Detecting alpha rhythm phase reset by phase sorting: caveats to consider

Phase of sensorimotor μ-oscillation modulates cortical responses to transcranial magnetic stimulation of the human motor cortex

KEY POINTS

Oscillatory brain activity coordinates the response of cortical neurons to synaptic inputs in a phase-dependent manner. Larger motor-evoked responses are obtained in a hand muscle when transcranial magnetic stimulation (TMS) is synchronized to the phase of the sensorimotor μ-rhythm. In this study we further showed that TMS applied at the negative vs. positive peak of the μ-rhythm is associated with higher absolute amplitude of the evoked EEG potential at 100 ms after stimulation. This demonstrates that cortical responses are sensitive to excitability fluctuation with brain oscillations Our results indicate that brain state-dependent stimulation is a new useful technique for the investigation of stimulus-related cortical dynamics.

ABSTRACT

Oscillatory brain activity coordinates the response of cortical neurons to synaptic inputs in a phase-dependent manner. Transcranial magnetic stimulation (TMS) of the human primary motor cortex elicits larger motor-evoked potentials (MEPs) when applied at the negative vs. positive peak of the sensorimotor μ-rhythm recorded with EEG, demonstrating that this phase represents a state of higher excitability of the cortico-spinal system. Here, we investigated the influence of the phase of the μ-rhythm on cortical responses to TMS as measured by EEG. We tested different stimulation intensities above and below resting motor threshold (RMT), and a realistic sham TMS condition. TMS at 110% RMT applied at the negative vs. positive peak of the μ-rhythm was associated with higher absolute amplitudes of TMS-evoked potentials at 70 ms (P70) and 100 ms (N100). Enhancement of the N100 was confirmed with negative peak-triggered 90% RMT TMS, while phase of the μ-rhythm did not influence evoked responses elicited by sham TMS. These findings extend the idea that TMS applied at the negative vs. positive peak of the endogenous μ-oscillation recruits a larger portion of neurons as a function of stimulation intensity. This further corroborates that brain oscillations determine fluctuations in cortical excitability and establishes phase-triggered EEG-TMS as a sensitive tool to investigate the effects of brain oscillations on stimulus-related cortical dynamics.

Dynamics of corticospinal motor control during overground and treadmill walking in humans

Increasing evidence suggests cortical involvement in the control of human gait. However, the nature of corticospinal interactions remains poorly understood. We performed time-frequency analysis of electrophysiological activity acquired during treadmill and overground walking in 22 healthy, young adults. Participants walked at their preferred speed (4.2, SD 0.4 km/h), which was matched across both gait conditions. Event-related power, corticomuscular coherence (CMC), and intertrial coherence (ITC) were assessed for EEG from bilateral sensorimotor cortices and EMG from the bilateral tibialis anterior (TA) muscles. Cortical power, CMC, and ITC at theta, alpha, beta, and gamma frequencies (4-45 Hz) increased during the double support phase of the gait cycle for both overground and treadmill walking. High beta (21-30 Hz) CMC and ITC of EMG was significantly increased during overground compared with treadmill walking, as well as EEG power in theta band (4-7 Hz). The phase spectra revealed positive time lags at alpha, beta, and gamma frequencies, indicating that the EEG response preceded the EMG response. The parallel increases in power, CMC, and ITC during double support suggest evoked responses at spinal and cortical populations rather than a modulation of ongoing corticospinal oscillatory interactions. The evoked responses are not consistent with the idea of synchronization of ongoing corticospinal oscillations but instead suggest coordinated cortical and spinal inputs during the double support phase. Frequency-band dependent differences in power, CMC, and ITC between overground and treadmill walking suggest differing neural control for the two gait modalities, emphasizing the task-dependent nature of neural processes during human walking. NEW & NOTEWORTHY We investigated cortical and spinal activity during overground and treadmill walking in healthy adults. Parallel increases in power, corticomuscular coherence, and intertrial coherence during double support suggest evoked responses at spinal and cortical populations rather than a modulation of ongoing corticospinal oscillatory interactions. These findings identify neurophysiological mechanisms that are important for understanding cortical control of human gait in health and disease.

The phase of pre-stimulus alpha oscillations influences the visual perception of stimulus timing

This study examined the influence of pre-stimulus alpha phase and attention on whether two visual stimuli occurring closely in time were perceived as simultaneous or asynchronous. The results demonstrated that certain phases of alpha in the period immediately preceding stimulus onset were associated with a higher proportion of stimuli judged to be asynchronous. Furthermore, this effect was shown to occur independently of both visuo-spatial attention and alpha amplitude. The findings are compatible with proposals that alpha phase reflects cyclic shifts in neuronal excitability. Importantly, however, the results further suggest that fluctuations in neuronal excitability can create a periodicity in neuronal transfer that can have functional consequences that are decoupled from changes in alpha amplitude. This study therefore provides evidence that perceptual processes fluctuate periodically although it remains uncertain whether this implies the discrete temporal framing of perception.

•

Pre-stimulus alpha phase influences the perceived timing of two visual stimuli.

•

Phase effects were independent of both spatial attention and alpha amplitude.

•

The results are compatible with the neural excitability hypothesis of alpha phase.

Exploring the Relationship Between Childhood Maltreatment and Addiction: A Review of the Neurocognitive Evidence

Childhood maltreatment has been shown to increase the risk of a range of psychiatric disorders including substance use disorders (SUDs) and is associated with the onset, course and severity of illness. We review the evidence for alterations in brain structure and neurocognitive processing in individuals who have experienced childhood maltreatment, focusing specifically on changes related to reward processing, executive functioning and affect processing. Changes in these neurocognitive systems have been documented in adults presenting with SUDs, who are typically characterized by heightened subcortico-striatal responses to salient stimuli and impairments in fronto-cingulate regulation. Maltreatment-specific effects in these processing domains may account for the particularly severe clinical presentation of SUDs in adults with histories of maltreatment in childhood. The findings are considered in relation to the theory of latent vulnerability, which contends that alterations in these neurocognitive systems may reflect calibration to early risk environments that in turn increases the risk of developing of SUDs later in life.

State-dependencies of learning across brain scales

Learning is a complex brain function operating on different time scales, from milliseconds to years, which induces enduring changes in brain dynamics. The brain also undergoes continuous “spontaneous” shifts in states, which, amongst others, are characterized by rhythmic activity of various frequencies. Besides the most obvious distinct modes of waking and sleep, wake-associated brain states comprise modulations of vigilance and attention. Recent findings show that certain brain states, particularly during sleep, are essential for learning and memory consolidation. Oscillatory activity plays a crucial role on several spatial scales, for example in plasticity at a synaptic level or in communication across brain areas. However, the underlying mechanisms and computational rules linking brain states and rhythms to learning, though relevant for our understanding of brain function and therapeutic approaches in brain disease, have not yet been elucidated. Here we review known mechanisms of how brain states mediate and modulate learning by their characteristic rhythmic signatures. To understand the critical interplay between brain states, brain rhythms, and learning processes, a wide range of experimental and theoretical work in animal models and human subjects from the single synapse to the large-scale cortical level needs to be integrated. By discussing results from experiments and theoretical approaches, we illuminate new avenues for utilizing neuronal learning mechanisms in developing tools and therapies, e.g., for stroke patients and to devise memory enhancement strategies for the elderly.

The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models

During the past two decades growing evidence indicates that brain oscillations in the alpha band (~10 Hz) not only reflect an "idle" state of cortical activity, but also take a more active role in the generation of complex cognitive functions. A recent study shows that more than 60% of the observed inter-subject variability in perceptual learning can be ascribed to ongoing alpha activity. This evidence indicates a significant role of alpha oscillations for perceptual learning and hence motivates to explore the potential underlying mechanisms. Hence, it is the purpose of this review to highlight existent evidence that ascribes intrinsic alpha oscillations a role in shaping our ability to learn. In the review, we disentangle the alpha rhythm into different neural signatures that control information processing within individual functional building blocks of perceptual learning. We further highlight computational studies that shed light on potential mechanisms regarding how alpha oscillations may modulate information transfer and connectivity changes relevant for learning. To enable testing of those model based hypotheses, we emphasize the need for multidisciplinary approaches combining assessment of behavior and multi-scale neuronal activity, active modulation of ongoing brain states and computational modeling to reveal the mathematical principles of the complex neuronal interactions. In particular we highlight the relevance of multi-scale modeling frameworks such as the one currently being developed by "The Virtual Brain" project.

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.

Statistical method for detecting phase shifts in alpha rhythm from human electroencephalogram data

We developed a statistical method for detecting discontinuous phase changes (phase shifts) in fluctuating alpha rhythms in the human brain from electroencephalogram (EEG) data obtained in a single trial. This method uses the state space models and the line process technique, which is a Bayesian method for detecting discontinuity in an image. By applying this method to simulated data, we were able to detect the phase and amplitude shifts in a single simulated trial. Further, we demonstrated that this method can detect phase shifts caused by a visual stimulus in the alpha rhythm from experimental EEG data even in a single trial. The results for the experimental data showed that the timings of the phase shifts in the early latency period were similar between many of the trials, and that those in the late latency period were different between the trials. The conventional averaging method can only detect phase shifts that occur at similar timings between many of the trials, and therefore, the phase shifts that occur at differing timings cannot be detected using the conventional method. Consequently, our obtained results indicate the practicality of our method. Thus, we believe that our method will contribute to studies examining the phase dynamics of nonlinear alpha rhythm oscillators.

The virtual brain integrates computational modeling and multimodal neuroimaging

Brain function is thought to emerge from the interactions among neuronal populations. Apart from traditional efforts to reproduce brain dynamics from the micro- to macroscopic scales, complementary approaches develop phenomenological models of lower complexity. Such macroscopic models typically generate only a few selected-ideally functionally relevant-aspects of the brain dynamics. Importantly, they often allow an understanding of the underlying mechanisms beyond computational reproduction. Adding detail to these models will widen their ability to reproduce a broader range of dynamic features of the brain. For instance, such models allow for the exploration of consequences of focal and distributed pathological changes in the system, enabling us to identify and develop approaches to counteract those unfavorable processes. Toward this end, The Virtual Brain (TVB) ( www.thevirtualbrain.org ), a neuroinformatics platform with a brain simulator that incorporates a range of neuronal models and dynamics at its core, has been developed. This integrated framework allows the model-based simulation, analysis, and inference of neurophysiological mechanisms over several brain scales that underlie the generation of macroscopic neuroimaging signals. In this article, we describe how TVB works, and we present the first proof of concept.

Towards a unified understanding of event-related changes in the EEG: the firefly model of synchronization through cross-frequency phase modulation

Although event-related potentials (ERPs) are widely used to study sensory, perceptual and cognitive processes, it remains unknown whether they are phase-locked signals superimposed upon the ongoing electroencephalogram (EEG) or result from phase-alignment of the EEG. Previous attempts to discriminate between these hypotheses have been unsuccessful but here a new test is presented based on the prediction that ERPs generated by phase-alignment will be associated with event-related changes in frequency whereas evoked-ERPs will not. Using empirical mode decomposition (EMD), which allows measurement of narrow-band changes in the EEG without predefining frequency bands, evidence was found for transient frequency slowing in recognition memory ERPs but not in simulated data derived from the evoked model. Furthermore, the timing of phase-alignment was frequency dependent with the earliest alignment occurring at high frequencies. Based on these findings, the Firefly model was developed, which proposes that both evoked and induced power changes derive from frequency-dependent phase-alignment of the ongoing EEG. Simulated data derived from the Firefly model provided a close match with empirical data and the model was able to account for i) the shape and timing of ERPs at different scalp sites, ii) the event-related desynchronization in alpha and synchronization in theta, and iii) changes in the power density spectrum from the pre-stimulus baseline to the post-stimulus period. The Firefly Model, therefore, provides not only a unifying account of event-related changes in the EEG but also a possible mechanism for cross-frequency information processing.

Repetitive tactile stimulation changes resting-state functional connectivity-implications for treatment of sensorimotor decline

Neurological disorders and physiological aging can lead to a decline of perceptual abilities. In contrast to the conventional therapeutic approach that comprises intensive training and practicing, passive repetitive sensory stimulation (RSS) has recently gained increasing attention as an alternative to countervail the sensory decline by improving perceptual abilities without the need of active participation. A particularly effective type of high-frequency RSS, utilizing Hebbian learning principles, improves perceptual acuity as well as sensorimotor functions and has been successfully applied to treat chronic stroke patients and elderly subjects. High-frequency RSS has been shown to induce plastic changes of somatosensory cortex such as representational map reorganization, but its impact on the brain's ongoing network activity and resting-state functional connectivity has not been investigated so far. Here, we applied high-frequency RSS in healthy human subjects and analyzed resting state Electroencephalography (EEG) functional connectivity patterns before and after RSS by means of imaginary coherency (ImCoh), a frequency-specific connectivity measure which is known to reduce over-estimation biases due to volume conduction and common reference. Thirty minutes of passive high-frequency RSS lead to significant ImCoh-changes of the resting state mu-rhythm in the individual upper alpha frequency band within distributed sensory and motor cortical areas. These stimulation induced distributed functional connectivity changes likely underlie the previously observed improvement in sensorimotor integration.

Evoked traveling alpha waves predict visual-semantic categorization-speed

In the present study we have tested the hypothesis that evoked traveling alpha waves are behaviorally significant. The results of a visual-semantic categorization task show that three early ERP components including the P1-N1 complex had a dominant frequency characteristic in the alpha range and behaved like traveling waves do. They exhibited a traveling direction from midline occipital to right lateral parietal sites. Phase analyses revealed that this traveling behavior of ERP components could be explained by phase-delays in the alpha but not theta and beta frequency range. Most importantly, we found that the speed of the traveling alpha wave was significantly and negatively correlated with reaction time indicating that slow traveling speed was associated with fast picture-categorization. We conclude that evoked alpha oscillations are functionally associated with early access to visual-semantic information and generate--or at least modulate--the early waveforms of the visual ERP.

Theta lingua franca: a common mid-frontal substrate for action monitoring processes

We present evidence that a multitude of mid-frontal event-related potential (ERP) components partially reflect a common theta band oscillatory process. Specifically, mid-frontal ERP components in the N2 time range and error-related negativity time range are parsimoniously characterized as reflections of theta band activities. Forty participants completed three different tasks with varying stimulus-response demands. Permutation tests were used to identify the dominant time-frequency responses of stimulus- and response-locked conditions as well as the enhanced responses to novelty, conflict, punishment, and error. A dominant theta band feature was found in all conditions, and both ERP component amplitudes and theta power measures were similarly modulated by novelty, conflict, punishment, and error. The findings support the hypothesis that generic and reactive medial prefrontal cortex processes are parsimoniously reflected by theta band activities.

Modulation of visually evoked cortical FMRI responses by phase of ongoing occipital alpha oscillations

Using simultaneous electroencephalography as a measure of ongoing activity and functional magnetic resonance imaging (fMRI) as a measure of the stimulus-driven neural response, we examined whether the amplitude and phase of occipital alpha oscillations at the onset of a brief visual stimulus affects the amplitude of the visually evoked fMRI response. When accounting for intrinsic coupling of alpha amplitude and occipital fMRI signal by modeling and subtracting pseudo-trials, no significant effect of prestimulus alpha amplitude on the evoked fMRI response could be demonstrated. Regarding the effect of alpha phase, we found that stimuli arriving at the peak of the alpha cycle yielded a lower blood oxygenation level-dependent (BOLD) fMRI response in early visual cortex (V1/V2) than stimuli presented at the trough of the cycle. Our results therefore show that phase of occipital alpha oscillations impacts the overall strength of a visually evoked response, as indexed by the BOLD signal. This observation complements existing evidence that alpha oscillations reflect periodic variations in cortical excitability and suggests that the phase of oscillations in postsynaptic potentials can serve as a mechanism of gain control for incoming neural activity. Finally, our findings provide a putative neural basis for observations of alpha phase dependence of visual perceptual performance.

Pre-stimulus alpha phase-alignment predicts P1-amplitude

Since years there is a hotly discussed dispute whether event-related potentials are either generated by an evoked component or by resetting of ongoing phase. We argue that phase-reset must not be proven in order to accept the general involvement of phase in ERP-generation as it is only one of several possible mechanisms influencing or generating certain ERP-components. Supporting data are presented showing that positive peaks of ongoing pre-stimulus alpha activity are not randomly distributed in time across trials. Most importantly, we found that a certain kind of pre-stimulus phase concentration that represents a continuous development of an alpha wave up to the time window where the P1 is generated is associated with an enlarged event-related component. We conclude that ongoing oscillations cannot be considered random background noise (even before stimulus onset) and that there are probably more phase-mechanisms that can contribute to the ERP-generation.

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.

Rhythmic pulsing: linking ongoing brain activity with evoked responses

The conventional assumption in human cognitive electrophysiology using EEG and MEG is that the presentation of a particular event such as visual or auditory stimuli evokes a “turning on” of additional brain activity that adds to the ongoing background activity. Averaging multiple event-locked trials is thought to result in the cancellation of the seemingly random phased ongoing activity while leaving the evoked response. However, recent work strongly challenges this conventional view and demonstrates that the ongoing activity is not averaged out due to specific non-sinusoidal properties. As a consquence, systematic modulations in ongoing activity can produce slow cortical evoked responses reflecting cognitive processing. In this review we introduce the concept of “rhythmic pulsing” to account for this specific non-sinusoidal property. We will explain how rhythmic pulsing can create slow evoked responses from a physiological perspective. We will also discuss how the notion of rhythmic pulsing provides a unifying framework linking ongoing oscillations, evoked responses and the brain's capacity to process incoming information.

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

Evoked EEG/MEG responses are a primary real-time measure of perceptual and cognitive activity in the human brain, but their neuronal generator mechanisms are not yet fully understood. Arguments have been put forward in favor of either "phase-reset" of ongoing oscillations or "added-energy" models. Instead of advocating for one or the other model, here we show theoretically that the differentiation between these two generation mechanisms might not be possible if based solely on macroscopic EEG/MEG recordings. Using mathematical modeling, we show that a simultaneous phase reset of multiple oscillating neuronal (microscopic) sources contributing to EEG/MEG can produce evoked responses in agreement with both, the "added-energy" and the "phase-reset" model. We observe a smooth transition between the two models by just varying the strength of synchronization between the multiple microscopic sources. Consequently, because precise knowledge about the strength of microscopic ensemble synchronization is commonly not available in noninvasive EEG/MEG studies, they cannot, in principle, differentiate between the two mechanisms for macroscopic-evoked responses.

Multi-frequency phase locking in human somatosensory cortex

Cortical population responses to sensory input arise from the interaction between external stimuli and the intrinsic dynamics of the densely interconnected neuronal population. Although there is a large body of knowledge regarding single neuron responses to periodic stimuli, responses at the scale of cortical populations are incompletely understood. The characteristics of large-scale neuronal activity during periodic stimulation speak directly to the mechanisms underlying collective neuronal activity. Their accurate elucidation is hence a vital prelude to constructing and evaluating large-scale computational and biophysical models of the brain. Electroencephalographic data was recorded from eight human subjects while periodic vibrotactile stimuli were applied to the fingertip. Time-frequency decomposition was performed on the multi-channel data in order to investigate relative changes in the power and phase distributions at stimulus-related frequencies. We observed phase locked oscillatory activity at multiple stimulus-specific frequencies, in particular at ratios of 1:1, 2:1 and 2:3 to the stimulus frequency. These phase locked components were found to be modulated differently across the range of stimulus frequencies, with oscillatory responses most robustly sustained around 30 Hz. In contrast, no robust frequency-locked responses were apparent in the power changes. These results demonstrate n:m phase synchronization between cortical oscillations in the somatosensory system and an external periodic signal. We argue that neuronal populations evidence a collective nonlinear response to periodic sensory input. The existence of n:m phase synchronization demonstrates the contribution of intrinsic cortical dynamics to stimulus encoding and provides a novel phenomenological criteria for the validation of large-scale models of the brain.

From prestimulus alpha oscillation to visual-evoked response: an inverted-U function and its attentional modulation

Understanding the relation between prestimulus neural activity and subsequent stimulus processing has become an area of active investigation. Computational modeling, as well as in vitro and in vivo single-unit recordings in animal preparations, have explored mechanisms by which background synaptic activity can influence the responsiveness of cortical neurons to afferent input. How these mechanisms manifest in humans is not well understood. Although numerous EEG/MEG studies have considered the role of prestimulus alpha oscillations in the genesis of visual-evoked potentials, no consensus has emerged, and divergent reports continue to appear. The present work addresses this problem in three stages. First, a theoretical model was developed in which the background synaptic activity and the firing rate of a neural ensemble are related through a sigmoidal function. The derivative of this function, referred to as local gain, has an inverted-U shape and is postulated to be proportional to the trial-by-trial response evoked by a transient stimulus. Second, the theoretical model was extended to noninvasive studies of human visual processing, where the model variables are reinterpreted in terms of ongoing EEG oscillations and event-related potentials. Predictions were derived from the model and tested by recording high-density scalp EEG from healthy volunteers performing a trial-by-trial cued spatial visual attention task. Finally, enhanced stimulus processing by attention was linked to an increase in the overall slope of the sigmoidal function. The commonly observed reduction of alpha magnitude with attention was interpreted as signaling a shift of the underlying neural ensemble toward an optimal excitability state that enables the increase in global gain.

Graph-based variability estimation in single-trial event-related neural responses

Extracting information from multitrial magnetoencephalography or electroencephalography (EEG) recordings is challenging because of the very low SNR, and because of the inherent variability of brain responses. The problem of low SNR is commonly tackled by averaging multiple repetitions of the recordings, also called trials, but the variability of response across trials leads to biased results and limits interpretability. This paper proposes to decode the variability of neural responses by making use of graph representations. Our approach has several advantages compared to other existing methods that process single-trial data: first, it avoids the a priori definition of a model for the waveform of the neural response; second, it does not make use of the average data for parameter estimation; third, it does not suffer from initialization problems by providing solutions that are global optimum of cost functions; and last, it is fast. We proceed in two steps. First, a manifold learning algorithm, based on a graph Laplacian, offers an efficient way of ordering trials with respect to the response variability, under the condition that this variability itself depends on a single parameter. Second, the estimation of the variability is formulated as a combinatorial optimization that can be solved very efficiently using graph cuts. Details and validation of this second step are provided for latency estimation. Performance and robustness experiments are conducted on synthetic data, and results are presented on EEG data from a P300 oddball experiment.

Frontal theta links prediction errors to behavioral adaptation in reinforcement learning

Investigations into action monitoring have consistently detailed a frontocentral voltage deflection in the event-related potential (ERP) following the presentation of negatively valenced feedback, sometimes termed the feedback-related negativity (FRN). The FRN has been proposed to reflect a neural response to prediction errors during reinforcement learning, yet the single-trial relationship between neural activity and the quanta of expectation violation remains untested. Although ERP methods are not well suited to single-trial analyses, the FRN has been associated with theta band oscillatory perturbations in the medial prefrontal cortex. Mediofrontal theta oscillations have been previously associated with expectation violation and behavioral adaptation and are well suited to single-trial analysis. Here, we recorded EEG activity during a probabilistic reinforcement learning task and fit the performance data to an abstract computational model (Q-learning) for calculation of single-trial reward prediction errors. Single-trial theta oscillatory activities following feedback were investigated within the context of expectation (prediction error) and adaptation (subsequent reaction time change). Results indicate that interactive medial and lateral frontal theta activities reflect the degree of negative and positive reward prediction error in the service of behavioral adaptation. These different brain areas use prediction error calculations for different behavioral adaptations, with medial frontal theta reflecting the utilization of prediction errors for reaction time slowing (specifically following errors), but lateral frontal theta reflecting prediction errors leading to working memory-related reaction time speeding for the correct choice.

Oscillatory brain states interact with late cognitive components of the somatosensory evoked potential

The question of interaction between ongoing neuronal activity and evoked responses has been addressed for different species, sensory systems and measurement modalities. Among other findings, there is converging evidence for an interaction of occipital alpha-rhythm amplitude with the visual evoked potential. Here, we test the hypothesis that the modulatory role of an ongoing rhythm might not be confined to the visual system and the occipital alpha rhythm, but instead may be generalized to other sensory systems. Using an online EEG analysis approach, we investigated the influence of the Rolandic alpha-rhythm on the somatosensory evoked potential (SEP). We triggered vibrotactile stimulation during periods of high Rolandic alpha-rhythm amplitude. Analysis revealed significant effects of pre-stimulus Rolandic alpha amplitude on the amplitude of the N140 and P260 components of the SEP, known to be linked to cognitive processing, but not on early sensory components. The N140-P260 complex shows a different focus in topography than the early sensory components and the pre-stimulus Rolandic alpha rhythm. These results indicate an involvement of Rolandic alpha-rhythm in higher cognitive processing. In more general terms--and in the context of similar studies in the visual system--our findings suggest that modulation of late EP components by ongoing rhythms might be a characteristic and possibly universal feature of sensory systems.