Theta-alpha cross-frequency synchronization facilitates working memory control - a modeling study

Activity Stabilization in a Population Model of Working Memory by Sinusoidal and Noisy Inputs

According to mechanistic theories of working memory (WM), information is retained as stimulus-dependent persistent spiking activity of cortical neural networks. Yet, how this activity is related to changes in the oscillatory profile observed during WM tasks remains a largely open issue. We explore joint effects of input gamma-band oscillations and noise on the dynamics of several firing rate models of WM. The considered models have a metastable active regime, i.e., they demonstrate long-lasting transient post-stimulus firing rate elevation. We start from a single excitatory-inhibitory circuit and demonstrate that either gamma-band or noise input could stabilize the active regime, thus supporting WM retention. We then consider a system of two circuits with excitatory intercoupling. We find that fast coupling allows for better stabilization by common noise compared to independent noise and stronger amplification of this effect by in-phase gamma inputs compared to anti-phase inputs. Finally, we consider a multi-circuit system comprised of two clusters, each containing a group of circuits receiving a common noise input and a group of circuits receiving independent noise. Each cluster is associated with its own local gamma generator, so all its circuits receive gamma-band input in the same phase. We find that gamma-band input differentially stabilizes the activity of the "common-noise" groups compared to the "independent-noise" groups. If the inter-cluster connections are fast, this effect is more pronounced when the gamma-band input is delivered to the clusters in the same phase rather than in the anti-phase. Assuming that the common noise comes from a large-scale distributed WM representation, our results demonstrate that local gamma oscillations can stabilize the activity of the corresponding parts of this representation, with stronger effect for fast long-range connections and synchronized gamma oscillations.

From thoughtless awareness to effortful cognition: alpha - theta cross-frequency dynamics in experienced meditators during meditation, rest and arithmetic

Neural activity is known to oscillate within discrete frequency bands and the synchronization between these rhythms is hypothesized to underlie information integration in the brain. Since strict synchronization is only possible for harmonic frequencies, a recent theory proposes that the interaction between different brain rhythms is facilitated by transient harmonic frequency arrangements. In this line, it has been recently shown that the transient occurrence of 2:1 harmonic cross-frequency relationships between alpha and theta rhythms (i.e. f_alpha≈ 12 Hz; f_theta≈ 6 Hz) is enhanced during effortful cognition. In this study, we tested whether achieving a state of ‘mental emptiness’ during meditation is accompanied by a relative decrease in the occurrence of 2:1 harmonic cross-frequency relationships between alpha and theta rhythms. Continuous EEG recordings (19 electrodes) were obtained from 43 highly experienced meditators during meditation practice, rest and an arithmetic task. We show that the occurrence of transient alpha:theta 2:1 harmonic relationships increased linearly from a meditative to an active cognitive processing state (i.e. meditation < rest < arithmetic task). It is argued that transient EEG cross-frequency arrangements that prevent alpha:theta cross-frequency coupling could facilitate the experience of ‘mental emptiness’ by avoiding the interaction between the memory and executive components of cognition.

Tracking Transient Changes in the Neural Frequency Architecture: Harmonic Relationships between Theta and Alpha Peaks Facilitate Cognitive Performance

The synchronization between neural oscillations at different frequencies has been proposed as a core mechanism for the coordination and integration of neural systems at different spatiotemporal scales. Because neural oscillations of different frequencies can only fully synchronize when their "peak" frequencies form harmonic relationships (e.g., f ₂ = f ₁/2), the present study explored whether the transient occurrence of harmonic cross-frequency relationship between task-relevant rhythms underlies efficient cognitive processing. Continuous EEG recordings (51 human participants; 14 males) were obtained during an arithmetic task, rest and breath focus. In two separate experiments, we consistently show that the proportion of epochs displaying a 2:1 harmonic relationship between alpha (8-14 Hz) and theta (4-8 Hz) peak frequencies (i.e., alpha_peak ≈ 10.6 Hz; theta_peak ≈ 5.3 Hz), was significantly higher when cognitive demands increased. In addition, a higher incidence of 2:1 harmonic cross-frequency relationships was significantly associated with increased alpha-theta phase synchrony and improved arithmetic task performance, thereby underlining the functional relevance of this cross-frequency configuration. Notably, opposite dynamics were identified for a specific range of "nonharmonic" alpha-theta cross-frequency relationships (i.e., alpha_peak/theta_peak = 1.1-1.6), which showed a higher incidence during rest compared with the arithmetic task. The observation that alpha and theta rhythms shifted into harmonic versus nonharmonic cross-frequency relationships depending on (cognitive) task demands is in line with the notion that the neural frequency architecture entails optimal frequency arrangements to facilitate cross-frequency "coupling" and "decoupling".SIGNIFICANCE STATEMENT Neural activity is known to oscillate within discrete frequency bands and the interplay between these brain rhythms is hypothesized to underlie cognitive functions. A recent theory posits that shifts in the peak frequencies of oscillatory rhythms form the principal mechanism by which cross-frequency coupling and decoupling is implemented in the brain. In line with this notion, we show that the occurrence of a cross-frequency arrangement that mathematically enables coupling between alpha and theta rhythms is more prominent during active cognitive processing (compared with rest and non-cognitively demanding tasks) and is associated with improved cognitive performance. Together, our results open new vistas for future research on cross-frequency dynamics in the brain and their functional role in cognitive processing.

Electroencephalography Amplitude Modulation Analysis for Automated Affective Tagging of Music Video Clips

The quantity of music content is rapidly increasing and automated affective tagging of music video clips can enable the development of intelligent retrieval, music recommendation, automatic playlist generators, and music browsing interfaces tuned to the users' current desires, preferences, or affective states. To achieve this goal, the field of affective computing has emerged, in particular the development of so-called affective brain-computer interfaces, which measure the user's affective state directly from measured brain waves using non-invasive tools, such as electroencephalography (EEG). Typically, conventional features extracted from the EEG signal have been used, such as frequency subband powers and/or inter-hemispheric power asymmetry indices. More recently, the coupling between EEG and peripheral physiological signals, such as the galvanic skin response (GSR), have also been proposed. Here, we show the importance of EEG amplitude modulations and propose several new features that measure the amplitude-amplitude cross-frequency coupling per EEG electrode, as well as linear and non-linear connections between multiple electrode pairs. When tested on a publicly available dataset of music video clips tagged with subjective affective ratings, support vector classifiers trained on the proposed features were shown to outperform those trained on conventional benchmark EEG features by as much as 6, 20, 8, and 7% for arousal, valence, dominance and liking, respectively. Moreover, fusion of the proposed features with EEG-GSR coupling features showed to be particularly useful for arousal (feature-level fusion) and liking (decision-level fusion) prediction. Together, these findings show the importance of the proposed features to characterize human affective states during music clip watching.

Causal Interactions between Frontal(θ) - Parieto-Occipital(α2) Predict Performance on a Mental Arithmetic Task

Many neuroimaging studies have demonstrated the different functional contributions of spatially distinct brain areas to working memory (WM) subsystems in cognitive tasks that demand both local information processing and interregional coordination. In WM cognitive task paradigms employing electroencephalography (EEG), brain rhythms such as θ and α have been linked to specific functional roles over given brain areas, but their functional coupling has not been extensively studied. Here we analyzed an arithmetic task with five cognitive workload levels (CWLs) and demonstrated functional/effective coupling between the two WM subsystems: the central executive located over frontal (F) brain areas that oscillates on the dominant θ rhythm (Frontal^θ/F^θ) and the storage buffer located over parieto-occipital (PO) brain areas that operates on the α₂ dominant brain rhythm (Parieto-Occipital^α2/PO^α2). We focused on important differences between and within WM subsystems in relation to behavioral performance. A repertoire of brain connectivity estimators was employed to elucidate the distinct roles of amplitude, phase within and between frequencies, and the hierarchical role of functionally specialized brain areas related to the task. Specifically, for each CWL, we conducted a) a conventional signal power analysis within both frequency bands at F^θ and PO^α2, b) the intra- and inter-frequency phase interactions between F^θ and PO^α2, and c) their causal phase and amplitude relationship. We found no significant statistical difference of signal power or phase interactions between correct and wrong answers. Interestingly, the study of causal interactions between F^θ and PO^α2 revealed frontal brain region(s) as the leader, while the strength differentiated between correct and wrong responses in every CWL with absolute accuracy. Additionally, zero time-lag between bilateral F^θ and right PO^a2 could serve as an indicator of mental calculation failure. Overall, our study highlights the significant role of coordinated activity between F^θ and PO^α2 via their causal interactions and the timing for arithmetic performance.

Neurophysiological correlates in Mild Cognitive Impairment detected using group Independent Component Analysis

Alzheimer's disease is the most prevalent cause of dementia. Mild Cognitive Impairment (MCI) is defined as a grey area between intact cognitive functioning and clinical dementia. Electroencephalography (EEG) has been used to identify biomarkers in dementia. Currently, there is a great interest in translating the study from raw signals to signal generators, trying to keep the relationship with neurophysiology. In the current study, EEG recordings during an encoding task were acquired in MCI subjects and healthy controls. Data was decomposed using group Independent Component Analysis (gICA) and the most neuronal components were analyzed using Phase Intertrial Coherence (PIC) and Phase shift Intertrial Coherence (PsIC). MCI subjects exhibited an increase of PIC in the theta band, while controls showed increase in PsIC in the alpha band. Correlation between PIC and PsIC and clinical scales were also found. Those findings indicate that the methodology proposed based in gICA can help to extract information from EEG recordings with neurophysiological meaning.

Acute intracerebral treatment with amyloid-beta (1-42) alters the profile of neuronal oscillations that accompany LTP induction and results in impaired LTP in freely behaving rats

Accumulation of amyloid plaques comprises one of the major hallmarks of Alzheimer’s disease (AD). In rodents, acute treatment with amyloid-beta (Aβ; 1–42) elicits immediate debilitating effects on hippocampal long-term potentiation (LTP). Whereas LTP contributes to synaptic information storage, information is transferred across neurons by means of neuronal oscillations. Furthermore, changes in theta-gamma oscillations, that appear during high-frequency stimulation (HFS) to induce LTP, predict whether successful LTP will occur. Here, we explored if intra-cerebral treatment with Aβ(1–42), that prevents LTP, also results in alterations of hippocampal oscillations that occur during HFS of the perforant path-dentate gyrus synapse in 6-month-old behaving rats. HFS resulted in LTP that lasted for over 24 h. In Aβ-treated animals, LTP was significantly prevented. During HFS, spectral power for oscillations below 100 Hz (δ, θ, α, β and γ) was significantly higher in Aβ-treated animals compared to controls. In addition, the trough-to-peak amplitudes of theta and gamma cycles were higher during HFS in Aβ-treated animals. We also observed a lower amount of envelope-to-signal correlations during HFS in Aβ-treated animals. Overall, the characteristic profile of theta-gamma oscillations that accompany successful LTP induction was disrupted. These data indicate that alterations in network oscillations accompany Aβ-effects on hippocampal LTP. This may comprise an underlying mechanism through which disturbances in synaptic information storage and hippocampus-dependent memory occurs in AD.