A novel mechanism for evoked responses in the human brain

A closed-loop auditory stimulation approach selectively modulates alpha oscillations and sleep onset dynamics in humans

Alpha oscillations play a vital role in managing the brain's resources, inhibiting neural activity as a function of their phase and amplitude, and are changed in many brain disorders. Developing minimally invasive tools to modulate alpha activity and identifying the parameters that determine its response to exogenous modulators is essential for the implementation of focussed interventions. We introduce Alpha Closed-Loop Auditory Stimulation (αCLAS) as an EEG-based method to modulate and investigate these brain rhythms in humans with specificity and selectivity, using targeted auditory stimulation. Across a series of independent experiments, we demonstrate that αCLAS alters alpha power, frequency, and connectivity in a phase, amplitude, and topography-dependent manner. Using single-pulse-αCLAS, we show that the effects of auditory stimuli on alpha oscillations can be explained within the theoretical framework of oscillator theory and a phase-reset mechanism. Finally, we demonstrate the functional relevance of our approach by showing that αCLAS can interfere with sleep onset dynamics in a phase-dependent manner.

A generative model of electrophysiological brain responses to stimulation

Each brain response to a stimulus is, to a large extent, unique. However this variability, our perceptual experience feels stable. Standard decoding models, which utilise information across several areas to tap into stimuli representation and processing, are fundamentally based on averages. Therefore, they can focus precisely on the features that are most stable across stimulus presentations. But which are these features exactly is difficult to address in the absence of a generative model of the signal. Here, I introduce genephys, a generative model of brain responses to stimulation publicly available as a Python package that, when confronted with a decoding algorithm, can reproduce the structured patterns of decoding accuracy that we observe in real data. Using this approach, I characterise how these patterns may be brought about by the different aspects of the signal, which in turn may translate into distinct putative neural mechanisms. In particular, the model shows that the features in the data that support successful decoding-and, therefore, likely reflect stable mechanisms of stimulus representation-have an oscillatory component that spans multiple channels, frequencies, and latencies of response; and an additive, slower response with a specific (cross-frequency) relation to the phase of the oscillatory component. At the individual trial level, still, responses are found to be highly variable, which can be due to various factors including phase noise and probabilistic activations.

Event-related modulation of alpha rhythm explains the auditory P300-evoked response in EEG

Evoked responses and oscillations represent two major electrophysiological phenomena in the human brain yet the link between them remains rather obscure. Here we show how most frequently studied EEG signals: the P300-evoked response and alpha oscillations (8-12 Hz) can be linked with the baseline-shift mechanism. This mechanism states that oscillations generate evoked responses if oscillations have a non-zero mean and their amplitude is modulated by the stimulus. Therefore, the following predictions should hold: (1) the temporal evolution of P300 and alpha amplitude is similar, (2) spatial localisations of the P300 and alpha amplitude modulation overlap, (3) oscillations are non-zero mean, (4) P300 and alpha amplitude correlate with cognitive scores in a similar fashion. To validate these predictions, we analysed the data set of elderly participants (N=2230, 60-82 years old), using (a) resting-state EEG recordings to quantify the mean of oscillations, (b) the event-related data, to extract parameters of P300 and alpha rhythm amplitude envelope. We showed that P300 is indeed linked to alpha rhythm, according to all four predictions. Our results provide an unifying view on the interdependency of evoked responses and neuronal oscillations and suggest that P300, at least partly, is generated by the modulation of alpha oscillations.

Precise Spatial Tuning of Visually Driven Alpha Oscillations in Human Visual Cortex

Neuronal oscillations at about 10 Hz, called alpha oscillations, are often thought to arise from synchronous activity across occipital cortex, reflecting general cognitive states such as arousal and alertness. However, there is also evidence that modulation of alpha oscillations in visual cortex can be spatially specific. Here, we used intracranial electrodes in human patients to measure alpha oscillations in response to visual stimuli whose location varied systematically across the visual field. We separated the alpha oscillatory power from broadband power changes. The variation in alpha oscillatory power with stimulus position was then fit by a population receptive field (pRF) model. We find that the alpha pRFs have similar center locations to pRFs estimated from broadband power (70-180 Hz), but are several times larger. The results demonstrate that alpha suppression in human visual cortex can be precisely tuned. Finally, we show how the pattern of alpha responses can explain several features of exogenous visual attention.

Significance Statement

The alpha oscillation is the largest electrical signal generated by the human brain. An important question in systems neuroscience is the degree to which this oscillation reflects system-wide states and behaviors such as arousal, alertness, and attention, versus much more specific functions in the routing and processing of information. We examined alpha oscillations at high spatial precision in human patients with intracranial electrodes implanted over visual cortex. We discovered a surprisingly high spatial specificity of visually driven alpha oscillations, which we quantified with receptive field models. We further use our discoveries about properties of the alpha response to show a link between these oscillations and the spread of visual attention.

Non-zero mean alpha oscillations revealed with computational model and empirical data

Ongoing oscillations and evoked responses are two main types of neuronal activity obtained with diverse electrophysiological recordings (EEG/MEG/iEEG/LFP). Although typically studied separately, they might in fact be closely related. One possibility to unite them is to demonstrate that neuronal oscillations have non-zero mean which predicts that stimulus- or task-triggered amplitude modulation of oscillations can contribute to the generation of evoked responses. We validated this mechanism using computational modelling and analysis of a large EEG data set. With a biophysical model, we indeed demonstrated that intracellular currents in the neuron are asymmetric and, consequently, the mean of alpha oscillations is non-zero. To understand the effect that neuronal currents exert on oscillatory mean, we varied several biophysical and morphological properties of neurons in the network, such as voltage-gated channel densities, length of dendrites, and intensity of incoming stimuli. For a very large range of model parameters, we observed evidence for non-zero mean of oscillations. Complimentary, we analysed empirical rest EEG recordings of 90 participants (50 young, 40 elderly) and, with spatio-spectral decomposition, detected at least one spatially-filtred oscillatory component of non-zero mean alpha oscillations in 93% of participants. In order to explain a complex relationship between the dynamics of amplitude-envelope and corresponding baseline shifts, we performed additional simulations with simple oscillators coupled with different time delays. We demonstrated that the extent of spatial synchronisation may obscure macroscopic estimation of alpha rhythm modulation while leaving baseline shifts unchanged. Overall, our results predict that amplitude modulation of neural oscillations should at least partially explain the generation of evoked responses. Therefore, inference about changes in evoked responses with respect to cognitive conditions, age or neuropathologies should be constructed while taking into account oscillatory neuronal dynamics.

Harmoni: a Method for Eliminating Spurious Interactions due to the Harmonic Components in Neuronal Data

Cross-frequency synchronization (CFS) has been proposed as a mechanism for integrating spatially and spectrally distributed information in the brain. However, investigating CFS in Magneto- and Electroencephalography (MEG/EEG) is hampered by the presence of spurious neuronal interactions due to the non-sinusoidal waveshape of brain oscillations. Such waveshape gives rise to the presence of oscillatory harmonics mimicking genuine neuronal oscillations. Until recently, however, there has been no methodology for removing these harmonics from neuronal data. In order to address this long-standing challenge, we introduce a novel method (called HARMOnic miNImization - Harmoni) that removes the signal components which can be harmonics of a non-sinusoidal signal. Harmoni's working principle is based on the presence of CFS between harmonic components and the fundamental component of a non-sinusoidal signal. We extensively tested Harmoni in realistic EEG simulations. The simulated couplings between the source signals represented genuine and spurious CFS and within-frequency phase synchronization. Using diverse evaluation criteria, including ROC analyses, we showed that the within- and cross-frequency spurious interactions are suppressed significantly, while the genuine activities are not affected. Additionally, we applied Harmoni to real resting-state EEG data revealing intricate remote connectivity patterns which are usually masked by the spurious connections. Given the ubiquity of non-sinusoidal neuronal oscillations in electrophysiological recordings, Harmoni is expected to facilitate novel insights into genuine neuronal interactions in various research fields, and can also serve as a steppingstone towards the development of further signal processing methods aiming at refining within- and cross-frequency synchronization in electrophysiological recordings.

Inhibitory Control in the Absence of Awareness: Interactions Between Frontal and Motor Cortex Oscillations Mediate Implicitly Learned Responses

Cognitive control of action is associated with conscious effort and is hypothesised to be reflected by increased frontal theta activity. However, the functional role of these increases in theta power, and how they contribute to cognitive control remains unknown. We conducted an MEG study to test the hypothesis that frontal theta oscillations interact with sensorimotor signals in order to produce controlled behaviour, and that the strength of these interactions will vary with the amount of control required. We measured neuromagnetic activity in 16 healthy adults performing a response inhibition (Go/Switch) task, known from previous work to modulate cognitive control requirements using hidden patterns of Go and Switch cues. Learning was confirmed by reduced reaction times (RT) to patterned compared to random Switch cues. Concurrent measures of pupil diameter revealed changes in subjective cognitive effort with stimulus probability, even in the absence of measurable behavioural differences, revealing instances of covert variations in cognitive effort. Significant theta oscillations were found in five frontal brain regions, with theta power in the right middle frontal and right premotor cortices parametrically increasing with cognitive effort. Similar increases in oscillatory power were also observed in motor cortical gamma, suggesting an interaction. Right middle frontal and right precentral theta activity predicted changes in pupil diameter across all experimental conditions, demonstrating a close relationship between frontal theta increases and cognitive control. Although no theta-gamma cross-frequency coupling was found, long-range theta phase coherence among the five significant sources between bilateral middle frontal, right inferior frontal, and bilateral premotor areas was found, thus providing a mechanism for the relay of cognitive control between frontal and motor areas via theta signalling. Furthermore, this provides the first evidence for the sensitivity of frontal theta oscillations to implicit motor learning and its effects on cognitive load. More generally these results present a possible a mechanism for this frontal theta network to coordinate response preparation, inhibition and execution.

Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability

The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Ongoing fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on intracranial electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.

Subliminal perception is continuous with conscious vision and can be predicted from prestimulus electroencephalographic activity

Individuals are able to discriminate visual stimuli they report not consciously seeing. This phenomenon is known as "subliminal perception." Such capacity is often assumed to be relatively automatic in nature and rely on stimulus-driven activity in low-level cortical areas. Instead, here we asked to what extent neural activity before stimulus presentation influences subliminal perception. We asked participants to discriminate the location of a briefly presented low-contrast visual stimulus and then rate how well they saw the stimulus. Consistent with previous studies, participants correctly discriminated with slightly above chance-level accuracy the location of a stimulus they reported not seeing. Signal detection analyses indicated that while subjects categorized their percepts as "unconscious," their capacity to discriminate these stimuli lay on the same continuum as conscious vision. We show that the accuracy of discriminating the location of a subliminal stimulus could be predicted with relatively high accuracy (AUC = 0.70) based on lateralized electroencephalographic (EEG) activity before the stimulus, the hemifield where the stimulus was presented, and the accuracy of previous trial's discrimination response. Altogether, our results suggest that rather than being a separate unconscious capacity, subliminal perception is based on similar processes as conscious vision.

Sensorimotor Functional Connectivity: A Neurophysiological Factor Related to BCI Performance

Brain-Computer Interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. About 20% of potential users of sensorimotor BCIs do not gain reliable control of the system. The inefficiency to decode user's intentions requires the identification of neurophysiological factors determining “good” and “poor” BCI performers. One of the important neurophysiological aspects in BCI research is that the neuronal oscillations, used to control these systems, show a rich repertoire of spatial sensorimotor interactions. Considering this, we hypothesized that neuronal connectivity in sensorimotor areas would define BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in a calibration and an online feedback session recorded on the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post- as well as pre-stimulus connectivity in the calibration recording is significantly correlated to online feedback performance in μ and feedback frequency bands. Importantly, the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study demonstrates that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sensorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance.

Event-related potential and EEG oscillatory predictors of verbal memory in mild cognitive impairment

Reliable biomarkers of memory decline are critical for the early detection of Alzheimer's disease. Previous work has found three EEG measures, namely the event-related brain potential P600, suppression of oscillatory activity in the alpha frequency range (∼10 Hz) and cross-frequency coupling between low theta/high delta and alpha/beta activity, each of which correlates strongly with verbal learning and memory abilities in healthy elderly and patients with mild cognitive impairment or prodromal Alzheimer's disease. In the present study, we address the question of whether event-related or oscillatory measures, or a combination thereof, best predict the decline of verbal memory in mild cognitive impairment and Alzheimer's disease. Single-trial correlation analyses show that despite a similarity in their time courses and sensitivities to word repetition, the P600 and the alpha suppression components are minimally correlated with each other on a trial-by-trial basis (generally |r _s| < 0.10). This suggests that they are unlikely to stem from the same neural mechanism. Furthermore, event-related brain potentials constructed from bandpass filtered (delta, theta, alpha, beta or gamma bands) single-trial data indicate that only delta band activity (1-4 Hz) is strongly correlated (r = 0.94, P < 0.001) with the canonical P600 repetition effect; event-related potentials in higher frequency bands are not. Importantly, stepwise multiple regression analyses reveal that the three event-related brain potential/oscillatory measures are complementary in predicting California Verbal Learning Test scores (overall R ^{2 '} s in 0.45-0.63 range). The present study highlights the importance of combining EEG event-related potential and oscillatory measures to better characterize the multiple mechanisms of memory failure in individuals with mild cognitive impairment or prodromal Alzheimer's disease.

Resolving the Connectome, Spectrally-Specific Functional Connectivity Networks and Their Distinct Contributions to Behavior

The resting human brain exhibits spontaneous patterns of activity that reflect features of the underlying neural substrate. Examination of interareal coupling of resting-state oscillatory activity has revealed that the brain’s resting activity is composed of functional networks, whose topographies differ depending on oscillatory frequency, suggesting a role for carrier frequency as a means of creating multiplexed, or functionally segregated, communication channels between brain areas. Using canonical correlation analysis (CCA), we examined spectrally resolved resting-state connectivity patterns derived from magnetoencephalography (MEG) recordings to determine the relationship between connectivity intrinsic to different frequency channels and a battery of over a hundred behavioral and demographic indicators, in a group of 89 young healthy participants. We demonstrate that each of the classical frequency bands in the range 1–40 Hz (δ, θ, α, β, and γ) delineates a subnetwork that is behaviorally relevant, spatially distinct, and whose expression is either negatively or positively predictive of individual traits, with the strongest link in the α-band being negative and networks oscillating at different frequencies, such as θ, β, and γ carrying positive function.

The oscillatory mechanisms associated with syntactic binding in healthy ageing

Older adults frequently display differential patterns of brain activity compared to young adults in the same task, alongside widespread neuroanatomical changes. Differing functional activity patterns in older adults are commonly interpreted as being compensatory (e.g., Cabeza et al., 2002). We examined the oscillatory activity in the EEG during syntactic binding in young and older adults, as well as the relationship between oscillatory activity and behavioural performance on a syntactic judgement task within the older adults. 19 young and 41 older adults listened to two-word sentences that differentially load onto morpho-syntactic binding: correct syntactic binding (morpho-syntactically correct, e.g., "I dotch"); incorrect syntactic binding (morpho-syntactic agreement violation, e.g., "they dotches") and no syntactic binding (minimizing morpho-syntactic binding, e.g., "dotches spuff"). Behavioural performance, assessed in a syntactic judgement task, was characterized by inter-individual variability especially in older adults, with accuracy ranging from 76 to 100% in young adults and 58-100% in older adults. Compared to young adults, older adults were slower, but not less accurate. Functional neural signatures for syntactic binding were assessed as the difference in oscillatory power between the correct and no syntactic binding condition. In older adults, syntactic binding was associated with a smaller increase in theta (4-7 Hz), alpha (8-12 Hz) and beta (15-20 Hz) power in a time window surrounding the second word. There was a significant difference between the older and young adults: in the alpha range, the condition difference seemed to be in the opposite direction for older versus young adults. Our findings thus suggest that the neural signature associated with syntactic binding in older adults is different from young adults. However, we found no evidence of a significant association between behavioural performance and the neural signatures of syntactic binding for older adults, which does not readily support the predictions of compensatory models of language and ageing.

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

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

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

Transient spectral events in resting state MEG predict individual task responses

Even in response to simple tasks such as hand movement, human brain activity shows remarkable inter-subject variability. Recently, it has been shown that individual spatial variability in fMRI task responses can be predicted from measurements collected at rest; suggesting that the spatial variability is a stable feature, inherent to the individual's brain. However, it is not clear if this is also true for individual variability in the spatio-spectral content of oscillatory brain activity. Here, we show using MEG (N ​= ​89) that we can predict the spatial and spectral content of an individual's task response using features estimated from the individual's resting MEG data. This works by learning when transient spectral 'bursts' or events in the resting state tend to reoccur in the task responses. We applied our method to motor, working memory and language comprehension tasks. All task conditions were predicted significantly above chance. Finally, we found a systematic relationship between genetic similarity (e.g. unrelated subjects vs. twins) and predictability. Our approach can predict individual differences in brain activity and suggests a link between transient spectral events in task and rest that can be captured at the level of individuals.

Cortical Responses to Input From Distant Areas are Modulated by Local Spontaneous Alpha/Beta Oscillations

Any given area in human cortex may receive input from multiple, functionally heterogeneous areas, potentially representing different processing threads. Alpha (8-13 Hz) and beta oscillations (13-20 Hz) have been hypothesized by other investigators to gate local cortical processing, but their influence on cortical responses to input from other cortical areas is unknown. To study this, we measured the effect of local oscillatory power and phase on cortical responses elicited by single-pulse electrical stimulation (SPES) at distant cortical sites, in awake human subjects implanted with intracranial electrodes for epilepsy surgery. In 4 out of 5 subjects, the amplitudes of corticocortical evoked potentials (CCEPs) elicited by distant SPES were reproducibly modulated by the power, but not the phase, of local oscillations in alpha and beta frequencies. Specifically, CCEP amplitudes were higher when average oscillatory power just before distant SPES (-110 to -10 ms) was high. This effect was observed in only a subset (0-33%) of sites with CCEPs and, like the CCEPs themselves, varied with stimulation at different distant sites. Our results suggest that although alpha and beta oscillations may gate local processing, they may also enhance the responsiveness of cortex to input from distant cortical sites.

Brain-Switches for Asynchronous Brain–Computer Interfaces: A Systematic Review

A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance.

Multiple mechanisms link prestimulus neural oscillations to sensory responses

Spontaneous fluctuations of neural activity may explain why sensory responses vary across repeated presentations of the same physical stimulus. To test this hypothesis, we recorded electroencephalography in humans during stimulation with identical visual stimuli and analyzed how prestimulus neural oscillations modulate different stages of sensory processing reflected by distinct components of the event-related potential (ERP). We found that strong prestimulus alpha- and beta-band power resulted in a suppression of early ERP components (C1 and N150) and in an amplification of late components (after 0.4 s), even after controlling for fluctuations in 1/f aperiodic signal and sleepiness. Whereas functional inhibition of sensory processing underlies the reduction of early ERP responses, we found that the modulation of non-zero-mean oscillations (baseline shift) accounted for the amplification of late responses. Distinguishing between these two mechanisms is crucial for understanding how internal brain states modulate the processing of incoming sensory information.

Explaining event-related fields by a mechanistic model encapsulating the anatomical structure of auditory cortex

Event-related fields of the magnetoencephalogram are triggered by sensory stimuli and appear as a series of waves extending hundreds of milliseconds after stimulus onset. They reflect the processing of the stimulus in cortex and have a highly subject-specific morphology. However, we still have an incomplete picture of how event-related fields are generated, what the various waves signify, and why they are so subject-specific. Here, we focus on this problem through the lens of a computational model which describes auditory cortex in terms of interconnected cortical columns as part of hierarchically placed fields of the core, belt, and parabelt areas. We develop an analytical approach arriving at solutions to the system dynamics in terms of normal modes: damped harmonic oscillators emerging out of the coupled excitation and inhibition in the system. Each normal mode is a global feature which depends on the anatomical structure of the entire auditory cortex. Further, normal modes are fundamental dynamical building blocks, in that the activity of each cortical column represents a combination of all normal modes. This approach allows us to replicate a typical auditory event-related response as a weighted sum of the single-column activities. Our work offers an alternative to the view that the event-related field arises out of spatially discrete, local generators. Rather, there is only a single generator process distributed over the entire network of the auditory cortex. We present predictions for testing to what degree subject-specificity is due to cross-subject variations in dynamical parameters rather than in the cortical surface morphology.

Steady state visual evoked potentials in reading aloud: Effects of lexicality, frequency and orthographic familiarity

The present study explored the possibility to use Steady-State Visual Evoked Potentials (SSVEPs) as a tool to investigate the core mechanisms in visual word recognition. In particular, we investigated three benchmark effects of reading aloud: lexicality (words vs. pseudowords), frequency (high-frequency vs. low-frequency words), and orthographic familiarity ('familiar' versus 'unfamiliar' pseudowords). We found that words and pseudowords elicited robust SSVEPs. Words showed larger SSVEPs than pseudowords and high-frequency words showed larger SSVEPs than low-frequency words. SSVEPs were not sensitive to orthographic familiarity. We further localized the neural generators of the SSVEP effects. The lexicality effect was located in areas associated with early level of visual processing, i.e. in the right occipital lobe and in the right precuneus. Pseudowords produced more activation than words in left sensorimotor areas, rolandic operculum, insula, supramarginal gyrus and in the right temporal gyrus. These areas are devoted to speech processing and/or spelling-to-sound conversion. The frequency effect involved the left temporal pole and orbitofrontal cortex, areas previously implicated in semantic processing and stimulus-response associations respectively, and the right postcentral and parietal inferior gyri, possibly indicating the involvement of the right attentional network.

Responses of Patients with Disorders of Consciousness to Habit Stimulation: A Quantitative EEG Study

Whether habit stimulation is effective in DOC patient arousal has not been reported. In this paper, we analyzed the responses of DOC patients to habit stimulation. Nineteen DOC patients with alcohol consumption or smoking habits were recruited and 64-channel EEG signals were acquired both at the resting state and at three stimulation states. Wavelet transformation and nonlinear dynamics were used to extract the features of EEG signals and four brain lobes were selected to investigate the degree of EEG response to habit stimulation. Results showed that the highest degree of EEG response was from the call-name stimulation, followed by habit and music stimulations. Significant differences in EEG wavelet energy and response coefficient were found both between habit and music stimulation, and between habit and call-name stimulation. These findings prove that habit stimulation induces relatively more intense EEG responses in DOC patients than music stimulation, suggesting that it may be a relevant additional method for eliciting patient arousal.

Nonsinusoidal neuronal oscillations: bug or feature?

There is compiling evidence suggesting that independent neuronal ensembles are coordinated in time and space through cross-frequency coupling (CFC). However, recent studies have convincingly demonstrated that nonsinusoidal oscillations produce serious biases in state of the art CFC metrics. Although most of studies treat nonsinusoidal waves as a nuisance or just ignore them, fortunately some scientists are starting to exploit their neurophysiological relevance opening new research vistas with critical implications.

On the Physiological Modulation and Potential Mechanisms Underlying Parieto-Occipital Alpha Oscillations

The parieto-occipital alpha (8–13 Hz) rhythm is by far the strongest spectral fingerprint in the human brain. Almost 90 years later, its physiological origin is still far from clear. In this Research Topic I review human pharmacological studies using electroencephalography (EEG) and magnetoencephalography (MEG) that investigated the physiological mechanisms behind posterior alpha. Based on results from classical and recent experimental studies, I find a wide spectrum of drugs that modulate parieto-occipital alpha power. Alpha frequency is rarely affected, but this might be due to the range of drug dosages employed. Animal and human pharmacological findings suggest that both GABA enhancers and NMDA blockers systematically decrease posterior alpha power. Surprisingly, most of the theoretical frameworks do not seem to embrace these empirical findings and the debate on the functional role of alpha oscillations has been polarized between the inhibition vs. active poles hypotheses. Here, I speculate that the functional role of alpha might depend on physiological excitation as much as on physiological inhibition. This is supported by animal and human pharmacological work showing that GABAergic, glutamatergic, cholinergic, and serotonergic receptors in the thalamus and the cortex play a key role in the regulation of alpha power and frequency. This myriad of physiological modulations fit with the view that the alpha rhythm is a complex rhythm with multiple sources supported by both thalamo-cortical and cortico-cortical loops. Finally, I briefly discuss how future research combining experimental measurements derived from theoretical predictions based of biophysically realistic computational models will be crucial to the reconciliation of these disparate findings.

Alpha Oscillations Reduce Temporal Long-Range Dependence in Spontaneous Human Brain Activity

Ongoing neural dynamics comprise both frequency-specific oscillations and broadband-features, such as long-range dependence (LRD). Despite both being behaviorally relevant, little is known about their potential interactions. In humans, 8-12 Hz α oscillations constitute the strongest deviation from 1/f power-law scaling, the signature of LRD. We postulated that α oscillations, believed to exert active inhibitory gating, downmodulate the temporal width of LRD in slower ongoing brain activity. In two independent "resting-state" datasets (electroencephalography surface recordings and magnetoencephalography source reconstructions), both across space and dynamically over time, power of α activity covaried with the power slope <5 Hz (i.e., greater α activity shortened LRD). Causality of α activity dynamics was implied by its temporal precedence over changes of slope. A model where power-law fluctuations of the α envelope inhibit baseline activity closely replicated our results. Thus, α oscillations may provide an active control mechanism to adaptively regulate LRD of brain activity at slow temporal scales, thereby shaping internal states and cognitive processes.SIGNIFICANCE STATEMENT The two prominent features of ongoing brain activity are oscillations and temporal long-range dependence. Both shape behavioral performance, but little is known about their interaction. Here, we demonstrate such an interaction in EEG and MEG recordings of task-free human brain activity. Specifically, we show that spontaneous dynamics in alpha activity explain ensuing variations of dependence in the low and ultra-low-frequency range. In modeling, two features of alpha oscillations are critical to account for the observed effects on long-range dependence, scale-free properties of alpha oscillations themselves, and a modulation of baseline levels, presumably inhibitory. Both these properties have been observed empirically, and our study hence establishes alpha oscillations as a regulatory mechanism governing long-range dependence or "memory" in slow ongoing brain activity.

Oscillatory activity in auditory cortex reflects the perceptual level of audio-tactile integration

Cross-modal interactions between sensory channels have been shown to depend on both the spatial disparity and the perceptual similarity between the presented stimuli. Here we investigate the behavioral and neural integration of auditory and tactile stimulus pairs at different levels of spatial disparity. Additionally, we modulated the amplitudes of both stimuli in either a coherent or non-coherent manner. We found that both auditory and tactile localization performance was biased towards the stimulus in the respective other modality. This bias linearly increases with stimulus disparity and is more pronounced for coherently modulated stimulus pairs. Analyses of electroencephalographic (EEG) activity at temporal–cortical sources revealed enhanced event-related potentials (ERPs) as well as decreased alpha and beta power during bimodal as compared to unimodal stimulation. However, while the observed ERP differences are similar for all stimulus combinations, the extent of oscillatory desynchronization varies with stimulus disparity. Moreover, when both stimuli were subjectively perceived as originating from the same direction, the reduction in alpha and beta power was significantly stronger. These observations suggest that in the EEG the level of perceptual integration is mainly reflected by changes in ongoing oscillatory activity.

Neuronal Oscillations with Non-sinusoidal Morphology Produce Spurious Phase-to-Amplitude Coupling and Directionality

Neuronal oscillations support cognitive processing. Modern views suggest that neuronal oscillations do not only reflect coordinated activity in spatially distributed networks, but also that there is interaction between the oscillations at different frequencies. For example, invasive recordings in animals and humans have found that the amplitude of fast oscillations (>40 Hz) occur non-uniformly within the phase of slower oscillations, forming the so-called cross-frequency coupling (CFC). However, the CFC patterns might be influenced by features in the signal that do not relate to underlying physiological interactions. For example, CFC estimates may be sensitive to spectral correlations due to non-sinusoidal properties of the alpha band wave morphology. To investigate this issue, we performed CFC analysis using experimental and synthetic data. The former consisted in a double-blind magnetoencephalography pharmacological study in which participants received either placebo, 0.5 or 1.5 mg of lorazepam (LZP; GABAergic enhancer) in different experimental sessions. By recording oscillatory brain activity with during rest and working memory (WM), we were able to demonstrate that posterior alpha (8-12 Hz) phase was coupled to beta-low gamma band (20-45 Hz) amplitude envelope during all sessions. Importantly, bicoherence values around the harmonics of the alpha frequency were similar both in magnitude and topographic distribution to the cross-frequency coherence (CFCoh) values observed in the alpha-phase to beta-low gamma coupling. In addition, despite the large CFCoh we found no significant cross-frequency directionality (CFD). Critically, simulations demonstrated that a sizable part of our empirical CFCoh between alpha and beta-low gamma coupling and the lack of CFD could be explained by two-three harmonics aligned in zero phase-lag produced by the physiologically characteristic alpha asymmetry in the amplitude of the peaks relative to the troughs. Furthermore, we showed that periodic signals whose waveform deviate from pure sine waves produce non-zero CFCoh with predictable CFD. Our results reveal the important role of the non-sinusoidal wave morphology on state of the art CFC metrics and we recommend caution with strong physiological interpretations of CFC and suggest basic data quality checks to enhance the mechanistic understanding of CFC.

Detecting neuromagnetic synchrony in the presence of noise

BACKGROUND

Synchrony between neuroelectric oscillations in distant brain areas is currently used as an indicator of functional connectivity between the involved neural substrates. Coherence measures, which quantify synchrony, are affected by concurrent brain activities, commonly subsumed as noise.

NEW METHOD

Using Monte-Carlo simulation, we analysed the properties of circular statistics and how those are affected by noise. We considered three different models of neuroelectric signal generation, which are an additive model, phase-reset, and reciprocal phase-interaction. Using the receiver-operating characteristic method, we compared the performances of currently implemented algorithms for coherence detection such as phase-coherence or phase-locking factor, magnitude-squared coherence, and phase-lagging index, all based on circular statistics, and a more general approach to synchrony, using measures of mutual information. We compared inter-trial coherence as a method for signal detection with coherence between multiple sources as measure of source interaction and connectivity.

RESULTS

Charts of performance characteristics showed that the choice of methods depend on the underlying signal generation model. Detection of coherence requires in general a higher signal-to-noise ratio than detection of the signal itself, and again, the difference in performance depends strongly on the underlying model of signal generation.

COMPARISON WITH EXISTING METHODS

Previous comparisons of the performances of different algorithms for signal detection and coherence have not considered systematically the underlying neural generation mechanisms.

CONCLUSION

Detection of coherence generated by additive signals or a phase-reset requires largely higher signal-to-noise ratio compared to signal detection. Only in case of true phase interaction, signal detection and coherence measures are similarly sensitive.

Averaging auditory evoked magnetoencephalographic and electroencephalographic responses: a critical discussion

In the analysis of data from magnetoencephalography (MEG) and electroencephalography (EEG), it is common practice to arithmetically average event-related magnetic fields (ERFs) or event-related electric potentials (ERPs) across single trials and subsequently across subjects to obtain the so-called grand mean. Comparisons of grand means, e.g. between conditions, are then often performed by subtraction. These operations, and their statistical evaluation with parametric tests such as ANOVA, tacitly rely on the assumption that the data follow the additive model, have a normal distribution, and have a homogeneous variance. This may be true for single trials, but these conditions are rarely met when ERFs/ERPs are compared between subjects, meaning that the additive model is seldom the correct model for computing grand mean waveforms. Here, we summarize some of our recent work and present new evidence, from auditory-evoked MEG and EEG results, that the non-normal distributions and the heteroscedasticity observed instead result because ERFs/ERPs follow a mixed model with additive and multiplicative components. For peak amplitudes, such as the auditory M100 and N100, the multiplicative component dominates. These findings emphasize that the common practice of simply subtracting arithmetic means of auditory-evoked ERFs or ERPs is problematic without prior adequate transformation of the data. Application of the area sinus hyperbolicus (asinh) transform to data following the mixed model transforms them into the requested additive model with its normal distribution and homogeneous variance. We therefore advise checking the data for compliance with the additive model and using the asinh transform if required.

A general framework for dynamic cortical function: the function-through-biased-oscillations (FBO) hypothesis

A central goal of neuroscience is to determine how the brain's relatively static anatomy can support dynamic cortical function, i.e., cortical function that varies according to task demands. In pursuit of this goal, scientists have produced a large number of experimental results and established influential conceptual frameworks, in particular communication-through-coherence (CTC) and gating-by-inhibition (GBI), but these data and frameworks have not provided a parsimonious view of the principles that underlie cortical function. Here I synthesize these existing experimental results and the CTC and GBI frameworks, and propose the function-through-biased-oscillations (FBO) hypothesis as a model to understand dynamic cortical function. The FBO hypothesis suggests that oscillatory voltage amplitude is the principal measurement that directly reflects cortical excitability, that asymmetries in voltage amplitude explain a range of brain signal phenomena, and that predictive variations in such asymmetric oscillations provide a simple and general model for information routing that can help to explain dynamic cortical function.

Prestimulus amplitudes modulate P1 latencies and evoked traveling alpha waves

Traveling waves have been well documented in the ongoing, and more recently also in the evoked EEG. In the present study we investigate what kind of physiological process might be responsible for inducing an evoked traveling wave. We used a semantic judgment task which already proved useful to study evoked traveling alpha waves that coincide with the appearance of the P1 component. We found that the P1 latency of the leading electrode is significantly correlated with prestimulus amplitude size and that this event is associated with a transient change in alpha frequency. We assume that cortical background excitability, as reflected by an increase in prestimulus amplitude, is responsible for the observed change in alpha frequency and the initiation of an evoked traveling trajectory.

The contribution of pre-stimulus neural oscillatory activity to spontaneous response time variability

Large variability between individual response times, even in identical conditions, is a ubiquitous property of animal behavior. However, the origins of this stochasticity and its relation to action decisions remain unclear. Here we focus on the state of the perception-action network in the pre-stimulus period and its influence on subsequent saccadic response time and choice in humans. We employ magnetoencephalography (MEG) and a correlational source reconstruction approach to identify the brain areas where pre-stimulus oscillatory activity predicted saccadic response time to visual targets. We find a relationship between future response time and pre-stimulus power, but not phase, in occipital (including V1), parietal, posterior cingulate and superior frontal cortices, consistently across alpha, beta and low gamma frequencies, each accounting for between 1 and 4% of the RT variance. Importantly, these correlations were not explained by deterministic sources of variance, such as experimental factors and trial history. Our results further suggest that occipital areas mainly reflect short-term (trial to trial) stochastic fluctuations, while the frontal contribution largely reflects longer-term effects such as fatigue or practice. Parietal areas reflect fluctuations at both time scales. We found no evidence of lateralization: these effects were indistinguishable in both hemispheres and for both saccade directions, and non-predictive of choice - a finding with fundamental consequences for models of action decision, where independent, not coupled, noise is normally assumed.

Identifying Granger causal relationships between neural power dynamics and variables of interest

Power modulations of oscillations in electro- and magnetoencephalographic (EEG/MEG) signals have been linked to a wide range of brain functions. To date, most of the evidence is obtained by correlating bandpower fluctuations to specific target variables such as reaction times or task ratings, while the causal links between oscillatory activity and behavior remain less clear. Here, we propose to identify causal relationships by the statistical concept of Granger causality, and we investigate which methods are bests suited to reveal Granger causal links between the power of brain oscillations and experimental variables. As an alternative to testing such causal links on the sensor level, we propose to linearly combine the information contained in each sensor in order to create virtual channels, corresponding to estimates of underlying brain oscillations, the Granger-causal relations of which may be assessed. Such linear combinations of sensor can be given by source separation methods such as, for example, Independent Component Analysis (ICA) or by the recently developed Source Power Correlation (SPoC) method. Here we compare Granger causal analysis on power dynamics obtained from i) sensor directly, ii) spatial filtering methods that do not optimize for Granger causality (ICA and SPoC), and iii) a method that directly optimizes spatial filters to extract sources the power dynamics of which maximally Granger causes a given target variable. We refer to this method as Granger Causal Power Analysis (GrangerCPA). Using both simulated and real EEG recordings, we find that computing Granger causality on channel-wise spectral power suffers from a poor signal-to-noise ratio due to volume conduction, while all three multivariate approaches alleviate this issue. In real EEG recordings from subjects performing self-paced foot movements, all three multivariate methods identify neural oscillations with motor-related patterns at a similar performance level. In an auditory perception task, the application of GrangerCPA reveals significant Granger-causal links between alpha oscillations and reaction times in more subjects compared to conventional methods.

Regression-based estimation of ERP waveforms: I. The rERP framework

ERP averaging is an extraordinarily successful method, but can only be applied to a limited range of experimental designs. We introduce the regression-based rERP framework, which extends ERP averaging to handle arbitrary combinations of categorical and continuous covariates, partial confounding, nonlinear effects, and overlapping responses to distinct events, all within a single unified system. rERPs enable a richer variety of paradigms (including high-N naturalistic designs) while preserving the advantages of traditional ERPs. This article provides an accessible introduction to what rERPs are, why they are useful, how they are computed, and when we should expect them to be effective, particularly in cases of partial confounding. A companion article discusses how nonlinear effects and overlap correction can be handled within this framework, as well as practical considerations around baselining, filtering, statistical testing, and artifact rejection. Free software implementing these techniques is available.

The dorsal medial frontal cortex mediates automatic motor inhibition in uncertain contexts: evidence from combined fMRI and EEG studies

Response inhibition is commonly thought to rely on voluntary, reactive, selective, and relatively slow prefrontal mechanisms. In contrast, we suggest here that response inhibition is achieved automatically, nonselectively, within very short delays in uncertain environments. We modified a classical go/nogo protocol to probe context-dependent inhibitory mechanisms. Because no single neuroimaging method can definitely disentangle neural excitation and inhibition, we combined fMRI and EEG recordings in healthy humans. Any stimulus (go or nogo) presented in an uncertain context requiring action restraint was found to evoke activity changes in the supplementary motor complex (SMC) with respect to a control condition in which no response inhibition was required. These changes included: (1) An increase in event-related BOLD activity, (2) an attenuation of the early (170 ms) event related potential generated by a single, consistent source isolated by advanced blind source separation, and (3) an increase in the evoked-EEG Alpha power of this source. Considered together, these results suggest that the BOLD signal evoked by any stimulus in the SMC when the situation is unpredictable can be driven by automatic, nonselective, context-dependent inhibitory activities. This finding reveals the paradoxical mechanisms by which voluntary control of action may be achieved. The ability to provide controlled responses in unpredictable environments would require setting-up the automatic self-inhibitory circuitry within the SMC. Conversely, enabling automatic behavior when the environment becomes predictable would require top-down control to deactivate anticipatorily and temporarily the inhibitory set.

TMS-EEG signatures of GABAergic neurotransmission in the human cortex

Combining transcranial magnetic stimulation (TMS) and electroencephalography (EEG) constitutes a powerful tool to directly assess human cortical excitability and connectivity. TMS of the primary motor cortex elicits a sequence of TMS-evoked EEG potentials (TEPs). It is thought that inhibitory neurotransmission through GABA-A receptors (GABAAR) modulates early TEPs (<50 ms after TMS), whereas GABA-B receptors (GABABR) play a role for later TEPs (at ∼100 ms after TMS). However, the physiological underpinnings of TEPs have not been clearly elucidated yet. Here, we studied the role of GABAA/B-ergic neurotransmission for TEPs in healthy subjects using a pharmaco-TMS-EEG approach. In Experiment 1, we tested the effects of a single oral dose of alprazolam (a classical benzodiazepine acting as allosteric-positive modulator at α1, α2, α3, and α5 subunit-containing GABAARs) and zolpidem (a positive modulator mainly at the α1 GABAAR) in a double-blind, placebo-controlled, crossover study. In Experiment 2, we tested the influence of baclofen (a GABABR agonist) and diazepam (a classical benzodiazepine) versus placebo on TEPs. Alprazolam and diazepam increased the amplitude of the negative potential at 45 ms after stimulation (N45) and decreased the negative component at 100 ms (N100), whereas zolpidem increased the N45 only. In contrast, baclofen specifically increased the N100 amplitude. These results provide strong evidence that the N45 represents activity of α1-subunit-containing GABAARs, whereas the N100 represents activity of GABABRs. Findings open a novel window of opportunity to study alteration of GABAA-/GABAB-related inhibition in disorders, such as epilepsy or schizophrenia.

Magnetoencephalographic signatures of right prefrontal cortex involvement in response inhibition

The prefrontal cortex has a pivotal role in top-down control of cognitive and sensory functions. In complex go-nogo tasks, the right dorsolateral prefrontal cortex is considered to be important for guiding the response inhibition. However, little is known about the temporal dynamics and neurophysiological nature of this activity. To address this issue, we recorded magnetoencephalographic brain activity in 20 women during a visual go-nogo task. The right dorsolateral prefrontal cortex showed an increase for the amplitude of the event-related fields and an increase in induced alpha frequency band activity for nogo in comparison to go trials. The peak of this prefrontal activity preceded the mean reaction time of around 360 ms for go trials, and thus supports the proposed role of right dorsolateral prefrontal cortex in gating the response inhibition and further suggests that right prefrontal alpha band activity might be involved in this gating. However, the results in right dorsolateral prefrontal cortex were similar for both successful and unsuccessful response inhibition. In these conditions, we instead observed pre- and poststimulus differences in alpha band activity in occipital and central areas. Thus, successful response inhibition seemed to additionally depend on prestimulus anticipatory alpha desynchronization in sensory areas as it was reduced prior to unsuccessful response inhibition. In conclusion, we suggest a role for functional inhibition by alpha synchronization not only in sensory, but also in prefrontal areas.

Individual trial-to-trial variability of different components of neuromagnetic signals associated with self-paced finger movements

We measured magnetic cortical responses to self-paced finger movements. Wide frequency band measurements revealed sharp elements of the response wave-shape, and allowed analysis of individual trials. The signal time course was decomposed into three components in the time window from 600ms before to 600ms after the movement. Each component had its own wave-shape and highly individual behavior. Two components displayed large trial-to-trial amplitude variations, whereas the amplitude of the third, high-frequency component remained stable. The frequency spectrum of the high-frequency component decayed exponentially, which indicates deterministic dynamics for the processes generating this magnetic signal. In spite of the large variations in the movement-related cortical signals, the movement itself, as measured by accelerometer attached to the finger tip, remained stable from trial to trial. The magnetic measurements are well-suited to reveal fine details of the process of movement initiation.

Average is optimal: an inverted-U relationship between trial-to-trial brain activity and behavioral performance

It is well known that even under identical task conditions, there is a tremendous amount of trial-to-trial variability in both brain activity and behavioral output. Thus far the vast majority of event-related potential (ERP) studies investigating the relationship between trial-to-trial fluctuations in brain activity and behavioral performance have only tested a monotonic relationship between them. However, it was recently found that across-trial variability can correlate with behavioral performance independent of trial-averaged activity. This finding predicts a U- or inverted-U- shaped relationship between trial-to-trial brain activity and behavioral output, depending on whether larger brain variability is associated with better or worse behavior, respectively. Using a visual stimulus detection task, we provide evidence from human electrocorticography (ECoG) for an inverted-U brain-behavior relationship: When the raw fluctuation in broadband ECoG activity is closer to the across-trial mean, hit rate is higher and reaction times faster. Importantly, we show that this relationship is present not only in the post-stimulus task-evoked brain activity, but also in the pre-stimulus spontaneous brain activity, suggesting anticipatory brain dynamics. Our findings are consistent with the presence of stochastic noise in the brain. They further support attractor network theories, which postulate that the brain settles into a more confined state space under task performance, and proximity to the targeted trajectory is associated with better performance.

The human brain is notoriously “noisy”. Even with identical physical sensory inputs and task demands, brain responses and behavioral output vary tremendously from trial to trial. Such brain and behavioral variability and the relationship between them have been the focus of intense neuroscience research for decades. Traditionally, it is thought that the relationship between trial-to-trial brain activity and behavioral performance is monotonic: the highest or lowest brain activity levels are associated with the best behavioral performance. Using invasive recordings in neurosurgical patients, we demonstrate an inverted-U relationship between brain and behavioral variability. Under such a relationship, moderate brain activity is associated with the best performance, while both very low and very high brain activity levels are predictive of compromised performance. These results have significant implications for our understanding of brain functioning. They further support recent theoretical frameworks that view the brain as an active nonlinear dynamical system instead of a passive signal-processing device.

Combined neurostimulation and neuroimaging in cognitive neuroscience: past, present, and future

Modern neurostimulation approaches in humans provide controlled inputs into the operations of cortical regions, with highly specific behavioral consequences. This enables causal structure–function inferences, and in combination with neuroimaging, has provided novel insights into the basic mechanisms of action of neurostimulation on distributed networks. For example, more recent work has established the capacity of transcranial magnetic stimulation (TMS) to probe causal interregional influences, and their interaction with cognitive state changes. Combinations of neurostimulation and neuroimaging now face the challenge of integrating the known physiological effects of neurostimulation with theoretical and biological models of cognition, for example, when theoretical stalemates between opposing cognitive theories need to be resolved. This will be driven by novel developments, including biologically informed computational network analyses for predicting the impact of neurostimulation on brain networks, as well as novel neuroimaging and neurostimulation techniques. Such future developments may offer an expanded set of tools with which to investigate structure–function relationships, and to formulate and reconceptualize testable hypotheses about complex neural network interactions and their causal roles in cognition.

Effects of mnemonic load on cortical activity during visual working memory: linking ongoing brain activity with evoked responses

The mechanisms generating task-locked changes in cortical potentials remain poorly understood, despite a wealth of research. It has recently been proposed that ongoing brain oscillations are not symmetric, so that task-related amplitude modulations generate a baseline shift that does not average out, leading to slow event-related potentials. We test this hypothesis using multivariate methods to formally assess the co-variation between task-related evoked potentials and spectral changes in scalp EEG during a visual working memory task, which is known to elicit both evoked and sustained cortical activities across broadly distributed cortical regions. 64-channel EEG data were acquired from eight healthy human subjects who completed a visuo-spatial associative working memory task as memory load was parametrically increased from easy to hard. As anticipated, evoked activity showed a complex but robust spatio-temporal waveform maximally expressed bilaterally in the parieto-occipital and anterior midline regions, showing robust effects of memory load that were specific to the stage of the working memory trial. Similarly, memory load was associated with robust spectral changes in the theta and alpha range, throughout encoding in posterior regions and through maintenance and retrieval in anterior regions, consistent with the additional resources required for decision making in prefrontal cortex. Analysis of the relationship between event-related changes in slow potentials and cortical rhythms, using partial least squares, is indeed consistent with the notion that the former make a causal contribution to the latter.

No somatotopy of sensorimotor alpha-oscillation responses to differential finger stimulation

The somatotopic layout of the primary somatosensory cortex is known for its fine spatial structure as delineated in single cell recordings and macroscopic EEG evoked responses. While a gross somatotopic layout has been revealed also for neuronal oscillations responding to sensorimotor stimulation of distant body parts (e.g. hand vs. foot), it is still unclear whether these oscillatory dynamics exhibit fine spatial layout comparable to those found in evoked responses. In twelve healthy subjects we applied electric stimuli to the first (D1) and fifth finger (D5) of the same hand while performing high-density electroencephalography. We used Common Spatial Pattern analysis to optimally extract components showing the strongest Event-Related Desynchronization (ERD) in neuronal alpha oscillations. In agreement with the previous studies, dipole locations of Somatosensory Evoked Potentials (SEPs) confirmed the existence of spatially distinct representations of each finger. In contrast, dipole locations of alpha-ERD patterns did not yield spatially different source locations, indicating that the stimulation of different fingers most likely resulted in oscillatory activity of overlapping neuronal populations. When both fingers were stimulated simultaneously the SEP dipole strength was found increased in comparison to a stimulation of either finger alone, in agreement with spatially distinct SEP to finger stimulation. The strength of ERD, on the other hand, was the same regardless of whether either one or both fingers were stimulated. Our findings might reflect anatomical constraints on the sequential temporal activation of fingers' skin where almost simultaneous activation of many fingers usually occurs in everyday activities, such as grasping or holding objects. Such simultaneity is unlikely to benefit from slow amplitude modulation of alpha oscillations, which would rather be beneficial for contrasting somatosensory processing of distinct body parts.

Distinction between added-energy and phase-resetting mechanisms in non-invasively detected somatosensory evoked responses

Non-invasively recorded averaged event-related potentials (ERP) represent a convenient opportunity to investigate human brain perceptive and cognitive processes. Nevertheless, generative ERP mechanisms are still debated. Two previous approaches have been contested in the past: the added-energy model in which the response raises independently from the ongoing background activity, and the phase-reset model, based on stimulus-driven synchronization of oscillatory ongoing activity. Many criteria for the distinction of these two models have been proposed, but there is no definitive methodology to disentangle them, owing also to the limited information at the single trial level. Here, we propose a new approach combining low-noise EEG technology and multivariate decomposition techniques. We present theoretical analyses based on simulated data and identify in high-frequency somatosensory evoked responses an optimal target for the distinction between the two mechanisms.

α-band oscillations, attention, and controlled access to stored information

Alpha-band oscillations are the dominant oscillations in the human brain and recent evidence suggests that they have an inhibitory function. Nonetheless, there is little doubt that alpha-band oscillations also play an active role in information processing. In this article, I suggest that alpha-band oscillations have two roles (inhibition and timing) that are closely linked to two fundamental functions of attention (suppression and selection), which enable controlled knowledge access and semantic orientation (the ability to be consciously oriented in time, space, and context). As such, alpha-band oscillations reflect one of the most basic cognitive processes and can also be shown to play a key role in the coalescence of brain activity in different frequencies.

Detrended fluctuation analysis: a scale-free view on neuronal oscillations

Recent years of research have shown that the complex temporal structure of ongoing oscillations is scale-free and characterized by long-range temporal correlations. Detrended fluctuation analysis (DFA) has proven particularly useful, revealing that genetic variation, normal development, or disease can lead to differences in the scale-free amplitude modulation of oscillations. Furthermore, amplitude dynamics is remarkably independent of the time-averaged oscillation power, indicating that the DFA provides unique insights into the functional organization of neuronal systems. To facilitate understanding and encourage wider use of scaling analysis of neuronal oscillations, we provide a pedagogical explanation of the DFA algorithm and its underlying theory. Practical advice on applying DFA to oscillations is supported by MATLAB scripts from the Neurophysiological Biomarker Toolbox (NBT) and links to the NBT tutorial website http://www.nbtwiki.net/. Finally, we provide a brief overview of insights derived from the application of DFA to ongoing oscillations in health and disease, and discuss the putative relevance of criticality for understanding the mechanism underlying scale-free modulation of oscillations.