We often learn about what the brain is doing by
observing what the body is doing when the brain is focused on a task. This is
true for investigations into rhythmic timing perception. Many insights into
timing have resulted from careful observation of sensorimotor synchronization
with auditory rhythms. This draws from the works of Bruno Repp that suggest
that perception of auditory rhythms relies on covert action—that synchronizing
with a sequence is not so different than simply perceiving a sequence without
moving along with it.
More specifically, in order to synchronize a
finger-tap, or any other body movement, with an auditory stream, some timing
prediction is necessary in order to perform all movement planning, effector
assembly and execution in time with the auditory beat instead of several
milliseconds too late. If we must plan for a synchronized movement in advance, and
there is some automaticity to this planning when we listen to auditory rhythms,
then it is reasonable to ask whether we also perform some degree of motor
planning every time we perceive a rhythm even if we do not move any body part in
time with it.
The evidence suggests we do use our motor systems,
or at least that our motor systems are actively being used for some purpose
while perceiving rhythms when we are not synchronizing. Brain images during
rhythm perception experiments consistently show activation in areas of the
brain that are known to be involved in movement of the body. These areas
include primary motor cortex, premotor cortices, the basal ganglia, supplementary
motor area, and cerebellum. Details about covert motor activity are still being
investigated, but some theories suggest covert motor activity plays an
essential role in rhythmic timing perception, a theory many music cognition
researchers find intriguing.
But first, what does the neuroimaging literature
actually say about which motor networks are active, and which rhythm perception
tasks elicit this covert action? Each study uses musical stimuli that vary on a
number of features, give different instructions to the subjects on how to
attend to or experience the stimuli, and these differences induce varying emotional
states, arousal, familiarity, attention and memory. However, across all this
stimulus variability, motor networks still robustly present themselves as
players in rhythm perception. Interestingly, the stimulus variability shows up
less in whether we see covert action and more in which motor networks are
covertly activated.
In a recent meta-analysis of neuroimaging studies on
passive musical rhythm perception, Chelsea Gordon, Patrice Cobb and Ramesh
Balasubramaniam (2018) asked which covert motor activations are most reliable
and consistent across studies. They used the Activation Likelihood Estimation
(ALE; Turkeltaub et al., 2002), derived from peak activations in Talairach or
MNI space, to compare coordinates across all PET and fMRI studies with passive
music listening conditions in typically healthy human subjects. Their sample
included 42 experiments that met the criteria for inclusion. As expected, the
results of the ALE meta-analysis revealed clear and consistent covert motor
activations in various regions during passive music listening. These
activations were in premotor cortex (bilaterally), right primary motor cortex,
and a region of left cerebellum. Premotor activation patterns could not be
further localized to dorsal or ventral subregions of premotor cortex, but were
dorsal, ventral or both dorsal and ventral. Right primary motor activations
might have been excitatory or inhibitory, and were stronger in studies that
asked subjects to anticipate later tapping to a beat in subsequent trials or to
subvocalize humming. Most consistent across studies were premotor and left
cerebellum activations, supporting predictive theories of covert motor activity
during passive music listening.
One surprising aspect of these results is that the
ALE meta-analysis did not find consistent activation in SMA, pre-SMA or the
basal ganglia. The authors suggest that basal-ganglia-thalamocortical circuits
may be specifically involved in subjects with musical training, or only in
tasks with specific instructions to attend to the rhythmic timing of the
stimuli instead of to listen passively.
An important concern Gordon and colleagues raised in
the discussion is that of how publication bias contributes to ALE results. Also
described by Acar et al. (2018), unpublished data deemed uninteresting can lead
to biases in meta-analytic techniques (known as the file drawer problem),
including in the ALE measure. Gordon et al. attempted to account for the file
drawer problem by contacting all authors of the analyzed manuscripts to ask for
the full datasets from each study to use in their ALE analysis. However, many
authors did not provide this data for unreported brain activations, leading to
limitations in number of explanatory contrasts that could be performed and a
possible influence of publication bias on the ALE results.
The ALE technique is a powerful tool in performing
large-scale neuroimaging study meta-analyses, but as with any meta-analysis
technique of published results could be susceptible to the pitfalls of the file
drawer problem. That being said, covert motor activity during passive music
listening presents consistently across studies, even with considerable stimulus
variability. This may support that timing prediction uses premotor and
cerebellar networks.
–Jessica M. Ross (jross4@bidmc.harvard.edu)
Source:
Gordon,
C.L., Cobb, P.R., Balasubramaniam, R. (2018). Recruitment of the motor system
during music listening: An ALE meta-analysis of fMRI data. PLoS ONE, 13(11),
e0207213. https://doi.org/10.1371/journal.pone.0207213
http://chelseagordon.me/index.html
https://www.patricehazam.com/
https://www.rameshlab.com/
References:
Acar,
F., Seurinck, R., Eickhoff, S.B., Moerkerke, B. (2018). Assessing robustness
against potential publication bias in Activation Likelihood Estimation (ALE)
meta-analyses for fMRI. PLoS ONE, 13(11), e0208177.
https://doi.org/10.1371/journal.pone.0208177
Turkeltaub,
P.E., Eden, G.F., Jones, K.M., & Zeffiro, T.A. (2002). Meta-analysis of the
functional neuroanatomy of single-word reading: Method and validation.
Neuroimage, 16, 765–780. https://doi.org/10.1006/nimg.2002.1131
