Bursting with Potential: How Sensorimotor Beta Bursts Develop from Infancy to Adulthood.

Beta activity is thought to play a critical role in sensorimotor processes. However, little is known about how activity in this frequency band develops. Here, we investigated the developmental trajectory of sensorimotor beta activity from infancy to adulthood. We recorded EEG from 9-month-old, 12-month-old, and adult humans (male and female) while they observed and executed grasping movements. We analyzed "beta burst" activity using a novel method that combines time-frequency decomposition and principal component analysis. We then examined the changes in burst rate and waveform motifs along the selected principal components. Our results reveal systematic changes in beta activity during action execution across development. We found a decrease in beta burst rate during movement execution in all age groups, with the greatest decrease observed in adults. Additionally, we identified three principal components that defined waveform motifs that systematically changed throughout the trial. We found that bursts with waveform shapes closer to the median waveform were not rate-modulated, whereas those with waveform shapes further from the median were differentially rate-modulated. Interestingly, the decrease in the rate of certain burst motifs occurred earlier during movement and was more lateralized in adults than in infants, suggesting that the rate modulation of specific types of beta bursts becomes increasingly refined with age.SIGNIFICANCE STATEMENT We demonstrate that, like in adults, sensorimotor beta activity in infants during reaching and grasping movements occurs in bursts, not oscillations like thought traditionally. Furthermore, different beta waveform shapes were differentially modulated with age, including more lateralization in adults. Aberrant beta activity characterizes various developmental disorders and motor difficulties linked to early brain injury, so looking at burst waveform shape could provide more sensitivity for early identification and treatment of affected individuals before any behavioral symptoms emerge. More generally, comparison of beta burst activity in typical versus atypical motor development will also be instrumental in teasing apart the mechanistic functional roles of different types of beta bursts.

Alteration of body representation in typical and atypical motor development.

Developmental coordination disorder (DCD) impacts the quality of life and ability to perform coordinated actions in 5% of school-aged children. The quality of body representations of individuals with DCD has been questioned, but never assessed. We hypothesize that children with DCD have imprecise body representations in the sensory and motor domains. Twenty neurotypical children, seventeen children with DCD (8-12 years old) and twenty neurotypical adults (25-45 years old) performed both sensory and motor body representation tasks: a limb identification and a limb movement task. We observed lower accuracy in the sensory task but not in the motor task. In both tasks, we observe a larger amplitude of errors, or synkinesis, in children with DCD than in neurotypical children. In neurotypical children, accuracy was lower than in neurotypical adults in the motor and sensory task, and the amplitude of sensory errors and synkinesis was higher than in neurotypical adults. Using a linear regression model, we showed that sensory accuracy is a good predictor of synkinesis production, and that synkinesis production is a good predictor of sensory accuracy, as can be expected by the perception-action loop. Results support the hypothesis of an imprecision of body representation in DCD. We suggest that this imprecision arises from noise in the body representation used at the level of internal models of action. Future studies may assess whether slower plasticity of body representations, initial imprecision, or both may account for this observation. At the clinical level, prevention strategies targeting body representation in early childhood are strategically important to limit such impairments. RESEARCH HIGHLIGHTS: Body representation is impaired in children with DCD and has a significant cost in terms of the accuracy of sensory identification of body parts and associated movements. Inaccuracies in the body representation measured in perception and in action (error amplitude and synkinesis) are related in both NT children and adults. In typical development, we provide evidence of a strong link between body schema and body image.

Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex.

Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analysing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity but also because of changes in the signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analysing such data in terms of discrete latent states, but previous approaches have not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modelled condition-specific differences. We present a multilevel Bayesian HMM addresses these shortcomings by incorporating multivariate Poisson log-normal emission probability distributions, multilevel parameter estimation and trial-specific condition covariates. We applied this framework to multi-unit neural spiking data recorded using chronically implanted multi-electrode arrays from macaque primary motor cortex during a cued reaching, grasping and placing task. We show that, in line with previous work, the model identifies latent neural population states which are tightly linked to behavioural events, despite the model being trained without any information about event timing. The association between these states and corresponding behaviour is consistent across multiple days of recording. Notably, this consistency is not observed in the case of a single-level HMM, which fails to generalise across distinct recording sessions. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long-term plasticity in neural populations.

Longitudinal effects of early psychosocial deprivation on macaque executive function: Evidence from computational modelling.

Executive function (EF) describes a group of cognitive processes underlying the organization and control of goal-directed behaviour. Environmental experience appears to play a crucial role in EF development, with early psychosocial deprivation often linked to EF impairment. However, many questions remain concerning the developmental trajectories of EF after exposure to deprivation, especially concerning specific mechanisms. Accordingly, using an 'A-not-B' paradigm and a macaque model of early psychosocial deprivation, we investigated how early deprivation influences EF development longitudinally from adolescence into early adulthood. The contribution of working memory and inhibitory control mechanisms were examined specifically via the fitting of a computational model of decision making to the choice behaviour of each individual. As predicted, peer-reared animals (i.e. those exposed to early psychosocial deprivation) performed worse than mother-reared animals across time, with the fitted model parameters yielding novel insights into the functional decomposition of group-level EF differences underlying task performance. Results indicated differential trajectories of inhibitory control and working memory development in the two groups. Such findings not only extend our knowledge of how early deprivation influences EF longitudinally, but also provide support for the utility of computational modelling to elucidate specific mechanisms linking early psychosocial deprivation to long-term poor outcomes.

Perturbation of Right Dorsolateral Prefrontal Cortex Makes Power Holders Less Resistant to Tempting Bribes.

Bribery is a common form of corruption that takes place when a briber suborns a power holder to achieve an advantageous outcome at the cost of moral transgression. Although bribery has been extensively investigated in the behavioral sciences, its underlying neurobiological basis remains poorly understood. Here, we employed transcranial direct-current stimulation (tDCS) in combination with a novel paradigm (N = 119 adults) to investigate whether disruption of right dorsolateral prefrontal cortex (rDLPFC) causally changed bribe-taking decisions of power holders. Perturbing rDLPFC via tDCS specifically made participants more willing to take bribes as the relative value of the offer increased. This tDCS-induced effect could not be explained by changes in other measures. Model-based analyses further revealed that such neural modulation alters the concern for generating profits for oneself via taking bribes and reshapes the concern for the distribution inequity between oneself and the briber, thereby influencing the subsequent decisions. These findings reveal a causal role of rDLPFC in modulating corrupt behavior.

Human motor cortical beta bursts relate to movement planning and response errors.

Motor cortical beta activity (13-30 Hz) is a hallmark signature of healthy and pathological movement, but its behavioural relevance remains unclear. Using high-precision magnetoencephalography (MEG), we show that during the classical event-related desynchronisation (ERD) and event-related synchronisation (ERS) periods, motor cortical beta activity in individual trials (n > 12,000) is dominated by high amplitude, transient, and infrequent bursts. Beta burst probability closely matched the trial-averaged beta amplitude in both the pre- and post-movement periods, but individual bursts were spatially more focal than the classical ERS peak. Furthermore, prior to movement (ERD period), beta burst timing was related to the degree of motor preparation, with later bursts resulting in delayed response times. Following movement (ERS period), the first beta burst was delayed by approximately 100 milliseconds when an incorrect response was made. Overall, beta burst timing was a stronger predictor of single trial behaviour than beta burst rate or single trial beta amplitude. This transient nature of motor cortical beta provides new constraints for theories of its role in information processing within and across cortical circuits, and its functional relevance for behaviour in both healthy and pathological movement.

Laminar dynamics of high amplitude beta bursts in human motor cortex.

Motor cortical activity in the beta frequency range is one of the strongest and most studied movement-related neural signals. At the single trial level, beta band activity is often characterized by transient, high amplitude, bursting events rather than slowly modulating oscillations. The timing of these bursting events is tightly linked to behavior, suggesting a more dynamic functional role for beta activity than previously believed. However, the neural mechanisms underlying beta bursts in sensorimotor circuits are poorly understood. To address this, we here leverage and extend recent developments in high precision MEG for temporally resolved laminar analysis of burst activity, combined with a neocortical circuit model that simulates the biophysical generators of the electrical currents which drive beta bursts. This approach pinpoints the generation of beta bursts in human motor cortex to distinct excitatory synaptic inputs to deep and superficial cortical layers, which drive current flow in opposite directions. These laminar dynamics of beta bursts in motor cortex align with prior invasive animal recordings within the somatosensory cortex, and suggest a conserved mechanism for somatosensory and motor cortical beta bursts. More generally, we demonstrate the ability for uncovering the laminar dynamics of event-related neural signals in human non-invasive recordings. This provides important constraints to theories about the functional role of burst activity for movement control in health and disease, and crucial links between macro-scale phenomena measured in humans and micro-circuit activity recorded from animal models.

Estimates of cortical column orientation improve MEG source inversion.

Determining the anatomical source of brain activity non-invasively measured from EEG or MEG sensors is challenging. In order to simplify the source localization problem, many techniques introduce the assumption that current sources lie on the cortical surface. Another common assumption is that this current flow is orthogonal to the cortical surface, thereby approximating the orientation of cortical columns. However, it is not clear which cortical surface to use to define the current source locations, and normal vectors computed from a single cortical surface may not be the best approximation to the orientation of cortical columns. We compared three different surface location priors and five different approaches for estimating dipole vector orientation, both in simulations and visual and motor evoked MEG responses. We show that models with source locations on the white matter surface and using methods based on establishing correspondences between white matter and pial cortical surfaces dramatically outperform models with source locations on the pial or combined pial/white surfaces and which use methods based on the geometry of a single cortical surface in fitting evoked visual and motor responses. These methods can be easily implemented and adopted in most M/EEG analysis pipelines, with the potential to significantly improve source localization of evoked responses.

Grasping Neurons in the Ventral Premotor Cortex of Macaques Are Modulated by Social Goals.

Although it is established that F5 neurons can distinguish between nonsocial goals such as bringing food to the mouth for eating or placing it in a container, it is not clear whether they discriminate between social and nonsocial goals. Here, we recorded single-unit activity in the ventral premotor cortex of two female macaques and used a simple reach-to-grasp motor task in which a monkey grasped an object with a precision grip in three conditions, which only differed in terms of their final goal, that is, a subsequent motor act that was either social (placing in the experimenter's hand ["Hand" condition]) or nonsocial (placing in a container ["Container" condition] or bringing to the mouth for eating ["Mouth" condition]). We found that, during the execution of the grasping motor act, the response of a sizable proportion of F5 motor neurons was modulated by the final goal of the action, with some having a preference for the social goal condition. Our results reveal that the representation of goal-directed actions in ventral premotor cortex is influenced by contextual information not only extracted from physical cues but also from cues endowed with biological or social value. Our study suggests that the activity of grasping neurons in the premotor cortex is modulated by social context.

High-precision magnetoencephalography for reconstructing amygdalar and hippocampal oscillations during prediction of safety and threat.

Learning to associate neutral with aversive events in rodents is thought to depend on hippocampal and amygdala oscillations. In humans, oscillations underlying aversive learning are not well characterised, largely due to the technical difficulty of recording from these two structures. Here, we used high-precision magnetoencephalography (MEG) during human discriminant delay threat conditioning. We constructed generative anatomical models relating neural activity with recorded magnetic fields at the single-participant level, including the neocortex with or without the possibility of sources originating in the hippocampal and amygdalar structures. Models including neural activity in amygdala and hippocampus explained MEG data during threat conditioning better than exclusively neocortical models. We found that in both amygdala and hippocampus, theta oscillations during anticipation of an aversive event had lower power compared to safety, both during retrieval and extinction of aversive memories. At the same time, theta synchronisation between hippocampus and amygdala increased over repeated retrieval of aversive predictions, but not during safety. Our results suggest that high-precision MEG is sensitive to neural activity of the human amygdala and hippocampus during threat conditioning and shed light on the oscillation-mediated mechanisms underpinning retrieval and extinction of fear memories in humans.

Quantifying the performance of MEG source reconstruction using resting state data.

In magnetoencephalography (MEG) research there are a variety of inversion methods to transform sensor data into estimates of brain activity. Each new inversion scheme is generally justified against a specific simulated or task scenario. The choice of this scenario will however have a large impact on how well the scheme performs. We describe a method with minimal selection bias to quantify algorithm performance using human resting state data. These recordings provide a generic, heterogeneous, and plentiful functional substrate against which to test different MEG recording and reconstruction approaches. We used a Hidden Markov model to spatio-temporally partition data into self-similar dynamic states. To test the anatomical precision that could be achieved, we then inverted these data onto libraries of systematically distorted subject-specific cortical meshes and compared the quality of the fit using cross validation and a Free energy metric. This revealed which inversion scheme was able to identify the least distorted (most accurate) anatomical models, and allowed us to quantify an upper bound on the mean anatomical distortion accordingly. We used two resting state datasets, one recorded with head-casts and one without. In the head-cast data, the Empirical Bayesian Beamformer (EBB) algorithm showed the best mean anatomical discrimination (3.7 mm) compared with Minimum Norm/LORETA (6.0 mm) and Multiple Sparse Priors (9.4 mm). This pattern was replicated in the second (conventional dataset) although with a marginally poorer (non-significant) prediction of the missing (cross-validated) data. Our findings suggest that the abundant resting state data now commonly available could be used to refine and validate MEG source reconstruction methods and/or recording paradigms.

Non-invasive laminar inference with MEG: Comparison of methods and source inversion algorithms.

Magnetoencephalography (MEG) is a direct measure of neuronal current flow; its anatomical resolution is therefore not constrained by physiology but rather by data quality and the models used to explain these data. Recent simulation work has shown that it is possible to distinguish between signals arising in the deep and superficial cortical laminae given accurate knowledge of these surfaces with respect to the MEG sensors. This previous work has focused around a single inversion scheme (multiple sparse priors) and a single global parametric fit metric (free energy). In this paper we use several different source inversion algorithms and both local and global, as well as parametric and non-parametric fit metrics in order to demonstrate the robustness of the discrimination between layers. We find that only algorithms with some sparsity constraint can successfully be used to make laminar discrimination. Importantly, local t-statistics, global cross-validation and free energy all provide robust and mutually corroborating metrics of fit. We show that discrimination accuracy is affected by patch size estimates, cortical surface features, and lead field strength, which suggests several possible future improvements to this technique. This study demonstrates the possibility of determining the laminar origin of MEG sensor activity, and thus directly testing theories of human cognition that involve laminar- and frequency-specific mechanisms. This possibility can now be achieved using recent developments in high precision MEG, most notably the use of subject-specific head-casts, which allow for significant increases in data quality and therefore anatomically precise MEG recordings. SECTION: Analysis methods. CLASSIFICATIONS: Source localization: inverse problem; Source localization: other.

Effects of Infant Cleft Lip on Adult Gaze and Perceptions of "Cuteness".

OBJECTIVE: Early mother-infant interactions are impaired in the context of infant cleft lip and are associated with adverse child psychological outcomes, but the nature of these interaction difficulties is not yet fully understood. The aim of this study was to explore adult gaze behavior and cuteness perception, which are particularly important during early social exchanges, in response to infants with cleft lip, in order to investigate potential foundations for the interaction difficulties seen in this population. METHODS: Using an eye tracker, eye movements were recorded as adult participants viewed images of infant faces with and without cleft lip. Participants also rated each infant on a scale of cuteness. RESULTS: Participants fixated significantly longer on the mouths of infants with cleft lip, which occurred at the expense of fixation on eyes. Severity of cleft lip was associated with the strength of fixation bias, with participants looking even longer at the mouths of infants with the most severe clefts. Infants with cleft lip were rated as significantly less cute than unaffected infants. Men rated infants as less cute than women overall but gave particularly low ratings to infants with cleft lip. CONCLUSIONS: Results demonstrate that the limited disturbance in infant facial configuration of cleft lip can significantly alter adult gaze patterns and cuteness perception. Our findings could have important implications for early interactions and may help in the development of interventions to foster healthy development in infants with cleft lip.

Inactivation of Parietal Reach Region Affects Reaching But Not Saccade Choices in Internally Guided Decisions.

The posterior parietal cortex (PPC) has traditionally been considered important for awareness, spatial perception, and attention. However, recent findings provide evidence that the PPC also encodes information important for making decisions. These findings have initiated a running argument of whether the PPC is critically involved in decision making. To examine this issue, we reversibly inactivated the parietal reach region (PRR), the area of the PPC that is specialized for reaching movements, while two monkeys performed a memory-guided reaching or saccade task. The task included choices between two equally rewarded targets presented simultaneously in opposite visual fields. Free-choice trials were interleaved with instructed trials, in which a single cue presented in the peripheral visual field defined the reach and saccade target unequivocally. We found that PRR inactivation led to a strong reduction of contralesional choices, but only for reaches. On the other hand, saccade choices were not affected by PRR inactivation. Importantly, reaching and saccade movements to single instructed targets remained largely intact. These results cannot be explained as an effector-nonspecific deficit in spatial attention or awareness, since the temporary "lesion" had an impact only on reach choices. Hence, the PPR is a part of a network for reach decisions and not just reach planning. SIGNIFICANCE STATEMENT: There has been an ongoing debate on whether the posterior parietal cortex (PPC) represents only spatial awareness, perception, and attention or whether it is also involved in decision making for actions. In this study we explore whether the parietal reach region (PRR), the region of the PPC that is specialized for reaches, is involved in the decision process. We inactivated the PRR while two monkeys performed reach and saccade choices between two targets presented simultaneously in both hemifields. We found that inactivation affected only the reach choices, while leaving saccade choices intact. These results cannot be explained as a deficit in attention, since the temporary lesion affected only the reach choices. Thus, PRR is a part of a network for making reach decisions.

Cerebellar tDCS dissociates the timing of perceptual decisions from perceptual change in speech.

Neuroimaging studies suggest that the cerebellum might play a role in both speech perception and speech perceptual learning. However, it remains unclear what this role is: does the cerebellum help shape the perceptual decision, or does it contribute to the timing of perceptual decisions? To test this, we used transcranial direct current stimulation (tDCS) in combination with a speech perception task. Participants experienced a series of speech perceptual tests designed to measure and then manipulate (via training) their perception of a phonetic contrast. One group received cerebellar tDCS during speech perceptual learning, and a different group received sham tDCS during the same task. Both groups showed similar learning-related changes in speech perception that transferred to a different phonetic contrast. For both trained and untrained speech perceptual decisions, cerebellar tDCS significantly increased the time it took participants to indicate their decisions with a keyboard press. By analyzing perceptual responses made by both hands, we present evidence that cerebellar tDCS disrupted the timing of perceptual decisions, while leaving the eventual decision unaltered. In support of this conclusion, we use the drift diffusion model to decompose the data into processes that determine the outcome of perceptual decision-making and those that do not. The modeling suggests that cerebellar tDCS disrupted processes unrelated to decision-making. Taken together, the empirical data and modeling demonstrate that right cerebellar tDCS dissociates the timing of perceptual decisions from perceptual change. The results provide initial evidence in healthy humans that the cerebellum critically contributes to speech timing in the perceptual domain.

A biologically plausible computational theory for value integration and action selection in decisions with competing alternatives.

Decision making is a vital component of human and animal behavior that involves selecting between alternative options and generating actions to implement the choices. Although decisions can be as simple as choosing a goal and then pursuing it, humans and animals usually have to make decisions in dynamic environments where the value and the availability of an option change unpredictably with time and previous actions. A predator chasing multiple prey exemplifies how goals can dynamically change and compete during ongoing actions. Classical psychological theories posit that decision making takes place within frontal areas and is a separate process from perception and action. However, recent findings argue for additional mechanisms and suggest the decisions between actions often emerge through a continuous competition within the same brain regions that plan and guide action execution. According to these findings, the sensorimotor system generates concurrent action-plans for competing goals and uses online information to bias the competition until a single goal is pursued. This information is diverse, relating to both the dynamic value of the goal and the cost of acting, creating a challenging problem in integrating information across these diverse variables in real time. We introduce a computational framework for dynamically integrating value information from disparate sources in decision tasks with competing actions. We evaluated the framework in a series of oculomotor and reaching decision tasks and found that it captures many features of choice/motor behavior, as well as its neural underpinnings that previously have eluded a common explanation.

Learning to grasp and extract affordances: the Integrated Learning of Grasps and Affordances (ILGA) model.

The activity of certain parietal neurons has been interpreted as encoding affordances (directly perceivable opportunities) for grasping. Separate computational models have been developed for infant grasp learning and affordance learning, but no single model has yet combined these processes in a neurobiologically plausible way. We present the Integrated Learning of Grasps and Affordances (ILGA) model that simultaneously learns grasp affordances from visual object features and motor parameters for planning grasps using trial-and-error reinforcement learning. As in the Infant Learning to Grasp Model, we model a stage of infant development prior to the onset of sophisticated visual processing of hand-object relations, but we assume that certain premotor neurons activate neural populations in primary motor cortex that synergistically control different combinations of fingers. The ILGA model is able to extract affordance representations from visual object features, learn motor parameters for generating stable grasps, and generalize its learned representations to novel objects.

Associative learning is necessary but not sufficient for mirror neuron development.

Existing computational models of the mirror system demonstrate the additional circuitry needed for mirror neurons to display the range of properties that they exhibit. Such models emphasize the need for existing connectivity to form visuomotor associations, processing to reduce the space of possible inputs, and demonstrate the role neurons with mirror properties might play in monitoring one's own actions.

Beta bursts question the ruling power for brain-computer interfaces.

Objective: Current efforts to build reliable brain-computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience.Approach: In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in 'left vs. right hand' motor imagery (MI) tasks. Current decoding approaches for such tasks take advantage of the fact that MI generates time-locked changes in induced power in the sensorimotor cortex and rely on band-passed power changes in single or multiple channels. Although little is known about the dynamics of beta burst activity during MI, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements.Main results and Significance: We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.

High precision magnetoencephalography reveals increased right-inferior frontal gyrus beta power during response conflict.

Flexibility of behavior and the ability to rapidly switch actions is critical for adaptive living in humans. It is well established that the right-inferior frontal gyrus (R-IFG) is recruited during outright action-stopping, relating to increased beta (12-30 Hz) power. It has also been posited that inhibiting incorrect response tendencies and switching is central to motor flexibility. However, it is not known if the commonly reported R-IFG beta signature of response inhibition in action-stopping is also recruited during response conflict, which would suggest overlapping networks for stopping and switching. In the current study, we analyzed high precision magnetoencephalography (hpMEG) data recorded with multiple within subject recording sessions (trials n > 10,000) from 8 subjects during different levels of response conflict. We hypothesized that a R-IFG-triggered network for response inhibition is domain general and therefore also involved in mediating response conflict. We tested whether R-IFG showed increased beta power dependent on the level of response conflict. Using event-related spectral perturbations and linear mixed modeling, we found that R-IFG beta power increased for response conflict trials. The R-IFG beta increase was specific to trials with strong response conflict, and increased R-IFG beta power related to less error. This supports a more generalized role for R-IFG beta, beyond simple stopping behavior towards response switching.