We introduce the open-source software MUedit and we describe its use for identifying the discharge timing of motor units from all types of electromyographic (EMG) signals recorded with multi-channel systems. MUedit performs EMG decomposition using a blind-source separation approach. Following this, users can display the estimated motor unit pulse trains and inspect the accuracy of the automatic detection of discharge times. When necessary, users can correct the automatic detection of discharge times and recalculate the motor unit pulse train with an updated separation vector. Here, we provide an open-source software and a tutorial that guides the user through i) the parameters and steps of the decomposition algorithm, and ii) the manual editing of motor unit pulse trains. Further, we provide simulated and experimental EMG signals recorded with grids of surface electrodes and intramuscular electrode arrays to benchmark the performance of MUedit. Finally, we discuss advantages and limitations of the blind-source separation approach for the study of motor unit behaviour during tonic muscle contractions. 

Tutorial on MUedit: An open-source software for identifying and analysing the discharge timing of motor units from electromyographic signals.

Evolution of surface electromyography: From muscle electrophysiology towards neural recording and interfacing.

Decoding the activity of individual neural cells during natural behaviours allows neuroscientists to study how the nervous system generates and controls movements. Contrary to other neural cells, the activity of spinal motor neurons can be determined non-invasively (or minimally invasively) from the decomposition of electromyographic (EMG) signals into motor unit discharge activities. For some interfacing and neuro-feedback investigations, EMG decomposition needs to be performed in real-time. Here, we introduce an open-source software that performs real-time decoding of spinal motor neurons using a blind-source separation approach for multichannel EMG signal processing. Separation vectors (motor unit filters) are identified for each motor unit from a baseline contraction and then re-applied in real-time during test contractions. In this way, the discharge activity of multiple motor units can be provided as visual feedback in real-time. We provide a complete framework with guidelines and examples of recordings to guide researchers who aim to study movement control at the motor neuron level. We tested the software on data collected using either grids of surface electrodes or intramuscular electrode arrays from five lower limb muscles (gastrocnemius lateralis and medialis, vastus lateralis and medialis, and tibialis anterior). We assessed how the muscle, or variation of contraction intensity between the baseline contraction and the test contraction impacted the accuracy of the real-time decomposition. This open-source interface provides a set of tools for neuroscientists to design experimental paradigms where participants can receive real-time feedback on the output of the spinal cord circuits. 

I-Spin live: An open-source software based on blind-source separation for decoding the activity of spinal alpha motor neurons in real-time

In two papers dated 1928 to 1929 in The Journal of Physiology, Edgar Adrian and Detlev Bronk described recordings from motor nerve and muscle fibres. The recordings from motor nerve fibres required progressive dissection of the nerve until a few fibres remained, from which isolated single fibre activity could be detected. The muscle fibre recordings were performed in humans during voluntary contractions with an intramuscular electrode - the concentric needle electrode - that they describe for the first time in the second paper. They recognised that muscle fibres would respond to each impulse sent by the innervating motor neurone and that therefore muscle fibre recordings provided information on the times of activation of the motor nerve fibres which were as accurate as a direct record from the nerve. These observations and the description of the concentric needle electrode opened the era of motor unit recordings in humans, which have continued for almost a century and have provided a comprehensive view of the neural control of movement at the motor unit level. Despite important advances in technology, many of the principles of motor unit behaviour that would be investigated in the subsequent decades were canvassed in the two papers by Adrian and Bronk. For example, they described the concomitant motor neurones' recruitment and rate coding for force modulation, synchronisation of motor unit discharges, and the dependence of discharge rate on motor unit recruitment threshold. Here, we summarise their observations and discuss the impact of their work. We highlight the advent of the concentric needle, and its subsequent influence on motor control research.

The neural control of movement: a century of in vivo motor unit recordings is the legacy of Adrian and Bronk.

Decoding the activity of individual neural cells during natural behaviours allows neuroscientists to study how the nervous system generates and controls movements. Contrary to other neural cells, the activity of spinal motor neurons can be determined non-invasively (or minimally invasively) from the decomposition of electromyographic (EMG) signals into motor unit discharge activities. For some interfacing and neuro-feedback investigations, EMG decomposition needs to be performed in real-time. Here, we introduce an open-source software that performs real-time decoding of spinal motor neurons using a blind-source separation approach for multichannel EMG signal processing. Separation vectors (motor unit filters) are identified for each motor unit from a baseline contraction and then re-applied in real-time during test contractions. In this way, the discharge activity of multiple motor units can be provided as visual feedback in real-time. We provide a complete framework with guidelines and examples of recordings to guide researchers who aim to study movement control at the motor neuron level. We tested the software on data collected using either grids of surface electrodes or intramuscular electrode arrays from five lower limb muscles (gastrocnemius lateralis and medialis, vastus lateralis and medialis, and tibialis anterior). We assessed how the muscle, or variation of contraction intensity between the baseline contraction and the test contraction impacted the accuracy of the real-time decomposition. This open-source interface provides a set of tools for neuroscientists to design experimental paradigms where participants can receive real-time feedback on the output of the spinal cord circuits.

/pdf/i-spin-live-an-open-source-software-based-on-blind-source-839ff6ef.pdf

Flexible sensitivity to inputs during skilled tongue movements

Perception, a cognitive construct, emerges through sensorimotor integration (SMI). The molecular and cellular mechanisms that shape SMI within circuits that promote cognition are poorly understood. Here, we demonstrate that expression of the autism/intellectual disability gene, Syngap1, in mouse cortical excitatory neurons promotes touch sensitivity required to elicit perceptual behaviors. Cortical Syngap1 expression enabled touch-induced feedback signals within sensorimotor loops by assembling circuits that support tactile sensitivity. These circuits also encoded correlates of attention that promoted self-generated whisker movements underlying purposeful and sustained object exploration. As Syngap1 deficient animals explored objects with whiskers, relatively weak touch signals were integrated with relatively strong motor signals. This produced a signal-to-noise deficit consistent with impaired tactile sensitivity, reduced tactile exploration, and weak tactile learning. Thus, Syngap1 expression in cortex promotes tactile perception by assembling circuits that integrate touch and whisker motor signals. Deficient Syngap1 expression likely contributes to cognitive impairment through abnormal top-down SMI.

Sensorimotor Integration Supporting Perception Requires Syngap1 Expression in Cortex

Sensory feedback is essential for motor performance and must adapt to task demands. Muscle spindle afferents (MSAs) are a major primary source of feedback about movement, and their responses are readily modulated online by gain-controller fusimotor neurons and other mechanisms. They are therefore a powerful site for implementing flexible sensorimotor control. We recorded from MSAs innervating the jaw musculature during performance of a directed lick sequence task. Jaw MSAs encoded complex jaw–tongue kinematics. However, kinematic encoding alone accounted for less than half of MSA spiking variability. MSA representations of kinematics changed based on sequence progression (beginning, middle, or end of the sequence, or reward consumption), suggesting that MSAs are flexibly tuned across the task. Dynamic control of incoming feedback signals from MSAs may be a strategy for adaptable sensorimotor control during performance of complex behaviors.

Muscle spindles provide flexible sensory feedback for movement sequences

The mammalian tongue is richly innervated with somatosensory, gustatory and motor fibers. These form the basis of many ethologically important functions such as eating, speaking and social grooming. Despite its high tactile acuity and sensitivity, the neural basis of tongue mechanosensation remains largely mysterious. Here we explored the organization of mechanosensory afferents in the tongue and found that each lingual papilla is innervated by Piezo2+ trigeminal neurons. Notably, each fungiform papilla contained highly specialized ring-like sensory neuron terminations that asymmetrically circumscribe the taste buds. Myelinated lingual afferents in the mouse lingual papillae did not form corpuscular sensory end organs but rather had only free nerve endings. In vivo single-unit recordings from the trigeminal ganglion revealed lingual low-threshold mechanoreceptors (LTMRs) with conduction velocities in the Aδ range or above and distinct adaptation properties ranging from intermediately adapting (IA) to rapidly adapting (RA). IA units were sensitive to both static indentation and stroking, while RA units had a preference for tangential forces applied by stroking. Lingual LTMRs were not directly responsive to rapid cooling or chemicals that can induce astringent or numbing sensations. Sparse labeling of lingual afferents in the tongue revealed distinct terminal morphologies and innervation patterns in fungiform and filiform papillae. Together, our results indicate that fungiform papillae are mechanosensory structures, while suggesting a simple model that links the functional and anatomical properties of tactile sensory neurons in the tongue.

Trigeminal innervation and tactile responses in mouse tongue

Neural and behavioral consequences of bilateral maps in primary somatosensory cortex

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