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We propose a computational framework for high-dimensional brain-body states as transient embodiments of nested internal and external dynamics governed by interoception. Unifying recent theoretical work, we suggest ways to reduce arbitrary state complexity to an observable number of features in order to accurately predict and intervene in pathological trajectories., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Bodily rhythms such as respiration are increasingly acknowledged to modulate neural oscillations underlying human action, perception, and cognition. Conversely, the link between respiration and aperiodic brain activity - a non-oscillatory reflection of excitation-inhibition (E:I) balance - has remained unstudied. Aiming to disentangle potential respiration-related dynamics of periodic and aperiodic activity, we applied recently developed algorithms of time-resolved parameter estimation to resting-state MEG and EEG data from two labs (total N = 78 participants). We provide evidence that fluctuations of aperiodic brain activity (1/f slope) are phase-locked to the respiratory cycle, which suggests that spontaneous state shifts of excitation-inhibition balance are at least partly influenced by peripheral bodily signals. Moreover, differential temporal dynamics in their coupling to non-oscillatory and oscillatory activity raise the possibility of a functional distinction in the way each component is related to respiration. Our findings highlight the role of respiration as a physiological influence on brain signalling., (© 2023. Springer Nature Limited.)

Pain emerges from the integration of sensory information about threats and contextual information such as an individual's expectations. However, how sensory and contextual effects on pain are served by the brain is not fully understood so far. To address this question, we applied brief painful stimuli to 40 healthy human participants and independently varied stimulus intensity and expectations. Concurrently, we recorded electroencephalography. We assessed local oscillatory brain activity and interregional functional connectivity in a network of six brain regions playing key roles in the processing of pain. We found that sensory information predominantly influenced local brain oscillations. In contrast, expectations exclusively influenced interregional connectivity. Specifically, expectations altered connectivity at alpha (8 to 12 hertz) frequencies from prefrontal to somatosensory cortex. Moreover, discrepancies between sensory information and expectations, i.e., prediction errors, influenced connectivity at gamma (60 to 100 hertz) frequencies. These findings reveal how fundamentally different brain mechanisms serve sensory and contextual effects on pain.

Bioelectromagnetism has contributed some of the most commonly used techniques to human neuroscience such as magnetoencephalography (MEG), electroencephalography (EEG), transcranial magnetic stimulation (TMS), and transcranial electric stimulation (TES). The considerable differences in their technical design and practical use give rise to the impression that these are quite different techniques altogether. Here, we review, discuss and illustrate the fundamental principle of Helmholtz reciprocity that provides a common ground for all four techniques. We show that, more than 150 years after its discovery by Helmholtz in 1853, reciprocity is important to appreciate the strengths and limitations of these four classical tools in neuroscience. We build this case by explaining the concept of Helmholtz reciprocity, presenting a methodological account of this principle for all four methods and, finally, by illustrating its application in practical clinical studies.

SignificanceThe modulation of growth hormone secretagogue receptor-1a (GHSR 1a ) signaling is a promising strategy for treating brain conditions of metabolism, aging, and addiction. GHSR 1a activation results in pleiotropic physiological outcomes through distinct and pharmacologically separable G protein- and β-arrestin (βarr)-dependent signaling pathways. Thus, pathway-selective modulation can enable improved pharmacotherapeutics that can promote therapeutic efficacy while mitigating side effects. Here, we describe the discovery of a brain-penetrant small molecule, N8279 (NCATS-SM8864), that biases GHSR 1a conformations toward Gα q activation and reduces aberrant dopaminergic behavior in mice. N8279 represents a promising chemical scaffold to advance the development of better treatments for GHSR 1a -related brain disorders involving the pathological dysregulation of dopamine.

Fluctuations in arousal, controlled by subcortical neuromodulatory systems, continuously shape cortical state, with profound consequences for information processing. Yet, how arousal signals influence cortical population activity in detail has so far only been characterized for a few selected brain regions. Traditional accounts conceptualize arousal as a homogeneous modulator of neural population activity across the cerebral cortex. Recent insights, however, point to a higher specificity of arousal effects on different components of neural activity and across cortical regions. Here, we provide a comprehensive account of the relationships between fluctuations in arousal and neuronal population activity across the human brain. Exploiting the established link between pupil size and central arousal systems, we performed concurrent magnetoencephalographic (MEG) and pupillographic recordings in a large number of participants, pooled across three laboratories. We found a cascade of effects relative to the peak timing of spontaneous pupil dilations: Decreases in low-frequency (2-8 Hz) activity in temporal and lateral frontal cortex, followed by increased high-frequency (>64 Hz) activity in mid-frontal regions, followed by monotonic and inverted U relationships with intermediate frequency-range activity (8-32 Hz) in occipito-parietal regions. Pupil-linked arousal also coincided with widespread changes in the structure of the aperiodic component of cortical population activity, indicative of changes in the excitation-inhibition balance in underlying microcircuits. Our results provide a novel basis for studying the arousal modulation of cognitive computations in cortical circuits., Competing Interests: TP, CK, DK, AK, AR, GT, JG No competing interests declared, TD Reviewing editor, eLife, (© 2022, Pfeffer et al.)

Analyses of cerebro-peripheral connectivity aim to quantify ongoing coupling between brain activity (measured by MEG/EEG) and peripheral signals such as muscle activity, continuous speech, or physiological rhythms (such as pupil dilation or respiration). Due to the distinct rhythmicity of these signals, undirected connectivity is typically assessed in the frequency domain. This leaves the investigator with two critical choices, namely a) the appropriate measure for spectral estimation (i.e., the transformation into the frequency domain) and b) the actual connectivity measure. As there is no consensus regarding best practice, a wide variety of methods has been applied. Here we systematically compare combinations of six standard spectral estimation methods (comprising fast Fourier and continuous wavelet transformation, bandpass filtering, and short-time Fourier transformation) and six connectivity measures (phase-locking value, Gaussian-Copula mutual information, Rayleigh test, weighted pairwise phase consistency, magnitude squared coherence, and entropy). We provide performance measures of each combination for simulated data (with precise control over true connectivity), a single-subject set of real MEG data, and a full group analysis of real MEG data. Our results show that, overall, WPPC and GCMI tend to outperform other connectivity measures, while entropy was the only measure sensitive to bimodal deviations from a uniform phase distribution. For group analysis, choosing the appropriate spectral estimation method appears to be more critical than the connectivity measure. We discuss practical implications (sampling rate, SNR, computation time, and data length) and aim to provide recommendations tailored to particular research questions., Competing Interests: Declaration of Competing Interest None., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Skull conductivity has a substantial influence on EEG and combined EEG and MEG source analysis as well as on optimized transcranial electric stimulation. To overcome the use of standard literature values, we propose a non-invasive two-level calibration procedure to estimate skull conductivity individually in a group study with twenty healthy adults. Our procedure requires only an additional run of combined somatosensory evoked potential and field data, which can be easily integrated in EEG/MEG experiments. The calibration procedure uses the P20/N20 topographies and subject-specific realistic head models from MRI. We investigate the inter-subject variability of skull conductivity and relate it to skull thickness, age and gender of the subjects, to the individual scalp P20/N20 surface distance between the P20 potential peak and the N20 potential trough as well as to the individual source depth of the P20/N20 source. We found a considerable inter-subject variability for (calibrated) skull conductivity (8.44 ± 4.84 mS/m) and skull thickness (5.97 ± 1.19 mm) with a statistically significant correlation between them (rho = 0.52). Age showed a statistically significant negative correlation with skull conductivity (rho = -0.5). Furthermore, P20/N20 surface distance and source depth showed large inter-subject variability of 12.08 ± 3.21 cm and 15.45 ± 4.54 mm, respectively, but there was no significant correlation between them. We also found no significant differences among gender subgroups for the investigated measures. It is thus important to take the inter-subject variability of skull conductivity and thickness into account by means of using subject-specific calibrated realistic head modeling., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 University Hospital of Münster. Published by Elsevier Inc. All rights reserved.)

Natural speech builds on contextual relations that can prompt predictions of upcoming utterances. To study the neural underpinnings of such predictive processing we asked 10 healthy adults to listen to a 1-h-long audiobook while their magnetoencephalographic (MEG) brain activity was recorded. We correlated the MEG signals with acoustic speech envelope, as well as with estimates of Bayesian word probability with and without the contextual word sequence (N-gram and Unigram, respectively), with a focus on time-lags. The MEG signals of auditory and sensorimotor cortices were strongly coupled to the speech envelope at the rates of syllables (4-8 ​Hz) and of prosody and intonation (0.5-2 ​Hz). The probability structure of word sequences, independently of the acoustical features, affected the ≤ 2-Hz signals extensively in auditory and rolandic regions, in precuneus, occipital cortices, and lateral and medial frontal regions. Fine-grained temporal progression patterns occurred across brain regions 100-1000 ​ms after word onsets. Although the acoustic effects were observed in both hemispheres, the contextual influences were statistically significantly lateralized to the left hemisphere. These results serve as a brain signature of the predictability of word sequences in listened continuous speech, confirming and extending previous results to demonstrate that deeply-learned knowledge and recent contextual information are employed dynamically and in a left-hemisphere-dominant manner in predicting the forthcoming words in natural speech., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Visual speech carried by lip movements is an integral part of communication. Yet, it remains unclear in how far visual and acoustic speech comprehension are mediated by the same brain regions. Using multivariate classification of full-brain MEG data, we first probed where the brain represents acoustically and visually conveyed word identities. We then tested where these sensory-driven representations are predictive of participants' trial-wise comprehension. The comprehension-relevant representations of auditory and visual speech converged only in anterior angular and inferior frontal regions and were spatially dissociated from those representations that best reflected the sensory-driven word identity. These results provide a neural explanation for the behavioural dissociation of acoustic and visual speech comprehension and suggest that cerebral representations encoding word identities may be more modality-specific than often upheld., Competing Interests: AK, JG, CK No competing interests declared, (© 2020, Keitel et al.)

Chronic pain is a common and severely disabling disease whose treatment is often unsatisfactory. Insights into the brain mechanisms of chronic pain promise to advance the understanding of the underlying pathophysiology and might help to develop disease markers and novel treatments. Here, we systematically exploited the potential of electroencephalography to determine abnormalities of brain function during the resting state in chronic pain. To this end, we performed state-of-the-art analyses of oscillatory brain activity, brain connectivity, and brain networks in 101 patients of either sex suffering from chronic pain. The results show that global and local measures of brain activity did not differ between chronic pain patients and a healthy control group. However, we observed significantly increased connectivity at theta (4-8 Hz) and gamma (>60 Hz) frequencies in frontal brain areas as well as global network reorganization at gamma frequencies in chronic pain patients. Furthermore, a machine learning algorithm could differentiate between patients and healthy controls with an above-chance accuracy of 57%, mostly based on frontal connectivity. These results suggest that increased theta and gamma synchrony in frontal brain areas are involved in the pathophysiology of chronic pain. Although substantial challenges concerning the reproducibility of the findings and the accuracy, specificity, and validity of potential electroencephalography-based disease markers remain to be overcome, our study indicates that abnormal frontal synchrony at theta and gamma frequencies might be promising targets for noninvasive brain stimulation and/or neurofeedback approaches.

Magnetoencephalography (MEG) is an invaluable tool to study the dynamics and connectivity of large-scale brain activity and their interactions with the body and the environment in functional and dysfunctional body and brain states. This primer introduces the basic concepts of MEG, discusses its strengths and limitations in comparison to other brain imaging techniques, showcases interesting applications, and projects exciting current trends into the near future, in a way that might more fully exploit the unique capabilities of MEG., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Rhythms are a fundamental and defining feature of neuronal activity in animals including humans. This rhythmic brain activity interacts in complex ways with rhythms in the internal and external environment through the phenomenon of 'neuronal entrainment', which is attracting increasing attention due to its suggested role in a multitude of sensory and cognitive processes. Some senses, such as touch and vision, sample the environment rhythmically, while others, like audition, are faced with mostly rhythmic inputs. Entrainment couples rhythmic brain activity to external and internal rhythmic events, serving fine-grained routing and modulation of external and internal signals across multiple spatial and temporal hierarchies. This interaction between a brain and its environment can be experimentally investigated and even modified by rhythmic sensory stimuli or invasive and non-invasive neuromodulation techniques. We provide a comprehensive overview of the topic and propose a theoretical framework of how neuronal entrainment dynamically structures information from incoming neuronal, bodily and environmental sources. We discuss the different types of neuronal entrainment, the conceptual advances in the field, and converging evidence for general principles., (Copyright © 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Emotion regulation is thought to involve communication between and within large-scale brain networks that underlie emotion reactivity and cognitive control. Aberrant network interaction might therefore be a key neural feature of mental disorders that involve emotion dysregulation. Here we tested whether connectivity hierarchies within and between emotion reactivity and cognitive reappraisal networks distinguishes social anxiety disorder (SAD) patients (n = 70) from healthy controls (HC) (n = 25). To investigate network organization, we implemented a graph-theory method called Dependency Network Analysis. Participants underwent fMRI while watching or reappraising video clips involving interpersonal verbal criticism. During reappraisal, the reappraisal network exerted less influence on the reactivity network in SAD participants. Specifically, the influence of the right inferior frontal gyrus on both reappraisal and reactivity networks was significantly reduced in SAD compared with HC, and correlated negatively with negative emotion ratings among SAD participants. Surprisingly, the amygdala exhibited reduced influence on the reappraisal network in SAD relative to HC. Yet, during the watch condition, the left amygdala's influence on the reactivity network increased with greater social anxiety symptoms among SAD participants. These findings refine our understanding of network organization that contributes to efficient reappraisal or to disturbances in applying this strategy in SAD., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Oscillatory neural activity is a fundamental characteristic of the mammalian brain spanning multiple levels of spatial and temporal scale. Current theories of neural oscillations and analysis techniques employed to investigate their functional significance are based on an often implicit assumption: In the absence of experimental manipulation, the spectral content of any given EEG- or MEG-recorded neural oscillator remains approximately stationary over the course of a typical experimental session (∼1 h), spontaneously fluctuating only around its dominant frequency. Here, we examined this assumption for ongoing neural oscillations in the alpha-band (8-13 Hz). We found that alpha peak frequency systematically decreased over time, while alpha-power increased. Intriguingly, these systematic changes showed partial independence of each other: Statistical source separation (independent component analysis) revealed that while some alpha components displayed concomitant power increases and peak frequency decreases, other components showed either unique power increases or frequency decreases. Interestingly, we also found these components to differ in frequency. Components that showed mixed frequency/power changes oscillated primarily in the lower alpha-band (∼8-10 Hz), while components with unique changes oscillated primarily in the higher alpha-band (∼9-13 Hz). Our findings provide novel clues on the time-varying intrinsic properties of large-scale neural networks as measured by M/EEG, with implications for the analysis and interpretation of studies that aim at identifying functionally relevant oscillatory networks or at driving them through external stimulation., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Two largely independent research lines use rhythmic sensory stimulation to study visual processing. Despite the use of strikingly similar experimental paradigms, they differ crucially in their notion of the stimulus-driven periodic brain responses: one regards them mostly as synchronized (entrained) intrinsic brain rhythms; the other assumes they are predominantly evoked responses [classically termed steady-state responses (SSRs)] that add to the ongoing brain activity. This conceptual difference can produce contradictory predictions about, and interpretations of, experimental outcomes. The effect of spatial attention on brain rhythms in the alpha band (8-13 Hz) is one such instance: alpha-range SSRs have typically been found to increase in power when participants focus their spatial attention on laterally presented stimuli, in line with a gain control of the visual evoked response. In nearly identical experiments, retinotopic decreases in entrained alpha-band power have been reported, in line with the inhibitory function of intrinsic alpha. Here we reconcile these contradictory findings by showing that they result from a small but far-reaching difference between two common approaches to EEG spectral decomposition. In a new analysis of previously published human EEG data, recorded during bilateral rhythmic visual stimulation, we find the typical SSR gain effect when emphasizing stimulus-locked neural activity and the typical retinotopic alpha suppression when focusing on ongoing rhythms. These opposite but parallel effects suggest that spatial attention may bias the neural processing of dynamic visual stimulation via two complementary neural mechanisms. SIGNIFICANCE STATEMENT Attending to a visual stimulus strengthens its representation in visual cortex and leads to a retinotopic suppression of spontaneous alpha rhythms. To further investigate this process, researchers often attempt to phase lock, or entrain, alpha through rhythmic visual stimulation under the assumption that this entrained alpha retains the characteristics of spontaneous alpha. Instead, we show that the part of the brain response that is phase locked to the visual stimulation increased with attention (as do steady-state evoked potentials), while the typical suppression was only present in non-stimulus-locked alpha activity. The opposite signs of these effects suggest that attentional modulation of dynamic visual stimulation relies on two parallel cortical mechanisms-retinotopic alpha suppression and increased temporal tracking., Competing Interests: The authors declare no competing financial interests., (Copyright © 2019 Keitel et al.)

Pain is a complex phenomenon involving perceptual, motor, and autonomic responses, but how the brain translates noxious stimuli into these different dimensions of pain is unclear. Here, we assessed perceptual, motor, and autonomic responses to brief noxious heat stimuli and recorded brain activity using electroencephalography (EEG) in humans. Multilevel mediation analysis reveals that each pain dimension is subserved by a distinct pattern of EEG responses and, conversely, that each EEG response differentially contributes to the different dimensions of pain. In particular, the translation of noxious stimuli into autonomic and motor responses involved the earliest N1 wave, whereas pain perception was mediated by later N2 and P2 waves. Gamma oscillations mediated motor responses rather than pain perception. These findings represent progress towards a mechanistic understanding of the brain processes translating noxious stimuli into pain and suggest that perceptual, motor, and autonomic dimensions of pain are partially independent rather than serial processes.

Human perception of perithreshold stimuli critically depends on oscillatory EEG activity prior to stimulus onset. However, it remains unclear exactly which aspects of perception are shaped by this pre-stimulus activity and what role stochastic (trial-by-trial) variability plays in driving these relationships. We employed a novel jackknife approach to link single-trial variability in oscillatory activity to psychometric measures from a task that requires judgement of the relative length of two line segments (the landmark task). The results provide evidence that pre-stimulus alpha fluctuations influence perceptual bias. Importantly, a mediation analysis showed that this relationship is partially driven by long-term (deterministic) alpha changes over time, highlighting the need to account for sources of trial-by-trial variability when interpreting EEG predictors of perception. These results provide fundamental insight into the nature of the effects of ongoing oscillatory activity on perception. The jackknife approach we implemented may serve to identify and investigate neural signatures of perceptual relevance in more detail., (© 2017 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open-source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541-1573, 2017. © 2016 Wiley Periodicals, Inc., (2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.)

Noxious stimuli induce physiological processes which commonly translate into pain. However, under certain conditions, pain intensity can substantially dissociate from stimulus intensity, e.g. during longer-lasting pain in chronic pain syndromes. How stimulus intensity and pain intensity are differentially represented in the human brain is, however, not yet fully understood. We therefore used electroencephalography (EEG) to investigate the cerebral representation of noxious stimulus intensity and pain intensity during 10min of painful heat stimulation in 39 healthy human participants. Time courses of objective stimulus intensity and subjective pain ratings indicated a dissociation of both measures. EEG data showed that stimulus intensity was encoded by decreases of neuronal oscillations at alpha and beta frequencies in sensorimotor areas. In contrast, pain intensity was encoded by gamma oscillations in the medial prefrontal cortex. Contrasting right versus left hand stimulation revealed that the encoding of stimulus intensity in contralateral sensorimotor areas depended on the stimulation side. In contrast, a conjunction analysis of right and left hand stimulation revealed that the encoding of pain in the medial prefrontal cortex was independent of the side of stimulation. Thus, the translation of noxious stimulus intensity into pain is associated with a change from a spatially specific representation of stimulus intensity by alpha and beta oscillations in sensorimotor areas to a spatially independent representation of pain by gamma oscillations in brain areas related to cognitive and affective-motivational processes. These findings extend the understanding of the brain mechanisms of nociception and pain and their dissociations during longer-lasting pain as a key symptom of chronic pain syndromes., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Pain is an integrative phenomenon that results from dynamic interactions between sensory and contextual (i.e., cognitive, emotional, and motivational) processes. In the brain the experience of pain is associated with neuronal oscillations and synchrony at different frequencies. However, an overarching framework for the significance of oscillations for pain remains lacking. Recent concepts relate oscillations at different frequencies to the routing of information flow in the brain and the signaling of predictions and prediction errors. The application of these concepts to pain promises insights into how flexible routing of information flow coordinates diverse processes that merge into the experience of pain. Such insights might have implications for the understanding and treatment of chronic pain., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

The human brain can be parcellated into diverse anatomical areas. We investigated whether rhythmic brain activity in these areas is characteristic and can be used for automatic classification. To this end, resting-state MEG data of 22 healthy adults was analysed. Power spectra of 1-s long data segments for atlas-defined brain areas were clustered into spectral profiles ("fingerprints"), using k-means and Gaussian mixture (GM) modelling. We demonstrate that individual areas can be identified from these spectral profiles with high accuracy. Our results suggest that each brain area engages in different spectral modes that are characteristic for individual areas. Clustering of brain areas according to similarity of spectral profiles reveals well-known brain networks. Furthermore, we demonstrate task-specific modulations of auditory spectral profiles during auditory processing. These findings have important implications for the classification of regional spectral activity and allow for novel approaches in neuroimaging and neurostimulation in health and disease.

Neuronal oscillations provide a window for understanding the brain dynamics that organize the flow of information from sensory to memory areas. While it has been suggested that gamma power reflects feedforward processing and alpha oscillations feedback control, it remains unknown how these oscillations dynamically interact. Magnetoencephalography (MEG) data was acquired from healthy subjects who were cued to either remember or not remember presented pictures. Our analysis revealed that in anticipation of a picture to be remembered, alpha power decreased while the cross-frequency coupling between gamma power and alpha phase increased. A measure of directionality between alpha phase and gamma power predicted individual ability to encode memory: stronger control of alpha phase over gamma power was associated with better memory. These findings demonstrate that encoding of visual information is reflected by a state determined by the interaction between alpha and gamma activity.

The model of the brain as an information processing machine is a profound hypothesis in which neuroscience, psychology and theory of computation are now deeply rooted. Modern neuroscience aims to model the brain as a network of densely interconnected functional nodes. However, to model the dynamic information processing mechanisms of perception and cognition, it is imperative to understand brain networks at an algorithmic level--i.e. as the information flow that network nodes code and communicate. Here, using innovative methods (Directed Feature Information), we reconstructed examples of possible algorithmic brain networks that code and communicate the specific features underlying two distinct perceptions of the same ambiguous picture. In each observer, we identified a network architecture comprising one occipito-temporal hub where the features underlying both perceptual decisions dynamically converge. Our focus on detailed information flow represents an important step towards a new brain algorithmics to model the mechanisms of perception and cognition.

Brain oscillations are increasingly the subject of electrophysiological studies probing their role in the functioning and dysfunction of the human brain. In recent years this research area has seen rapid and significant changes in the experimental approaches and analysis methods. This article reviews these developments and provides a structured overview of experimental approaches, spectral analysis techniques and methods to establish relationships between brain oscillations and behaviour., (Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.)

Modulations of occipito-parietal α-band (8-14 Hz) power that are opposite in direction (α-enhancement vs. α-suppression) and origin of generation (ipsilateral vs. contralateral to the locus of attention) are a robust correlate of anticipatory visuospatial attention. Yet, the neural generators of these α-band modulations, their interdependence across homotopic areas, and their respective contribution to subsequent perception remain unclear. To shed light on these questions, we employed magnetoencephalography, while human volunteers performed a spatially cued detection task. Replicating previous findings, we found α-power enhancement ipsilateral to the attended hemifield and contralateral α-suppression over occipito-parietal sensors. Source localization (beamforming) analysis showed that α-enhancement and suppression were generated in 2 distinct brain regions, located in the dorsal and ventral visual streams, respectively. Moreover, α-enhancement and suppression showed different dynamics and contribution to perception. In contrast to the initial and transient dorsal α-enhancement, α-suppression in ventro-lateral occipital cortex was sustained and influenced subsequent target detection. This anticipatory biasing of ventro-lateral extrastriate α-activity probably reflects increased receptivity in the brain region specialized in processing upcoming target features. Our results add to current models on the role of α-oscillations in attention orienting by showing that α-enhancement and suppression can be dissociated in time, space, and perceptual relevance.

Cortical oscillations are likely candidates for segmentation and coding of continuous speech. Here, we monitored continuous speech processing with magnetoencephalography (MEG) to unravel the principles of speech segmentation and coding. We demonstrate that speech entrains the phase of low-frequency (delta, theta) and the amplitude of high-frequency (gamma) oscillations in the auditory cortex. Phase entrainment is stronger in the right and amplitude entrainment is stronger in the left auditory cortex. Furthermore, edges in the speech envelope phase reset auditory cortex oscillations thereby enhancing their entrainment to speech. This mechanism adapts to the changing physical features of the speech envelope and enables efficient, stimulus-specific speech sampling. Finally, we show that within the auditory cortex, coupling between delta, theta, and gamma oscillations increases following speech edges. Importantly, all couplings (i.e., brain-speech and also within the cortex) attenuate for backward-presented speech, suggesting top-down control. We conclude that segmentation and coding of speech relies on a nested hierarchy of entrained cortical oscillations., Competing Interests: The authors have declared that no competing interests exist.

Allocation of attention during goal-directed behavior entails simultaneous processing of relevant and attenuation of irrelevant information. How the brain delegates such processes when confronted with dynamic (biological motion) stimuli and harnesses relevant sensory information for sculpting prospective responses remains unclear. We analyzed neuromagnetic signals that were recorded while participants attentively tracked an actor's pointing movement that ended at the location where subsequently the response-cue indicated the required response. We found the observers' spatial allocation of attention to be dynamically reflected in lateralized parieto-occipital alpha (8-12 Hz) activity and to have a lasting influence on motor preparation. Specifically, beta (16-25 Hz) power modulation reflected observers' tendency to selectively prepare for a spatially compatible response even before knowing the required one. We discuss the observed frequency-specific and temporally evolving neural activity within a framework of integrated visuomotor processing and point towards possible implications about the mechanisms involved in action observation., (Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.)

Over the past decade, various techniques have been proposed for localization of cerebral sources of oscillatory activity on the basis of magnetoencephalography (MEG) or electroencephalography recordings. Beamformers in the frequency domain, in particular, have proved useful in this endeavor. However, the localization accuracy and efficacy of such spatial filters can be markedly limited by bias from correlation between cerebral sources and short duration of source activity, both essential issues in the localization of brain data. Here, we evaluate a method for frequency-domain localization of oscillatory neural activity based on the relevance vector machine (RVM). RVM is a Bayesian algorithm for learning sparse models from possibly overcomplete data sets. The performance of our frequency-domain RVM method (fdRVM) was compared with that of dynamic imaging of coherent sources (DICS), a frequency-domain spatial filter that employs a minimum variance adaptive beamformer (MVAB) approach. The methods were tested both on simulated and real data. Two types of simulated MEG data sets were generated, one with continuous source activity and the other with transiently active sources. The real data sets were from slow finger movements and resting state. Results from simulations show comparable performance for DICS and fdRVM at high signal-to-noise ratios and low correlation. At low SNR or in conditions of high correlation between sources, fdRVM performs markedly better. fdRVM was successful on real data as well, indicating salient focal activations in the sensorimotor area. The resulting high spatial resolution of fdRVM and its sensitivity to low-SNR transient signals could be particularly beneficial when mapping event-related changes of oscillatory activity.

In this work, we investigate the feasibility to estimating causal interactions between brain regions based on multivariate autoregressive models (MAR models) fitted to magnetoencephalographic (MEG) sensor measurements. We first demonstrate the theoretical feasibility of estimating source level causal interactions after projection of the sensor-level model coefficients onto the locations of the neural sources. Next, we show with simulated MEG data that causality, as measured by partial directed coherence (PDC), can be correctly reconstructed if the locations of the interacting brain areas are known. We further demonstrate, if a very large number of brain voxels is considered as potential activation sources, that PDC as a measure to reconstruct causal interactions is less accurate. In such case the MAR model coefficients alone contain meaningful causality information. The proposed method overcomes the problems of model nonrobustness and large computation times encountered during causality analysis by existing methods. These methods first project MEG sensor time-series onto a large number of brain locations after which the MAR model is built on this large number of source-level time-series. Instead, through this work, we demonstrate that by building the MAR model on the sensor-level and then projecting only the MAR coefficients in source space, the true casual pathways are recovered even when a very large number of locations are considered as sources. The main contribution of this work is that by this methodology entire brain causality maps can be efficiently derived without any a priori selection of regions of interest., (Copyright © 2011 Wiley Periodicals, Inc.)

Oscillations are an important aspect of neuronal activity. Interestingly, oscillatory patterns are also observed in behaviour, such as in visual performance measures after the presentation of a brief sensory event in the visual or another modality. These oscillations in visual performance cycle at the typical frequencies of brain rhythms, suggesting that perception may be closely linked to brain oscillations. We here investigated this link for a prominent rhythm of the visual system (the alpha-rhythm, 8-12 Hz) by applying rhythmic visual stimulation at alpha-frequency (10.6 Hz), known to lead to a resonance response in visual areas, and testing its effects on subsequent visual target discrimination. Our data show that rhythmic visual stimulation at 10.6 Hz: 1) has specific behavioral consequences, relative to stimulation at control frequencies (3.9 Hz, 7.1 Hz, 14.2 Hz), and 2) leads to alpha-band oscillations in visual performance measures, that 3) correlate in precise frequency across individuals with resting alpha-rhythms recorded over parieto-occipital areas. The most parsimonious explanation for these three findings is entrainment (phase-locking) of ongoing perceptually relevant alpha-band brain oscillations by rhythmic sensory events. These findings are in line with occipital alpha-oscillations underlying periodicity in visual performance, and suggest that rhythmic stimulation at frequencies of intrinsic brain-rhythms can be used to reveal influences of these rhythms on task performance to study their functional roles.

The attentional blink phenomenon is the reduced ability to report a second target (T2) after identifying a first target (T1) in a rapid serial visual presentation (RSVP) of stimuli (e.g., letters), which are presented at approximately 10 items per second. Several explanations have been proposed, which focus primarily on cognitive aspects, such as attentional filter-, capacity limitation- and retrieval failure-processes. Here, we focus on the hypothesis that an entrainment of alpha oscillations (with a frequency of about 10Hz) is a critical factor for the attentional blink phenomenon. Our hypothesis is based on the fact that item presentation rate in the RSVP typically lies in the alpha frequency range and is motivated by theories assuming an inhibitory function for alpha. We predict that entrainment--during the time window of T2 presentation--is larger for attentional blink (AB) items (when T2 cannot be reported) than for NoAB trials (when T2 cannot be reported). The results support our hypothesis and show that alpha entrainment as measured by the amplitude of the alpha evoked response and the extent of alpha phase concentration is larger for AB than for NoAB trials. Together with the lack of differences in alpha power these findings demonstrate that the differences between AB and NoAB trials--during presentation onset of T2--are due to an entrainment of alpha phase and not due to an amplitude modulation. Thus, we conclude that alpha entrainment may be considered the critical factor underlying the attentional blink phenomenon., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Oscillations in brain activity have long been known, but many fundamental aspects of such brain rhythms, particularly their functional importance, have been unclear. As we review here, new insights into these issues are emerging from the application of intervention approaches. In these approaches, the timing of brain oscillations is manipulated by non-invasive brain stimulation, either through sensory input or transcranially, and the behavioural consequence then monitored. Notably, such manipulations have led to rapid, periodic fluctuations in behavioural performance, which co-cycle with underlying brain oscillations. Such findings establish a causal relationship between brain oscillations and behaviour, and are allowing novel tests of longstanding models about the functions of brain oscillations., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

In MEG experiments an electromagnetic field is measured at a very high temporal resolution in many sensors located in a helmet-shaped dewar, producing a very large dataset. Filtering techniques are commonly used to reduce the noise in the data. In this paper, spatiotemporal smoothing across space and time simultaneously is used, not simply as a pre-processing step, but as the central focus of a modelling technique intended to estimate the structure of the spatial and temporal response to stimulus. A particular advantage of this approach is the ability to study responses from individual replicates, rather than averages. The benefits of this form of smoothing are discussed and simulation used to evaluate its performance. The methods are illustrated on an application with real data., (Copyright © 2011 Elsevier B.V. All rights reserved.)

In the past decade several multivariate causality measures based on Granger causality have been suggested to assess directionality of neural signals. To date, however, a detailed evaluation of the reliability of these measures is largely missing. We systematically evaluated the performance of five different causality measures (squared partial directed coherence (sPDC), partial directed coherence (PDC), directed transfer function (DTF), direct directed transfer function (dDTF) and transfer function) depending upon data length, noise level, coupling strength, and model order and performed simulations based on four different neural data recording procedures (magnetoencephalography, electroencephalography, electromyography, intraoperative local field potentials). Moreover, we analyzed the effect of two common numerical methods to determine the significance of the particular causality measure (random permutation and the leave one out method (LOOM)). The simulations showed the sPDC combined with the LOOM to be the most reliable and robust choice for assessing directionality in neural data. While DTF and H by construction were unable to distinguish between direct and indirect connections, the dDTF also failed this test. Finally, we applied the causality measures to a real data set. This showed the usefulness of our simulation results for practical applications in order to draw correct inferences and distinguish between conflicting evidence obtained with different causality measures., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Neural oscillations are ubiquitous measurements of cognitive processes and dynamic routing and gating of information. The fundamental and so far unresolved problem for neuroscience remains to understand how oscillatory activity in the brain codes information for human cognition. In a biologically relevant cognitive task, we instructed six human observers to categorize facial expressions of emotion while we measured the observers' EEG. We combined state-of-the-art stimulus control with statistical information theory analysis to quantify how the three parameters of oscillations (i.e., power, phase, and frequency) code the visual information relevant for behavior in a cognitive task. We make three points: First, we demonstrate that phase codes considerably more information (2.4 times) relating to the cognitive task than power. Second, we show that the conjunction of power and phase coding reflects detailed visual features relevant for behavioral response--that is, features of facial expressions predicted by behavior. Third, we demonstrate, in analogy to communication technology, that oscillatory frequencies in the brain multiplex the coding of visual features, increasing coding capacity. Together, our findings about the fundamental coding properties of neural oscillations will redirect the research agenda in neuroscience by establishing the differential role of frequency, phase, and amplitude in coding behaviorally relevant information in the brain., Competing Interests: The authors have declared that no competing interests exist.

Studying the interaction between brain regions is important to increase our understanding of brain function. Magnetoencephalography (MEG) is well suited to investigate brain connectivity, because it provides measurements of activity of the whole brain at very high temporal resolution. Typically, brain activity is reconstructed from the sensor recordings with an inverse method such as a beamformer, and subsequently a connectivity metric is estimated between predefined reference regions-of-interest (ROIs) and the rest of the source space. Unfortunately, this approach relies on a robust estimate of the relevant reference regions and on a robust estimate of the activity in those reference regions, and is not generally applicable to a wide variety of cognitive paradigms. Here, we investigate the possibility to perform all-to-all pairwise connectivity analysis, thus removing the need to define ROIs. Particularly, we evaluate the effect of nonhomogeneous spatial smoothing of differential connectivity maps. This approach is inspired by the fact that the spatial resolution of source reconstructions is typically spatially nonhomogeneous. We use this property to reduce the spatial noise in the cerebro-cerebral connectivity map, thus improving interpretability. Using extensive data simulations we show a superior detection rate and a substantial reduction in the number of spurious connections. We conclude that nonhomogeneous spatial smoothing of cerebro-cerebral connectivity maps could be an important improvement of the existing analysis tools to study neuronal interactions noninvasively., (Copyright © 2010 Wiley-Liss, Inc.)

Background: One common criterion for classifying electrophysiological brain responses is based on the distinction between transient (i.e. event-related potentials, ERPs) and steady-state responses (SSRs). The generation of SSRs is usually attributed to the entrainment of a neural rhythm driven by the stimulus train. However, a more parsimonious account suggests that SSRs might result from the linear addition of the transient responses elicited by each stimulus. This study aimed to investigate this possibility., Methodology/principal Findings: We recorded brain potentials elicited by a checkerboard stimulus reversing at different rates. We modeled SSRs by sequentially shifting and linearly adding rate-specific ERPs. Our results show a strong resemblance between recorded and synthetic SSRs, supporting the superposition hypothesis. Furthermore, we did not find evidence of entrainment of a neural oscillation at the stimulation frequency., Conclusions/significance: This study provides evidence that visual SSRs can be explained as a superposition of transient ERPs. These findings have critical implications in our current understanding of brain oscillations. Contrary to the idea that neural networks can be tuned to a wide range of frequencies, our findings rather suggest that the oscillatory response of a given neural network is constrained within its natural frequency range.

It has been proposed that the workings of the brain are mainly intrinsically generated recurrent neuronal activity, with sensory inputs as modifiers of such activity in both sensory and higher order modality non-specific regions. This is supported by the demonstration of recurrent neuronal activity in the visual system as a response to visual stimulation. In contrast recurrent activity has never been demonstrated before in higher order modality non-specific regions. Using magneto-encephalography and Granger causality analysis, we tested in a paralimbic network the hypothesis that stimulation may enhance causal recurrent interaction between higher-order, modality non-specific regions. The network includes anterior cingulate/medial prefrontal and posterior cingulate/medial parietal cortices together with pulvinar thalami, a network known to be effective in autobiographic memory retrieval and self-awareness. Autobiographic memory retrieval of previous personal judgments of visually presented words was used as stimuli. It is demonstrated that the prestimulus condition is characterized by causal, recurrent oscillations which are maximal in the lower gamma range. When retrieving previous judgments of visually presented adjectives, this activity is dramatically increased during the stimulus task as ascertained by Granger causality analysis. Our results confirm the hypothesis that stimulation may enhance causal interaction between higher order, modality non-specific brain regions, exemplified in a network of autobiographical memory retrieval.

Peripheral and central structures are involved in the onset of tinnitus. Neuronal plasticity is of special importance for the occurrence of central tinnitus and its persistent form. Neuronal plasticity is the ability of the brain to adapt its own structure (synapses, nerve cells, or even whole areas of the brain) and its organization to modified biological requirements. Neuroplasticity is an ongoing dynamic process. Generally speaking, there are two types of plasticity: synaptic and cortical. Cortical plasticity involves activity-dependent changes in size, connectivity, or in the activation pattern of cortical networks. Synaptic plasticity refers to the activity-dependent change in the strength of synaptic transmission and can affect both the morphology and physiology of the synapse. The stimulation of afferent fibers leads to long-lasting changes in synaptic transmission. This phenomenon is called long-term potentiation (LTP) or long-term depression (LTD). From the perspective of molecular biology, synaptic plasticity is of particular importance for the development of tinnitus and its persistence. Ultimately, the damage to the hair cells, auditory nerve, and excitotoxicity results in an imbalance between LTP and LTD and thus in changes of synaptic plasticity. After excessive acoustic stimulation, LTP can be induced by the increase of afferent inputs, whereas decreased afferent inputs generate LTD. The imbalance between LTP and LTD leads to changes in gene expression and involves changes in neurotransmission, in the expression of the receptors, ion channels, regulatory enzymes, and in direct changes on the synapses. This causes an increase of activity on the cellular level. As a result, the imbalance can lead to hyperactivity in the dorsal cochlear nucleus, inferior colliculus, and in the auditory cortex and, later on, to changes in cortical plasticity leading to tinnitus.

In the past, causality measures based on Granger causality have been suggested for assessing directionality in neural signals. In frequency domain analyses (power or coherence) of neural data, it is common to preprocess the time series by filtering or decimating. However, in other fields, it has been shown theoretically that filtering in combination with Granger causality may lead to spurious or missed causalities. We investigated whether this result translates to multivariate causality methods derived from Granger causality with (a) a simulation study and (b) an application to magnetoencephalographic data. To this end, we performed extensive simulations of the effect of applying different filtering techniques and evaluated the performance of five different multivariate causality measures in combination with two numerical significance measures (random permutation and leave one out method). The analysis included three of the most widely used filters (high-pass, low-pass, notch filter), four different filter types (Butterworth, Chebyshev I and II, elliptic filter), variation of filter order, decimating and interpolation. The simulation results suggest that preprocessing without a strong prior about the artifact to be removed disturbs the information content and time ordering of the data and leads to spurious and missed causalities. Only if apparent artifacts like a current or movement artifact are present, filtering out the respective disturbance seems advisable. While oversampling poses no problem, decimation by a factor greater than the minimum time shift between the time series may lead to wrong inferences. In general, the multivariate causality measures are very sensitive to data preprocessing., (Copyright 2009 Elsevier Inc. All rights reserved.)

A coherent and meaningful percept of the world is essential for human nature. Consequently, much speculation has focused on how this is achieved in the brain. It is thought that all conscious experiences have reference to the self. Self-reference may either be minimal or extended, i.e., autonoetic. In minimal self-reference subjective experiences are self-aware in the weak sense that there is something it feels like for the subject to experience something. In autonoetic consciousness, consciousness emerges, by definition, by retrieval of memories of personally experienced events (episodic memory). It has been shown with transcranial magnetic stimulation (TMS) that a medial paralimbic circuitry is critical for self-reference. This circuitry includes anterior cingulate/medial prefrontal and posterior cingulate/medial parietal cortices, connected directly and via thalamus. We here hypothesized that interaction in the circuitry may bind conscious experiences with widely different degrees of self-reference through synchrony of high frequency oscillations as a common neural event. This hypothesis was confirmed with magneto-encephalography (MEG). The observed coupling between the neural events in conscious experience may explain the sense of unity of consciousness and the severe symptoms associated with paralimbic dysfunction., ((c) 2009 Wiley-Liss, Inc.)

Despite the fact that essential tremor (ET) is the most prevalent movement disorder, the underlying pathological mechanisms are not fully understood. There is accumulating evidence that this specific type of tremor is mainly of central origin, in particular involving inferior olive, cerebellum, thalamus, and primary motor cortex. We studied 8 patients with ET recording simultaneously neural activity with a whole-scalp neuromagnetometer and tremor activity with surface electromyography (EMG). Subjects performed an isometric contraction of the left forearm. Tremor frequency of 5 to 7 Hz and its first harmonic were clearly evident in power spectra of EMG recordings. We used the localization technique dynamic imaging of coherent sources (DICS) to identify cerebral areas coherent to the EMG signal at tremor frequency and its first harmonic. All subjects showed coherence to the contralateral primary motor cortex. In a further step, DICS was used to identify areas of significant cerebro-cerebral coherence. The analysis revealed a network of areas consisting of contralateral primary motor cortex, premotor cortex, thalamus, brainstem, and ipsilateral cerebellum. These results are consistent with the view that in ET, a network of cerebral areas including brainstem shows oscillatory interactions, which lead to a rhythmic modulation of muscle activity becoming apparent as tremor., (2009 Movement Disorder Society.)

Interactions between functionally specialized brain regions are crucial for normal brain function. Magnetoencephalography (MEG) and electroencephalography (EEG) are techniques suited to capture these interactions, because they provide whole head measurements of brain activity in the millisecond range. More than one sensor picks up the activity of an underlying source. This field spread severely limits the utility of connectivity measures computed directly between sensor recordings. Consequentially, neuronal interactions should be studied on the level of the reconstructed sources. This article reviews several methods that have been applied to investigate interactions between brain regions in source space. We will mainly focus on the different measures used to quantify connectivity, and on the different strategies adopted to identify regions of interest. Despite various successful accounts of MEG and EEG source connectivity, caution with respect to the interpretation of the results is still warranted. This is due to the fact that effects of field spread can never be completely abolished in source space. However, in this very exciting and developing field of research this cautionary note should not discourage researchers from further investigation into the connectivity between neuronal sources., ((c) 2009 Wiley-Liss, Inc.)

Resting tremor in idiopathic Parkinson's disease (PD) is associated with an oscillatory network comprising cortical as well as subcortical brain areas. To shed light on the effect of levodopa on these network interactions, we investigated 10 patients with tremor-dominant PD and reanalyzed data in 11 healthy volunteers mimicking PD resting tremor. To this end, we recorded surface electromyograms of forearm muscles and neuromagnetic activity using a 122-channel whole-head magnetometer (MEG). Measurements were performed after overnight withdrawal of levodopa (OFF) and 30 min after oral application of fast-acting levodopa (ON). During OFF, patients showed the typical antagonistic resting tremor. Using the analysis tool Dynamic Imaging of Coherent Sources, we identified the oscillatory network associated with tremor comprising contralateral primary sensorimotor cortex (S1/M1), supplementary motor area (SMA), contralateral premotor cortex (PMC), thalamus, secondary somatosensory cortex (S2), posterior parietal cortex (PPC), and ipsilateral cerebellum oscillating at 8 to 10 Hz. After intake of levodopa, we found a significant decrease of cerebro-cerebral coupling between thalamus and motor cortical areas. Similarly, in healthy controls mimicking resting tremor, we found a significant decrease of functional interaction within a thalamus-premotor-motor network during rest. However, in patients with PD, decrease of functional interaction between thalamus and PMC was significantly stronger when compared with healthy controls. These data support the hypothesis that (1) in patients with PD the basal ganglia and motor cortical structures become more closely entrained and (2) levodopa is associated with normalization of the functional interaction between thalamus and motor cortical areas.

Common neurological manifestation of Wilson's disease (WD) is a postural tremor of the upper extremities. Recently, the primary sensorimotor cortex (S1/M1) has been shown to be involved in WD postural tremor generation. However, neuropathological changes in WD are mostly observed in subcortical structures. We therefore aimed to investigate whether S1/M1 may be functionally interconnected with other brain areas. In five WD patients, we used magnetoencephalography and surface electromyography (EMG) to record simultaneously cerebral neuronal activity and muscular activity during sustained posture of the right forearm. As demonstrated previously, the strongest coupling to tremor EMG was observed in the contralateral S1/M1. This area was taken as reference in order to identify and localize cerebro-cerebral coherence at tremor frequency and its first harmonic. The analysis revealed significant coherence within an oscillatory network including S1/M1, higher cortical motor areas (premotor cortex, PM; supplementary motor area, SMA), posterior parietal cortex (PPC) and thalamus contralateral as well as the cerebellum ipsilateral to the tremor forearm. Flow of information was mainly of bidirectional nature. Taken together, our results indicate that WD postural tremor is generated within a synchronized cerebello-thalamo-cortical network, comprising S1/M1, higher cortical motor areas (SMA, PM), and PPC.

Pain signals threat and drives the individual into a behavioral response that significantly depends on a short stimulus-response latency. Paradoxically, the peripheral and spinal conduction velocities of pain are much slower than of tactile information. However, cerebral processing times and reaction times of touch and pain have not yet been fully assessed. Here we show that reaction times to selective nociceptive cutaneous laser stimuli are substantially faster than expected from the peripheral conduction velocities. Furthermore, by using magnetoencephalography, we found that latencies between earliest stimulus-evoked cortical responses and reaction times are approximately 60 ms shorter for nociceptive than for tactile stimuli. These findings reveal that cerebral processing of pain is substantially faster than processing of tactile information and relatively compensates for the slow peripheral and spinal conduction velocities of pain. Our observation shows how the cerebral organization of pain processing enhances motor responses to potentially harmful stimuli and thereby subserves the particular behavioral demands of pain.

Adequate interaction with our physical and social environment requires accurate timing abilities. Since planning and control of movements is closely related to sensorimotor synchronization, the investigation of synchronization abilities may allow insights into fundamental principles of motor behaviour. The finger-tapping task has frequently been used to study the synchronization of one's own movements in relation to external events. Data from behavioural studies gave rise to the assumption that it is not the peripheral event (i.e., finger-tap or pacing signal) that is synchronized but its central representation. The neural foundations of sensorimotor synchronization have only recently been investigated and are still poorly understood. The present article reviews data from neurophysiological studies investigating sensorimotor synchronization to shed light on the neurophysiological processes associated with sensorimotor synchronization. This review focuses on studies investigating neuroelectric and neuromagnetic activity associated with simple repetitive synchronization tasks.