Scanning for oscillations

Frontal midline theta power during the cue-target-interval reflects increased cognitive effort in rewarded task-switching

Cognitive performance largely depends on how much effort is invested during task-execution. This also means that we rarely perform as good as we could. Cognitive effort is adjusted to the expected outcome of performance, meaning that it is driven by motivation. The results from recent studies suggest that the expenditure of cognitive control is particularly prone to being affected by modulations of cognitive effort. Although recent EEG studies investigated the neural underpinnings of the interaction of effort and control, reports on how cognitive effort is reflected by oscillatory activity of the EEG are quite sparse. It is the goal of the present study to bridge this gap by performing an exploratory analysis of high-density EEG data from a switching-task using manipulations of monetary incentives. A beamformer approach is used to localize the sensor-level effects in source-space. The results indicate that the manipulation of cognitive effort was successful. The participants reported significantly higher motivation and cognitive effort in high versus low reward trials. Performance was also significantly increased. The analysis of the EEG data revealed that the increase of cognitive effort was reflected by an increased mid-frontal theta activity during the cue-target interval, suggesting an increased use of proactive control. This interpretation is supported by the result from a regression analysis performed on single-trial data, showing higher mid-frontal theta power prior to target-onset being associated with faster responses. Alpha-desynchronization throughout the trial was also more pronounced in high reward trials, signaling a bias of attention towards the processing of external stimuli. Source reconstruction suggests that these effects are located in areas related to cognitive control, and visual processing.

A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings

Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100s to 1000s. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post hoc manner from univariate analyses or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgment in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here, we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state-space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as oscillation component analysis. These parameters - the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations - all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.

A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings

Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100's to 1000's. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post-hoc manner from univariate analyses, or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgement in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as Oscillation Component Analysis (OCA). These parameters - the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations - all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.

Methodological considerations for studying neural oscillations

Neural oscillations are ubiquitous across recording methodologies and species, broadly associated with cognitive tasks, and amenable to computational modelling that investigates neural circuit generating mechanisms and neural population dynamics. Because of this, neural oscillations offer an exciting potential opportunity for linking theory, physiology and mechanisms of cognition. However, despite their prevalence, there are many concerns-new and old-about how our analysis assumptions are violated by known properties of field potential data. For investigations of neural oscillations to be properly interpreted, and ultimately developed into mechanistic theories, it is necessary to carefully consider the underlying assumptions of the methods we employ. Here, we discuss seven methodological considerations for analysing neural oscillations. The considerations are to (1) verify the presence of oscillations, as they may be absent; (2) validate oscillation band definitions, to address variable peak frequencies; (3) account for concurrent non-oscillatory aperiodic activity, which might otherwise confound measures; measure and account for (4) temporal variability and (5) waveform shape of neural oscillations, which are often bursty and/or nonsinusoidal, potentially leading to spurious results; (6) separate spatially overlapping rhythms, which may interfere with each other; and (7) consider the required signal-to-noise ratio for obtaining reliable estimates. For each topic, we provide relevant examples, demonstrate potential errors of interpretation, and offer suggestions to address these issues. We primarily focus on univariate measures, such as power and phase estimates, though we discuss how these issues can propagate to multivariate measures. These considerations and recommendations offer a helpful guide for measuring and interpreting neural oscillations.

Tensor Decomposition of Human Narrowband Oscillatory Brain Activity in Frequency, Space and Time

Many brain processes in health and disease are associated with modulation of narrowband brain oscillations (NBOs) in the scalp-recorded EEG, which exhibit specific frequency spectra and scalp topography. Isolating and tracking NBOs over time using algorithms is useful in domains such as brain-computer interfaces or when measuring the EEG effects of experimental manipulations. Previously, we successfully applied modified tensor methods for identifying and tracking NBO activity over time or conditions. We introduced frequency and spatial constraints that greatly improved their physiological plausibility. In this paper we rigorously demonstrate the power and precision of tensor methods to separate, isolate and track NBOs using sources simulated with forward models. This allows us to control the attributes of NBOs and validate tensor solutions. We find that tensor methods can accurately identify, separate and track NBOs over time, using realistic sources either alone or in combination, and compare favorably to well-known spatio-spectral decomposition method for NBO estimation.

A multidimensional evaluation of the benefits of an ecologically realistic training based on pretend play for preschoolers' cognitive control and self-regulation: From behavior to the underlying theta neuro-oscillatory activity

To what extent can cognitive control, self-regulation, and the underlying midfrontal theta oscillatory activity of preschool children be modified by an ecologically realistic training based on pretend play? To answer this question, 70 children aged 4-6 years (37 boys) were assigned to a training group or a control group using a pairing randomization procedure. Children were administered 20 play sessions over 10 weeks. Benefits were evaluated with a pre-post design. The intervention helped children to engage more in self-regulation within the training activities. However, the intervention did not promote self-regulation outside of the training context, nor did it influence cognitive control and theta activity. These results provide a better understanding of the limitations of an ecologically realistic approach to cognitive control training.

A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiology

The goal of this paper is to present a theoretical and practical introduction to generalized eigendecomposition (GED), which is a robust and flexible framework used for dimension reduction and source separation in multichannel signal processing. In cognitive electrophysiology, GED is used to create spatial filters that maximize a researcher-specified contrast. For example, one may wish to exploit an assumption that different sources have different frequency content, or that sources vary in magnitude across experimental conditions. GED is fast and easy to compute, performs well in simulated and real data, and is easily adaptable to a variety of specific research goals. This paper introduces GED in a way that ties together myriad individual publications and applications of GED in electrophysiology, and provides sample MATLAB and Python code that can be tested and adapted. Practical considerations and issues that often arise in applications are discussed.

Enhancing oscillations in intracranial electrophysiological recordings with data-driven spatial filters

In invasive electrophysiological recordings, a variety of neural oscillations can be detected across the cortex, with overlap in space and time. This overlap complicates measurement of neural oscillations using standard referencing schemes, like common average or bipolar referencing. Here, we illustrate the effects of spatial mixing on measuring neural oscillations in invasive electrophysiological recordings and demonstrate the benefits of using data-driven referencing schemes in order to improve measurement of neural oscillations. We discuss referencing as the application of a spatial filter. Spatio-spectral decomposition is used to estimate data-driven spatial filters, a computationally fast method which specifically enhances signal-to-noise ratio for oscillations in a frequency band of interest. We show that application of these data-driven spatial filters has benefits for data exploration, investigation of temporal dynamics and assessment of peak frequencies of neural oscillations. We demonstrate multiple use cases, exploring between-participant variability in presence of oscillations, spatial spread and waveform shape of different rhythms as well as narrowband noise removal with the aid of spatial filters. We find high between-participant variability in the presence of neural oscillations, a large variation in spatial spread of individual rhythms and many non-sinusoidal rhythms across the cortex. Improved measurement of cortical rhythms will yield better conditions for establishing links between cortical activity and behavior, as well as bridging scales between the invasive intracranial measurements and noninvasive macroscale scalp measurements.

Inverse effects of time-on-task in task-related and task-unrelated theta activity

The phenomenon of mental fatigue has recently been investigated extensively by means of the EEG. Studies deploying spectral analysis consistently reported an increase of spectral power in the lower frequencies with increasing time-on-task, whereas event-related studies observed decreases in various measures related to task engagement and attentional resources. The results from these two lines of research cannot be aligned easily. (Frontal) theta power has been linked to cognitive control and was found to increase with time-on-task. In contrast, theoretical frameworks on mental fatigue suggest a decline in task-engagement as causal for the performance decline observed in mental fatigue. The goal of the present study was to investigate mental fatigue in time-frequency space using linear regression on single-trial data in order to obtain a better understanding about how time-on-task affects theta oscillatory activity. A data-driven analysis approach indicated an increase of alpha and theta power during the intertrial interval. In contrast, task-related theta activity declined. This reduction of stimulus-locked theta power may be interpreted as a reduction of task engagement with increasing mental fatigue. The increase of theta spectral power in the intertrial interval, moreover, could possibly be explained by an increased idling of cognitive control networks. Alternatively, it might be the case that the increase of theta power with time-on-task is a by-product an alpha power increase. As alpha peak frequency systematically decreases with time-on-task, the theta band might be affected as well.

Beta2 Oscillations in Hippocampal-Cortical Circuits During Novelty Detection

Novelty detection is a core feature of behavioral adaptation and involves cascades of neuronal responses-from initial evaluation of the stimulus to the encoding of new representations-resulting in the behavioral ability to respond to unexpected inputs. In the past decade, a new important novelty detection feature, beta2 (~20-30 Hz) oscillations, has been described in the hippocampus (HC). However, the interactions between beta2 and the hippocampal network are unknown, as well as the role-or even the presence-of beta2 in other areas involved with novelty detection. In this work, we combined multisite local field potential (LFP) recordings with novelty-related behavioral tasks in mice to describe the oscillatory dynamics associated with novelty detection in the CA1 region of the HC, parietal cortex, and mid-prefrontal cortex. We found that transient beta2 power increases were observed only during interaction with novel contexts and objects, but not with familiar contexts and objects. Also, robust theta-gamma phase-amplitude coupling was observed during the exploration of novel environments. Surprisingly, bursts of beta2 power had strong coupling with the phase of delta-range oscillations. Finally, the parietal and mid-frontal cortices had strong coherence with the HC in both theta and beta2. These results highlight the importance of beta2 oscillations in a larger hippocampal-cortical circuit, suggesting that beta2 plays a role in the mechanism for detecting and modulating behavioral adaptation to novelty.

Synchronization between Keyboard Typing and Neural Oscillations

Rhythmic neural activity synchronizes with certain rhythmic behaviors, such as breathing, sniffing, saccades, and speech. The extent to which neural oscillations synchronize with higher-level and more complex behaviors is largely unknown. Here, we investigated electrophysiological synchronization with keyboard typing, which is an omnipresent behavior daily engaged by an uncountably large number of people. Keyboard typing is rhythmic, with frequency characteristics roughly the same as neural oscillatory dynamics associated with cognitive control, notably through midfrontal theta (4-7 Hz) oscillations. We tested the hypothesis that synchronization occurs between typing and midfrontal theta and breaks down when errors are committed. Thirty healthy participants typed words and sentences on a keyboard without visual feedback, while EEG was recorded. Typing rhythmicity was investigated by interkeystroke interval analyses and by a kernel density estimation method. We used a multivariate spatial filtering technique to investigate frequency-specific synchronization between typing and neuronal oscillations. Our results demonstrate theta rhythmicity in typing (around 6.5 Hz) through the two different behavioral analyses. Synchronization between typing and neuronal oscillations occurred at frequencies ranging from 4 to 15 Hz, but to a larger extent for lower frequencies. However, peak synchronization frequency was idiosyncratic across participants, therefore not specific to theta nor to midfrontal regions, and correlated somewhat with peak typing frequency. Errors and trials associated with stronger cognitive control were not associated with changes in synchronization at any frequency. As a whole, this study shows that brain-behavior synchronization does occur during keyboard typing but is not specific to midfrontal theta.

Narrowband multivariate source separation for semi-blind discovery of experiment contrasts

BACKGROUND

Electrophysiological recordings contain mixtures of signals from distinct neural sources, impeding a straightforward interpretation of the sensor-level data. This mixing is particularly detrimental when distinct sources resonate in overlapping frequencies. Fortunately, the mixing is linear and instantaneous. Multivariate source separation methods may therefore successfully separate statistical sources, even with overlapping spatial distributions.

NEW METHOD

We demonstrate a feature-guided multivariate source separation method that is tuned to narrowband frequency content as well as binary condition differences. This method - comparison scanning generalized eigendecomposition, csGED - harnesses the covariance structure of multichannel data to find directions (i.e., eigenvectors) that maximally separate two subsets of data. To drive condition specificity and frequency specificity, our data subsets were taken from different task conditions and narrowband-filtered prior to applying GED.

RESULTS

To validate the method, we simulated MEG data in two conditions with shared noise characteristics and unique signal. csGED outperformed the best sensor at reconstructing the ground truth signals, even in the presence of large amounts of noise. We next applied csGED to a published empirical MEG dataset on visual perception vs. imagery. csGED identified sources in alpha, beta, and gamma bands, and successfully separated distinct networks in the same frequency band.

COMPARISON WITH EXISTING METHOD(S)

GED is a flexible feature-guided decomposition method that has previously successfully been applied. Our combined frequency- and condition-tuning is a novel adaptation that extends the power of GED in cognitive electrophysiology.

CONCLUSIONS

We demonstrate successful condition-specific source separation by applying csGED to simulated and empirical data.

Multiple Midfrontal Thetas Revealed by Source Separation of Simultaneous MEG and EEG

Theta-band (∼6 Hz) rhythmic activity within and over the medial PFC ("midfrontal theta") has been identified as a distinctive signature of "response conflict," the competition between multiple actions when only one action is goal-relevant. Midfrontal theta is traditionally conceptualized and analyzed under the assumption that it is a unitary signature of conflict that can be uniquely identified at one electrode (typically FCz). Here we recorded simultaneous MEG and EEG (total of 328 sensors) in 9 human subjects (7 female) and applied a feature-guided multivariate source-separation decomposition to determine whether conflict-related midfrontal theta is a unitary or multidimensional feature of the data. For each subject, a generalized eigendecomposition yielded spatial filters (components) that maximized the ratio between theta and broadband activity. Components were retained based on significance thresholding and midfrontal EEG topography. All of the subjects individually exhibited multiple (mean 5.89, SD 2.47) midfrontal components that contributed to sensor-level midfrontal theta power during the task. Component signals were temporally uncorrelated and asynchronous, suggesting that each midfrontal theta component was unique. Our findings call into question the dominant notion that midfrontal theta represents a unitary process. Instead, we suggest that midfrontal theta spans a multidimensional space, indicating multiple origins, but can manifest as a single feature at the sensor level because of signal mixing.SIGNIFICANCE STATEMENT "Midfrontal theta" is a rhythmic electrophysiological signature of the competition between multiple response options. Midfrontal theta is traditionally considered to reflect a single process. However, this assumption could be erroneous because of "mixing" (multiple sources contributing to the activity recorded at a single electrode). We investigated the dimensionality of midfrontal theta by applying advanced multivariate analysis methods to a multimodal MEG/EEG dataset. We identified multiple topographically overlapping neural sources that drove response conflict-related midfrontal theta. Midfrontal theta thus reflects multiple uncorrelated signals that manifest with similar EEG scalp projections. In addition to contributing to the cognitive control literature, we demonstrate both the feasibility and the necessity of signal demixing to understand the narrowband neural dynamics underlying cognitive processes.

How to test for phasic modulation of neural and behavioural responses

Research on whether perception or other processes depend on the phase of neural oscillations is rapidly gaining popularity. However, it is unknown which methods are optimally suited to evaluate the hypothesized phase effect. Using a simulation approach, we here test the ability of different methods to detect such an effect on dichotomous (e.g., "hit" vs "miss") and continuous (e.g., scalp potentials) response variables. We manipulated parameters that characterise the phase effect or define the experimental approach to test for this effect. For each parameter combination and response variable, we identified an optimal method. We found that methods regressing single-trial responses on circular (sine and cosine) predictors perform best for all of the simulated parameters, regardless of the nature of the response variable (dichotomous or continuous). In sum, our study lays a foundation for optimized experimental designs and analyses in future studies investigating the role of phase for neural and behavioural responses. We provide MATLAB code for the statistical methods tested.

Filters: When, Why, and How (Not) to Use Them

Filters are commonly used to reduce noise and improve data quality. Filter theory is part of a scientist's training, yet the impact of filters on interpreting data is not always fully appreciated. This paper reviews the issue and explains what a filter is, what problems are to be expected when using them, how to choose the right filter, and how to avoid filtering by using alternative tools. Time-frequency analysis shares some of the same problems that filters have, particularly in the case of wavelet transforms. We recommend reporting filter characteristics with sufficient details, including a plot of the impulse or step response as an inset.

Midfrontal theta phase coordinates behaviorally relevant brain computations during cognitive control

Neural oscillations are thought to provide a cyclic time frame for orchestrating brain computations. Following this assumption, midfrontal theta oscillations have recently been proposed to temporally organize brain computations during conflict processing. Using a multivariate analysis approach, we show that brain-behavior relationships during conflict tasks are modulated according to the phase of ongoing endogenous midfrontal theta oscillations recorded by scalp EEG. We found reproducible results in two independent datasets, using two different conflict tasks: brain-behavior relationships (correlation between reaction time and theta power) were theta phase-dependent in a subject-specific manner, and these "behaviorally optimal" theta phases were also associated with fronto-parietal cross-frequency dynamics emerging as theta phase-locked beta power bursts. These effects were present regardless of the strength of conflict. Thus, these results provide empirical evidence that midfrontal theta oscillations are involved in cyclically orchestrating brain computations likely related to response execution during the tasks rather than purely related to conflict processing. More generally, this study supports the hypothesis that phase-based computation is an important mechanism giving rise to cognitive processing.

Mining Within-Trial Oscillatory Brain Dynamics to Address the Variability of Optimized Spatial Filters

Data-driven spatial filtering algorithms optimize scores, such as the contrast between two conditions to extract oscillatory brain signal components. Most machine learning approaches for the filter estimation, however, disregard within-trial temporal dynamics and are extremely sensitive to changes in training data and involved hyperparameters. This leads to highly variable solutions and impedes the selection of a suitable candidate for, e.g., neurotechnological applications. Fostering component introspection, we propose to embrace this variability by condensing the functional signatures of a large set of oscillatory components into homogeneous clusters, each representing specific within-trial envelope dynamics. The proposed method is exemplified by and evaluated on a complex hand force task with a rich within-trial structure. Based on electroencephalography data of 18 healthy subjects, we found that the components' distinct temporal envelope dynamics are highly subject-specific. On average, we obtained seven clusters per subject, which were strictly confined regarding their underlying frequency bands. As the analysis method is not limited to a specific spatial filtering algorithm, it could be utilized for a wide range of neurotechnological applications, e.g., to select and monitor functionally relevant features for brain-computer interface protocols in stroke rehabilitation.

Delta Vs Gamma Auditory Steady State Synchrony in Schizophrenia

Background

Delta band (1-4 Hz) neuronal responses support the precision and stability of auditory processing, and a deficit in delta band synchrony may be relevant to auditory domain symptoms in schizophrenia patients.

Methods

Delta band synchronization elicited by a 2.5 Hz auditory steady state response (ASSR) paradigm, along with those from theta (5 Hz), alpha (10 Hz), beta (20 Hz), gamma (40 Hz), and high gamma (80 Hz) frequency ASSR, were compared in 128 patients with schizophrenia, 108 healthy controls, and 55 first-degree relatives (FDR) of patients.

Results

Delta band synchronization was significantly impaired in patients compared with controls (F = 18.3, P < .001). There was a significant 2.5 Hz by 40 Hz ASSR interaction (P = .023), arising from a greater reduction of 2.5 Hz ASSR than of 40 Hz ASSR, in patients compared with controls. Greater deficit in delta ASSR was associated with auditory perceptual abnormality (P = .007) and reduced verbal working memory (P < .001). Gamma frequency ASSR impairment was also significant but more modest (F = 8.7, P = .004), and this deficit was also present in FDR (P = .022).

Conclusions

The ability to sustain delta band oscillation entrainment in the auditory pathway is significantly reduced in schizophrenia patients and appears to be clinically relevant.

Individual Alpha Peak Frequency Predicts 10 Hz Flicker Effects on Selective Attention

Rhythmic visual stimulation ("flicker") is primarily used to "tag" processing of low-level visual and high-level cognitive phenomena. However, preliminary evidence suggests that flicker may also entrain endogenous brain oscillations, thereby modulating cognitive processes supported by those brain rhythms. Here we tested the interaction between 10 Hz flicker and endogenous alpha-band (∼10 Hz) oscillations during a selective visuospatial attention task. We recorded EEG from human participants (both genders) while they performed a modified Eriksen flanker task in which distractors and targets flickered within (10 Hz) or outside (7.5 or 15 Hz) the alpha band. By using a combination of EEG source separation, time-frequency, and single-trial linear mixed-effects modeling, we demonstrate that 10 Hz flicker interfered with stimulus processing more on incongruent than congruent trials (high vs low selective attention demands). Crucially, the effect of 10 Hz flicker on task performance was predicted by the distance between 10 Hz and individual alpha peak frequency (estimated during the task). Finally, the flicker effect on task performance was more strongly predicted by EEG flicker responses during stimulus processing than during preparation for the upcoming stimulus, suggesting that 10 Hz flicker interfered more with reactive than proactive selective attention. These findings are consistent with our hypothesis that visual flicker entrained endogenous alpha-band networks, which in turn impaired task performance. Our findings also provide novel evidence for frequency-dependent exogenous modulation of cognition that is determined by the correspondence between the exogenous flicker frequency and the endogenous brain rhythms.SIGNIFICANCE STATEMENT Here we provide novel evidence that the interaction between exogenous rhythmic visual stimulation and endogenous brain rhythms can have frequency-specific behavioral effects. We show that alpha-band (10 Hz) flicker impairs stimulus processing in a selective attention task when the stimulus flicker rate matches individual alpha peak frequency. The effect of sensory flicker on task performance was stronger when selective attention demands were high, and was stronger during stimulus processing and response selection compared with the prestimulus anticipatory period. These findings provide novel evidence that frequency-specific sensory flicker affects online attentional processing, and also demonstrate that the correspondence between exogenous and endogenous rhythms is an overlooked prerequisite when testing for frequency-specific cognitive effects of flicker.

Individual Differences in Frequency and Topography of Slow and Fast Sleep Spindles

Sleep spindles are transient oscillatory waveforms that occur during non-rapid eye movement (NREM) sleep across widespread cortical areas. In humans, spindles can be classified as either slow or fast, but large individual differences in spindle frequency as well as methodological difficulties have hindered progress towards understanding their function. Using two nights of high-density electroencephalography recordings from 28 healthy individuals, we first characterize the individual variability of NREM spectra and demonstrate the difficulty of determining subject-specific spindle frequencies. We then introduce a novel spatial filtering approach that can reliably separate subject-specific spindle activity into slow and fast components that are stable across nights and across N2 and N3 sleep. We then proceed to provide detailed analyses of the topographical expression of individualized slow and fast spindle activity. Group-level analyses conform to known spatial properties of spindles, but also uncover novel differences between sleep stages and spindle classes. Moreover, subject-specific examinations reveal that individual topographies show considerable variability that is stable across nights. Finally, we demonstrate that topographical maps depend nontrivially on the spindle metric employed. In sum, our findings indicate that group-level approaches mask substantial individual variability of spindle dynamics, in both the spectral and spatial domains. We suggest that leveraging, rather than ignoring, such differences may prove useful to further our understanding of the physiology and functional role of sleep spindles.

Exploring the temporal dynamics of sustained and transient spatial attention using steady-state visual evoked potentials

While the behavioral dynamics as well as the functional network of sustained and transient attention have extensively been studied, their underlying neural mechanisms have most often been investigated in separate experiments. In the present study, participants were instructed to perform an audio-visual spatial attention task. They were asked to attend to either the left or the right hemifield and to respond to deviant transient either auditory or visual stimuli. Steady-state visual evoked potentials (SSVEPs) elicited by two task irrelevant pattern reversing checkerboards flickering at 10 and 15 Hz in the left and the right hemifields, respectively, were used to continuously monitor the locus of spatial attention. The amplitude and phase of the SSVEPs were extracted for single trials and were separately analyzed. Sustained attention to one hemifield (spatial attention) as well as to the auditory modality (intermodal attention) increased the inter-trial phase locking of the SSVEP responses, whereas briefly presented visual and auditory stimuli decreased the single-trial SSVEP amplitude between 200 and 500 ms post-stimulus. This transient change of the single-trial amplitude was restricted to the SSVEPs elicited by the reversing checkerboard in the spatially attended hemifield and thus might reflect a transient re-orienting of attention towards the brief stimuli. Thus, the present results demonstrate independent, but interacting neural mechanisms of sustained and transient attentional orienting.

Multivariate cross-frequency coupling via generalized eigendecomposition

This paper presents a new framework for analyzing cross-frequency coupling in multichannel electrophysiological recordings. The generalized eigendecomposition-based cross-frequency coupling framework (gedCFC) is inspired by source-separation algorithms combined with dynamics of mesoscopic neurophysiological processes. It is unaffected by factors that confound traditional CFC methods—such as non-stationarities, non-sinusoidality, and non-uniform phase angle distributions—attractive properties considering that brain activity is neither stationary nor perfectly sinusoidal. The gedCFC framework opens new opportunities for conceptualizing CFC as network interactions with diverse spatial/topographical distributions. Five specific methods within the gedCFC framework are detailed, these are validated in simulated data and applied in several empirical datasets. gedCFC accurately recovers physiologically plausible CFC patterns embedded in noise that causes traditional CFC methods to perform poorly. The paper also demonstrates that spike-field coherence in multichannel local field potential data can be analyzed using the gedCFC framework, which provides significant advantages over traditional spike-field coherence analyses. Null-hypothesis testing is also discussed.

DOI:http://dx.doi.org/10.7554/eLife.21792.001

Rhythmic entrainment source separation: Optimizing analyses of neural responses to rhythmic sensory stimulation

Steady-state evoked potentials (SSEPs) are rhythmic brain responses to rhythmic sensory stimulation, and are often used to study perceptual and attentional processes. We present a data analysis method for maximizing the signal-to-noise ratio of the narrow-band steady-state response in the frequency and time-frequency domains. The method, termed rhythmic entrainment source separation (RESS), is based on denoising source separation approaches that take advantage of the simultaneous but differential projection of neural activity to multiple electrodes or sensors. Our approach is a combination and extension of existing multivariate source separation methods. We demonstrate that RESS performs well on both simulated and empirical data, and outperforms conventional SSEP analysis methods based on selecting electrodes with the strongest SSEP response, as well as several other linear spatial filters. We also discuss the potential confound of overfitting, whereby the filter captures noise in absence of a signal. Matlab scripts are available to replicate and extend our simulations and methods. We conclude with some practical advice for optimizing SSEP data analyses and interpreting the results.