Role of neuronal synchrony in the generation of evoked EEG/MEG responses

Single neuron contributions to the auditory brainstem EEG

The auditory brainstem response (ABR) is an acoustically evoked EEG potential that is an important diagnostic tool for hearing loss, especially in newborns. The ABR originates from the response sequence of auditory brainstem nuclei, and a click-evoked ABR typically shows three positive peaks ('waves') within the first six milliseconds. However, an assignment of the waves of the ABR to specific sources is difficult, and a quantification of contributions to the ABR waves is not available. Here, we exploit the large size and physical separation of the barn owl first-order cochlear nucleus magnocellularis (NM) to estimate single-cell contributions to the ABR. We simultaneously recorded NM neurons' spikes and the EEG, and found that ≳ 5, 000 spontaneous single-cell spikes are necessary to isolate a significant spike-triggered average response at the EEG electrode. An average single-neuron contribution to the ABR was predicted by convolving the spike-triggered average with the cell's peri-stimulus time histogram. Amplitudes of predicted contributions of single NM cells typically reached 32.9 ± 1.1 nV (mean ± SE, range: 2.5 - 162.7 nV), or 0.07 ± 0.02% (median ± SE range: 0.01 - 4.0%) of the ABR amplitude. The time of the predicted peak coincided best with the peak of the ABR wave II, and this coincidence was independent of the click sound level. Our results suggest that wave II of the ABR is shaped by a small fraction of NM units.

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.

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.

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.

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.

Focal Suppression of Distractor Sounds by Selective Attention in Auditory Cortex

Auditory selective attention is required for parsing crowded acoustic environments, but cortical systems mediating the influence of behavioral state on auditory perception are not well characterized. Previous neurophysiological studies suggest that attention produces a general enhancement of neural responses to important target sounds versus irrelevant distractors. However, behavioral studies suggest that in the presence of masking noise, attention provides a focal suppression of distractors that compete with targets. Here, we compared effects of attention on cortical responses to masking versus non-masking distractors, controlling for effects of listening effort and general task engagement. We recorded single-unit activity from primary auditory cortex (A1) of ferrets during behavior and found that selective attention decreased responses to distractors masking targets in the same spectral band, compared with spectrally distinct distractors. This suppression enhanced neural target detection thresholds, suggesting that limited attention resources serve to focally suppress responses to distractors that interfere with target detection. Changing effort by manipulating target salience consistently modulated spontaneous but not evoked activity. Task engagement and changing effort tended to affect the same neurons, while attention affected an independent population, suggesting that distinct feedback circuits mediate effects of attention and effort in A1.

Changes in Dimensionality and Fractal Scaling Suggest Soft-Assembled Dynamics in Human EEG

Humans are high-dimensional, complex systems consisting of many components that must coordinate in order to perform even the simplest of activities. Many behavioral studies, especially in the movement sciences, have advanced the notion of soft-assembly to describe how systems with many components coordinate to perform specific functions while also exhibiting the potential to re-structure and then perform other functions as task demands change. Consistent with this notion, within cognitive neuroscience it is increasingly accepted that the brain flexibly coordinates the networks needed to cope with changing task demands. However, evaluation of various indices of soft-assembly has so far been absent from neurophysiological research. To begin addressing this gap, we investigated task-related changes in two distinct indices of soft-assembly using the established phenomenon of EEG repetition suppression. In a repetition priming task, we assessed evidence for changes in the correlation dimension and fractal scaling exponents during stimulus-locked event-related potentials, as a function of stimulus onset and familiarity, and relative to spontaneous non-task-related activity. Consistent with predictions derived from soft-assembly, results indicated decreases in dimensionality and increases in fractal scaling exponents from resting to pre-stimulus states and following stimulus onset. However, contrary to predictions, familiarity tended to increase dimensionality estimates. Overall, the findings support the view from soft-assembly that neural dynamics should become increasingly ordered as external task demands increase, and support the broader application of soft-assembly logic in understanding human behavior and electrophysiology.

Encoding of natural sounds by variance of the cortical local field potential

Neural encoding of sensory stimuli is typically studied by averaging neural signals across repetitions of the same stimulus. However, recent work has suggested that the variance of neural activity across repeated trials can also depend on sensory inputs. Here we characterize how intertrial variance of the local field potential (LFP) in primary auditory cortex of awake ferrets is affected by continuous natural sound stimuli. We find that natural sounds often suppress the intertrial variance of low-frequency LFP (<16 Hz). However, the amount of the variance reduction is not significantly correlated with the amplitude of the mean response at the same recording site. Moreover, the variance changes occur with longer latency than the mean response. Although the dynamics of the mean response and intertrial variance differ, spectro-temporal receptive field analysis reveals that changes in LFP variance have frequency tuning similar to multiunit activity at the same recording site, suggesting a local origin for changes in LFP variance. In summary, the spectral tuning of LFP intertrial variance and the absence of a correlation with the amplitude of the mean evoked LFP suggest substantial heterogeneity in the interaction between spontaneous and stimulus-driven activity across local neural populations in auditory cortex.

A Role of Phase-Resetting in Coordinating Large Scale Neural Networks During Attention and Goal-Directed Behavior

Short periods of oscillatory activation are ubiquitous signatures of neural circuits. A broad range of studies documents not only their circuit origins, but also a fundamental role for oscillatory activity in coordinating information transfer during goal directed behavior. Recent studies suggest that resetting the phase of ongoing oscillatory activity to endogenous or exogenous cues facilitates coordinated information transfer within circuits and between distributed brain areas. Here, we review evidence that pinpoints phase resetting as a critical marker of dynamic state changes of functional networks. Phase resets: (1) set a "neural context" in terms of narrow band frequencies that uniquely characterizes the activated circuits; (2) impose coherent low frequency phases to which high frequency activations can synchronize, identifiable as cross-frequency correlations across large anatomical distances; (3) are critical for neural coding models that depend on phase, increasing the informational content of neural representations; and (4) likely originate from the dynamics of canonical E-I circuits that are anatomically ubiquitous. These multiple signatures of phase resets are directly linked to enhanced information transfer and behavioral success. We survey how phase resets re-organize oscillations in diverse task contexts, including sensory perception, attentional stimulus selection, cross-modal integration, Pavlovian conditioning, and spatial navigation. The evidence we consider suggests that phase-resets can drive changes in neural excitability, ensemble organization, functional networks, and ultimately, overt behavior.

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.

Theta-band phase tracking in the two-talker problem

It is usually easy to understand speech, but when several people are talking at once it becomes difficult. The brain must select one speech stream and ignore distracting streams. We tested a theory about the neural and computational mechanisms of attentional selection. The theory is that oscillating signals in brain networks phase-lock with amplitude fluctuations in speech. By doing this, brain-wide networks acquire information from the selected speech, but ignore other speech signals on the basis of their non-preferred dynamics. Two predictions were supported: first, attentional selection boosted the power of neuroelectric signals that were phase-locked with attended speech, but not ignored speech. Second, this phase selectivity was associated with better recall of the attended speech.

Transcranial magnetic stimulation-induced global propagation of transient phase resetting associated with directional information flow

Electroencephalogram (EEG) phase synchronization analyses can reveal large-scale communication between distant brain areas. However, it is not possible to identify the directional information flow between distant areas using conventional phase synchronization analyses. In the present study, we applied transcranial magnetic stimulation (TMS) to the occipital area in subjects who were resting with their eyes closed, and analyzed the spatial propagation of transient TMS-induced phase resetting by using the transfer entropy (TE), to quantify the causal and directional flow of information. The time-frequency EEG analysis indicated that the theta (5 Hz) phase locking factor (PLF) reached its highest value at the distant area (the motor area in this study), with a time lag that followed the peak of the transient PLF enhancements of the TMS-targeted area at the TMS onset. Phase-preservation index (PPI) analyses demonstrated significant phase resetting at the TMS-targeted area and distant area. Moreover, the TE from the TMS-targeted area to the distant area increased clearly during the delay that followed TMS onset. Interestingly, the time lags were almost coincident between the PLF and TE results (152 vs. 165 ms), which provides strong evidence that the emergence of the delayed PLF reflects the causal information flow. Such tendencies were observed only in the higher-intensity TMS condition, and not in the lower-intensity or sham TMS conditions. Thus, TMS may manipulate large-scale causal relationships between brain areas in an intensity-dependent manner. We demonstrated that single-pulse TMS modulated global phase dynamics and directional information flow among synchronized brain networks. Therefore, our results suggest that single-pulse TMS can manipulate both incoming and outgoing information in the TMS-targeted area associated with functional changes.

Functional and effective connectivity in subthalamic local field potential recordings of patients with Parkinson's disease

In Parkinson's disease (PD) levodopa-associated changes in the power and long-range temporal correlations of beta oscillations have been demonstrated, yet the presence and modulation of genuine connectivity in local field potentials (LFP) recorded from the subthalamic nucleus (STN) remains an open question. The present study investigated LFP recorded bilaterally from the STN at wakeful rest in ten patients with PD after overnight withdrawal of levodopa (OFF) and after a single dose levodopa administration (ON). We utilized connectivity measures being insensitive to volume conduction (functional connectivity: non-zero imaginary part of coherency; effective connectivity: phase-slope index). We demonstrated the presence of neuronal interactions in the frequency range of 10-30 Hz in STN-LFP without a preferential directionality of interactions between different contacts along the electrode tracks. While the direction of neuronal interactions per se was preserved after levodopa administration, functional connectivity and the ventral-dorsal information flow were modulated by medication. The OFF-ON differences in functional connectivity were correlated with the levodopa-induced improvement in clinical Unified Parkinson's Disease Rating Scale scores. We hypothesize that regional neuronal interactions, as reflected in STN-LFP connectivity, might represent a basis for the intra-nuclear spatial specificity of deep brain stimulation. Moreover, our results suggest the potential use of volume conduction-insensitive measures of connectivity in STN-LFP as a marker of clinical motor symptoms in PD.

State-dependent perceptual learning

Learning constitutes a fundamental property of the human brain-yet an unresolved puzzle is the profound variability of the learning success between individuals. Here we highlight the relevance of individual ongoing brain states as sources of the learning variability in exposure-based somatosensory perceptual learning. Electroencephalogram recordings of ongoing rhythmic brain activity before and during learning revealed that prelearning parietal alpha oscillations as well as during-learning stimulus-induced contralateral central alpha changes are predictive for the learning outcome. These two distinct alpha rhythm sources predicted up to 64% of the observed learning variability, one source representing an idling state with posteroparietal focus and a potential link to the default mode network, the other representing the sensorimotor mu rhythm, whose desynchronization is indicative for the degree of engagement of sensorimotor neuronal populations during application of the learning stimuli. Unspecific effects due to global shifts of attention or vigilance do not explain our observations. Our study thus suggests a brain state-dependency of perceptual learning success in humans opening new avenues for supportive learning tools in the clinical and educational realms.

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.

Effects of neural synchrony on surface EEG

It has long been assumed that the surface electroencephalography (EEG) signal depends on both the amplitude and spatial synchronization of underlying neural activity, though isolating their respective contribution remains elusive. To address this, we made simultaneous surface EEG measurements along with intracortical recordings of local field potentials (LFPs) in the primary visual cortex of behaving nonhuman primates. We found that trial-by-trial fluctuations in EEG power could be explained by a linear combination of LFP power and interelectrode temporal synchrony. This effect was observed in both stimulus and stimulus-free conditions and was particularly strong in the gamma range (30-100 Hz). Subsequently, we used pharmacological manipulations to show that neural synchrony can produce a positively modulated EEG signal even when the LFP signal is negatively modulated. Taken together, our results demonstrate that neural synchrony can modulate EEG signals independently of amplitude changes in neural activity. This finding has strong implications for the interpretation of EEG in basic and clinical research, and helps reconcile EEG response discrepancies observed in different modalities (e.g., EEG vs. functional magnetic resonance imaging) and different spatial scales (e.g., EEG vs. intracranial EEG).

Power and phase properties of oscillatory neural responses in the presence of background activity

Natural sensory inputs, such as speech and music, are often rhythmic. Recent studies have consistently demonstrated that these rhythmic stimuli cause the phase of oscillatory, i.e. rhythmic, neural activity, recorded as local field potential (LFP), electroencephalography (EEG) or magnetoencephalography (MEG), to synchronize with the stimulus. This phase synchronization, when not accompanied by any increase of response power, has been hypothesized to be the result of phase resetting of ongoing, spontaneous, neural oscillations measurable by LFP, EEG, or MEG. In this article, however, we argue that this same phenomenon can be easily explained without any phase resetting, and where the stimulus-synchronized activity is generated independently of background neural oscillations. It is demonstrated with a simple (but general) stochastic model that, purely due to statistical properties, phase synchronization, as measured by 'inter-trial phase coherence', is much more sensitive to stimulus-synchronized neural activity than is power. These results question the usefulness of analyzing the power and phase of stimulus-synchronized activity as separate and complementary measures; particularly in the case of attempting to demonstrate whether stimulus-synchronized neural activity is generated by phase resetting of ongoing neural oscillations.

Are high-frequency (600 Hz) oscillations in human somatosensory evoked potentials due to phase-resetting phenomena?

OBJECTIVE

Median nerve somatosensory evoked potentials (SEP) contain a brief oscillatory wavelet burst at about 600 Hz (σ-burst) superimposed on the initial cortical component (N20). While invasive single-cell recordings suggested that this burst is generated by increased neuronal spiking activity in area 3b, recent non-invasive scalp recordings could not reveal concomitant single-trial added-activity, suggesting that the SEP burst might instead be generated by phase-reset of ongoing high-frequency EEG. Here, a statistical model and exemplary data are presented reconciling these seemingly contradictory results.

METHODS

A statistical model defined the conditions required to detect added-activity in a set of single-trial SEP. Its predictions were tested by analyzing human single-trial scalp SEP recorded with custom-made low-noise amplifiers.

RESULTS

The noise level in previous studies did not allow to detect single-trial added-activity in the period concomitant with the trial-averaged σ-burst. In contrast, optimized low-noise recordings do reveal added-activity in a set of single-trials.

CONCLUSIONS

The experimental noise level is the decisive factor determining the detectability of added-activity in single-trials. A low-noise experiment provided direct evidence that the SEP σ-burst is at least partly generated by added-activity matching earlier invasive single-cell recordings.

SIGNIFICANCE

Quantitative criteria are provided for the feasibility of single-trial detectability of band-limited added-activity.

The neuromagnetic response to spoken sentences: co-modulation of theta band amplitude and phase

Speech elicits a phase-locked response in the auditory cortex that is dominated by theta (3-7 Hz) frequencies when observed via magnetoencephalography (MEG). This phase-locked response is potentially explained as new phase-locked activity superimposed on the ongoing theta oscillation or, alternatively, as phase-resetting of the ongoing oscillation. The conventional method used to distinguish between the two hypotheses is the comparison of post- to prestimulus amplitude for the phase-locked frequency across a set of trials. In theory, increased amplitude indicates the presence of additive activity, while unchanged amplitude points to phase-resetting. However, this interpretation may not be valid if the amplitude of ongoing background activity also changes following the stimulus. In this study, we employ a new approach that circumvents this problem. Specifically, we utilize a fine-grained time-frequency analysis of MEG channel data to examine the co-modulation of amplitude change and phase coherence in the post-stimulus theta-band response. If the phase-locked response is attributable solely to phase-resetting of the ongoing theta oscillation, then amplitude and phase coherence should be uncorrelated. In contrast, additive activity should produce a positive correlation. We find significant positive correlation not only during the onset response but also throughout the response period. In fact, transient increases in phase coherence are accompanied by transient increases in amplitude in accordance with a "signal plus background" model of the evoked response. The results support the hypothesis that the theta-band phase-locked response to attended speech observed using MEG is dominated by additive phase-locked activity.

A comparison of methods for assessing alpha phase resetting in electrophysiology, with application to intracerebral EEG in visual areas

There are two competing views on the mechanisms underlying the generation of visual evoked potentials/fields in EEG/MEG. The classical hypothesis assumes an additive wave on top of background noise. Another hypothesis states that the evoked activity can totally or partially arise from a phase resetting of the ongoing alpha rhythm. There is no consensus however, on the best tools for distinguishing between these two hypotheses. In this study, we have tested different measures on a large series of simulations under a variety of scenarios, involving in particular trial-to-trial variability and different dynamics of ongoing alpha rhythm. No single measure or set of measures was found to be necessary or sufficient for defining phase resetting in the context of our simulations. Still, simulations permitted to define criteria that were the most reliable in practice for distinguishing additive and phase resetting hypotheses. We have then applied these criteria on intracerebral EEG data recordings in the visual areas during a visual discrimination task. We investigated the intracerebral channels that presented both ERP and ongoing alpha oscillations (n=37). Within these channels, a total of 30% fulfilled phase resetting criteria during the generation of the visual evoked potential, based on criteria derived from simulations. Moreover, 19% of the 37 channels presented dependence of the ERP on the level of pre-stimulus alpha. Only 5% of channels fulfilled both the simulation-related criteria and dependence on baseline alpha level. Our simulation study points out to the difficulty of clearly assessing phase resetting based on observed macroscopic electrophysiological signals. Still, some channels presented an indication of phase resetting in the context of our simulations. This needs to be confirmed by further work, in particular at a smaller recording scale.