Got your hands full? – How the brain plans actions with different body parts
STEM editor: Francesca Farina
Imagine you’re carrying a laundry basket in your hand, dutifully pursuing your domestic tasks. You open the door with your knee, press the light switch with your elbow, and pick up a lost sock with your foot. Easy, right? Normally, we perform these kinds of goal-directed movements with our hands. Unsurprisingly, hands are also the most widely studied body part, or so-called effector, in research on action planning. We do know a fair bit about how the brain prepares movements with a hand (not to be confused with movement execution). You see something desirable, say, a chocolate bar, and that image goes from your retina to the visual cortex, which is roughly located at the back of your brain. At the same time, an estimate of where your hand is in space is generated in somatosensory cortex, which is located more frontally. Between these two areas sits an area called posterior parietal cortex (PPC), in an ideal position to bring these two pieces of information – the seen location of the chocolate bar and the felt location of your hand – together (for a detailed description of these so-called coordinate transformations see ). From here, the movement plan is sent to primary motor cortex, which directly controls movement execution through the spinal cord. What’s interesting about motor cortex is that it is organised like a map of the body, so the muscles that are next to each other on the “outside” are also controlled by neuronal populations that are next to each other on the “inside”. Put simply, there is a small patch of brain for each body part we have, a phenomenon known as the motor homunculus .
As we all know from everyday experience, it is pretty simple to use a body part other than the hand to perform a purposeful action. But the findings from studies investigating movement planning with different effectors are not clear-cut. Usually, the paradigm used in this kind of research works as follows: The participants look at a centrally presented fixation mark and rest their hand in front of the body midline. Next, a dot indicating the movement goal is presented to the left or right of fixation. The colour of the dot tells the participants, whether they have to use their hand or their eyes to move towards the dot. Only when the fixation mark disappears, the participants are allowed to perform the movement with the desired effector. The delay between the presentation of the goal and the actual movement is important, because muscle activity affects the signal that is measured from the brain (and not in a good way). The subsequent analyses usually focus on this delay period, as the signal emerging throughout is thought to reflect movement preparation. Many studies assessing the activity preceding eye and hand movements have suggested that PPC is organised in an effector-specific manner, with different sub-regions representing different body parts . Other studies report contradicting results, with overlapping activity for hand and eye .
But here’s the thing: We cannot stare at a door until it finally opens itself and I imagine picking up that lost piece of laundry with my eye to be rather uncomfortable. Put more scientifically, hands and eyes are functionally different. Whereas we use our hands to interact with the environment, our eyes are a key player in perception. This is why my supervisor came up with the idea to compare hands and feet, as virtually all goal-directed actions we typically perform using our hands can also be performed with our feet (e.g., see http://www.mfpa.uk for mouth and foot painting artists). Surprisingly, it turned out that the portion of PPC that was previously thought to be exclusively dedicated to hand movement planning showed virtually the same fMRI activation during foot movement planning . That is, the brain does not seem to differentiate between the two limbs in PPC. Wait, the brain? Whereas fMRI is useful to show us where in the brain something is happening, it does not tell us much about what exactly is going on in neuronal populations. Here, the high temporal resolution of EEG allows for a more detailed investigation of brain activity. During my PhD, I used EEG to look at hands and feet from different angles (literally – I looked at a lot of feet). One way to quantify possible effects is to analyse the signal in the frequency domain. Different cognitive functions have been associated with power changes in different frequency bands. Based on a study that found eye and hand movement planning to be encoded in different frequencies , my project focused on identifying a similar effect for foot movements.
This is not as straightforward as it might sound, because there are a number of things that need to be controlled for: To make a comparison between the two limbs as valid as possible, movements should start from a similar position and end at the same spot. And to avoid expectancy effects, movements with both limbs should alternate randomly. As you can imagine, it is quite challenging to find a comfortable position to complete this task (most participants did still talk to me after the experiment, though). Another important thing to keep in mind is the fact that foot movements are somewhat more sluggish than hand movements, owing to physical differences between the limbs. This circumstance can be accounted for by performing different types of movements; some easy, some difficult. When the presented movement goal is rather big, it’s easier to hit than when it’s smaller. Unsurprisingly, movements to easy targets are faster than movements to difficult targets, an effect that has long been known for the hand  but had not been shown for the foot yet. Even though this effect is obviously observed during movement execution, it has been shown to already arise during movement planning .
So, taking a closer look at actual movements can also tell us a fair bit about the underlying planning processes. In my case, “looking closer” meant recording hand and foot movements using infrared lights, a procedure called motion capture. Basically the same method is used to create the characters in movies like Avatar and the Hobbit, but rather than making fancy films I used the trajectories to extract kinematic measures like velocity and acceleration. Again, it turned out that hands and feet have more in common than it may seem at first sight. And it makes sense – as we evolved from quadrupeds (i.e., mammals walking on all fours) to bipeds (walking on two feet), the neural pathways that used to control locomotion with all fours likely evolved into the system now controlling skilled hand movements .
What’s most fascinating to me is the incredible speed and flexibility with which all of this happens. We hardly ever give a thought to the seemingly simple actions we perform every minute (and it’s useful not to, otherwise we’d probably stand rooted to the spot). Our brain is able to take in such a vast amount of information – visually, auditory, somatosensory – filter it effectively and generate motor commands in the range of milliseconds. And we haven’t even found out a fraction of how all of it works. Or to use a famous quote : “If the human brain were so simple that we could understand it, we would be so simple that we couldn’t.”
 Batista, A. (2002). Inner space: Reference frames. Current Biology, 12(11), R380-R383.
 Penfield, W., & Boldrey, E. (1937). Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation. Brain, 60(4), 389-443.
 Connolly, J. D., Andersen, R. A., & Goodale, M. A. (2003). FMRI evidence for a ‘parietal reach region’ in the human brain. Experimental Brain Research, 153(2), 140-145.
 Beurze, S. M., Lange, F. P. de, Toni, I., & Medendorp, W. P. (2009). Spatial and Effector Processing in the Human Parietofrontal Network for Reaches and Saccades. Journal of Neurophysiology, 101(6), 3053–3062
 Heed, T., Beurze, S. M., Toni, I., Röder, B., & Medendorp, W. P. (2011). Functional rather than effector-specific organization of human posterior parietal cortex. The Journal of Neuroscience, 31(8), 3066-3076.
 Van Der Werf, J., Jensen, O., Fries, P., & Medendorp, W. P. (2010). Neuronal synchronization in human posterior parietal cortex during reach planning. Journal of Neuroscience, 30(4), 1402-1412.
 Fitts, P. M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of experimental psychology, 47(6), 381.
 Bertucco, M., Cesari, P., & Latash, M. L. (2013). Fitts’ Law in early postural adjustments. Neuroscience, 231, 61-69.
 Georgopoulos, A. P., & Grillner, S. (1989). Visuomotor coordination in reaching and locomotion. Science, 245(4923), 1209–1210.
 Pugh, Edward M, quoted in George Pugh (1977). The Biological Origin of Human Values.