Motor Systems

Understanding the way in which the brain interacts with the rest of the body, and consequently how it interacts with the environment, is a process that is a huge focus in neurotechnology. Thus, in order to understand how neurotech is intended to assist people who have neuromuscular disorder, one must understand the mechanism behind how the nervous system relays information to muscles. Starting at the most fundamental level is the motor unit.

The motor unit is a combination of the motor neuron and however many muscles it innervates. When an action potential occurs at the motor neuron, it allows for the excretion of acetylcholine across the neuromuscular junction to cause constriction of the muscle fibers it innervates. This motor unit action potential is a very useful tool in neurotechnology as can be read with the use of electromyography (EMG). This process of passing a small needle directly through the skin and into the muscle is considered the “gold standard” of neurophysiological assessments of the neuromuscular junction (Pitt et al., 2017). After doing so, one can observe the oscillations that occur through different levels of electrical activity in the motor unit (like the difference between slight and forceful contraction). EMG is also highly considered for adolescent use due to its safety and the fact that it can be done with conscious patients with still high predictive power (Pitt et al., 2017) .

There are several factors that play into what occurs in the muscle after acetylcholine is transmitted to it. To begin, there is a whole cascade of recruitment. The muscle fibers are recruited smallest to largest. When the first muscle fiber is recruited, it will receive that action potential over and over again until the strength of the contraction has been reached. If that fiber is unable to achieve the contraction necessary, the motor neuron will signal the next largest fiber while continuing to increase the signal to the first small fiber. This process will repeat until the proper contraction occurs. This mechanism has been a difficult one to incorporate into the neurotechnologies that are meant to rehabilitate individuals who have lost motor function. The primary inhibition arises from current lack of understanding of the threshold required for recruiting more motor neurons in an efficient manner. One possible approach that has been explored is the use of electrode arrays to non-invasively stimulate muscles. However, this specific technology causes electrodes to accidentally target many of the surrounding muscles as opposed to the individual one they wanted to target first.

One idea that has shown some promise in this area is the use of multiple pads to have a more specific area of target, and when needed the system can have gaps that aren’t necessary to the function they are attempting to stimulate. The other problem these kinds of technologies often ran into was the idea of fatigue. Muscle fibers often fall into two different categories: fast twitch and slow twitch. These are measures of how quickly the muscles can contract, but consequently it is also the speed at which they become fatigued. If a muscle fiber is fast twitch, it will contract very quickly after stimulation, but it will also fatigue faster and lose its ability to exert force. The opposite is true for slow twitch fibers, which can exert a smaller force over a longer period of time. This fatigue made it very difficult for the functional electrical stimulation systems using electrode arrays to operate as they are not highly selective in their recruitment and repeatedly activate muscles that normally wouldn’t be targeted for a particular function. To circumvent this, researchers have recently focused on either varying the pulse’s temporal characteristics or using predictive models that account for fatigue to control the stimulation. These kinds of changes include closed-loop control strategies, adaptive control, neural networks, and the use of proportional integral derivative control (Katsuo et al., 2016).

If we take a step back from the motor unit, it gets us to the spinal cord. The motor unit, and the nerves contained within it, serve as the primary form of communication between the brain and the peripheral nervous system. As such, any kind of problem that occurs here can be very debilitating. A person can lose function of extremities, both in feeling and motor control. Fixing damage in the spinal cord has become is a huge focus in neurotechnology. A recent study (Fletcher et al., 2021), permitted as a clinical trial by the FDA, was the use of a brain-computer interface on a tetraplegic patient. The brain computer interface (BCI) worked to transfer synaptic signals in order to control a prosthetic arm that had intracortical microstimulation. This intracortical microstimulation, 88 wired electrodes implanted in the area of the motor cortex and decodes movement intent, allows patients to have tactile sensation through the prosthetic arm. In fact, they reported that the sensations that came from use of the prosthetic arm felt as though they came from the patient’s own hand. This study has helped solve one of the major setbacks for prosthetics, the loss of tactile sensation (Fletcher et al., 2021). Without it, movements were clumsy and inaccurate. The brain needs these sensations in order to command the fine motor system how to respond.

Finally, the last aspect to examine in motor systems is the motor cortex itself. It is split into two different sections: the premotor cortex and the primary motor cortex. The premotor cortex is in control of kinematic processes, context dependent processes, and relies heavily on input from the surrounding sensory areas. The primary motor cortex controls kinetics, plans muscle movements, and relies heavily on input from the premotor cortex. These functions have been very important for the development of neurotechnologies and have become all the more clear through electroencephalography (EEG). In one study, they utilized movement-related cortical potential (MRCP), which is a low-frequency negative shift in an EEG recording that takes place about 2 seconds prior to voluntary movement production, to predict and quantify an upcoming real or imaginary movement (Shakeel et al., 2015). This “intention of movement” information could help to make prosthetics far more accurate as they could operate as fast as a nervous system signal normally would with the extra 2 seconds of preparation. In addition, this information could help improve integration with machine algorithms. By priming the code to run before any action is taken, this could help in motion before it is done. The use of the EEG on the motor cortex has made monumental strides in our understanding of these processes and can more generally help further the technological advancements within neurotechnology.