Learning movement sequences with music changes brain connectivity. Behavioural relevance pending…

Research Spotlight #2 Moore, E., Schaefer, R., Bastin, M., Roberts, N., & Overy, K. (2017). Diffusion tensor MRI tractography reveals increased fractional anisotropy (FA) in arcuate fasciculus following music-cued motor training. Brain and Cognition, 116, 40-46. 

A couple of weeks ago my mum emailed me a link to a BBC news article which she rightly knew would be of interest to me. It reported on a study by researchers mainly from the University of Edinburgh, and the title of the BBC article was: “Learning with music can change brain structure, says study“. This of course grabbed my attention, as I thought it could provide a neuroscientific counterpart to my PhD student John Dyer’s recent work, which had found that triggering a melody with one’s movements helped in learning and recalling a complex bimanual coordination task (tracing a diamond with one hand and a triangle with the other to create a 4:3 rhythm). The study by Moore et al. on which the BBC article was based is indeed interesting, but probably creates more questions than it answers. It also ties in neatly with an ongoing issue in neuroscience – trying to draw conclusions from neural measures when the behavioural framework within which they should fit is incomplete.

The study involved people learning different patterns of finger movements on their left (non-dominant hand), e.g. index-ring-middle-pinkie-ring-index-ring-middle. You can try yourself and see that it is not that easy – hence room for some learning to happen. Everyone in the study learned the task with a kind of Guitar Hero style video to show them which order the fingers had to be pressed. Importantly, one group practiced with just the video (‘control group’), while the other additionally heard musical pitches (presumably like keys on a piano) to indicate the finger order of the sequence to press. This was the key difference between the groups – one had a ‘musical’ version of the task while the other didn’t. They learned the sequences at home, and the task sped up as they got more confident at producing the sequences. They were tested at performing the rehearsed sequences as well as some sequences that they hadn’t rehearsed at three stages: before training, midway through training and after training. MRI scans of their brains were also recorded before and after training. These were used to measure changes in the organisation of fibres which connects neural cells between the auditory and motor regions of the right-hemisphere of the brain (i.e. the side of the brain which would be responsible for sending muscle signals to the left hand). The hypothesis was that learning with the musical sounds may entail greater auditory-motor linkages in the brain hemisphere involved in coordinating the task-to-be-learned.

The results of the study showed that both groups got better at the task, that is, they completed a greater number of correct sequences as a result of training. However, there were no differences in how much the music and control groups improved, that is both groups were getting pretty much the same number of correct sequences by the end of training. This is rightly taken to show that the musical tones did not influence learning (at least in terms of correct sequences). There was also evidence of a training-dependent change in the organisational structure of the axons connecting auditory and motor regions of the brain for the musical group in the right hemisphere, but not in the left hemisphere or at all in the control group. It could be noted that the changes in neural organisation were small (4% change in the main measure, all measures hovering around the 0.05 p-value), but for such a ‘light’ intervention, this may be all the more compelling – if just adding a little music to the task can change the brain, imagine what you could do if you really went to town with musical movement training!

As I said earlier, these results are certainly interesting. The lack of difference in the learning measure between the group, when one might expect the sound to be helpful, points to a need to understand better if and how sound could lead to enhanced learning of the task (e.g. through movement sonification rather than just as a guide). Also, the apparent change in neural fibres in the music group, although small, may represent a fairly surprising amount of neural plasticity in such a short space of time, more than would be expected (although I cannot comment on whether a 4% difference is that surprising or not as I am not up on what counts as small/large changes in neuroplasticity measures).

In spite of these interesting aspects, there are some limitations on what can be taken from this paper and a number of further questions raised. Firstly, it is a shame that the performance of the task was only examined using correct sequences performed. While it is always a good idea to have a primary learning measure, other measures like timing accuracy or movement kinematics may have revealed group differences which would help with understanding what the neural changes correspond to. If the neural changes are not functional for the movements involved, but rather underpin some audio-motor mapping or association which is simultaneously being acquired by the learners, then this might have been examined by looking for disruptions in performance for the music group when an incongruous finger-tone mapping is introduced following training. It is also possible that using movement sonification (fingers trigger the sounds), rather than sounds as mere cues, may have led to enhanced learning for the music group. Each of these additional measures or experimental conditions might have led to observable behavioural effects, through which the neural changes could be interpreted. As it is, however, it cannot be determined yet whether the observed changes in brain have some functional significance, or are merely an artifact, comparable to an indentation in one’s middle finger following a period of intensive handwriting.

To be fair, the authors do acknowledge that the lack of measured learning differences between the two groups limits the conclusions that can be made with regard to the neural data. Nevertheless, they do propose that the study may be valuable for clinical practice, e.g. for movement rehabilitation in Stroke survivors. The mismatch between the excitement over the seeming neural transformation (less in the paper itself but more in its media coverage) and the lack of corresponding behavioural differences between the learning conditions is telling. Recently, a group of pretty eminent neuroscientists published an excellent critique of a trend in the neurosciences to focus disproportionately on the activity and structures of individual neural cells, or networks of cells, without giving due attention to the situated task and behaviour of the organism that such neural activity is supposed to subserve. Studying the properties of stomach acid molecules is supposed to help us understand digestion; studying the brain should help us understand behaviour.

One might ask – if the researchers had carried out the study as a purely behavioural experiment, would they have then been motivated to repeat it with MRI analysis? My guess is that the behavioural component would need to have been more convincing, in which case the cart may be in front of the horse on this one.


Movements that cause sound are different from those that don’t

Research spotlight #1

Neszmélyi, B. & Horváth, J. (in press). Consequences matter: Self‐induced tones are used as feedback to optimize tone‐eliciting actions, Psychophysiology. doi: 10.1111/psyp.12845*

This paper addresses a conceptual and methodological issue in research which studies attenuated perception of sensory feedback of one’s own actions. Research has indicated that neural responses to sensations that are caused by one’s actions (e.g. the sound of a bell you press yourself) result in reduced neural activity when compared to sensations that are not self-generated (e.g. the sound of a bell you didn’t press). This idea makes intuitive sense: you are likely be more sensitive to things in your environment that you did not immediately cause to happen, since things you did cause can be anticipated. How this is studied is by recording brain EEG activity (electrical signals on the scalp) when someone makes a movement that causes a sound, subtracting the activity due to the movement itself (without sound), and comparing the result to just listening to the sound with no movement. The big assumption here, which this paper tackles, is that the muscle movements which cause sound and the same movements without auditory consequences are essentially controlled in the same way.

In the experiment reported, participants had to pinch a Force Sensing Resistor (FSR) with a pre-set amount of force. This constituted the movement of the task. After some training with a visual display to get the required amount of force right, participants then performed the task under two conditions. In one condition, their pinch was effectively sonified. When they applied the right level of force, a 1000 Hz sine tone sounded (audio-motor condition: AM). In another condition, they had to pinch the FSR without any sound feedback (motor only: M). A final condition involved listening to recordings of sounds created by their previous pinches during which they made no finger movements (audio-only: A). The AM condition always came first, while the order of the A and M conditions were varied between participants. Participants did 300 repetitions of each condition (to get good EEG data, lots of repetitions of a given event are necessary). Pinch force was recorded in the AM and M conditions, and EEG activity was recorded in all three conditions.

The first striking thing that was found in the results was that the pinch force applied differed dramatically between the condition in which pinches caused the tone (AM) versus the no-tone condition (M). Force applied was a lot lighter when the pinch produced a sound than when it didn’t. The second result of interest was that as a consequence of the differences in movements when there was an auditory consequence versus none could account for the attenuated EEG response to self-generated sounds, when applying the subtraction approach described above. While this second result is of interest to people working on the neuroscience of agency, for me the first finding is the most interesting.

The difference in force applied when expecting a tone to be caused by your action versus silent pinching suggests that auditory perceptual feedback from movement can materially alter the nature of coordination with the task at hand. When sounds feedback from movements is present, the movements change because the interaction has changed. This needs to be considered when comparing, for example, behaviour during movement sonification with non-sonified movement interactions (something I would routinely do in my own research on sonification in skill learning). It needs to be considered how the auditory feedback may not only change the way that movement is guided, but also the entire nature of the task as experienced by the agent involved (actor, learner, etc.). A shift from an experimenter’s-eye-view to a participant’s-eye-view is, as always, essential.

Another important idea which this study highlights was masterly laid out by John Dewey’s in his 1896 critique of the Reflex Arc Concept in Psychology (essentially the fixation on stimulus-response causality). In the AM condition, the tones may be the result of the pinches, but they also seem to modulate the coordination of subsequent pinch movements. Essentially, participants pinched more softly when they knew a sound would result. Thus, the sensory consequences of one action modified the control of the subsequent actions across the block of trials. Behaviour is here best characterised as an ongoing coordination between action and perception, not a set of isolated stimulus-response pairs.

A final idea that this study highlights which I want to mention is the potential folly of assuming that one can study coordination and the brain by adding and subtracting perceptual and motor elements of a task. In this study,  neural activity in the AM condition was not simply the sum of activity in A and M, as had previously been assumed. If the brain is a non-linear dynamical system (and it surely is), then the different ways that elements of a task inter-relate and affect the overall state of the system need to be very carefully considered.

Of course, there are limitations in the experiment (as there are in every individual scientific study). The sound used (1000 Hz tone) is typical of a lab-based experiment, but is frustratingly un-ecological. A more ‘pinch-relevant’ auditory consequence might have revealed further interesting motor and neural behaviours. The movement itself (pinch force) is rather limited and one-dimensional, so it is not clear how far this effect could generalise to more complex actions. Also, having the A condition after the AM condition for everyone is problematic as this might mean that neural responses to the tones in the listening condition are affected by the newly formed action-sound mapping from the previous 300 AM trials. Still, this paper did address a potentially pernicious assumption in the neural agency literature, and in so doing highlighted important concepts for thinking about auditory-motor coordination processes.


*If you cannot access this article, use the DOI in Sci-Hub to get it. Science should be open to everyone!