Structural coupling between auditory and motor networks is associated with sensorimotor synchronisation performance

Paced finger tapping tasks have been used extensively in brain imaging research to investigate the sensory and motor networks involved in the coordination of rhythmic movements. In comparison, much less is known about how these networks communicate to produce precisely timed actions. A paper published recently in Neuroimage provides new insight into the structural brain connections that underpin sensorimotor synchronisation (SMS) performance.

The study, conducted by Tal Blecher, Idan Tal and Michael Ben-Shachar at Bar Ilan University, explored the structural networks associated with two latent processes widely assumed to be associated with SMS performance: adaptation and anticipation. Adaptation and anticipation are two dissociable sensorimotor processes that are argued to help stabilise performance in SMS tasks. Adaptation refers to various reactive correction mechanisms that fine tune motor plans to minimise the asynchrony between actions and events. Anticipation on the other hand has been linked to the observation that actions (typically finger taps) tend to precede the pacing stimulus. Termed the negative mean asynchrony – the propensity for actions to occur before stimulus onset in SMS tasks suggests that participants do not merely react to stimulus onsets, but instead predict the timing of future events to ensure motor commands coincide with target stimuli.

To assess anticipation and adaptation, participants were instructed to tap in time with an auditory pacing stimulus that incorporated meter. Meter was marked by emphasising either every second tone (1 / 2 meter) or every third tone (3 / 4 meter). Participants were instructed to tap with their index finger for each emphasised tone, and to tap with their middle finger for all tones that were not emphasised. To assess adaptation, the meter presented to participants was changed at random intervals. The time taken to adjust the coordination of the index and middle fingers to the new meter – called time to resynchronise – was used as an index of adaptation. In contrast, to measure anticipation the mean asynchrony was calculated from performance data collected during auditory sequences that did not incorporate changes in meter (constant meter condition).

To examine the structural brain networks associated with adaptation and anticipation, mean asynchrony and time to resynchronise were correlated with brain imaging measures of white matter integrity. The authors used diffusion tensor imaging (DTI) – a technique that measures water diffusion – to identify the major white matter pathways in the brain. DTI exploits the propensity of water to diffuse freely only along the longitudinal axis of axons to delineate tissues that are composed of axons with uniform orientation, such as the major fibre tracts. In addition to tract identification, DTI can be used to estimate the microstructural integrity of the white matter pathways. One measure – called fractional anisotropy – quantifies the proportion of the total diffusion observed within a voxel that coincides with the primary direction of diffusion. High fractional anisotropy is related to microstructural tissue properties, such as the degree of axonal myelination, that are argued to facilitate communication between connected brain regions.

Using deterministic tractography, the authors focused their analysis on two white matter pathways involved in sensorimotor integration: the arcuate fasciculus and the corpus callosum. The arcuate fasciculus connects the superior temporal, inferior parietal and frontal lobes, and plays a prominent role in speech production, speech perception and action observation. In contrast, the corpus callosum connects homologous cortical regions in the left and right hemisphere. To limit the analysis of the corpus callosum to fibre tracts that link motor and auditory regions, the authors only examined the sections of the corpus callosum that corresponded to the pathways connecting bilateral pre-central gyrus (i.e., motor cortex) and bilateral temporal lobes (auditory cortex).

Analysis of the left arcuate fasciculus revealed a significant positive correlation between mean asynchrony and fractional anisotropy that was confined to an anterior portion of the tract. Given that observed mean asynchrony values were negative (i.e., distributed between -150ms and 0ms), this result indicates that participants with higher fractional anisotropy values were better able to synchronise with the auditory stimulus. The authors concluded that this finding adds evidence to the view that sensory motor integration relies on bidirectional coupling of brain regions involved in perception and action. Interpreting mean asynchrony as a measure of anticipation, these findings suggest that feedforward and feedback signals between frontal and temporal regions may be used to form predictions about the timing of upcoming auditory stimuli.

Fractional anisotropy in the pre-central segment of the corpus callosum was found to be negatively correlated with the time to resynchronise measure, indicating that increased integrity of the tract linking the left and right motor cortex was related to faster adaptation to changes in meter. To understand the behavioural significance of this finding, the authors decomposed the changing meter task into several underlying cognitive processes; meter change detection, new meter analysis, old meter inhibition, and execution of new motor plans. Based on evidence that callosal connections are predominantly inhibitory, the authors suggest that the pre-central callosal connections facilitate adaptation in the changing meter task via inhibition of the old meter.

Unexpectedly, fractional anisotropy in the temporal segment of the corpus callosum was found to be negatively correlated with mean asynchrony. Moreover, fractional anisotropy in this tract also correlated negatively with the standard deviation of asynchronies observed in the constant meter task. Taken together, these results indicate that participants with increased fractional anisotropy in this tract demonstrated less accurate and more variable performance in the tapping tasks. The authors provide two possible explanations to account for these apparently contradictory findings. Firstly the authors point out that the transmission of action potentials can be facilitated by either increased myelination and thicker axons. However, fibres comprising neurons with thicker axons should also demonstrate lower fractional anisotropy, as water would be free to diffuse more in directions perpendicular to the orientation of the axon. Alternatively, the authors also suggest that analysis of the auditory input might simply benefit from more lateralised analysis. In this case, sensorimotor synchronisation performance would benefit from decreased communication between the hemispheres.

In summary, these results seemingly point to the view that SMS performance is related to intra-hemispheric coupling between sensorimotor networks, with inter-hemispheric communication benefiting more complex tasks incorporating inhibitory processing. However, it is worth noting that the measures of SMS performance, particularly adaptation, depart considerably from those typically examined in sensorimotor synchronisation research. As noted by the authors, the changing meter task is likely associated with a range of cognitive processes. In contrast, models of SMS focus on much simpler forms of adaptation namely phase correction and period correction. These processes are thought to be carried about by functionally segregated timing networks not examined in this study. Future studies will need to examine these fundamental adaptive processes to determine whether they rely on different timing networks.

Author: Bronson