotional processing: Theoretical and empirical considerations

Although inhibitory control appears to support successful emotion regulation (ER; Joorman and Gotlib, 2010; McCabe et al., 2010), few emotion inhibition studies position themselves in the literature on ER, and even fewer ER studies reference the role of emotion inhibition. Perhaps contributing to this, the ER literature is frequently divided into implicit or "automatic" (which subsumes emotion inhibition) and explicit or "effortful" control (Braunstein et al., 2017; Gyurak et al., 2011). The present paper evaluates relationships among constructs of inhibitory control, emotion inhibition, and ER to assess neural evidence for and against distinctions between implicit and explicit ER. We argue that, whereas the distinction between implicit and explicit ER may appear organizationally or conceptually helpful, such categorical distinctions are not supported by available research and in fact contribute to imbalances in the research literature.

Cross-frequency interactions between frontal theta and posterior alpha control mechanisms foster working memory

Neural oscillatory activity in the theta (4-8 Hz) and alpha (8-14 Hz) bands has been associated with the implementation of executive function, with theta in midline frontal cortex and alpha in posterior parietal cortex related to working memory (WM) load. To identify how these spatially and spectrally distinct neural phenomena interact within a large-scale fronto-parietal network organized in service of WM, EEG was recorded while subjects performed an N-back WM task. Frontal theta power increase, paralleled by posterior alpha decrease, tracked participants' successful WM performance. These power fluctuations were inversely related both across and within trials and predicted reaction time, suggesting a functionally important communication channel within the fronto-parietal network. Granger causality analysis revealed directed parietal to frontal communication via alpha and frontal to parietal communication via theta. Results encourage consideration of these bidirectional, power-to-power, cross-frequency control mechanisms as an important feature of cerebral network organization supporting executive function.