Coordinate Transformation is First Completed Downstream of Primary Motor Cortex

It is well known that discharge of neurons in the primary motor cortex (M1) depends on end-point force and limb posture. However, the details of these relations remain unresolved. With the development of brain-machine interfaces (BMIs), these issues have taken on practical as well as theoretical importance. We examined how the M1 encodes movement by comparing single-neuron and electromyographic (EMG) preferred directions (PDs) and by predicting force and EMGs from multiple neurons recorded during an isometric wrist task. Monkeys moved a cursor from a central target to one of eight peripheral targets by exerting force about the wrist while the forearm was held in one of two postures. We fit tuning curves to both EMG and M1 activity measured during the hold period, from which we computed both PDs and the change in PD between forearm postures (⌬PD). We found a unimodal distribution of these ⌬PDs, the majority of which were intermediate between the typical muscle response and an unchanging, extrinsic coordinate system. We also discovered that while most neuron-to-EMG predictions generalized well across forearm postures, end-point force measured in extrinsic coordinates did not. The lack of force generalization was due to musculoskeletal changes with posture. Our results show that the dynamics of most of the recorded M1 signals are similar to those of muscle activity and imply that a BMI designed to drive an actuator with dynamics like those of muscles might be more robust and easier to learn than a BMI that commands forces or movements in external coordinates. muscle activity; wrist movement; single-neuron recording; brainmachine interface; electromyography

Section: Discussionsupporting

confidence: 85%

supporting

confidence: 87%

Movement representation in the primary motor cortex and its contribution to generalizable EMG predictions

Oby

Éthier

Miller

2013

“…Frontal and parietal regions, such as PMA and posterior parietal cortex, carry combined gaze and hand signals, and these areas project to M1. While it has not been definitively established that individual PMA and parietal neurons exhibit combined eye-hand signals, the overwhelming evidence points to a convergence in M1 of gaze and hand position signals ); these could then become used in the process that generates hand movements, although most clearly further processing is done in the spinal interneurons downstream of M1 (Yanai et al 2008).…”

Section: Discussionmentioning

confidence: 99%

Gaze and Hand Position Effects on Finger-Movement-Related Human Brain Activation

Bédard

Sanes²

2009

Bédard P, Sanes JN. Gaze and hand position effects on finger-movement-related human brain activation. J Neurophysiol 101: 834 -842, 2009. First published November 12, 2008 doi:10.1152/jn.90683.2008. Humans commonly use their hands to move and to interact with their environment by processing visual and proprioceptive information to determine the location of a goal-object and the initial hand position. It remains elusive, however, how the human brain fully uses this sensory information to generate accurate movements. In monkeys, it appears that frontal and parietal areas use and combine gaze and hand signals to generate movements, whereas in humans, prior work has separately assessed how the brain uses these two signals. Here we investigated whether and how the human brain integrates gaze orientation and hand position during simple visually triggered finger tapping. We hypothesized that parietal, frontal, and subcortical regions involved in movement production would also exhibit modulation of movementrelated activation as a function of gaze and hand positions. We used functional MRI to measure brain activation while healthy young adults performed a visually cued finger movement and fixed gaze at each of three locations and held the arm in two different configurations. We found several areas that exhibited activation related to a mixture of these hand and gaze positions; these included the sensorymotor cortex, supramarginal gyrus, superior parietal lobule, superior frontal gyrus, anterior cingulate, and left cerebellum. We also found regions within the left insula, left cuneus, left midcingulate gyrus, left putamen, and right tempo-occipital junction with activation driven only by gaze orientation. Finally, clusters with hand position effects were found in the cerebellum bilaterally. Our results indicate that these areas integrate at least two signals to perform visual-motor actions and that these could be used to subserve sensory-motor transformations.

“…Recent studies demonstrate that variability in the firing of premotor structures is correlated to trial-by-trial variability in behavioral features such as pitch in birdsong (Sober et al 2008), reaching speed in monkeys (Churchland et al 2006), and eye movement in monkeys (Medina and Lisberger 2007). Variability in motor output may therefore be centrally controlled and passed to the behavior through downstream sensorimotor transformations (Soechting and Flanders 2008;Yanai et al 2008) of reduced dimension (Flash and Hochner 2005;Lockhart and Ting 2007). Similarly, intertrial variations in muscle synergy activations might arise from descending influences such as expectation, habituation, or emotion, which are factors that have been shown to affect EMG responses (Carpenter et al 2004;Woollacott and Shumway-Cook 2002).…”

Section: 14%mentioning

confidence: 99%

Subject-Specific Muscle Synergies in Human Balance Control Are Consistent Across Different Biomechanical Contexts

Torres-Oviedo

Ting

2010

265

216

Torres-Oviedo G, Ting LH. Subject-specific muscle synergies in human balance control are consistent across different biomechanical contexts. J Neurophysiol 103: 3084 -3098, 2010. First published April 14, 2010 doi:10.1152/jn.00960.2009. The musculoskeletal redundancy of the body provides multiple solutions for performing motor tasks. We have proposed that the nervous system solves this unconstrained problem through the recruitment of motor modules or functional muscle synergies that map motor intention to action. Consistent with this hypothesis, we showed that trial-by-trial variations in muscle activation for multidirectional balance control in humans were constrained by a small set of muscle synergies. However, apparent muscle synergy structures could arise from characteristic patterns of sensory input resulting from perturbations or from low-dimensional optimal motor solutions. Here we studied electromyographic (EMG) responses for balance control across a range of biomechanical contexts, which alter not only the sensory inflow generated by postural perturbations, but also the muscle activation patterns used to restore balance. Support-surface translations in 12 directions were delivered to subjects standing in six different postural configurations: one-leg, narrow, wide, very wide, crouched, and normal stance. Muscle synergies were extracted from each condition using nonnegative matrix factorization. In addition, muscle synergies from the normal stance condition were used to reconstruct muscle activation patterns across all stance conditions. A consistent set of muscle synergies were recruited by each subject across conditions. When balance demands were extremely different from the normal stance (e.g., one-legged or crouched stance), task-specific muscle synergies were recruited in addition to the preexisting ones, rather generating de novo muscle synergies. Taken together, our results suggest that muscle synergies represent consistent motor modules that map intention to action, regardless of the biomechanical context of the task. I N T R O D U C T I O NThe control of balance requires sensorimotor transformations that allow the nervous system to rapidly interpret multiple sensory input signals from all segments of the body to produce context-dependent muscle activation patterns that stabilize the body. In humans, we previously observed that the variations in muscle activation patterns evoked in response to different directions of support-surface movements in the horizontal plane could be described by a limited set of muscle synergies (Torres-Oviedo and Ting 2007). We define muscle synergies as invariant patterns of activation across multiple muscles that are combined to produce complex muscle activation patterns. We hypothesize that variations in muscle activity for movement are generated through the flexible recruitment of a limited set of muscle synergies (for review see Ting and McKay 2007; Tresch and Jarc 2009). To challenge our hypothesis, we investigated whether muscle synergy structures are consist...