Load-Dependent Increases in Delay-Period Alpha-Band Power Track the Gating of Task-Irrelevant Inputs to Working Memory

The association between working memory precision and the nonlinear dynamics of frontal and parieto-occipital EEG activity

Electrophysiological working memory (WM) research shows brain areas communicate via macroscopic oscillations across frequency bands, generating nonlinear amplitude modulation (AM) in the signal. Traditionally, AM is expressed as the coupling strength between the signal and a prespecified modulator at a lower frequency. Therefore, the idea of AM and coupling cannot be studied separately. In this study, 33 participants completed a color recall task while their brain activity was recorded through EEG. The AM of the EEG data was extracted using the Holo-Hilbert spectral analysis (HHSA), an adaptive method based on the Hilbert-Huang transforms. The results showed that WM load modulated parieto-occipital alpha/beta power suppression. Furthermore, individuals with higher frontal theta power and lower parieto-occipital alpha/beta power exhibited superior WM precision. In addition, the AM of parieto-occipital alpha/beta power predicted WM precision after presenting a target-defining probe array. The phase-amplitude coupling (PAC) between the frontal theta phase and parieto-occipital alpha/beta AM increased with WM load while processing incoming stimuli, but the PAC itself did not predict the subsequent recall performance. These results suggest frontal and parieto-occipital regions communicate through theta-alpha/beta PAC. However, the overall recall precision depends on the alpha/beta AM following the onset of the retro cue.

Interhemispheric Connectivity Supports Load-Dependent Working Memory Maintenance for Complex Visual Stimuli

Abstract Introduction: One manipulation used to study the neural basis of working memory (WM) is to vary the information load at encoding, then measure activity and connectivity during maintenance in the delay period. A hallmark finding is increased delay activity and connectivity between frontoparietal brain regions with increased load. Most WM studies, however, employ simple stimuli during encoding and unfilled intervals during the delay. In this study, we asked how delay period activity and connectivity change during low and high load maintenance of complex stimuli. Methods: Twenty-two participants completed a modified Sternberg WM task with two or five naturalistic scenes as stimuli during scalp electroencephalography (EEG). On each trial, the delay was filled with phase-scrambled scenes to provide a visual perceptual control with similar color and spatial frequency as presented during encoding. Functional connectivity during the delay was assessed by the phase-locking value (PLV). Results: Results showed reduced theta/alpha delay activity amplitude during high compared with low WM load across frontal, central, and parietal sources. A network with higher connectivity during low load consisted of increased PLV between (1) left frontal and right posterior temporal sources in the theta/alpha bands, (2) right anterior temporal and left central sources in the alpha and lower beta bands, and (3) left anterior temporal and posterior temporal sources in the theta, alpha, and lower beta bands. Discussion: The findings suggest a role for interhemispheric connectivity during WM maintenance of complex stimuli with load modulation when limited attentional resources are essential for filtering. Impact statement The patterns of brain connectivity subserving working memory (WM) have largely been investigated to date using simple stimuli, including letters, digits, and shapes and during unfilled WM delay intervals. Fewer studies describe functional connectivity changes during the maintenance of more naturalistic stimuli in the presence of distractors. In the present study, we employed a scene-based WM task during electroencephalography in healthy humans and found that during low-load WM maintenance with distractors increased interhemispheric connectivity in frontotemporal networks. These findings suggest a role for increased interhemispheric connectivity during maintenance of complex stimuli when attentional resources are essential for filtering.

The Effect of Music Listening on EEG Functional Connectivity of Brain: A Short-Duration and Long-Duration Study

Music is considered a powerful brain stimulus, as listening to it can activate several brain networks. Music of different kinds and genres may have a different effect on the human brain. The goal of this study is to investigate the change in the brain’s functional connectivity (FC) when music is used as a stimulus. Secondly, the effect of listening to the subject’s favorite music is compared with listening to specifically formulated relaxing music with alpha binaural beats. Finally, the effect of the duration of music listening is studied. Subjects’ electroencephalographic (EEG) signals were captured as they listened to favorite and relaxing music. After preprocessing and artifact removal, the EEG recordings were decomposed into the delta, theta, alpha, and beta frequency bands, and the grand-averaged connectivity matrices were generated using Inter-Site Phase Clustering (ISPC) for each frequency band and each type of music. Furthermore, each lobe of the brain was analyzed separately to understand the effect of music on specific regions of the brain. EEG-FC among different channels was accessed by using graph theory and Network-based Statistics (NBS). To determine the significance of the changes in brain networks after listening to music, statistical analysis was conducted using Analysis of Variance (ANOVA) and t-test. The study of listening to music for a short duration verifies that either favorite or preferred music can affect the FC of the subject and induce a relaxation state. The short duration study also verifies a significant (ANOVA and t-test: p < 0.05) effectiveness of relaxing music over favorite music to induce relaxation and alertness in the subject. In the study of long duration, it is concluded that listening to relaxing music can increase functional connectivity and connections strength in the frontal lobe of the subject. A significant increase (ANOVA and t-test: p < 0.05) in FC in alpha and theta band and a significant decrease (ANOVA and t-test: p < 0.05) in FC in beta band in the frontal and parietal lobe of the brain verifies the hypothesis that the relaxing music can help the subject to achieve relaxation, activeness, and alertness.

Distractor filtering and its electrophysiological correlates in schizophrenia

OBJECTIVE

Patients with schizophrenia are characterized by compromised working memory (WM) performance and increased distractibility. Theta synchronization (especially over the frontal midline areas) is related to cognitive control and executive processes during WM encoding and retention. Alpha event-related desynchronization (ERD) is associated with information processing and attention.

METHODS

Participants (35 patients and 39 matched controls) performed a modified Sternberg WM task, containing salient and non-salient distractor items in the retention period. A high-density 128 channel EEG was recorded during the task. Theta (4-7 Hz) and fast alpha (10-13 Hz) event-related spectral perturbation (ERSP) were analyzed during the retention and encoding period.

RESULTS

Patients with schizophrenia showed worse WM performance and increased attentional distractibility in terms of lower hit rates and increased distractor-related commission errors compared to healthy controls. Theta synchronization was modulated by condition (learning vs. distractor) in both groups but it was modulated by salience only in controls. Furthermore, salience of distractors modulated less the fast alpha ERD in patients.

CONCLUSIONS

Our results suggest that patients with schizophrenia process salient and non-salient distracting information less efficiently and show weaker cognitive control compared to controls.

SIGNIFICANCE

These differences may partly account for diminished WM performance and increased distractibility in schizophrenia.

Decoding working memory task condition using magnetoencephalography source level long-range phase coupling patterns

OBJECTIVE

The objective of the study is to identify phase coupling patterns that are shared across subjects via a machine learning approach that utilises source space magnetoencephalography (MEG) phase coupling data from a working memory (WM) task. Indeed, phase coupling of neural oscillations is putatively a key factor for communication between distant brain areas and is therefore crucial in performing cognitive tasks, including WM. Previous studies investigating phase coupling during cognitive tasks have often focused on a few a priori selected brain areas or a specific frequency band, and the need for data-driven approaches has been recognised. Machine learning techniques have emerged as valuable tools for the analysis of neuroimaging data since they catch fine-grained differences in the multivariate signal distribution. Here, we expect that these techniques applied to MEG phase couplings can reveal WM-related processes that are shared across individuals.

APPROACH

We analysed WM data collected as part of the Human Connectome Project. The MEG data were collected while subjects (n = 83) performed N-back WM tasks in two different conditions, namely 2-back (WM condition) and 0-back (control condition). We estimated phase coupling patterns (multivariate phase slope index) for both conditions and for theta, alpha, beta, and gamma bands. The obtained phase coupling data were then used to train a linear support vector machine in order to classify which task condition the subject was performing with an across-subject cross-validation approach. The classification was performed separately based on the data from individual frequency bands and with all bands combined (multiband). Finally, we evaluated the relative importance of the different features (phase couplings) for classification by the means of feature selection probability.

MAIN RESULTS

The WM condition and control condition were successfully classified based on the phase coupling patterns in the theta (62% accuracy) and alpha bands (60% accuracy) separately. Importantly, the multiband classification showed that phase coupling patterns not only in the theta and alpha but also in the gamma bands are related to WM processing, as testified by improvement in classification performance (71%).

SIGNIFICANCE

Our study successfully decoded WM tasks using MEG source space functional connectivity. Our approach, combining across-subject classification and a multidimensional metric recently developed by our group, is able to detect patterns of connectivity that are shared across individuals. In other words, the results are generalisable to new individuals and allow meaningful interpretation of task-relevant phase coupling patterns.

EEG, MEG and neuromodulatory approaches to explore cognition: Current status and future directions

Neural oscillations and their association with brain states and cognitive functions have been object of extensive investigation over the last decades. Several electroencephalography (EEG) and magnetoencephalography (MEG) analysis approaches have been explored and oscillatory properties have been identified, in parallel with the technical and computational advancement. This review provides an up-to-date account of how EEG/MEG oscillations have contributed to the understanding of cognition. Methodological challenges, recent developments and translational potential, along with future research avenues, are discussed.

Oscillatory brain activity and maintenance of verbal and visual working memory: A systematic review

Brain oscillations likely play a significant role in the storage of information in working memory (WM). Despite the wide popularity of the topic, current attempts to summarize the research in the field are narrative reviews. We address this gap by providing a descriptive systematic review, in which we investigated oscillatory correlates of maintenance of verbal and visual information in WM. The systematic approach enabled us to challenge some common views popularized by previous research. The identified literature (100 EEG/MEG studies) highlighted the importance of theta oscillations in verbal WM: frontal midline theta enhanced with load in most verbal studies, while more equivocal results have been obtained in visual studies. Increasing WM load affected alpha activity in most studies, but the direction of the effect was inconsistent: the ratio of studies that found alpha increase versus decrease with increasing load was 80/20% in the verbal WM domain and close to 60/40% in the visual domain. Alpha asymmetry (left < right) was a common finding in both verbal and visual WM studies. Beta and gamma activity studies yielded the least convincing data: a diversity in the spatial and frequency distribution of beta activity prevented us from making a coherent conclusion; gamma rhythm was virtually neglected in verbal WM studies with no systematic support for sustained gamma changes during the delay in EEG studies in general.

Salient distractors open the door of perception: alpha desynchronization marks sensory gating in a working memory task

Focusing attention on relevant information while ignoring distracting stimuli is essential to the efficacy of working memory. Alpha- and theta-band oscillations have been linked to the inhibition of anticipated and attentionally avoidable distractors. However, the neurophysiological background of the rejection of task-irrelevant stimuli appearing in the focus of attention is not fully understood. We aimed to examine whether theta and alpha-band oscillations serve as an indicator of successful distractor rejection. Twenty-four students were enrolled in the study. 64-channel EEG was recorded during a modified Sternberg working memory task where weak and strong (salient) distractors were presented during the retention period. Event-related spectral perturbation in the alpha frequency band was significantly modulated by the saliency of the distracting stimuli, while theta oscillation was modulated by the need for cognitive control. Moreover, stronger alpha desynchronization to strong relative to weak distracting stimuli significantly increased the probability of mistakenly identifying the presented distractor as a member of the memory sequence. Therefore, our results suggest that alpha activity reflects the vulnerability of attention to distracting salient stimuli.

Two good reasons to say 'change!' - ensemble representations as well as item representations impact standard measures of VWM capacity

Visual working memory (VWM) is a central bottleneck in human information processing. Its capacity is most often measured in terms of how many individual-item representations VWM can hold (k). In the standard task employed to estimate k, an array of highly discriminable colour patches is maintained and, after a short retention interval, compared to a test display (change detection). Recent research has shown that with more complex, structured displays, change-detection performance is, in addition to individual-item representations, supported by ensemble representations formed as a result of spatial subgroupings. Here, by asking participants to additionally localize the change, we reveal indication for an influence of ensemble representations even in the very simple, unstructured displays of the colour-patch change-detection task. Critically, pure-item models from which standard formulae of k are derived do not consider ensemble representations and, therefore, potentially overestimate k. To gauge this overestimation, we develop an item-plus-ensemble model of change detection and change localization. Estimates of k from this new model are about 1 item (~30%) lower than the estimates from traditional pure-item models, even if derived from the same data sets.