Biology subjects

Barsakcioglu, D. Y.

Publications and source records attributed to Barsakcioglu, D. Y..

3 recordsLinked to original sources

bioRxiv2022

Concurrent assessment of individual motoneuron discharges and muscle unit displacement velocity in humans by high-density EMG and ultrafast ultrasound

ObjectiveThe study of human neuromechanical control at the motor unit (MU) level has predominantly focussed on electrical activity and force generation, whilst the link between these, the muscle deformation, has not been widely studied. An important example of this is excitation-contraction coupling (E-C coupling) - the process by which electrical excitation is converted into contraction in the muscle fibres. Despite this being a clear marker for progression of certain diseases, it cannot be measured in vivo in natural contractions. To address this, we analyse the kinematics of muscle units in natural contractions. ApproachWe combine high density surface electromyography (HDsEMG) and ultrafast ultrasound (US) recordings of a mildly contracted muscle (tibialis anterior) to measure the deformation of the muscular tissue caused by individual MU twitches (decomposed from the HDsEMG). With a novel analysis on the US images we identified, with high spatio-temporal precision, the velocity maps associated with single muscle unit movements. From the individual MU profiles obtained from the velocity maps the region of movement, the duration of the mechanical twitch, the total and active contraction times, and the activation time (equivalent to E-C coupling) were computed. Main resultsThe E-C coupling was 3.8 {+/-} 3.0 ms (n = 390), providing the first measurement of this value in for single MUs in non-stimulated contractions. Furthermore, the experimental measures provided the first evidence of single muscle unit twisting during voluntary contractions and showed the presence of MUs with territories with multiple distinct split regions across the muscle region. SignificanceWe show that the combined use of HDsEMG and ultrafast US can allow for the study of kinematics of individual MU twitches, including measurement of the excitation-contraction coupling time under natural neural control conditions. These measurements and characterisations open new avenues for study of neuromechanics in healthy and pathological conditions.

bioengineering↗

bioRxiv2021

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

Recent developments in neural interfaces enable the real-time and non-invasive tracking of motor neuron spiking activity. Such novel interfaces provide a promising basis for human motor augmentation by extracting potential high-dimensional control signals directly from the human nervous system. However, it is unclear how flexibly humans can control the activity of individual motor neurones to effectively increase the number of degrees-of-freedom available to coordinate multiple effectors simultaneously. Here, we provided human subjects (N=7) with real-time feedback on the discharge patterns of pairs of motor units (MUs) innervating a single muscle (tibialis anterior) and encouraged them to independently control the MUs by tracking targets in a 2D space. Subjects learned control strategies to achieve the target-tracking task for various combinations of MUs. These strategies rarely corresponded to a volitional control of independent input signals to individual MUs. Conversely, MU activation was consistent with a common input to the MU pair, while individual activation of the MUs in the pair was predominantly achieved by alterations in de-recruitment order that could be explained with history-dependent changes in motor neuron excitability. These results suggest that flexible MU control based on independent synaptic inputs to single MUs is not a simple to learn control strategy.

neuroscience↗

bioRxiv2021

Non-invasive real-time access to the output of the spinal cord via a wrist wearable interface

Despite the promising features of neural interfaces, their trade-off between information transfer and invasiveness has limited translation and viability outside research settings. Here, we present a non-invasive neural interface that provides access to spinal motoneuron activities from a sensor band at the wrist. The interface decodes electric signals present at the tendon endings of the forearm muscles by using a model of signal generation and deconvolution. First, we evaluated the reliability of the interface to detect motoneuron firings, and thereafter we used the decoded neural activity for the prediction of finger movements in offline and real-time conditions. The results showed that motoneuron activity decoded from the wrist accurately predicted individual and combined finger commands and therefore allowed for highly accurate real-time control. These findings demonstrate the feasibility of a wearable, non-invasive, neural interface at the wrist for precise real-time control based on the output of the spinal cord.

neuroscience↗