Moving in synchrony with regular musical events (i.e., beat) is a basic and generalized human ability that can reach sophisticated levels in professional percussionists (Honing et al., 2015; Merchant et al., 2015b). Indeed, humans are extremely sensitive to auditory regularities and can entrain to auditory beats across a wide range of tempos, as well as use timed movements of different body parts (such as finger or foot taps or body swaying) to keep the beat (Mendoza and Merchant, 2014; Repp and Su, 2013). A classical task used to study rhythmic entrainment is the synchronization-continuation task (SCT), where subjects first entrain their tapping to a set of isochronous stimuli, also known as a metronome, and then continue tapping without the periodic stimulus using an internal clock (Merchant et al., 2008a; Wing, 2002; Wing and Kristofferson, 1973). In this type of task, humans show negative asynchronies, namely they tap a few milliseconds before the metronome, supporting the notion that entrainment depends on a predictive internal beat that is phase-locked to the stimuli (Lenc et al., 2021; Repp and Su, 2013; Zarco et al., 2009). Neurophysiological and functional imaging studies have shown that the internal beat representation during an SCT rests in the motor system, including the basal ganglia and supplementary motor regions (Bartolo et al., 2014; Merchant et al., 2013a; Merchant and Averbeck, 2017; Rao et al., 2001; Sánchez-Moncada et al., 2024). These areas produce a regenerating rhythmic signal (Crowe et al., 2014; Gámez et al., 2019; Merchant et al., 2015a) that dynamically interacts with the auditory areas, creating audiomotor loops where the motor prediction of the beat is flexibly compared and adjusted depending on changes in the input stream of rhythmic stimuli to phase-lock sensory and motor signals (Comstock et al., 2018; Merchant and Honing, 2014b; Patel and Iversen, 2014). In addition, neurons in the supplementary motor areas (SMAs) are tuned to the tempo duration of tapping (Merchant et al., 2014a; Merchant et al., 2013b), giving rise to interval-specific circuits that define a chronotopic map with short preferred intervals in the anterior portion and long preferred intervals in the posterior portion of the medial premotor areas (Protopapa et al., 2019; Merchant et al., 2024b).

The existence of neural circuits with preferred intervals comprising chronotopic maps is consistent with the human flexibility to tap in phase (with asynchronies close to zero) and high precision to isochronous stimuli over a wide range of interstimulus-onset intervals (ISIs), spanning from 400 to 1200 ms (Mates, 1994; Repp, 2005). Within this window, subjects demonstrate a spontaneous rhythmic tempo, which corresponds to the interval produced naturally when asked to tap in without external cues (McAuley et al., 2006; Zamm et al., 2018). This spontaneous or preferred tempo is around 600–750 ms in human adults (Fischl, 2012; Fraisse, 1978, but see Parncutt, 1994), but is faster in early childhood and slower in late adulthood (McAuley et al., 2006). A recent study demonstrated that the perception of rhythmic stimuli also has a preferred tempo, with an optimal sampling rate of ~1.4 Hz (ISI of 714 ms) in audition and ~0.7 Hz (ISI of 1428 ms) in vision. Furthermore, motor tapping helps to synchronize the temporal fluctuations of attention with maximal effects at ~1.7 Hz (ISI of 588 ms), but only for the auditory modality (Zalta et al., 2020). These findings support the notion that ongoing motor activity shapes attention and beat perception, as it imposes temporal constraints on the sampling of sensory information within a narrow frequency range (Morillon et al., 2019). Hence, the audiomotor system is built to optimally work at a preferred tempo.

Although rhythmic entrainment is prevalent across all human cultures and is a natural behavior for social interaction (Nettle, 2000; Jacoby et al., 2024), there are wide individual differences in the period (inter-tap interval) and phase (asynchronies) of movement synchronization. There are subjects who lack musical training, yet spontaneously synchronize to rhythmic stimuli ranging from strictly periodic metronomes to complex musical pieces, with performance comparable to that of trained musicians (Scheurich et al., 2018; Tranchant et al., 2016). Conversely, there are poor synchronizers (around 10% of the population) that show low period accuracy and large asynchronies to isochronous metronomes and musical excerpts (Phillips-Silver et al., 2011; Sowiński and Dalla Bella, 2013; Tranchant et al., 2016). Furthermore, non-musicians synchronize less flexibly and less precisely across tempos than musicians (Scheurich et al., 2018). Hence, both genetic and learning factors influence the beat-entrainment abilities of humans. A large-scale genome-wide association study (GWAS) recently demonstrated a highly polygenic architecture of the human capacity to synchronize to a musical beat. The GWAS phenotype for beat synchronization was related to performance in beat synchronization tasks and rhythm perception tasks (Niarchou et al., 2022). Nevertheless, genetic influences account for only a small portion of human variation in beat synchronization, while environmental influences are the primary drivers of rhythmic accuracy. In fact, functional imaging has revealed that individual differences in beat perception depend on activation differences in the SMA and the posterior auditory cortex (Grahn and McAuley, 2009). In addition, the putamen, SMA, and auditory cortex show greater functional connectivity during rhythm perception, with larger modulation for musicians than non-musicians (Grahn and Rowe, 2009; Grahn and Rowe, 2013). Consequently, the magnitude of the anatomofunctional association between the auditory and motor control areas of the cerebral cortex seems to covary with the individual difference in how humans perceive and entrain to simple regular beats. Such brain networks rely on the structural connectivity provided by white matter, which can be evaluated through diffusion-weighted imaging (DWI). Long-range anatomical connectivity is supported by deep white matter (DWM) bundles, while short-range connectivity is achieved through fibers that run tangentially to the cortical surface and connect adjacent and proximal cortical regions. These fibers are collectively termed U-fibers due to their shape (Schüz and Braitenberg, 2002; Schmahmann et al., 2006, Shastin et al., 2022). In this work, we individually analyze the role of these two forms of connectivity in rhythmic entrainment abilities. We hypothesize that if the cortical connectivity of the audiomotor system is defining rhythmic entrainment abilities, then individual differences in tapping synchronization should covary with the degree of anatomical connectivity (Assaneo et al., 2019; Steele et al., 2013). Previous evidence suggests that the audiomotor system is tuned at a limited interaction rate (Zalta et al., 2020; Morillon et al., 2019). Hence, we also hypothesize that the relationship between rhythmic tapping abilities and the structural connectivity of the audiomotor system should be more evident for intervals close to the preferred tempo.

To test these hypotheses, we acquired DWIs from 32 subjects that had previously performed an SCT using flashing visual or auditory tones as metronomes in the range of hundreds of milliseconds (Figure 1). SCT rhythmic performance across durations (ISI: 550, 650, 750, 850, or 950 ms) and modalities (auditory and visual) was characterized using the absolute asynchronies, the autocorrelation of the inter-tap interval time series during the synchronization epoch, the constant error, and the temporal variability during both synchronization and continuation epochs. These parameters measure the phase accuracy, error correction, period accuracy, and period precision of the rhythmic tapping of the subjects, respectively (Figure 2A). We used an ISI range of 550–950 ms because it contains the preferred interval and is within the optimal window for tap synchronization (Repp, 2005). Hence, with this ISI range, we could potentially identify structural correlates for both the preferred tempo and interval selectivity. With this in mind, we evaluated DWM fascicles using a fixel-based approach (Dhollander et al., 2021) and developed two metrics for superficial white matter (SWM): fiber density corresponding to fibers entering or exiting the cortex and U-fibers running tangentially to the cortex. Widespread correlations in the right audiomotor circuit were found between the tangential U-fiber density and the phase accuracy of subjects during the synchronization epoch of the auditory condition for the 650 and 750 ms intervals. The interval specificity in these associations suggests that the preferred tempo for rhythmic entrainment has its origins in the structural properties of the U-fibers running superficially across the audiomotor circuit. In addition, there was a significant association between asynchronies in the auditory condition and the density and bundle diameter of the corpus callosum (CC), forming an interval-selective map with an anterior–posterior gradient, similar to the topography of interval-tuned clusters observed with functional imaging. Crucially, the anatomo-behavioral associations were negative, indicating that subjects with good predictive abilities and small asynchronies exhibited large superficial and deep apparent fiber densities (AFDs), while subjects with large asynchronies showed low fiber densities.

Figure 1

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Figure 2 with 1 supplement see all

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The present research determined the intersubject association between the structural properties of the SWM and DWM and different measures of rhythmic timing during a synchronization-continuation tapping task. Our study supports five conclusions: First, the tapping phase and period during the SCT showed precision and accuracy, as well as error corrections that were biased toward auditory rather than visual metronomes, thereby confirming previous observations. Second, the right audiomotor system exhibited individual differences in SWM and U-fiber density. These differences were correlated with the degree of phase accuracy of the tapping synchronization across subjects. Notably, the correlations were selective for the synchronization epoch of the auditory condition and were specific to the 650 and 750 ms intervals. Third, there was a significant association between the rhythmic entrainment phase and the density and bundle diameter of the CC, forming an interval-selective map with an anterior–posterior trend. This implies that the behavioral and structural associations for short and long intervals tended to be in the anterior and posterior portions of the CC, respectively. Fourth, the fiber bundle diameter of the arcuate fasciculus, CC, forceps major, and superior longitudinal fasciculus showed a significant correlation with the mean asynchronies across all tested tempos. Finally, we found no significant associations between SWM and DWM properties and temporal variability, constant error, or lag 1 autocorrelation under the visual and auditory conditions during the synchronization and continuation epochs of the SCT. These last findings suggest that connectivity within the audiomotor system is tightly linked with the ability to synchronize in phase at the preferred tempo for auditory metronomes. We speculate that the structural white matter is associated with tapping-phase control, not with period representation, as the latter depends on neural population dynamics within the timing network (Gámez et al., 2019; Betancourt et al., 2023) and the former depends on a prior ability to detect changes in phase at the preferred tempo, whose intrinsic nature is defined in the audiomotor connectivity.

Many studies have shown that performance tapping synchronized to an auditory metronome is more precise and accurate than synchronization to a flashing visual metronome with the same timing characteristics (Chen et al., 2002; Hove et al., 2013; Merchant et al., 2008c; Patel et al., 2005; Repp and Penel, 2004; Zarco et al., 2009). This auditory–visual asymmetry can be cancelled out by visual moving metronomes (Hove et al., 2010; Pérez et al., 2023). Since the first processing relays, the auditory system has higher temporal resolution compared to the visual system (Duysens et al., 1996; He et al., 1997; Sayegh et al., 2011) and plays a critical role in time perception and reproduction across many tasks, not only in tapping SCTs (Grondin et al., 2005; Merchant et al., 2008b; Merchant and de Lafuente, 2014c). For example, when audiovisual stimuli are used in an oddball paradigm, the perceived duration is dominated by the auditory modality (Chen and Yeh, 2009). In addition, transcranial magnetic stimulation (TMS) disruption of the auditory cortex impaired time estimation for auditory and visual stimuli, while disruption in the visual cortex only produced timing impairments for visual stimuli (Kanai et al., 2011). Consequently, a current hypothesis is that the auditory cortex is engaged in multimodal temporal processing, and the interaction between the auditory and motor systems in the frontal lobe allows not only time encoding but also time prediction (Merchant and Honing, 2014b; Merchant and Yarrow, 2016; Patel and Iversen, 2014; Schwartze et al., 2012). Our present findings support this notion in different ways. First, the rhythmic timing performance of our subjects was biased toward the auditory condition across different behavioral measures. Second, the right audiomotor system showed widespread and significant correlations between the density of superficial U-fibers and the degree of sensorimotor phase accuracy across subjects. These anatomo-behavioral associations are selective to the intervals in the 650–750 ms range. Third, the fiber bundle diameter of the left arcuate fasciculus, a key tract connecting the parietotemporal auditory system with the frontal lobe, showed a significant correlation with the mean asynchronies across all tested tempos. Furthermore, the clear clustering of subjects as either good or bad synchronizers in a syllabic isochronous entrainment task correlates with both the difference in the activation magnitude in frontal areas and the changes in white matter pathways (i.e., left arcuate fasciculus) that connect the auditory system with the premotor cortical system (Assaneo et al., 2019). Hence, our results accentuate the audiomotor structural foundation for rhythmic entrainment (Honing and Merchant, 2014; Miyata et al., 2022). The lack of structural associations in the visual condition is probably due to the larger variability in rhythmic tapping for this modality, making it difficult to infer statistical correlations between our metrics. As described above, the null anatomo-behavioral associations for the visual condition could also be due to the slow sampling rate of visual periodic temporal attention, with a sampling interval that is longer than our tested durations (Zalta et al., 2020).

The observed associations between the measured SWM and DWM properties and the accuracy in the synchronization phase were negative. This result indicates that the white matter parameter values were greater in subjects with asynchronies close to zero and an accurate tapping phase with the metronome than in subjects with large gaps in time between the stimuli and taps. These results support the theory that intersubject differences in rhythmic entrainment phase depend on micro- and macrostructural white matter properties, which could have a genetic and/or learned substrate. From a genetics perspective, we could speculate that the existence of poor and superior synchronizers (Blecher et al., 2016) may depend on the FD of superficial U-fibers in the right audiomotor system, as well as on the density of deep tracts such as the CC and arcuate fasciculus. From a training perspective, these SWM and DWM bundles may develop larger density and myelination during intense musical practice, distinguishing the audiomotor tracts between musicians and non-musicians (Palomar-García et al., 2020; Vaquero et al., 2018; Zatorre et al., 2007).

The correlations between the density of tangential U-fibers in the right audiomotor circuit and the asynchronies for the auditory condition were interval-selective for the intermediate tested tempos. The observed interval specificity corroborates the existence of an spontaneous rhythmic tempo, already observed in many studies of rhythmic entrainment, and with values between 600 and 750 ms (Drake et al., 2000a; Drake et al., 2000b; McAuley et al., 2006: Dalla Bella et al., 2017). The biological intrinsic periodicity may depend on a biased distribution of preferred tempos toward 1.5 Hz in the interval-tuned neurons of the motor system (Bartolo et al., 2014; Bartolo and Merchant, 2009; Pérez et al., 2023; Pérez and Merchant, 2018). Interestingly, Zalta et al., 2020 showed that humans were best at following auditory rhythms at a rate of ~1.4 Hz and that overt motor activity optimizes auditory periodic temporal attention at a similar rate. This rate is close to the intervals with structural associations in the present study. Consequently, we suggest that cortico-cortical connectivity within the audiomotor system is especially designed to support the internal representation of an auditory beat at the preferred tempo, providing larger phase-locking abilities with the metronome for spontaneous motor tempos (Balasubramaniam et al., 2021). On the other hand, Zalta et al., 2020 also demonstrated that the best rate for visual rhythms was far slower, at ~0.7 Hz (Zalta et al., 2020). Therefore, it is possible that we did not get effects in the visual condition because our target interval of 950 ms was not slow enough for this modality.

Ultra-high-field (7T) functional imaging revealed that the medial premotor areas (SMA and pre-SMA) of the human brain possess neural circuits that are tuned to different durations, forming a topographical arrangement during a visual discrimination task. These chronomaps show units with enhanced responses for the preferred interval and suppressed activity for the non-preferred duration, and define a topographical gradient with short preferred intervals in the anterior portion and long preferred intervals in the posterior portion of the medial premotor areas (Protopapa et al., 2019; Schwartze et al., 2012). Protopapa et al., 2019 also showed chronomaps in the SMA during an interval reproduction task using auditory cues, which is similar to the task used in this study. Notably, chronomaps also showed an anterior–posterior gradient, but they represent relative rather than absolute time, and they presented some flexibility in the location of the preferred interval depending on the task context (Bueti et al., 2021). In addition, a recent imaging study described the existence of large chronomaps covering the cortical mantle from the dorsal and ventral premotor areas to the occipital pole (Harvey et al., 2020; Hendrikx et al., 2022). Our measurements of the FDA also revealed a topographical arrangement in the correlations between FD in the DWM and CC and the sensorimotor phase accuracy of subjects. We also found significant anatomo-behavioral associations in the anterior part of the CC for short intervals and in the posterior CC for long tapping tempos (Schwartze et al., 2012). Nevertheless, our interval-selective map is defined by the correlation between asynchronies and FDA, with no topographic model of the distribution of preferred intervals as shown in functional magnetic resonance imaging (fMRI) studies. These studies have not explored individual differences. Moreover, we found a frontal CC cluster of fixels with longer interval selectivity, producing a discontinuity in the anterior–posterior gradient of preferred intervals. These findings suggest that the map for duration selectivity starts anteriorly in the CC, which is linked to the premotor system, and ends in the CC of the visual areas of the occipital lobe. The anterior selectivity for the 650 and 750 ms ISI supports the notion that the motor system functions at 1.7 Hz during beat perception and entrainment and strongly influences the auditory system but not the visual system at this tempo (Zalta et al., 2020). The latter could explain why we did not find interval selectivity in the CC for the visual condition. Consequently, timing maps define a cortical processing framework for efficient timing integration that has both functional and anatomical bases, especially for the auditory modality (Merchant and de Lafuente, 2024a).

Previous studies have shown correlations between sensorimotor synchonization task performance and the microstructural characteristics of DWM. For instance, Blecher et al., 2016 found a positive association between the fractional anisotropy of the left arcuate fasciculus and CC and performance in an auditory-cued finger-tapping task. The SWM immediately below the cortex has received has been less studied than DWM fasciculi, even though it accounts for 60% of the total white matter volume and is pivotal in maintaining cortico-cortical connectivity (Schüz and Braitenberg, 2002; Schmahmann et al., 2006). Nonetheless, recent studies have implicated SWM abnormalities in epilepsy (Liu et al., 2016), autism spectrum disorder (Hong et al., 2019), Alzheimer’s disease (Phillips et al., 2016), schizophrenia (Nazeri et al., 2013), and stroke (Stockert et al., 2021). The SWM contains short-range association fibers that connect adjacent gyri (U-fibers) and the initial or final portions of long-range connections that traverse the DWM (Schüz and Braitenberg, 2002; Guevara et al., 2020; Kirilina et al., 2020; Yoshino et al., 2020). The SWM is difficult to study because of its complicated geometry and abundance of fiber crossings (Guevara et al., 2020). To better characterize the SWM microstructure, we separated the two components of the SWM and performed a surface- and depth-wise evaluation. Complementarily, we analyzed the DWM using FBA, which addresses many of the shortcomings of voxel-wise analysis of diffusion tensor imaging (Dhollander et al., 2021). This two-pronged approach allowed us to evaluate the entire white matter volume and show the association between its mesoscopic characteristics (i.e., AFD) and predictive tapping synchronization.

A potential limitation of our study is the relatively small number of participants, related to the time-consuming nature of the behavioral evaluation and the long scanning time. Moreover, many statistical tests were performed, relating several behavioral metrics to various diffusion metrics across the brain at different depths. To minimize the possibility of statistical errors, we performed an rCCA to jointly model the behavioral and imaging metrics, thus accounting for the numerous statistical tests and reducing the possibility of reporting false positive findings. This reinforced the importance of superficial cortico-cortical communication through U-fibers and the SCT for intervals around the preferred tempo. While we have attempted to control for statistical errors as much as possible, a sample of 32 young adults with specific inclusion and exclusion criteria may inevitably not represent the population. We expect our current findings to be replicated and extended in future studies.

In conclusion, our results showed that the subjects’ accuracy in SCT performance was associated with higher FC, FDC, or FD values. This is consistent with the literature that shows that better performance in different tasks is associated with higher values of FBA metrics. For example, better performance in a bimanual coordination task was associated with higher FBA values in the CC (Zivari Adab et al., 2020).

This study was approved by the Ethics Committee of our Institution (049H-RM).

Data is available at OSF: https://doi.org/10.17605/OSF.IO/YNVF3.

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Author details

1. Pamela Garcia-Saldivar

   Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Mexico

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3274-4955

2. Cynthia de León

   Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Mexico

   Contribution

   Software, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4488-2864

3. Felipe A Mendez Salcido

   Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Mexico

   Contribution

   Formal analysis, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1697-5203

4. Luis Concha

   1. Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Mexico
   2. International Laboratory for Brain, Music and Sound (BRAMS), Montreal, Canada

   Contribution

   Software, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing – review and editing

   For correspondence

   lconcha@unam.mx

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7842-3869

5. Hugo Merchant

   Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Mexico

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Writing - original draft, Writing – review and editing

   For correspondence

   hugomerchant@unam.mx

   Competing interests

   Reviewing editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0002-3488-9501

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