The control and training of single motor units in isometric tasks are constrained by a common synaptic input signal

Bräcklein, Mario; Ibáñez, Jaime; Barsakcioglu, Deren Y.; Eden, Jonathan; Burdet, Etienne; Mehring, Carsten; Farina, Dario

doi:10.1101/2021.08.03.454908

Cited by 5 publications

(5 citation statements)

References 36 publications

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“…Our findings are potentially consistent with the observation that recruitment can be altered using biofeedback training in humans 47,58–61,112 . An open question has been whether subjects learn to exploit the fact that rigid control embodies (fixed) exceptions to pure size-based recruitment 46,62 , or whether subjects gain direct voluntary control over previously unknown degrees of freedom. Our results indicate the latter is possible but don’t directly address this question; the prevalence of flexibility during voluntary movement doesn’t necessarily imply it can be controlled under biofeedback.…”

Section: Discussionmentioning

confidence: 99%

“…Another possibility is that latent flexibility may be revealed via biofeedback training 46,47,58–61 . However, it remains unclear whether subjects truly display flexible control or simply leverage known size-principle exceptions; biofeedback-based control appears quite limited 62 . Thomas 46 found biofeedback-based flexibility only in muscles with multiple actions and argued it is a trivial consequence of subjects learning to move differently (e.g., supinating versus flexing).…”

Section: Introductionmentioning

confidence: 99%

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Flexible neural control of motor units

Marshall

Glaser

Trautmann

et al. 2021

Preprint

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SummaryVoluntary movement requires communication from cortex to the spinal cord, where a dedicated pool of motor units (MUs) activates each muscle. The canonical description of MU function, established decades ago, rests upon two foundational tenets. First, cortex cannot control MUs independently1 but supplies each pool with a common drive that specifies force amplitude2,3. Second, as force rises, MUs are recruited in a consistent order4–13 typically described by Henneman’s size principle14–19. While this paradigm has considerable empirical support, a direct test requires simultaneous observations of many MUs over a range of behaviors. We developed an isometric task that allowed stable MU recordings during rapidly changing force production. MU responses were surprisingly flexible and behavior-dependent. MU activity could not be accurately described as reflecting common drive, even when fit with highly expressive latent factor models. Neuropixels probe recordings revealed that, consistent with the requirements of fully flexible control, the cortical population response displays a surprisingly large number of degrees of freedom. Furthermore, MUs were differentially recruited by microstimulation at neighboring cortical sites. Thus, MU activities are flexibly controlled to meet task demands, and cortex has the capacity to contribute to that ability.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flexible neural control of motor units

Marshall

Glaser

Trautmann

et al. 2021

Preprint

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“…Indeed, the effect of independent inputs on force modulation is negligible because force modulation is mainly influenced by the common synaptic input received by a population of motor neurons (Farina & Negro, 2015;Farina et al, 2016). Accordingly, Bräcklein et al (2022) observed that it is very challenging for humans to voluntarily disrupt the common input to motor neurons innervating a single muscle and thus to achieve independent control. There is a greater likelihood that movement is controlled via common inputs to groups of motor neurons.…”

Section: Introductionmentioning

confidence: 99%

Correlation networks of spinal motor neurons that innervate lower limb muscles during a multi‐joint isometric task

Hug

Avrillon

Sarcher

et al. 2022

The Journal of Physiology

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Movements are reportedly controlled through the combination of synergies that generate specific motor outputs by imposing an activation pattern on a group of muscles. To date, the smallest unit of analysis of these synergies has been the muscle through the measurement of its activation. However, the muscle is not the lowest neural level of movement control. In this human study (n = 10), we used a purely data‐driven method grounded on graph theory to extract networks of motor neurons based on their correlated activity during an isometric multi‐joint task. Specifically, high‐density surface electromyography recordings from six lower limb muscles were decomposed into motor neurons spiking activity. We analysed these activities by identifying their common low‐frequency components, from which networks of correlated activity to the motor neurons were derived and interpreted as networks of common synaptic inputs. The vast majority of the identified motor neurons shared common inputs with other motor neuron(s). In addition, groups of motor neurons were partly decoupled from their innervated muscle, such that motor neurons innervating the same muscle did not necessarily receive common inputs. Conversely, some motor neurons from different muscles‐including distant muscles‐received common inputs. The study supports the theory that movements are produced through the control of small numbers of groups of motor neurons via common inputs and that there is a partial mismatch between these groups of motor neurons and muscle anatomy. We provide a new neural framework for a deeper understanding of the structure of common inputs to motor neurons. Key points A central and unresolved question is how spinal motor neurons are controlled to generate movement. We decoded the spiking activities of dozens of spinal motor neurons innervating six muscles during a multi‐joint task, and we used a purely data‐driven method grounded on graph theory to extract networks of motor neurons based on their correlated activity (considered as common input). The vast majority of the identified motor neurons shared common inputs with other motor neuron(s). Groups of motor neurons were partly decoupled from their innervated muscle, such that motor neurons innervating the same muscle did not necessarily receive common inputs. Conversely, some motor neurons from different muscles, including distant muscles, received common inputs. The study supports the theory that movement is produced through the control of groups of motor neurons via common inputs and that there is a partial mismatch between these groups of motor neurons and muscle anatomy.

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“…Scientifically, a viable research platform, such as sEMG-RD, could enable studying, for example, the effects of neurofeedback on motor unit activity for novel human-machine interactions (Formento, Botros, and Carmena 2021;Bräcklein et al 2022Bräcklein et al , 2021, decoding of motor function (Stachaczyk et al 2020;Tanzarella et al 2023), or investigating the underpinning mechanisms of motor unit control (Marshall et al 2022;Hug et al 2023).…”

Section: Future Directionsmentioning

confidence: 99%

A generic noninvasive neuromotor interface for human-computer interaction

Sussillo,

Kaifosh,

Reardon

2024

Preprint

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Since the advent of computing, humans have sought computer input technologies that are expressive, intuitive, and universal. While diverse modalities have been developed, including keyboards, mice, and touchscreens, they require interaction with an intermediary device that can be limiting, especially in mobile scenarios. Gesture-based systems utilize cameras or inertial sensors to avoid an intermediary device, but they tend to perform well only for unobscured or overt movements. Brain computer interfaces (BCIs) have been imagined for decades to solve the interface problem by allowing for input to computers via thought alone. However high-bandwidth communication has only been demonstrated using invasive BCIs with decoders designed for single individuals, and so cannot scale to the general public. In contrast, neuromotor signals found at the muscle offer access to subtle gestures and force information. Here we describe the development of a noninvasive neuromotor interface that allows for computer input using surface electromyography (sEMG). We developed a highly-sensitive and robust hardware platform that is easily donned/doffed to sense myoelectric activity at the wrist and transform intentional neuromotor commands into computer input. We paired this device with an infrastructure optimized to collect training data from thousands of consenting participants, which allowed us to develop generic sEMG neural network decoding models that work across many people without the need for per-person calibration. Test users not included in the training set demonstrate closed-loop median performance of gesture decoding at 0.5 target acquisitions per second in a continuous navigation task, 0.9 gesture detections per second in a discrete gesture task, and handwriting at 17.0 adjusted words per minute. We demonstrate that input bandwidth can be further improved up to 30% by personalizing sEMG decoding models to the individual, anticipating a future in which humans and machines co-adapt to provide seamless translation of human intent. To our knowledge this is the first high-bandwidth neuromotor interface that directly leverages biosignals with performant out-of-the-box generalization across people.

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The control and training of single motor units in isometric tasks are constrained by a common synaptic input signal

Cited by 5 publications

References 36 publications

Flexible neural control of motor units

Flexible neural control of motor units

Correlation networks of spinal motor neurons that innervate lower limb muscles during a multi‐joint isometric task

A generic noninvasive neuromotor interface for human-computer interaction

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