A bicycle can be balanced by stochastic optimal feedback control but only with accurate speed estimates.

Balancing a bicycle is typical for the balance control humans perform as a part of a whole range of behaviors (walking, running, skating, skiing, etc.). This paper presents a general model of balance control and applies it to the balancing of a bicycle. Balance control has both a physics (mechanics) and a neurobiological component. The physics component pertains to the laws that govern the movements of the rider and his bicycle, and the neurobiological component pertains to the mechanisms via which the central nervous system (CNS) uses these laws for balance control. This paper presents a computational model of this neurobiological component, based on the theory of stochastic optimal feedback control (OFC). The central concept in this model is a computational system, implemented in the CNS, that controls a mechanical system outside the CNS. This computational system uses an internal model to calculate optimal control actions as specified by the theory of stochastic OFC. For the computational model to be plausible, it must be robust to at least two inevitable inaccuracies: (1) model parameters that the CNS learns slowly from interactions with the CNS-attached body and bicycle (i.e., the internal noise covariance matrices), and (2) model parameters that depend on unreliable sensory input (i.e., movement speed). By means of simulations, I demonstrate that this model can balance a bicycle under realistic conditions and is robust to inaccuracies in the learned sensorimotor noise characteristics. However, the model is not robust to inaccuracies in the movement speed estimates. This has important implications for the plausibility of stochastic OFC as a model for motor control.

What to Do If N Is Two?

The field of in vivo neurophysiology currently uses statistical standards that are based on tradition rather than formal analysis. Typically, data from two (or few) animals are pooled for one statistical test, or a significant test in a first animal is replicated in one (or few) further animals. The use of more than one animal is widely believed to allow an inference on the population. Here, we explain that a useful inference on the population would require larger numbers and a different statistical approach. The field should consider to perform studies at that standard, potentially through coordinated multicenter efforts, for selected questions of exceptional importance. Yet, for many questions, this is ethically and/or economically not justifiable. We explain why in those studies with two (or few) animals, any useful inference is limited to the sample of investigated animals, irrespective of whether it is based on few animals, two animals, or a single animal.

Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series.

Since its beginnings in the early 20th century, the psychophysiological study of human brain function has included research into the spectral properties of electrical and magnetic brain signals. Now, dramatic advances in digital signal processing, biophysics, and computer science have enabled increasingly sophisticated methodology for neural time series analysis. Innovations in hardware and recording techniques have further expanded the range of tools available to researchers interested in measuring, quantifying, modeling, and altering the spectral properties of neural time series. These tools are increasingly used in the field, by a growing number of researchers who vary in their training, background, and research interests. Implementation and reporting standards also vary greatly in the published literature, causing challenges for authors, readers, reviewers, and editors alike. The present report addresses this issue by providing recommendations for the use of these methods, with a focus on foundational aspects of frequency domain and time-frequency analyses. It also provides publication guidelines, which aim to (1) foster replication and scientific rigor, (2) assist new researchers who wish to enter the field of brain oscillations, and (3) facilitate communication among authors, reviewers, and editors.

Improving the sensitivity of cluster-based statistics for functional magnetic resonance imaging data.

Because of the high dimensionality of neuroimaging data, identifying a statistical test that is both valid and maximally sensitive is an important challenge. Here, we present a combination of two approaches for functional magnetic resonance imaging (fMRI) data analysis that together result in substantial improvements of the sensitivity of cluster-based statistics. The first approach is to create novel cluster definitions that optimize sensitivity to plausible effect patterns. The second is to adopt a new approach to combine test statistics with different sensitivity profiles, which we call the min(p) method. These innovations are made possible by using the randomization inference framework. In this article, we report on a set of simulations and analyses of real task fMRI data that demonstrate (a) that the proposed methods control the false-alarm rate, (b) that the sensitivity profiles of cluster-based test statistics vary depending on the cluster defining thresholds and cluster definitions, and (c) that the min(p) method for combining these test statistics results in a drastic increase of sensitivity (up to fivefold), compared to existing fMRI analysis methods. This increase in sensitivity is not at the expense of the spatial specificity of the inference.

Visual detection is locked to the internal dynamics of cortico-motor control.

Movements overtly sample sensory information, making sensory analysis an active-sensing process. In this study, we show that visual information sampling is not just locked to the (overt) movement dynamics but to the internal (covert) dynamics of cortico-motor control. We asked human participants to perform continuous isometric contraction while detecting unrelated and unpredictable near-threshold visual stimuli. The motor output (force) shows zero-lag coherence with brain activity (recorded via electroencephalography) in the beta-band, as previously reported. In contrast, cortical rhythms in the alpha-band systematically forerun the motor output by 200 milliseconds. Importantly, visual detection is facilitated when cortico-motor alpha (not beta) synchronization is enhanced immediately before stimulus onset, namely, at the optimal phase relationship for sensorimotor communication. These findings demonstrate an ongoing coupling between visual sampling and motor control, suggesting the operation of an internal and alpha-cycling visuomotor loop.

Effect of Field Spread on Resting-State Magneto Encephalography Functional Network Analysis: A Computational Modeling Study.

A popular way to analyze resting-state electroencephalography (EEG) and magneto encephalography (MEG) data is to treat them as a functional network in which sensors are identified with nodes and the interaction between channel time series and the network connections. Although conceptually appealing, the network-theoretical approach to sensor-level EEG and MEG data is challenged by the fact that EEG and MEG time series are mixtures of source activity. It is, therefore, of interest to assess the relationship between functional networks of source activity and the ensuing sensor-level networks. Since these topological features are of high interest in experimental studies, we address the question of to what extent the network topology can be reconstructed from sensor-level functional connectivity (FC) measures in case of MEG data. Simple simulations that consider only a small number of regions do not allow to assess network properties; therefore, we use a diffusion magnetic resonance imaging-constrained whole-brain computational model of resting-state activity. Our motivation lies behind the fact that still many contributions found in the literature perform network analysis at sensor level, and we aim at showing the discrepancies between source- and sensor-level network topologies by using realistic simulations of resting-state cortical activity. Our main findings are that the effect of field spread on network topology depends on the type of interaction (instantaneous or lagged) and leads to an underestimation of lagged FC at sensor level due to instantaneous mixing of cortical signals, instantaneous interaction is more sensitive to field spread than lagged interaction, and discrepancies are reduced when using planar gradiometers rather than axial gradiometers. We, therefore, recommend using lagged interaction measures on planar gradiometer data when investigating network properties of resting-state sensor-level MEG data.

Theta oscillations locked to intended actions rhythmically modulate perception.

Ongoing brain oscillations are known to influence perception, and to be reset by exogenous stimulations. Voluntary action is also accompanied by prominent rhythmic activity, and recent behavioral evidence suggests that this might be coupled with perception. Here, we reveal the neurophysiological underpinnings of this sensorimotor coupling in humans. We link the trial-by-trial dynamics of EEG oscillatory activity during movement preparation to the corresponding dynamics in perception, for two unrelated visual and motor tasks. The phase of theta oscillations (~4 Hz) predicts perceptual performance, even >1 s before movement. Moreover, theta oscillations are phase-locked to the onset of the movement. Remarkably, the alignment of theta phase and its perceptual relevance unfold with similar non-monotonic profiles, suggesting their relatedness. The present work shows that perception and movement initiation are automatically synchronized since the early stages of motor planning through neuronal oscillatory activity in the theta range.

Dynamic decomposition of spatiotemporal neural signals.

Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals.

Supramodal Theta, Gamma, and Sustained Fields Predict Modality-specific Modulations of Alpha and Beta Oscillations during Visual and Tactile Working Memory.

Flexible control over currently relevant sensory representations is an essential feature of primate cognition. We investigated the neurophysiological bases of such flexible control in humans during an intermodal working memory task in which participants retained visual or tactile sequences. Using magnetoencephalography, we first show that working memory retention engages early visual and somatosensory areas, as reflected in the sustained load-dependent suppression of alpha and beta oscillations. Next, we identify three components that are also load dependent but modality independent: medial prefrontal theta synchronization, frontoparietal gamma synchronization, and sustained parietal event-related fields. Critically, these domain-general components predict (across trials and within load conditions) the modality-specific suppression of alpha and beta oscillations, with largely unique contributions per component. Thus, working memory engages multiple complementary frontoparietal components that have discernible neuronal dynamics and that flexibly modulate retention-related activity in sensory areas in a manner that tracks the current contents of working memory.

Rhythmic Components in Extracranial Brain Signals Reveal Multifaceted Task Modulation of Overlapping Neuronal Activity.

Oscillatory neuronal activity is implicated in many cognitive functions, and its phase coupling between sensors may reflect networks of communicating neuronal populations. Oscillatory activity is often studied using extracranial recordings and compared between experimental conditions. This is challenging, because there is overlap between sensor-level activity generated by different sources, and this can obscure differential experimental modulations of these sources. Additionally, in extracranial data, sensor-level phase coupling not only reflects communicating populations, but can also be generated by a current dipole, whose sensor-level phase coupling does not reflect source-level interactions. We present a novel method, which is capable of separating and characterizing sources on the basis of their phase coupling patterns as a function of space, frequency and time (trials). Importantly, this method depends on a plausible model of a neurobiological rhythm. We present this model and an accompanying analysis pipeline. Next, we demonstrate our approach, using magnetoencephalographic (MEG) recordings during a cued tactile detection task as a case study. We show that the extracted components have overlapping spatial maps and frequency content, which are difficult to resolve using conventional pairwise measures. Because our decomposition also provides trial loadings, components can be readily contrasted between experimental conditions. Strikingly, we observed heterogeneity in alpha and beta sources with respect to whether their activity was suppressed or enhanced as a function of attention and performance, and this happened both in task relevant and irrelevant regions. This heterogeneity contrasts with the common view that alpha and beta amplitude over sensory areas are always negatively related to attention and performance.

Physiological Plausibility Can Increase Reproducibility in Cognitive Neuroscience.

Physiological measurements offer the unique opportunity to assess plausibility along multiple data dimensions, in addition to the significance of statistical tests. Increased emphasis hereon should help increase the reproducibility of research findings by turning what is often considered a threat to reproducibility (a large search space) into part of the solution.

Distinct α- and ß-band rhythms over rat somatosensory cortex with similar properties as in humans.

We demonstrate distinct α- (7-14 Hz) and ß-band (15-30 Hz) rhythms in rat somatosensory cortex in vivo using epidural electrocorticography recordings. Moreover, we show in rats that a genuine ß-rhythm coexists alongside ß-activity that reflects the second harmonic of the arch-shaped somatosensory α-rhythm. This demonstration of a genuine somatosensory ß-rhythm depends on a novel quantification of neuronal oscillations that is based on their rhythmic nature: lagged coherence. Using lagged coherence, we provide two lines of evidence that this somatosensory ß-rhythm is distinct from the second harmonic of the arch-shaped α-rhythm. The first is based on the rhythms' spatial properties: the α- and ß-rhythms are demonstrated to have significantly different topographies. The second is based on the rhythms' temporal properties: the lagged phase-phase coupling between the α- and ß-rhythms is demonstrated to be significantly less than would be expected if both reflected a single underlying nonsinusoidal rhythm. Finally, we demonstrate that 1) the lagged coherence spectrum is consistent between signals from rat and human somatosensory cortex; and 2) a tactile stimulus has the same effect on the somatosensory α- and ß-rhythms in both rats and humans, namely suppressing them. Thus we not only provide evidence for the existence of genuine α- and ß-rhythms in rat somatosensory cortex, but also for their homology to the primate sensorimotor α- and ß-rhythms.

Diverse Phase Relations among Neuronal Rhythms and Their Potential Function.

Neuronal oscillations at nearby sites in the brain often show phase relations that are consistent across time, yet diverse across space. We discuss recent demonstrations of this phase relation diversity, and show that, contrary to earlier beliefs, this diversity is a general property of oscillations that is neither restricted to low-frequency oscillations nor to periods outside of stimulus processing. Arguing for the computational relevance of phase relation diversity, we discuss that it can be modulated by sensory and motor events, and put forward the idea that phase relation diversity may support effective neuronal communication by (i) enhancing selectivity and (ii) allowing for the concurrent segregation of multiple information streams.

Withholding planned speech is reflected in synchronized beta-band oscillations.

When engaged in a conversation, speakers sometimes have to withhold a planned response, for example, before it is their turn to speak. In the present study, using magnetoencephalography (MEG) outside of a conversational setting, we investigate the oscillatory brain mechanisms involved in the process of withholding a planned verbal response until it is time to speak. Our participants viewed a sequence of four random consonant strings and one pseudoword, which they had to pronounce when the fifth string (the imperative stimulus) was presented. The pseudoword appeared either as the fourth or fifth stimulus in the sequence, creating two conditions. In the withhold condition, the pseudoword was the fourth string and the verbal response was withheld until the imperative stimulus was presented. In the control condition, the fifth string was the pseudoword, so no response was withheld. We compared oscillatory responses to the withhold relative to the control condition in the time period preceding speech. Alpha-beta power (8-30 Hz) decreased over occipital sensors in the withhold condition relative to the control condition. Source-level analysis indicated a posterior source (i.e., occipital cortex) associated with the alpha-beta power decreases. This occipital alpha-beta desynchronization likely reflects attentional allocation to the upcoming imperative stimulus. Moreover, beta (12-20 Hz) power increased over frontal sensors. Source-level analysis indicated a frontal source (i.e., middle and superior frontal gyri) associated with the beta-power increases. We interpret the frontal beta synchronization to reflect a mechanism aiding the maintenance of the current motor or cognitive state. Our results provide a window into a possible oscillatory mechanism implementing the ability of speakers to withhold a planned verbal response until they have to speak.

Touch automatically upregulates motor readiness in humans.

Goal-directed movements require effective integration of tactile input with ongoing movement. Here we investigated the functional consequences of such integration in healthy humans by probing the influence of spatially congruent and incongruent tactile stimuli on performance in a speeded button-press task. In addition, using magnetoencephalography (MEG), we evaluated whether the modulation of somato-motor beta (13-30 Hz) oscillations following tactile input-which has been shown to propagate to motor areas-could underlie this influence. We demonstrate that congruent tactile stimuli, despite being irrelevant to the motor task, lead to both faster and more accurate responses. We further show that this automatic upregulation of lateralized motor readiness 1) is specific to tactile input, 2) is independent of the spatial separation of the hands in peripersonal space, and 3) lasts (and remains facilitatory) for up to a second after the tactile input. This pattern of behavioral results is in line with recent physiological investigations showing that somatosensory and motor areas directly influence each other's processing capacity through joint changes in brain state. At the same time, however, the tactile-induced modulation of beta oscillations (one particular index of such a somato-motor state change) could not account for the observed movement facilitation, because it had a different time course.

Identifying neuronal oscillations using rhythmicity.

Neuronal oscillations are a characteristic feature of neuronal activity and are typically investigated through measures of power and coherence. However, neither of these measures directly reflects the distinctive feature of oscillations: their rhythmicity. Rhythmicity is the extent to which future phases can be predicted from the present one. Here, we present lagged coherence, a frequency-indexed measure that quantifies the rhythmicity of neuronal activity. We use this method to identify the sensorimotor alpha and beta rhythms in ongoing magnetoencephalographic (MEG) data, and to study their attentional modulation. Using lagged coherence, the sensorimotor rhythms become visible in ongoing activity as local rhythmicity peaks that are separated from the strong posterior activity in the same frequency bands. In contrast, using conventional power analyses, the sensorimotor rhythms cannot be identified in ongoing data, nor can they be separated from the posterior activity. We go on to show that the attentional modulation of these rhythms is also evident in lagged coherence and moreover, that in contrast to power, it can be visualised even without an experimental contrast. These findings suggest that the rhythmicity of neuronal activity is better suited to identify neuronal oscillations than the power in the same frequency band.

Beta oscillations reflect memory and motor aspects of spoken word production.

Two major components form the basis of spoken word production: the access of conceptual and lexical/phonological information in long-term memory, and motor preparation and execution of an articulatory program. Whereas the motor aspects of word production have been well characterized as reflected in alpha-beta desynchronization, the memory aspects have remained poorly understood. Using magnetoencephalography, we investigated the neurophysiological signature of not only motor but also memory aspects of spoken-word production. Participants named or judged pictures after reading sentences. To probe the involvement of the memory component, we manipulated sentence context. Sentence contexts were either constraining or nonconstraining toward the final word, presented as a picture. In the judgment task, participants indicated with a left-hand button press whether the picture was expected given the sentence. In the naming task, they named the picture. Naming and judgment were faster with constraining than nonconstraining contexts. Alpha-beta desynchronization was found for constraining relative to nonconstraining contexts pre-picture presentation. For the judgment task, beta desynchronization was observed in left posterior brain areas associated with conceptual processing and in right motor cortex. For the naming task, in addition to the same left posterior brain areas, beta desynchronization was found in left anterior and posterior temporal cortex (associated with memory aspects), left inferior frontal cortex, and bilateral ventral premotor cortex (associated with motor aspects). These results suggest that memory and motor components of spoken word production are reflected in overlapping brain oscillations in the beta band.

Uncovering phase-coupled oscillatory networks in electrophysiological data.

Phase consistent neuronal oscillations are ubiquitous in electrophysiological recordings, and they may reflect networks of phase-coupled neuronal populations oscillating at different frequencies. Because neuronal oscillations may reflect rhythmic modulations of neuronal excitability, phase-coupled oscillatory networks could be the functional building block for routing information through the brain. Current techniques are not suited for directly characterizing such networks. To be able to extract phase-coupled oscillatory networks we developed a new method, which characterizes networks by phase coupling between sites. Importantly, this method respects the fact that neuronal oscillations have energy in a range of frequencies. As a consequence, we characterize these networks by between-site phase relations that vary as a function of frequency, such as those that result from between-site temporal delays. Using human electrocorticographic recordings we show that our method can uncover phase-coupled oscillatory networks that show interesting patterns in their between-site phase relations, such as travelling waves. We validate our method by demonstrating it can accurately recover simulated networks from a realistic noisy environment. By extracting phase-coupled oscillatory networks and investigating patterns in their between-site phase relations we can further elucidate the role of oscillations in neuronal communication.

Temporal expectation and attention jointly modulate auditory oscillatory activity in the beta band.

The neural response to a stimulus is influenced by endogenous factors such as expectation and attention. Current research suggests that expectation and attention exert their effects in opposite directions, where expectation decreases neural activity in sensory areas, while attention increases it. However, expectation and attention are usually studied either in isolation or confounded with each other. A recent study suggests that expectation and attention may act jointly on sensory processing, by increasing the neural response to expected events when they are attended, but decreasing it when they are unattended. Here we test this hypothesis in an auditory temporal cueing paradigm using magnetoencephalography in humans. In our study participants attended to, or away from, tones that could arrive at expected or unexpected moments. We found a decrease in auditory beta band synchrony to expected (versus unexpected) tones if they were unattended, but no difference if they were attended. Modulations in beta power were already evident prior to the expected onset times of the tones. These findings suggest that expectation and attention jointly modulate sensory processing.

Movement preparation improves touch perception without awareness.

Movements are often directed at external objects, such as when reaching out for a glass to drink from. Surprisingly, however, it is largely unknown how movement plans influence the identification of such external somatosensory stimuli. To address this, we cued participants to prepare for a speeded button press with their left/right thumb and presented a spatially-patterned somatosensory stimulus at either the same or the opposite thumb with equal probability. In contrast to many previous investigations that focused on self-produced somatosensory input and reported attenuated perception, we show that the identification of external stimuli (touch perception) is facilitated by movement preparation. In line with analogous studies in vision, this suggests that movement preparation automatically allocates processing resources (attention) to the location and/or body part of the planned movement. We further show that, in contrast to deliberate somatosensory preparation, participants do not become more confident in their touch perception following movement preparation. These data suggest that the perceptual improvement during movement preparation occurs outside of awareness. Such an unconscious facilitatory process will ensure that relevant parts of the environment are processed with high fidelity, while sparing conscious resources for monitoring other processes in the course of action.