Modulation of Neural Variability in Premotor, Motor, and Posterior Parietal Cortex during Change of Motor Intention

Cortico-spinal modularity in the parieto-frontal system: A new perspective on action control

Classical neurophysiology suggests that the motor cortex (MI) has a unique role in action control. In contrast, this review presents evidence for multiple parieto-frontal spinal command modules that can bypass MI. Five observations support this modular perspective: (i) the statistics of cortical connectivity demonstrate functionally-related clusters of cortical areas, defining functional modules in the premotor, cingulate, and parietal cortices; (ii) different corticospinal pathways originate from the above areas, each with a distinct range of conduction velocities; (iii) the activation time of each module varies depending on task, and different modules can be activated simultaneously; (iv) a modular architecture with direct motor output is faster and less metabolically expensive than an architecture that relies on MI, given the slow connections between MI and other cortical areas; (v) lesions of the areas composing parieto-frontal modules have different effects from lesions of MI. Here we provide examples of six cortico-spinal modules and functions they subserve: module 1) arm reaching, tool use and object construction; module 2) spatial navigation and locomotion; module 3) grasping and observation of hand and mouth actions; module 4) action initiation, motor sequences, time encoding; module 5) conditional motor association and learning, action plan switching and action inhibition; module 6) planning defensive actions. These modules can serve as a library of tools to be recombined when faced with novel tasks, and MI might serve as a recombinatory hub. In conclusion, the availability of locally-stored information and multiple outflow paths supports the physiological plausibility of the proposed modular perspective.

Neural variability fingerprint predicts individuals' information security violation intentions

As the weakest links in information security defense are the individuals in an organizations, it is important to understand their information security behaviors. In the current study, we tested whether the neural variability pattern could predict an individual's intention to engage in information security violations. Because cognitive neuroscience methods can provide a new perspective into psychological processes without common methodological biases or social desirability, we combined an adapted version of the information security paradigm (ISP) with functional magnetic resonance imaging (fMRI) technology. While completing an adapted ISP task, participants underwent an fMRI scan. We adopted a machine learning method to build a neural variability predictive model. Consistent with previous studies, we found that people were more likely to take actions under neutral conditions than in minor violation contexts and major violation contexts. Moreover, the neural variability predictive model, including nodes within the task control, default mode, visual, salience and attention networks, can predict information security violation intentions. These results illustrate the predictive value of neural variability for information security violations and provide a new perspective for combining ISP with the fMRI technique to explore a neural predictive model of information security violation intention.

A Revised Computational Neuroanatomy for Motor Control

We discuss a new framework for understanding the structure of motor control. Our approach integrates existing models of motor control with the reality of hierarchical cortical processing and the parallel segregated loops that characterize cortical-subcortical connections. We also incorporate the recent claim that cortex functions via predictive representation and optimal information utilization. Our framework assumes that each cortical area engaged in motor control generates a predictive model of a different aspect of motor behavior. In maintaining these predictive models, each area interacts with a different part of the cerebellum and BG. These subcortical areas are thus engaged in domain-appropriate system identification and optimization. This refocuses the question of division of function among different cortical areas. What are the different aspects of motor behavior that are predictively modeled? We suggest that one fundamental division is between modeling of task and body whereas another is the model of state and action. Thus, we propose that the posterior parietal cortex, somatosensory cortex, premotor cortex, and motor cortex represent task state, body state, task action, and body action, respectively. In the second part of this review, we demonstrate how this division of labor can better account for many recent findings of movement encoding, especially in the premotor and posterior parietal cortices.

A Simulation Model of Neural Activity During Hand Reaching Movement

Introduction

The neural response is a noisy random process. The neural response to a sensory stimulus is completely equivalent to a list of spike times in the spike train. In previous studies, decreased neuronal response variability was observed in the cortex's various areas during motor preparatory in reaching tasks. The reasons for the reduction in Neural Variability (NV) are unclear. It could be influenced by an increased firing rate, or it could result from the intrinsic characteristic of cells during the Reaction Time (RT).

Methods

A neural response function with an underlying deterministic instantaneous firing rate signal and a random Poisson process spike generator was simulated in this research. Neural stimulation could help us understand the relationships between the complex data structures of cortical activities and their stability in detail during motor intention in arm-reaching tasks.

Results

Our measurements indicated a similar pattern of results to the cortex, a sharp reduction of the normalized variance of simulated spike trains across all trials. We also observed a reverse relationship between activity and normalized variance.

Conclusion

The present study findings could be applied to neural engineering and brain-machine interfaces for controlling external devices, like the movement of a robot arm.

Swallowing Pressure Variability as a Function of Pharyngeal Region, Bolus Volume, Age, and Sex

OBJECTIVES

Within-individual movement variability occurs in most motor domains. However, it is unknown how pharyngeal swallowing pressure varies in healthy individuals. We hypothesized that: 1) variability would differ among pharyngeal regions; 2) variability would decrease with increased bolus volume; 3) variability would increase with age; and 4) there would be no sex differences.

STUDY DESIGN

Case series.

METHODS

We used pharyngeal high-resolution manometry to measure swallowing pressure in the following regions: velopharynx, tongue base, hypopharynx, and upper esophageal sphincter. Data were collected from 97 healthy adults (41 male) aged 21 to 89 years during thin liquid swallows: 2 mL, 10 mL, and participant-selected comfortable volume. Pressure variability was measured using coefficient of variation. Repeated measures analysis of variance was used to assess impacts of region, bolus volume, age, and sex on pressure variability.

RESULTS

There was a significant region × volume interaction (P < .001) and significant main effect of age (P = .005). Pressures in the hypopharynx region were more variable than all other regions (P ≤ .028), and pressures in the tongue base region were less variable than all other regions (P ≤ .002) except at 2 mL volumes (P = .065). Swallowing pressure variability was significantly different in the velopharynx and upper esophageal sphincter regions, with comfortable volume and 2 mL swallows having greater variability than 10 mL swallows (P ≤ .026). Pressure variability significantly increased with increasing age (P = .002). There were no effects of sex on pressure variability (P ≥ .15).

CONCLUSION

Pharyngeal swallowing pressure variability differs according pharyngeal region, volume, and age but not sex. Abnormal swallowing pressure variability may reflect deviations in motor control in persons with swallowing impairment, and results from this study can be used as normative data for future investigations evaluating swallowing pressure generation.

LEVEL OF EVIDENCE

4 Laryngoscope, 131:E52-E58, 2021.

Decoding Changes of Mind in Voluntary Action-Dynamics of Intentional Choice Representations

Voluntary actions rely on appropriate flexibility of intentions. Usually, we should pursue our goals, but sometimes we should change goals if they become too costly to achieve. Using functional magnetic resonance imaging, we investigated the neural dynamics underlying the capacity to change one's mind based on new information after action onset. Multivariate pattern analyses revealed that in visual areas, neural representations of intentional choice between 2 visual stimuli were unchanged by additional decision-relevant information. However, in fronto-parietal cortex, representations changed dynamically as decisions evolved. Precuneus, angular gyrus, and dorsolateral prefrontal cortex encoded new externally cued rewards/costs that guided subsequent changes of mind. Activity in medial frontal cortex predicted changes of mind when participants detached from externally cued evidence, suggesting a role in endogenous decision updates. Finally, trials with changes of mind were associated with an increase in functional connectivity between fronto-parietal areas, allowing for integration of various endogenous and exogenous decision components to generate a distributed consensus about whether to pursue or abandon an initial intention. In conclusion, local and global dynamics of choice representations in fronto-parietal cortex allow agents to maintain the balance between adapting to changing environments versus pursuing internal goals.

Simultaneous motor preparation and execution in a last-moment reach correction task

Motor preparation typically precedes movement and is thought to determine properties of upcoming movements. However, preparation has mostly been studied in point-to-point delayed reaching tasks. Here, we ask whether preparation is engaged during mid-reach modifications. Monkeys reach to targets that occasionally jump locations prior to movement onset, requiring a mid-reach correction. In motor cortex and dorsal premotor cortex, we find that the neural activity that signals when to reach predicts monkeys’ jump responses on a trial-by-trial basis. We further identify neural patterns that signal where to reach, either during motor preparation or during motor execution. After a target jump, neural activity responds in both preparatory and movement-related dimensions, even though error in preparatory dimensions can be small at that time. This suggests that the same preparatory process used in delayed reaching is also involved in reach correction. Furthermore, it indicates that motor preparation and execution can be performed simultaneously.

Motor preparation processes guide movement. Here, by recording neural activity in monkeys reaching toward targets that can change location, the authors provide evidence that changing a prepared movement midway through completion reengages motor preparation.

The Puzzling Relationship between Attention and Motivation: Do Motor Biases Matter?

The relationship between attention and incentive motivation has been mostly examined by administering Posner style cueing tasks in humans and varying monetary stakes. These studies found that higher incentives improved performance independently of spatial attention. However, the ability of the cueing task to measure actual attentional orienting has been debated by several groups that have highlighted the function of the motor system in affecting the behavioral features that are commonly attributed to spatial attention. To determine the impact of motor influences on the interplay between attention and motivation, we administered 2 reaching versions of a cueing task to monkeys in various motor scenarios. In both tasks, a central stimulus indicated the reward stake and predicted the stimulus target location in 80% of trials. In Experiment 1, subjects were requested to report the detection of a target stimulus in each trial. In Experiment 2, the task was modified to fit a paradigm of Go/NoGo target identification. We found that attention and motivation interacted exclusively in Experiment 2, wherein anticipated motor activation was discouraged and more demanding visual processing was imposed. Consequently, we suggest a protocol that provides novel insights into the study of the relationship between spatial attention and motivation and highlights the influence of the arm motor system in the estimation of the deployment of spatial attention.

Finding the balance between model complexity and performance: Using ventral striatal oscillations to classify feeding behavior in rats

The ventral striatum (VS) is a central node within a distributed network that controls appetitive behavior, and neuromodulation of the VS has demonstrated therapeutic potential for appetitive disorders. Local field potential (LFP) oscillations recorded from deep brain stimulation (DBS) electrodes within the VS are a pragmatic source of neural systems-level information about appetitive behavior that could be used in responsive neuromodulation systems. Here, we recorded LFPs from the bilateral nucleus accumbens core and shell (subregions of the VS) during limited access to palatable food across varying conditions of hunger and food palatability in male rats. We used standard statistical methods (logistic regression) as well as the machine learning algorithm lasso to predict aspects of feeding behavior using VS LFPs. We were able to predict the amount of food eaten, the increase in consumption following food deprivation, and the type of food eaten. Further, we were able to predict whether the initiation of feeding was imminent up to 42.5 seconds before feeding began and classify current behavior as either feeding or not-feeding. In classifying feeding behavior, we found an optimal balance between model complexity and performance with models using 3 LFP features primarily from the alpha and high gamma frequencies. As shown here, unbiased methods can identify systems-level neural activity linked to domains of mental illness with potential application to the development and personalization of novel treatments.

As neuropsychiatry begins to leverage the power of computational methods to understand disease states and to develop better therapies, it is vital that we acknowledge the trade-offs between model complexity and performance. We show that computational methods can elucidate a neural signature of feeding behavior and we show how these methods could be used to discover neural patterns related to other behaviors and reveal new potential therapeutic targets. Further, our results help to contextualize both the limitations and potential of applying computational methods to neuropsychiatry by showing how changing the data being used to train predictive models (e.g., population vs. individual data) can have a large impact on how model performance generalizes across time, internal states, and individuals.

Neural Coding of Cell Assemblies via Spike-Timing Self-Information

Cracking brain's neural code is of general interest. In contrast to the traditional view that enormous spike variability in resting states and stimulus-triggered responses reflects noise, here, we examine the "Neural Self-Information Theory" that the interspike-interval (ISI), or the silence-duration between 2 adjoining spikes, carries self-information that is inversely proportional to its variability-probability. Specifically, higher-probability ISIs convey minimal information because they reflect the ground state, whereas lower-probability ISIs carry more information, in the form of "positive" or "negative surprisals," signifying the excitatory or inhibitory shifts from the ground state, respectively. These surprisals serve as the quanta of information to construct temporally coordinated cell-assembly ternary codes representing real-time cognitions. Accordingly, we devised a general decoding method and unbiasedly uncovered 15 cell assemblies underlying different sleep cycles, fear-memory experiences, spatial navigation, and 5-choice serial-reaction time (5CSRT) visual-discrimination behaviors. We further revealed that robust cell-assembly codes were generated by ISI surprisals constituted of ~20% of the skewed ISI gamma-distribution tails, conforming to the "Pareto Principle" that specifies, for many events-including communication-roughly 80% of the output or consequences come from 20% of the input or causes. These results demonstrate that real-time neural coding arises from the temporal assembly of neural-clique members via silence variability-based self-information codes.

Methods for measuring swallowing pressure variability using high-resolution manometry

Any movement performed repeatedly will be executed with inter-trial variability. Oropharyngeal swallowing is a complex sensorimotor action, and swallow-to-swallow variability can have consequences that impact swallowing safety. Our aim was to determine an appropriate method to measure swallowing pressure waveform variability. An ideal variability metric must be sensitive to known deviations in waveform amplitude, duration, and overall shape, without being biased by waveforms that have both positive and sub-atmospheric pressure profiles. Through systematic analysis of model waveforms, we found a coefficient of variability (CV) parameter on waveforms adjusted such that the overall mean was 0 to be best suited for swallowing pressure variability analysis. We then investigated pharyngeal swallowing pressure variability using high-resolution manometry data from healthy individuals to assess impacts of waveform alignment, pharyngeal region, and number of swallows investigated. The alignment that resulted in the lowest overall swallowing pressure variability was when the superior-most sensor in the upper esophageal sphincter reached half its maximum pressure. Pressures in the tongue base region of the pharynx were least variable and pressures in the hypopharynx region were most variable. Sets of 3 - 10 consecutive swallows had no overall difference in variability, but sets of 2 swallows resulted in significantly less variability than the other dataset sizes. This study identified variability in swallowing pressure waveform shape throughout the pharynx in healthy adults; we discuss implications for swallowing motor control.

Interpreting and Utilising Intersubject Variability in Brain Function

We consider between-subject variance in brain function as data rather than noise. We describe variability as a natural output of a noisy plastic system (the brain) where each subject embodies a particular parameterisation of that system. In this context, variability becomes an opportunity to: (i) better characterise typical versus atypical brain functions; (ii) reveal the different cognitive strategies and processing networks that can sustain similar tasks; and (iii) predict recovery capacity after brain damage by taking into account both damaged and spared processing pathways. This has many ramifications for understanding individual learning preferences and explaining the wide differences in human abilities and disabilities. Understanding variability boosts the translational potential of neuroimaging findings, in particular in clinical and educational neuroscience.

Precursor processes of human self-initiated action

A gradual buildup of electrical potential over motor areas precedes self-initiated movements. Recently, such “readiness potentials” (RPs) were attributed to stochastic fluctuations in neural activity. We developed a new experimental paradigm that operationalized self-initiated actions as endogenous ‘skip’ responses while waiting for target stimuli in a perceptual decision task. We compared these to a block of trials where participants could not choose when to skip, but were instead instructed to skip. Frequency and timing of motor action were therefore balanced across blocks, so that conditions differed only in how the timing of skip decisions was generated. We reasoned that across-trial variability of EEG could carry as much information about the source of skip decisions as the mean RP. EEG variability decreased more markedly prior to self-initiated compared to externally-triggered skip actions. This convergence suggests a consistent preparatory process prior to self-initiated action. A leaky stochastic accumulator model could reproduce this convergence given the additional assumption of a systematic decrease in input noise prior to self-initiated actions. Our results may provide a novel neurophysiological perspective on the topical debate regarding whether self-initiated actions arise from a deterministic neurocognitive process, or from neural stochasticity. We suggest that the key precursor of self-initiated action may manifest as a reduction in neural noise.

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Self-initiated action was operationalized in a novel perceptual decision making task.

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EEG variability decreased prior to self-initiated action.

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These findings could be accounted for by a leaky stochastic accumulator model.

Neurons in dorsal premotor cortex represent the switching of intended hand path in a delayed reaching task

Dorsal premotor cortex (PMd) is considered to play a crucial role in motor preparation, yet how the variation of neuronal activity affects the generation of different circumstances dependent movements remains unclear. Here we trained two monkeys to perform a delayed reaching task instructed by two sets of cues, one for indicating the target locations and another for indicating a conditionally presented virtual obstacle in the reaching path, which required the monkey to make a bypassing instead of straight reaching. We recorded the activity of PMd neurons and investigated how they responded to the switching of intended hand path induced by obstacle bypassing. Comparing the neuronal activity between hand bypassing trials and straight reaching trials, we found 30% of the total 687 set-related neurons showed different overall discharging level, and another 24% showed different onset time during the delay period. We also found 16% of the neurons were modulated only by target location and 14% were modulated by both target location and path switching. Our results demonstrate PMd neurons not only represent the planning of reaching to different target locations, as many previous studies have shown, but also represent the switching of intended reaching path induced by hand bypassing, suggesting how PMd neurons coordinate for such circumstances dependent motor planning.

Neural Code-Neural Self-information Theory on How Cell-Assembly Code Rises from Spike Time and Neuronal Variability

A major stumbling block to cracking the real-time neural code is neuronal variability - neurons discharge spikes with enormous variability not only across trials within the same experiments but also in resting states. Such variability is widely regarded as a noise which is often deliberately averaged out during data analyses. In contrast to such a dogma, we put forth the Neural Self-Information Theory that neural coding is operated based on the self-information principle under which variability in the time durations of inter-spike-intervals (ISI), or neuronal silence durations, is self-tagged with discrete information. As the self-information processor, each ISI carries a certain amount of information based on its variability-probability distribution; higher-probability ISIs which reflect the balanced excitation-inhibition ground state convey minimal information, whereas lower-probability ISIs which signify rare-occurrence surprisals in the form of extremely transient or prolonged silence carry most information. These variable silence durations are naturally coupled with intracellular biochemical cascades, energy equilibrium and dynamic regulation of protein and gene expression levels. As such, this silence variability-based self-information code is completely intrinsic to the neurons themselves, with no need for outside observers to set any reference point as typically used in the rate code, population code and temporal code models. Moreover, temporally coordinated ISI surprisals across cell population can inherently give rise to robust real-time cell-assembly codes which can be readily sensed by the downstream neural clique assemblies. One immediate utility of this self-information code is a general decoding strategy to uncover a variety of cell-assembly patterns underlying external and internal categorical or continuous variables in an unbiased manner.