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Sunday, 24 February 2013

What role do motor neurons play in basic bodily functions?

As you read this, muscles are contracting and relaxing regularly to move air in and out of your lungs. When you walk to the kitchen for a cup of coffee, muscles in your legs contract and relax in rhythmic sequence to move you forward. For animals to function, motor behaviours like breathing and walking must be reliably controlled by the nervous system. Muscles need to contract in the same order, for roughly the same duration, each time a breath or step is taken. There is a pattern of activity that must be maintained. But the system also has to be flexible enough to respond to changes in the environment, such as obstacles in your path that you have to step around. There are many open questions about how the nervous system controls rhythmic movements, permitting reliability and flexibility. What determines the timing of the motor pattern? Under what conditions can the timing be altered, and how? To what extent can these behaviours be recovered after injury?

Man and child walking. Eadweard Muybridge Animal Locomotion.
How does the nervous system produce the rhythmic sequence of movements required for walking?
Image credit: Eadward Muybridge 1887, via Boston Public Library

Rhythmic motor pattern generation
Example of a motor network, in which motor neurons are not part of the central pattern generating network
Example of a motor network, in which motor neurons are not part of the CPG. Image credit: E. McKiernan.
Rhythmic motor behaviours are controlled by networks of neurons which communicate electrically and chemically1. Although the exact organization of these networks varies, many can be divided into four principal groups of cells. One group includes a subset of neurons in the central nervous system called a central pattern generating (CPG) network, which produces the core rhythm. In some systems, motor neurons, which send signals directly to muscle fibres, participate in generating the rhythm and are part of the first group. However, in other systems, motor neurons do not belong to the CPG network and are considered a second group receiving input from the first. Muscle fibres constitute a third group of cells comprising whole muscles that contract or relax to produce movements. A fourth group, sensory neurons, responds to the movement of muscles and sends feedback to the central nervous system about the output that was produced, or whether there are environmental perturbations, like obstacles.

Role of motor neurons
Motor neuron holding telephone without opposable thumbs
Is neural communication like a game of telephone?
Illustration by John R. McKiernan.
At every level of the motor network there are fascinating open questions. Since motor neurons represent the direct connection between the nervous system and muscles, it is important to understand how they receive, integrate, and generate signals. Neurons are sometimes analogized to telephones, which brings to mind the game many of us played as children. A message starts in one place and after it travels through several relay stations, it arrives at its final destination a mutated version of its former self. Similarly, one can ask what happens to a message as it passes through the motor neurons and on to the muscles? Do motor neurons pass on a relatively similar message to the one they receive, or do they change it? If the latter, to what extent can they alter the message and how?

Importance of ion channels
Motor neurons express proteins that allow them to integrate and generate signals. These proteins, called ion channels, allow charged molecules to cross the membrane, producing currents that change the electrical activity of neurons2. Recent studies have presented tantalizing evidence for the importance of motor neurons currents in shaping rhythmic motor output3. But we don't know the intricacies of how motor neurons interact with the rest of the motor network and which genes encode the crucial channels.

Challenges in studying rhythmic motor control
Drosophila melanogaster fruitfly
The fruitfly is a great model system. Yet, there is a lot we still don't know about its nervous system.
Image credit: André Karwath (creative commons)
Investigating neural networks is tricky. First, one has to know who all the 'players' are and how they are connected. The organization of some motor networks is known, such as that controlling movements of the gut in crustaceans1. But mapping networks is not trivial. The fruitfly is considered a relatively simple model system. In the larva, we know all the motor neurons and how they connect to muscles producing crawling4. But almost nothing is known about the network upstream. Who sends signals to the motor neurons? And how are those neurons interconnected? All that uncertainty about an immature fly. Even recent successes in rats, recording eight neurons simultaneously, have only scraped the surface of what we want to know about network connectivity5. Take a more complicated system, like that of a human – with many more neurons, more connections – and our ignorance is astonishing.

Assuming the network organization is known, how do you isolate single players and take them out of the game? Many studies use drugs to block ion channels and change neurons' electrical activity. Although a lot has been learned from such approaches, two problems arise. First, many drugs are not as specific as desired. In one concentration they may affect only the channel of interest, while larger concentrations can affect more than one channel type. Such crosstalk can be avoided when the channel targets and relevant concentrations are known, but sometimes they aren't. Lack of specificity makes it difficult to identify the crucial channels – mechanisms – that may permit motor neurons to change the motor pattern. Second, these drugs are often applied in ways that affect more than one type of neuron. Genetic approaches offer increased specificity. Manipulations can be focused on single ion channel genes and targeted to identified neurons6. But even these approaches aren't perfect. The nervous system has redundancy that helps ensure vital behaviours like breathing and walking continue. Take out one element and the network compensates for its loss. Proteins are up- or down-regulated, connection strengths are modified, new connections are made. Does this mean the element taken out doesn't play a role? Not necessarily. It just means there are other elements that can take its place. One could argue this speaks to necessity versus sufficiency. But when trying to understand intricate interactions between neurons as they contribute to behaviours, necessary or sufficient – black or white – doesn't really tell us very much.

Moving forward
It is now possible to record from more neurons simultaneously, to image neurons in amazing detail, to control neural activity using pulses of light. We have genetic tools to express channel manipulations in small windows of development to reduce compensation. New methods are being developed every day. But fancy tools will get us nowhere if they aren't coupled with sound, creative experimental design and solid theory. We must think of new ways to combine techniques, develop theoretical frameworks that generate testable hypotheses and guide our experiments, and investigate using behaviourally relevant contexts.

It won't be easy. But the questions wouldn't be open if it was.

This guest post is by Erin C. McKiernan, an experimental and theoretical neuroscience researcher and professor of mathematics at Instituto Tecnológico y de Estudios Superiores de Monterrey in Xochitepec, Morelos, Mexico. Erin has her own blog where she writes about neuroscience, open science, and open access publishing, and can be found on Twitter as @emckiernan13.

The author thanks Marco Herrera Valdez and Ed Trollope for feedback on earlier versions of this article, and Robert Garisto for discussions on neural communication that inspired using the telephone analogy.

[1] Marder, E. & Bucher, D. (2001). Current Biol., 11: R986–R996
[2] Harris-Warrick, R.M. (2002). Current Op. Neurobiol., 12: 646-651.
[3] Wright Jr., TM & Calabrese R. (2011). J. Neurophys., 106: 538-553.
[4] Hoang, B. & Chiba, A. (2001). Dev. Biol., 229: 55-70.
[5] Jiang et al. (2013). Nature Neurosci., 16(2): 210-218.
[6] White et al., (2001). Current Biol., 11: R1041-1053.