Meta-analysis of neuroimaging during passive music listening: Motor network contributions to timing perception

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 (


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.


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.

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.

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