After a very successful TRF1 in 2017, this year saw the second bi-annual Timing Research Forum held from the 15-17th of October, 2019 in Queretaro, Mexico. The conference largely followed the format of its predecessor, with the extremely exciting addition of a Moonshot session and keynotes from Mehrdad Jazayeri, Albert Tsao and Kia Nobre. 

It would be difficult to comment on all the new ideas and findings, with so many exciting and inspiring talks/posters in one place, but some of the musings I took away were related to how we still have a long (and adventurous) road ahead of us in terms of defining what we truly mean when we speak of the specifics of timing and time perception. Specifically, several speakers proposed the idea of rethinking relative features of time such as what regularity truly means for the brain, and whether previously demonstrated functional networks account for all presentations of time as has been suggested (for example, beats and intervals), or rather whether different networks account for different aspects of time. The importance of rhythmic expectation via improved performance in behavioural measures were also touched upon, but again, extrapolating these behavioural effects to occur as a result of temporal expectation is a perilous stance that still requires further exploration (and may actually be more challenging than we initially anticipate). With every meeting dedicated to exploring time perception, it becomes increasingly obvious that we don’t all mean the same thing when we speak of time. Investigating the perception of ‘rhythms’ or ‘beat’ is distinctly different to the perception of ‘intervals’, and even that is different to the processing of ‘durations’. Perhaps, however, these differences are what make the study of time endlessly exciting and compelling – that there truly is so much more we are yet to discover (and eventually piece together). 

The Moonshot session was a novel and particularly exciting addition at this years meeting. The basic premise for the discussion was this – 

“For the next 5 years, all techniques, methods, and workforce are available to solve the question you think is essential to understand time in the brain. Which question would that be, and why?”. 

Speakers proposed several really thought-provoking and progressive advances (the session was fully livestreamed and if you didn’t get a chance to tune in, you can still catch up at https://www.pscp.tv/w/1djxXRwNmYyGZ). Some of the suggestions included a focus on the ontology of timing and a particular need to develop a comprehensive taxonomy of time, as several accounts of interval timing actually describe pattern timing. Another suggestion was the immediate need to explore if distinct neurochemical systems mediate distinct aspects of timing and more social approaches to this question, for example, why synchronous movements with others increases social affiliation (even as early as infancy), and in a similar vein, the comparison that unlike humans, other primates (monkeys) do not spontaneously perform periodic temporal prediction, highlighting that predictive motor entrainment is intrinsically rewarding to humans (..and why exactly to species differ)?. 

While the Moonshot allowed for many exciting ideas to be brought to the forefront, I was especially excited by Molly Henry’s radical approach to the perusal of needing to delimit the now, and what exactly this means. Molly described reports on an ‘expanded now’ from individuals on LSD and other hallucinogens, and proposed that if we truly were to aim for the moon, considering the use of such stimulants to explore the extent of the ‘now’ might actually be more fruitful and eye-opening (excuse the pun!) than we realise. Whilst this leaves much to think about (and in some cases, reconsider), there is something to be said for the feeling of each of us working together to piece this puzzle in our own creative ways.

In conclusion, it is no understatement to say TRF2 was a resounding success – presenting novel insights, but also reaffirming the need to continue pushing our own assumptions of approaching the enigma of timing creatively. Building on the foundations of its predecessor, TRF2 has paved the way for more exciting work to be presented at TRF3 in Groningen (The Netherlands). Thanks again to all the organisers and attendees. See you in 2021! 

– Aysha Motala is a postdoctoral fellow at the Brain and Mind Institute (Western University, Canada) working on cross-modal timing and neuroimaging of speech and rhythm processing 

(twitter:@aysha_motala)  

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