Motor planning brings human primary somatosensory cortex into action-specific preparatory states

Motor planning plays a critical role in producing fast and accurate movement. Yet, the neural processes that occur in human primary motor and somatosensory cortex during planning, and how they relate to those during movement execution, remain poorly understood. Here, we used 7T functional magnetic resonance imaging and a delayed movement paradigm to study single finger movement planning and execution. The inclusion of no-go trials and variable delays allowed us to separate what are typically overlapping planning and execution brain responses. Although our univariate results show widespread deactivation during finger planning, multivariate pattern analysis revealed finger-specific activity patterns in contralateral primary somatosensory cortex (S1), which predicted the planned finger action. Surprisingly, these activity patterns were as informative as those found in contralateral primary motor cortex (M1). Control analyses ruled out the possibility that the detected information was an artifact of subthreshold movements during the preparatory delay. Furthermore, we observed that finger-specific activity patterns during planning were highly correlated to those during execution. These findings reveal that motor planning activates the specific S1 and M1 circuits that are engaged during the execution of a finger press, while activity in both regions is overall suppressed. We propose that preparatory states in S1 may improve movement control through changes in sensory processing or via direct influence of spinal motor neurons

Spinal stretch reflexes support efficient hand control

© 2019, The Author(s), under exclusive licence to Springer Nature America, Inc. Motor behaviour is most efficiently controlled by correcting only disturbances that influence task success. It is currently thought that such control is computed within a transcortical feedback pathway. Here we show that, for postural hand control, even the fastest spinal feedback pathway can produce efficient corrective responses, forcing a re-evaluation of how the nervous system derives the control laws that support motor behavior

Mapping the Integration of Sensory Information across Fingers in Human Sensorimotor Cortex

The integration of somatosensory signals across fingers is essential for dexterous object manipulation. Previous experiments suggest that this integration occurs in neural populations in the primary somatosensory cortex (S1). However, the integration process has not been fully characterized, as previous studies have mainly used 2-finger stimulation paradigms. Here, we addressed this gap by stimulating all 31 single- and multifinger combinations. We measured population-wide activity patterns evoked during finger stimulation in human S1 and primary motor cortex (M1) using 7T fMRI in female and male participants. Using multivariate fMRI analyses, we found clear evidence of unique nonlinear interactions between fingers. In Brodmann area (BA) 3b, interactions predominantly occurred between pairs of neighboring fingers. In BA 2, however, we found equally strong interactions between spatially distant fingers, as well as interactions between finger triplets and quadruplets. We additionally observed strong interactions in the hand area of M1. In both M1 and S1, these nonlinear interactions did not reflect a general suppression of overall activity, suggesting instead that the interactions we observed reflect rich, nonlinear integration of sensory inputs from the fingers. We suggest that this nonlinear finger integration allows for a highly flexible mapping from finger sensory inputs to motor responses that facilitates dexterous object manipulation.SIGNIFICANCE STATEMENT Processing of somatosensory information in primary somatosensory cortex (S1) is essential for dexterous object manipulation. To successfully handle an object, the sensorimotor system needs to detect complex patterns of haptic information, which requires the nonlinear integration of sensory inputs across multiple fingers. Using multivariate fMRI analyses, we characterized brain activity patterns evoked by stimulating all single- and multifinger combinations. We report that progressively stronger multifinger interactions emerge in posterior S1 and in the primary motor cortex (M1), with interactions arising between inputs from neighboring and spatially distant fingers. Our results suggest that S1 and M1 provide the neural substrate necessary to support a flexible mapping from sensory inputs to motor responses of the hand

The planning horizon for movement sequences

When performing a long chain of actions in rapid sequence, future movements need to be planned concur-rently with ongoing action. However, how far ahead we plan, and whether this ability improves with practice, is currently unknown. Here, we designed an experiment in which healthy volunteers produced sequences of 14 finger presses quickly and accurately on a keyboard in response to numerical stimuli. On every trial, participants were only shown a fixed number of stimuli ahead of the current keypress. The size of this viewing window varied between 1 (next digit revealed with the pressing of the current key) and 14 (full view of the sequence). Participants practiced the task for 5 days, and their performance was continuously assessed on random sequences. Our results indicate that participants used the available visual information to plan multiple actions into the future, but that the planning horizon was limited: receiving information about more than three movements ahead did not result in faster sequence production. Over the course of practice, we found larger performance improvements for larger viewing windows and an expansion of the planning horizon. These find-ings suggest that the ability to plan future responses during ongoing movement constitutes an important as-pect of skillful movement. Based on the results, we propose a framework to investigate the neuronal processes underlying simultaneous planning and execution

Skin and muscle receptors shape coordinated fast feedback responses in the upper limb

© 2021 Elsevier Ltd Despite many real-world examples where skin and muscle receptors must function in concert to support movement control, responses based on these sensory modalities are usually separated from one another in laboratory studies. Proprioception is often considered the domain of muscle receptors, whereas the skin\u27s function is often assumed to be discriminative touch. This distinction understates the extent to which sensory feedback from skin and muscle work together to shape successful movement control. Here we review the functional characteristics and similarities between fast feedback responses of the upper limb originating from muscle stretch and skin slip. We place an emphasis on recent evidence of their highly inter-dependent nature and how they build on one another to implement common tasks like object manipulation in the face of external forces applied to the arm or hand

Structure of population activity in primary motor cortex for single finger flexion and extension

Copyright © 2020 the authors How is the primary motor cortex (M1) organized to control fine finger movements? We investigated the population activity in M1 for single finger flexion and extension, using 7T functional magnetic resonance imaging (fMRI) in female and male human participants and compared these results to the neural spiking patterns recorded in two male monkeys performing the identical task. fMRI activity patterns were distinct for movements of different fingers, but were quite similar for flexion and extension of the same finger. In contrast, spiking patterns in monkeys were quite distinct for both fingers and directions, which is similar to what was found for muscular activity patterns. The discrepancy between fMRI and electrophysiological measurements can be explained by two (non-mutually exclusive) characteristics of the organization of finger flexion and extension movements. Given that fMRI reflects predominantly input and recurrent activity, the results can be explained by an architecture in which neural populations that control flexion or extension of the same finger produce distinct outputs, but interact tightly with each other and receive similar inputs. Additionally, neurons tuned to different movement directions for the same finger (or combination of fingers) may cluster closely together, while neurons that control different finger combinations may be more spatially separated. When measuring this organization with fMRI at a coarse spatial scale, the activity patterns for flexion and extension of the same finger would appear very similar. Overall, we suggest that the discrepancy between fMRI and electrophysiological measurements provides new insights into the general organization of fine finger movements in M1

Sharing voxelwise neuroimaging results from rhesus monkeys and other species with Neurovault

© 2020 The Authors Animal neuroimaging studies can provide unique insights into brain structure and function, and can be leveraged to bridge the gap between animal and human neuroscience. In part, this power comes from the ability to combine mechanistic interventions with brain-wide neuroimaging. Due to their phylogenetic proximity to humans, nonhuman primate neuroimaging holds particular promise. Because nonhuman primate neuroimaging studies are often underpowered, there is a great need to share data amongst translational researchers. Data sharing efforts have been limited, however, by the lack of standardized tools and repositories through which nonhuman neuroimaging data can easily be archived and accessed. Here, we provide an extension of the Neurovault framework to enable sharing of statistical maps and related voxelwise neuroimaging data from other species and template-spaces. Neurovault, which was previously limited to human neuroimaging data, now allows researchers to easily upload and share nonhuman primate neuroimaging results. This promises to facilitate open, integrative, cross-species science while affording researchers the increased statistical power provided by data aggregation. In addition, the Neurovault code-base now enables the addition of other species and template-spaces. Together, these advances promise to bring neuroimaging data sharing to research in other species, for supplemental data, location-based atlases, and data that would otherwise be relegated to a file-drawer . As increasing numbers of researchers share their nonhuman neuroimaging data on Neurovault, this resource will enable novel, large-scale, cross-species comparisons that were previously impossible

Primary motor cortex and fast feedback responses to mechanical perturbations : a primer on what we know now and some suggestions on what we should find out next

Many researchers have drawn a clear distinction between fast feedback responses to mechanical perturbations (e.g., stretch responses) and voluntary control processes. But this simple distinction is difficult to reconcile with growing evidence that long-latency stretch responses share most of the defining capabilities of voluntary control. My general view-and I believe a growing consensus-is that the functional similarities between long-latency stretch responses and voluntary control processes can be readily understood based on their shared neural circuitry, especially a transcortical pathway through primary motor cortex. Here I provide a very brief and selective account of the human and monkey studies linking a transcortical pathway through primary motor cortex to the generation and functional sophistication of the long-latency stretch response. I then lay out some of the notable issues that are ready to be answered

Stretch reflexes

Many of us know about stretch reflexes from the doctor\u27s office, when a physician taps the tendon near our kneecap to elicit a quick knee extension. This procedure is used as a diagnostic tool to determine the integrity of the spinal cord and the extension response it elicits may seem otherwise useless. In fact, the tendon tap taps into one aspect of a critical building block of mammalian motor control, the stretch reflexes. Stretch reflexes are often thought to quickly resist unexpected changes in muscle length via a very simple circuit in the spinal cord, and this is one circuit that the tendon tap engages. It turns out, however, that stretch reflexes support a myriad of functions and are highly flexible. Under naturalistic conditions, stretch reflexes are shaped by peripheral physiology and engage neural circuits spanning the spinal cord, brainstem and cerebral cortex. In this Primer, we outline what is currently known about stretch reflex function and its underlying mechanisms, with a specific focus on how the cascade of nested responses collectively known as stretch reflexes interact with and build off of one another to support real-world motor behavior