Estimation of the transmission delays in the basal ganglia of the macaque monkey and subsequent predictions about oscillatory activity under dopamine depletion.

The timescales of the dynamics of a system depend on the combination of the timescales of its components and of its transmission delays between components. Here, we combine experimental stimulation data from 10 studies in macaque monkeys that reveal the timing of excitatory and inhibitory events in the basal ganglia circuit, to estimate its set of transmission delays. In doing so, we reveal possible inconsistencies in the existing data, calling for replications, and we propose two possible sets of transmission delays. We then integrate these delays in a model of the primate basal ganglia that does not rely on direct and indirect pathways' segregation and show that extrastriatal dopaminergic depletion in the external part of the globus pallidus and in the subthalamic nucleus is sufficient to generate ß-band oscillations (in the high part, 20-35 Hz, of the band). More specifically, we show that D2 and D5 dopamine receptors in these nuclei play opposing roles in the emergence of these oscillations, thereby explaining how completely deactivating D5 receptors in the subthalamic nucleus can, paradoxically, cancel oscillations.

A topological classifier to characterize brain states: When shape matters more than variance.

Despite the remarkable accuracies attained by machine learning classifiers to separate complex datasets in a supervised fashion, most of their operation falls short to provide an informed intuition about the structure of data, and, what is more important, about the phenomena being characterized by the given datasets. By contrast, topological data analysis (TDA) is devoted to study the shape of data clouds by means of persistence descriptors and provides a quantitative characterization of specific topological features of the dataset under scrutiny. Here we introduce a novel TDA-based classifier that works on the principle of assessing quantifiable changes on topological metrics caused by the addition of new input to a subset of data. We used this classifier with a high-dimensional electro-encephalographic (EEG) dataset recorded from eleven participants during a previous decision-making experiment in which three motivational states were induced through a manipulation of social pressure. We calculated silhouettes from persistence diagrams associated with each motivated state with a ready-made band-pass filtered version of these signals, and classified unlabeled signals according to their impact on each reference silhouette. Our results show that in addition to providing accuracies within the range of those of a nearest neighbour classifier, the TDA classifier provides formal intuition of the structure of the dataset as well as an estimate of its intrinsic dimension. Towards this end, we incorporated variance-based dimensionality reduction methods to our dataset and found that in most cases the accuracy of our TDA classifier remains essentially invariant beyond a certain dimension.

A machine learning approach to characterize sequential movement-related states in premotor and motor cortices.

Nonhuman primate (NHP) movement kinematics have been decoded from spikes and local field potentials (LFPs) recorded during motor tasks. However, the potential of LFPs to provide network-like characterizations of neural dynamics during planning and execution of sequential movements requires further exploration. Is the aggregate nature of LFPs suitable to construct informative brain state descriptors of movement preparation and execution? To investigate this, we developed a framework to process LFPs based on machine-learning classifiers and analyzed LFP from a primate, implanted with several microelectrode arrays covering the premotor cortex in both hemispheres and the primary motor cortex on one side. The monkey performed a reach-to-grasp task, consisting of five consecutive states, starting from rest until a rewarding target (food) was attained. We use this five-state task to characterize neural activity within eight frequency bands, using spectral amplitude and pairwise correlations across electrodes as features. Our results show that we could best distinguish all five movement-related states using the highest frequency band (200-500 Hz), yielding an 87% accuracy with spectral amplitude, and 60% with pairwise electrode correlation. Further analyses characterized each movement-related state, showing differential neuronal population activity at above-γ frequencies during the various stages of movement. Furthermore, the topological distribution for the high-frequency LFPs allowed for a highly significant set of pairwise correlations, strongly suggesting a concerted distribution of movement planning and execution function is distributed across premotor and primary motor cortices in a specific fashion, and is most significant in the low ripple (100-150 Hz), high ripple (150-200 Hz), and multiunit frequency bands. In summary, our results show that the concerted use of novel machine-learning techniques with coarse grained queue broad signals such as LFPs may be successfully used to track and decode fine movement aspects involving preparation, reach, grasp, and reward retrieval across several brain regions.NEW & NOTEWORTHY Local field potentials (LFPs), despite lower spatial resolution compared to single-neuron recordings, can be used with machine learning classifiers to decode sequential movements involving motor preparation, execution, and reward retrieval. Our results revealed heterogeneity of neural activity on small spatial scales, further evidencing the utility of micro-electrode array recordings for complex movement decoding. With further advancement, high-dimensional LFPs may become the gold standard for brain-computer interfaces such as neural prostheses in the near future.

Changes of Mind after Movement Onset Depend on the State of the Motor System.

Decision-making is traditionally described as a cognitive process of deliberation followed by commitment to an action choice, preceding the planning and execution of the chosen action. However, this is challenged by recent data suggesting that during situated decisions, multiple options are specified simultaneously and compete in premotor cortical areas for selection and execution. Previous studies focused on the competition during planning and left unaddressed the dynamics of decisions during movement. Does deliberation extend into the execution phase? Are nonselected options still considered? Here we studied a decision-making task in which human participants were instructed to select a reaching path trajectory from an origin to a rectangular target, where reward was distributed nonuniformly at the target. Critically, we applied mechanical perturbations to the arm during movement to study under which conditions such perturbations produce changes of mind. Our results show that participants initially selected the direction of movement toward the highest reward region and changed their mind most frequently when the two choices offered the same reward, showing that deliberation continues and follows cost-benefit considerations during movement. Furthermore, changes of mind were dependent on the intensity of the perturbation and the current state of the motor system, including velocity and distance to targets. Although reward remains most relevant, our results indicate that the state of the motor system when the perturbation occurs is a crucial determinant of changes of mind.

Visual-reward driven changes of movement during action execution.

Motor decision-making is often described as a sequential process, beginning with the assessment of available options and leading to the execution of a selected movement. While this view is likely to be accurate for decisions requiring significant deliberation, it would seem unfit for choices between movements in dynamic environments. In this study, we examined whether and how non-selected motor options may be considered post-movement onset. We hypothesized that a change in reward at any point in time implies a dynamic reassessment of options, even after an initial decision has been made. To test this, we performed a decision-making task in which human participants were instructed to execute a reaching movement from an origin to a rectangular target to attain a reward. Reward depended on arrival precision and on the specific distribution of reward presented along the target. On a third of trials, we changed the initial reward distribution post-movement onset. Our results indicated that participants frequently change their initially selected movements when a change is associated with an increase in reward. This process occurs quicker than overall, average reaction times. Finally, changes in movement are not only dependent on reward but also on the current state of the motor apparatus.

Correction: Perceived Effort for Motor Control and Decision-Making.

[This corrects the article DOI: 10.1371/journal.pbio.2002885.].

Perceived effort for motor control and decision-making.

How effort is internally quantified and how it influences both movement generation and decisions between potential movements are 2 difficult questions to answer. Physical costs are known to influence motor control and decision-making, yet we lack a general, principled characterization of how the perception of effort operates across tasks and conditions. Morel and colleagues introduce an insightful approach to that end, assessing effort indifference points and presenting a quadratic law between perceived effort and force production.

Motor Cost Influences Perceptual Decisions.

Perceptual decision making has been widely studied using tasks in which subjects are asked to discriminate a visual stimulus and instructed to report their decision with a movement. In these studies, performance is measured by assessing the accuracy of the participants' choices as a function of the ambiguity of the visual stimulus. Typically, the reporting movement is considered as a mere means of reporting the decision with no influence on the decision-making process. However, recent studies have shown that even subtle differences of biomechanical costs between movements may influence how we select between them. Here we investigated whether this purely motor cost could also influence decisions in a perceptual discrimination task in detriment of accuracy. In other words, are perceptual decisions only dependent on the visual stimulus and entirely orthogonal to motor costs? Here we show the results of a psychophysical experiment in which human subjects were presented with a random dot motion discrimination task and asked to report the perceived motion direction using movements of different biomechanical cost. We found that the pattern of decisions exhibited a significant bias towards the movement of lower cost, even when this bias reduced performance accuracy. This strongly suggests that motor costs influence decision making in visual discrimination tasks for which its contribution is neither instructed nor beneficial.

Balancing out dwelling and moving: optimal sensorimotor synchronization.

Sensorimotor synchronization is a fundamental skill involved in the performance of many artistic activities (e.g., music, dance). After a century of research, the manner in which the nervous system produces synchronized movements remains poorly understood. Typical rhythmic movements involve a motion and a motionless phase (dwell). The dwell phase represents a sizable fraction of the rhythm period, and scales with it. The rationale for this organization remains unexplained and is the object of this study. Twelve participants, four drummers (D) and eight nondrummers (ND), performed tapping movements paced at 0.5-2.5 Hz by a metronome. The participants organized their tapping behavior into dwell and movement phases according to two strategies: 1) Eight participants (1 D, 7 ND) maintained an almost constant ratio of movement time (MT) and dwell time (DT) irrespective of the metronome period. 2) Four participants increased the proportion of DT as the period increased. The temporal variabilities of both the dwell and movement phases were consistent with Weber's law, i.e., their variability increased with their durations, and the longest phase always exhibited the smallest variability. We developed an optimal statistical model that formalized the distribution of time into dwell and movement intervals as a function of their temporal variability. The model accurately predicted the participants' dwell and movement durations irrespective of their strategy and musical skill, strongly suggesting that the distribution of DT and MT results from an optimization process, dependent on each participant's skill to predict time during rest and movement.

Context-dependent urgency influences speed-accuracy trade-offs in decision-making and movement execution.

Speed-accuracy tradeoffs (SATs) exist in both decision-making and movement control, and are generally studied separately. However, in natural behavior animals are free to adjust the time invested in deciding and moving so as to maximize their reward rate. Here, we investigate whether shared mechanisms exist for SAT adjustment in both decisions and actions. Two monkeys performed a reach decision task in which they watched 15 tokens jump, one every 200 ms, from a central circle to one of two peripheral targets, and had to guess which target would ultimately receive the majority of tokens. The monkeys could decide at any time, and once a target was reached, the remaining token movements accelerated to either 50 ms ("fast" block) or 150 ms ("slow" block). Decisions were generally earlier and less accurate in fast than slow blocks, and in both blocks, the criterion of accuracy decreased over time within each trial. This could be explained by a simple model in which sensory information is combined with a linearly growing urgency signal. Remarkably, the duration of the reaching movements produced after the decision decreased over time in a similar block-dependent manner as the criterion of accuracy estimated by the model. This suggests that SATs for deciding and acting are influenced by a shared urgency/vigor signal. Consistent with this, we observed that the vigor of saccades performed during the decision process was higher in fast than in slow blocks, suggesting the influence of a context-dependent global arousal.

Rapid prediction of biomechanical costs during action decisions.

When given a choice between actions that yield the same reward, we tend to prefer the one that requires the least effort. Recent studies have shown that humans are remarkably accurate at evaluating the effort of potential reaching actions and can predict the subtle energetic demand caused by the nonisotropic biomechanical properties of the arm. In the present study, we investigated the time course over which such information is computed and comes to influence decisions. Two independent approaches were used. First, subjects performed a reach decision task in which the time interval for deciding between two candidate reaching actions was varied from 200 to 800 ms. Second, we measured motor-evoked potential (MEPs) to single-pulse transcranial magnetic stimulation (TMS) over the primary motor cortex (M1) to probe the evolving decision at different times after stimulus presentation. Both studies yielded a consistent conclusion: that a prediction of the effort associated with candidate movements is computed very quickly and influences decisions within 200 ms after presentation of the candidate actions. Furthermore, whereas the MEPs measured 150 ms after stimulus presentation were well correlated with the choices that subjects ultimately made, later in the trial the MEP amplitudes were primarily related to the muscular requirements of the chosen movement. This suggests that corticospinal excitability (CSE) initially reflects a competition between candidate actions and later changes to reflect the processes of preparing to implement the winning action choice.

Modelling the learning of biomechanics and visual planning for decision-making of motor actions.

Recent experiments showed that the bio-mechanical ease and end-point stability associated to reaching movements are predicted prior to movement onset, and that these factors exert a significant influence on the choice of movement. As an extension of these results, here we investigate whether the knowledge about biomechanical costs and their influence on decision-making are the result of an adaptation process taking place during each experimental session or whether this knowledge was learned at an earlier stage of development. Specifically, we analysed both the pattern of decision-making and its fluctuations during each session, of several human subjects making free choices between two reaching movements that varied in path distance (target relative distance), biomechanical cost, aiming accuracy and stopping requirement. Our main result shows that the effect of biomechanics is well established at the start of the session, and that, consequently, the learning of biomechanical costs in decision-making occurred at an earlier stage of development. As a means to characterise the dynamics of this learning process, we also developed a model-based reinforcement learning model, which generates a possible account of how biomechanics may be incorporated into the motor plan to select between reaching movements. Results obtained in simulation showed that, after some pre-training corresponding to a motor babbling phase, the model can reproduce the subjects' overall movement preferences. Although preliminary, this supports that the knowledge about biomechanical costs may have been learned in this manner, and supports the hypothesis that the fluctuations observed in the subjects' behaviour may adapt in a similar fashion.

The modulatory influence of end-point controllability on decisions between actions.

Recent work has shown that human subjects are able to predict the biomechanical ease of potential reaching movements and use these predictions to influence their choices. Here, we examined how reach decisions are influenced by specific biomechanical factors related to the control of end-point stability, such as aiming accuracy or stopping control. Human subjects made free choices between two potential reaching movements that varied in terms of path distance and biomechanical cost in four separate blocks that additionally varied two constraints: the width of the targets (narrow or wide) and the requirement of stopping in them. When movements were unconstrained (very wide targets and no requirement of stopping), subjects' choices were strongly biased toward directions aligned with the direction of maximal mobility. However, as the movements became progressively constrained, factors related to the control of the end point gained relevance, thus reducing this bias. This demonstrates that, before movement onset, constraints such as stopping and aiming participate in a remarkably adaptive and flexible action selection process that trades off the advantage of moving along directions of maximal mobility for unconstrained movements against exploiting biomechanical anisotropies to facilitate control of end-point stability whenever the movement constraints require it. These results support a view of decision making between motor actions as a highly context-dependent gradual process in which the subjective desirability of potential actions is influenced by their dynamic properties in relation to the intrinsic properties of the motor apparatus.

The influence of predicted arm biomechanics on decision making.

There is considerable debate on the extent to which biomechanical properties of movements are taken into account before and during voluntary movements. For example, while several models have described reach planning as primarily kinematic, some studies have suggested that implicit knowledge about biomechanics may also exert some influence on the planning of reaching movements. Here, we investigated whether decisions about reaching movements are influenced by biomechanical factors and whether these factors are taken into account before movement onset. To this end, we designed an experimental paradigm in which humans made free choices between two potential reaching movements where the options varied in path distance as well as biomechanical factors related to movement energy and stability. Our results suggest that the biomechanical properties of potential actions strongly influence the selection between them. In particular, in our task, subjects preferred movements whose final trajectory was better aligned with the major axis of the arm's mobility ellipse, even when the launching properties were very similar. This reveals that the nervous system can predict biomechanical properties of potential actions before movement onset and that these predictions, in addition to purely abstract criteria, may influence the decision-making process.