Cyclic, Condition-Independent Activity in Primary Motor Cortex Predicts Corrective Movement Behavior

Initial and corrective submovement encoding differences within primary motor cortex during precision reaching

Precision reaching often requires corrective submovements to obtain the desired goal. Most studies of reaching have focused on single initial movements, and implied the cortical encoding model was the same for all submovements. However, corrective submovements may show different encoding patterns from the initial submovement with distinct patterns of activation across the population. Two rhesus macaques performed a precision center-out-task with small targets. Neural activity from single units in the primary motor cortex and associated behavioral data were recorded to evaluate movement characteristics. Neural population data and individual neuronal firing rates identified with a peak finding algorithm to identify peaks in hand speed were examined for encoding differences between initial and corrective submovements. Individual neurons were fitted with a regression model that included the reach vector, position, and speed to predict firing rate. For both initial and corrective submovements, the largest effect remained movement direction. We observed a large subset changed their preferred direction greater than 45° between initial and corrective submovements. Neuronal depth of modulation also showed considerable variation when adjusted for movement speed. By using principal component analysis, neural trajectories of initial and corrective submovements progressed through different neural subspaces. These findings all suggest that different neural encoding patterns exist for initial and corrective submovements within the cortex. We hypothesize that this variation in how neurons change to encode small, corrective submovements might allow for a larger portion of the neural space being used to encode a greater range of movements with varying amplitudes and levels of precision.NEW & NOTEWORTHY Neuronal recordings matched with kinematic behavior were collected in a precision center-out task that often required corrective movements. We reveal large differences in preferred direction and depth of modulation between initial and corrective submovements across the neural population. We then present a model of the neural population describing how these shifts in tuning create different subspaces for signaling initial and corrective movements likely to improve motor precision.

Identifying Distinct Neural Features between the Initial and Corrective Phases of Precise Reaching Using AutoLFADS

Many initial movements require subsequent corrective movements, but how the motor cortex transitions to make corrections and how similar the encoding is to initial movements is unclear. In our study, we explored how the brain's motor cortex signals both initial and corrective movements during a precision reaching task. We recorded a large population of neurons from two male rhesus macaques across multiple sessions to examine the neural firing rates during not only initial movements but also subsequent corrective movements. AutoLFADS, an autoencoder-based deep-learning model, was applied to provide a clearer picture of neurons' activity on individual corrective movements across sessions. Decoding of reach velocity generalized poorly from initial to corrective submovements. Unlike initial movements, it was challenging to predict the velocity of corrective movements using traditional linear methods in a single, global neural space. We identified several locations in the neural space where corrective submovements originated after the initial reaches, signifying firing rates different than the baseline before initial movements. To improve corrective movement decoding, we demonstrate that a state-dependent decoder incorporating the population firing rates at the initiation of correction improved performance, highlighting the diverse neural features of corrective movements. In summary, we show neural differences between initial and corrective submovements and how the neural activity encodes specific combinations of velocity and position. These findings are inconsistent with assumptions that neural correlations with kinematic features are global and independent, emphasizing that traditional methods often fall short in describing these diverse neural processes for online corrective movements.

Neuronal travelling waves explain rotational dynamics in experimental datasets and modelling

Spatiotemporal properties of neuronal population activity in cortical motor areas have been subjects of experimental and theoretical investigations, generating numerous interpretations regarding mechanisms for preparing and executing limb movements. Two competing models, representational and dynamical, strive to explain the relationship between movement parameters and neuronal activity. A dynamical model uses the jPCA method that holistically characterizes oscillatory activity in neuron populations by maximizing the data rotational dynamics. Different rotational dynamics interpretations revealed by the jPCA approach have been proposed. Yet, the nature of such dynamics remains poorly understood. We comprehensively analyzed several neuronal-population datasets and found rotational dynamics consistently accounted for by a traveling wave pattern. For quantifying rotation strength, we developed a complex-valued measure, the gyration number. Additionally, we identified parameters influencing rotation extent in the data. Our findings suggest that rotational dynamics and traveling waves are typically the same phenomena, so reevaluation of the previous interpretations where they were considered separate entities is needed.

Scale-invariant changes in corticospinal excitability reflect multiplexed oscillations in the motor output

In the absence of disease, humans produce smooth and accurate movement trajectories. Despite such 'macroscopic' aspect, the 'microscopic' structure of movements reveals recurrent (quasi-rhythmic) discontinuities. To date, it is unclear how the sensorimotor system contributes to the macroscopic and microscopic architecture of movement. Here, we investigated how corticospinal excitability changes in relation to microscopic fluctuations that are naturally embedded within larger macroscopic variations in motor output. Participants performed a visuomotor tracking task. In addition to the 0.25 Hz modulation that is required for task fulfilment (macroscopic scale), the motor output shows tiny but systematic fluctuations at ∼2 and 8 Hz (microscopic scales). We show that motor-evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) during task performance are consistently modulated at all (time) scales. Surprisingly, MEP modulation covers a similar range at both micro- and macroscopic scales, even though the motor output differs by several orders of magnitude. Thus, corticospinal excitability finely maps the multiscale temporal patterning of the motor output, but it does so according to a principle of scale invariance. These results suggest that corticospinal excitability indexes a relatively abstract level of movement encoding that may reflect the hierarchical organisation of sensorimotor processes. KEY POINTS: Motor behaviour is organised on multiple (time)scales. Small but systematic ('microscopic') fluctuations are engrained in larger and slower ('macroscopic') variations in motor output, which are instrumental in deploying the desired motor plan. Corticospinal excitability is modulated in relation to motor fluctuations on both macroscopic and microscopic (time)scales. Corticospinal excitability obeys a principle of scale invariance, that is, it is modulated similarly at all (time)scales, possibly reflecting hierarchical mechanisms that optimise motor encoding.