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.

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.

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.

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.

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.

Phase-amplitude coupling in rat orbitofrontal cortex discriminates between correct and incorrect decisions during associative learning.

Cross-frequency interactions between oscillations in local field potentials (LFPs) are thought to support communication between brain structures by temporally coordinating neural activity. It is unknown, however, whether such interactions differentiate between different levels of performance in decision-making tasks. Here, we investigated theta (4-12 Hz) to gamma (30-100 Hz) phase-amplitude coupling in LFP recordings from rat orbitofrontal cortex. Across subsequent periods of a task in which rats learned to discriminate two odors associated with positive and negative outcomes, theta-to-gamma phase-amplitude coupling (PAC) was highest during the odor-sampling task period that preceded a Go/NoGo decision. This task-dependent modulation could not be explained by changes in oscillatory power and appeared to be time-locked to odor onset, not to the timing of the behavioral response. We found that PAC strength during odor sampling correlated with learning, as indexed by improved performance across trials. Moreover, this increase in PAC magnitude was apparent only on trials with correct Go and NoGo decisions, but not incorrect Go decisions. In addition, we found that PAC preferred coupling phase showed consistency over sessions only for correct, but not incorrect trials. In conclusion, orbitofrontal cortex theta-gamma PAC strength differentiates between different levels of performance in an olfactory decision-making task and may play a role in the generation and utilization of stimulus-based outcome predictions, necessary for adaptive decision-making.

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.

Both ongoing alpha and visually induced gamma oscillations show reliable diversity in their across-site phase-relations.

Neural oscillations have emerged as one of the major electrophysiological phenomena investigated in cognitive and systems neuroscience. These oscillations are typically studied with regard to their amplitude, phase, and/or phase coupling. Here we demonstrate the existence of another property that is intrinsic to neural oscillations but has hitherto remained largely unexplored in cognitive and systems neuroscience. This pertains to the notion that these oscillations show reliable diversity in their phase-relations between neighboring recording sites (phase-relation diversity). In contrast to most previous work, we demonstrate that this diversity is restricted neither to low-frequency oscillations nor to periods outside of sensory stimulation. On the basis of magnetoencephalographic (MEG) recordings in humans, we show that this diversity is prominent not only for ongoing alpha oscillations (8-12 Hz) but also for gamma oscillations (50-70 Hz) that are induced by sustained visual stimulation. We further show that this diversity provides a dimension within electrophysiological data that, provided a sufficiently high signal-to-noise ratio, does not covary with changes in amplitude. These observations place phase-relation diversity on the map as a prominent and general property of neural oscillations that, moreover, can be studied with noninvasive methods in healthy human volunteers. This opens important new avenues for investigating how neural oscillations contribute to the neural implementation of cognition and behavior.

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.

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.

Anticipation increases tactile stimulus processing in the ipsilateral primary somatosensory cortex.

Stimulus anticipation improves perception. To account for this improvement, we investigated how stimulus processing is altered by anticipation. In contrast to a large body of previous work, we employed a demanding perceptual task and investigated sensory responses that occur beyond early evoked activity in contralateral primary sensory areas: Stimulus-induced modulations of neural oscillations. For this, we recorded magnetoencephalography in 19 humans while they performed a cued tactile identification task involving the identification of either a proximal or a distal stimulation on the fingertips. We varied the cue-target interval between 0 and 1000 ms such that tactile targets occurred at various degrees of anticipation. This allowed us to investigate the influence of anticipation on stimulus processing in a parametric fashion. We observed that anticipation increases the stimulus-induced response (suppression of beta-band oscillations) originating from the ipsilateral primary somatosensory cortex. This occurs in the period in which the tactile memory trace is analyzed and is correlated with the anticipation-induced improvement in tactile perception. We propose that this ipsilateral response indicates distributed processing across bilateral primary sensory cortices, of which the extent increases with anticipation. This constitutes a new and potentially important mechanism contributing to perception and its improvement following anticipation.

Somatosensory demands modulate muscular Beta oscillations, independent of motor demands.

Neural oscillations in the beta band (15-30 Hz) occur coherently throughout the primate somatomotor network, comprising somatomotor cortices, basal ganglia, thalamus, cerebellum, and spinal cord, with the latter resulting in beta oscillations in muscular activity. In accordance with the anatomy of this network, these oscillations have traditionally been associated strictly with motor function. Here we show in humans that somatosensory demands, both in anticipation and during the processing of tactile stimuli, also modulate beta oscillations throughout this network. Specifically, somatosensory demands suppress the degree to which not only cortical activity but also muscular activity oscillates in the beta band. This suppression of muscular beta oscillations by perceptual demands is specific to demands in the somatosensory modality and occurs independent of movement preparation and execution: it occurs even when no movement is required at all. This places touch perception as an important computation within this widely distributed somatomotor beta network and suggests that, at least in healthy subjects, somatosensation and action should not be considered as separable processes, not even at the level of the muscles.

Attentional modulations of somatosensory alpha, beta and gamma oscillations dissociate between anticipation and stimulus processing.

What are the spectral signatures of somatosensory attention? Here we show that the answer to this question depends critically on the sensory context in which attention is deployed. We recorded magnetoencephalography (MEG) in humans and investigated tactile spatial attention in two different sensory contexts: in anticipation and during the processing of sustained tactile stimuli. We observe a double dissociation between these contexts and two key electrophysiological correlates of attention: in anticipation we primarily observe an attentional suppression of contralateral alpha and beta oscillations (8-12 and 15-30 Hz, respectively), whereas during stimulus processing we primarily observe an attentional amplification of contralateral gamma oscillations (55-75 Hz). This dissociation is well explained by the different neural states that occur prior and during the stimulus, and on which attention can exert its influence. In line with analogous observations in the visual modality, this suggests that the neural implementation of attention must be understood in relation to context and existing brain states. Consequently, different signatures of attention may contribute to perception in different contexts and, as our data reveals for the attentional modulation of alpha oscillations, these are not always required for attention to improve perception. At the same time, these data demonstrate that the attentional modulations of alpha and gamma oscillations (during, respectively, attentional orienting and attentional selection), are generalizable phenomena across the different sensory modalities.

Phase-amplitude coupling in human electrocorticography is spatially distributed and phase diverse.

Spatially distributed phase-amplitude coupling (PAC) is a possible mechanism for selectively routing information through neuronal networks. If so, two key properties determine its selectivity and flexibility, phase diversity over space, and frequency diversity. To investigate these issues, we analyzed 42 human electrocorticographic recordings from 27 patients performing a working memory task. We demonstrate that (1) spatially distributed PAC occurred at distances >10 cm, (2) involved diverse preferred coupling phases, and (3) involved diverse frequencies. Using a novel technique [N-way decomposition based on the PARAFAC (for Parallel Factor analysis) model], we demonstrate that (4) these diverse phases originated mainly from the phase-providing oscillations. With these properties, PAC can be the backbone of a mechanism that is able to separate spatially distributed networks operating in parallel.

Attentional cues affect accuracy and reaction time via different cognitive and neural processes.

We investigated whether symbolic endogenous attentional cues affect perceptual accuracy and reaction time (RT) via different cognitive and neural processes. We recorded magnetoencephalography in 19 humans while they performed a cued somatosensory discrimination task in which the cue-target interval was varied between 0 and 1000 ms. Comparing behavioral and neural measures, we show that (1) attentional cueing affects accuracy and RT with different time courses and (2) the time course of our neural measure (anticipatory suppression of neuronal oscillations in stimulus-receiving sensory cortex) only accounts for the accuracy time course. A model is proposed in which the effect on accuracy is explained by a single process (preparatory excitability increase in sensory cortex), whereas the effect on RT is explained by an additional process that is sensitive to cue-target compatibility (post-target comparison between expected and actual stimulus location). These data provide new insights into the mechanisms underlying behavioral consequences of attentional cueing.

Rhythmic neuronal synchronization in visual cortex entails spatial phase relation diversity that is modulated by stimulation and attention.

Groups of neurons tend to synchronize in distinct frequency bands. Within a given frequency band, synchronization is defined as the consistency of phase relations between site pairs, over time. This synchronization has been investigated in numerous studies and has been found to be modulated by sensory stimulation or cognitive conditions. Here, we investigate local field potentials (LFPs) and multi-unit activity (MUA) recorded from area V4 of two monkeys performing a selective visual attention task. We show that phase relations, that are consistent over time, are typically diverse across site pairs. That is, across site pairs, mean phase relations differ substantially and this across-site-pair phase-relation diversity (SPHARED, for Spatial PHAse RElation Diversity) is highly reliable. Furthermore, we show that visual stimulation and selective attention can shift the pattern of phase relations across site pairs. These shifts are again diverse and this across-site-pair phase-relation-shift diversity (SPHARESD) is again highly reliable. We find SPHARED for LFP-LFP, LFP-MUA and MUA-MUA pairs, stimulus-induced SPHARESD for LFP-LFP and LFP-MUA pairs, and attention-induced SPHARESD for LFP-LFP pairs. SPHARESD is a highly interesting signal from the perspective of impact on downstream neuronal activity. We provide several pieces of evidence for such a role.

Decoding the memorization of individual stimuli with direct human brain recordings.

Through decades of research, neuroscientists and clinicians have identified an array of brain areas that each activate when a person views a certain category of stimuli. However, we do not have a detailed understanding of how the brain represents individual stimuli within a category. Here we used direct human brain recordings and machine-learning algorithms to characterize the distributed patterns that distinguish specific cognitive states. Epilepsy patients with surgically implanted electrodes performed a working-memory task and we used machine-learning algorithms to predict the identity of each viewed stimulus. We found that the brain's representation of stimulus-specific information is distributed across neural activity at multiple frequencies, electrodes, and timepoints. Stimulus-specific neuronal activity was most prominent in the high-gamma (65-128 Hz) and theta/alpha (4-16 Hz) bands, but the properties of these signals differed significantly between individuals and for novel stimuli compared to common ones. Our findings are helpful for understanding the neural basis of memory and developing brain-computer interfaces by showing that the brain distinguishes specific cognitive states by diverse spatiotemporal patterns of neuronal.

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.