Once a pianist has learned to play a song, he or she can nearly effortlessly reproduce the sequence of finger movements needed to play the song with a particular rhythm. A skilled pianist can also improvise, pairing the same keystrokes with a different rhythm or playing the same rhythm with a slightly different sequence of keys. This ability to flexibly modify and recombine sequences of physical movements in space and time enables humans to exhibit great creativity in music, language, and many other tasks that require motor skills. However, the underlying brain mechanisms that allow this flexibility are only beginning to be explored.

Some scientists have theorized that networks of brain cells in the parts of the brain that control movement store a sequence in time and space as one inseparable unit. However, this theory doesn't explain why pianists and other skilled individuals can separate and recombine the physical movements and timing of a sequence in new ways. An alternate idea is that the brain captures the information necessary to execute a series of physical movements separately from the timing at which the movements are to be carried out. This would allow these features to be put together in new ways.

Kornysheva and Diedrichsen taught a group of volunteers a series of finger movements paired with particular rhythms. Half the volunteers performed the task using their left hand and the other half with their right hand. After training the volunteers performed better when producing sequences they had been trained on, even in trials where either the rhythm or the finger sequence was slightly changed.

The volunteers were also asked to perform the trained movements while their brain activity was monitored in a functional magnetic resonance imaging (fMRI) machine. Kornysheva and Diedrichsen looked for areas that showed similar patterns of increases and decreases in activity whenever a particular sequence was performed. This identified areas that showed unique patterns for each trained sequence combination of finger movements and rhythm, which could be distinguished from areas where the activity patterns for sequences remained similar across rhythms or across finger movements.

Kornysheva and Diedrichsen found that a region of the brain that controls movement encodes sequences on the opposite side of the brain from the moving hand. In this part of the brain, the movement and timing were encoded together as one unit. However, in premotor areas—which are known to help individuals to plan movements—the timing and the finger movements appeared to be encoded separately in overlapping patches on both sides of the brain. This automatic separation appears to be a fundamental function of the premotor cortex, enabling behavioural flexibility and the storage of complex sequences of movements in space and time.

Skilled performance in music, speech, or sports often involves long sequences of movements, which demand a precise sequential activation of different muscles in time. The ordering of these muscle activations—and hence the ordering of the movements of different body parts in space—is here referred to as the ‘spatial feature’ of a sequence. Additionally, movement sequences are often characterized by a stereotypical temporal structure or rhythm—their ‘temporal feature’. The latter can either emerge spontaneously as part of chunk formation (Sakai et al., 2003), or be directly relevant to the goal of the sequence, as in musical performance, dance, or speech (Shin and Ivry, 2002; Lewis and Miall, 2003; Repp, 2005; Kotz and Schwartze, 2010; Bläsing et al., 2012; Grahn, 2012; Penhune and Steele, 2012). One of the hallmarks of human motor performance is the ease with which experts can modify the temporal and spatial features of learned motor skills. For example, a pianist is able to play the same tune using different variations of the rhythm, and a fluent speaker can change separately the word order or the rhythmic profile of speech for effective communication. How is such flexibility in skilled actions achieved neurally?

There has been a long-standing debate on whether a dedicated representation of temporal structure of skilled movements exists, or whether it is tightly integrated with a representation of its spatial features (Conditt and Mussa-Ivaldi, 1999; Shin and Ivry, 2002; Ullén and Bengtsson, 2003; Medina et al., 2005; Spencer and Ivry, 2009; Ali et al., 2013). Recent work suggests that spatio-temporal trajectories of movements can be learned and produced by a dynamical network of neurons that encodes patterned muscle dynamics, instead of by representing different parameters of a movement sequence separately (Laje and Buonomano, 2013; Shenoy et al., 2013). This neural implementation has been advocated for the primary motor and premotor cortices and implies that temporal features are stored inseparably from the specific movement trajectory trained. From this perspective, a spatial sequence performed with two different temporal profiles would constitute two distinct behaviours and demand the training of independent neural generators.

Alternatively, the motor system may parse movement sequences into their constituent spatial and temporal features, which then are represented independently. Such an encoding scheme would explain the ability of both animals and humans to flexibly recombine learned temporal patterns with a new spatial sequence and vice versa (Ullén and Bengtsson, 2003; Ali et al., 2013; Kornysheva et al., 2013).

Neurally, sequence representations are characterised by the occurrence of sequence-specific tuning. For example, neurons in the supplementary motor area (SMA) vary their firing rate for specific movement transitions and whole sequences of movements rather than for individual movements in a sequence (Tanji and Shima, 1994). Inactivating the SMA, the primary motor cortex, the putamen or the dentate nucleus in monkeys disrupts sequential behaviour whilst sparing individual actions (Tanji and Shima, 1994; Shima and Tanji, 1998; Hikosaka et al., 1999; Lu and Ashe, 2005), with a similar effect demonstrated for the SMA/pre-SMA in humans with non-invasive stimulation techniques (Gerloff et al., 1997; Kennerley et al., 2004). Recent evidence in primates also argues for the existence of neurons tuned to specific temporal intervals between movements in the same area, with a subset also tuned to the position of an interval in a sequence (Merchant et al., 2013). However, it remains unknown whether these neurons are simply part of a dynamical network that represents spatial and temporal features in an integrated manner, or whether independent populations of neurons encode spatial and temporal features in isolation.

Here, we used fMRI to study the sequential tuning of individual voxels in the human brain. We hypothesized that following training, specific neuronal sub-populations will become differentially active for different sequences, as has been observed in neurophysiological studies for spatial sequence features (Tanji and Shima, 1994). If such sequence-specific tuning is sufficiently clustered, it should be detectable with the relatively low spatial resolution of fMRI (Kamitani and Tong, 2005; Swisher et al., 2010). Using a classification approach to evaluate these subtle differences in the local patterns of brain activity during sequence production, a recent imaging study (Wiestler and Diedrichsen, 2013) indeed showed that such sequential tuning can be detected in the human brain in a range of motor and premotor areas. This study, however, did not reveal whether and how these areas represented spatial or temporal features of sequences.

To this end, we developed a visually paced motor learning paradigm. Participants were trained on nine sequences consisting of unique combinations of three spatial and three temporal features (Figure 1). Half the participants were trained on the right and half on the left hand to probe whether possible differences between hemispheres reflected hemispheric specialisation or the difference between contra vs ipsilateral encoding. First, by looking at behavioural generalisation, we show transfer of trained temporal and spatial features to new combinations. Second, by employing separate classification procedures of fMRI voxel activity patterns and testing for generalization of patterns across temporal or spatial contexts, we were able to dissociate independent spatial and temporal from integrated representation profiles across the human motor system.

Figure 1

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Our study employed fMRI multivoxel pattern analysis that reflects the differential tuning of individual voxels (Kamitani and Tong, 2005; Kriegeskorte et al., 2006) to identify neural representations of spatial and temporal finger sequence features. We were able to dissociate independent feature representations in which voxel patterns related to spatial and temporal sequence features combined linearly, from integrated feature representations in which each spatio-temporal combination was associated with a unique activity pattern. We demonstrate that only the output stage of the cortical motor hierarchy, the primary motor cortex (M1) contralateral to the moving hand, encoded spatio-temporal features of finger sequences in an integrated fashion. In contrast, bilateral medial and lateral premotor cortices showed partly overlapping, but mutually independent representations of the spatial and temporal features. The independent encoding of sequence features in higher order motor areas paralleled our behavioural findings—the nervous system's ability to flexibly transfer both spatial and temporal features from trained to new sequence contexts.

The integrated sequence encoding found in the contralateral M1 is in line with electrophysiological data showing that 40% of neurons in the primary motor area in monkeys can exhibit tuning to sequences of muscle commands (Matsuzaka et al., 2007), evidence that inactivation of M1 via muscimol can selectively disrupt sequential behaviour (Lu and Ashe, 2005), as well as previous sequence learning studies in humans (Karni et al., 1995; Penhune and Doyon, 2005; Steele and Penhune, 2010). We found that the overall sequence encoding in the contralateral M1 covaried with the amount of behavioural advantages for the trained sequences, suggesting that our analysis uncovered skill-dependent representations. The fact that each spatio-temporal sequence combination had its unique activity pattern in M1 is consistent with a dynamical systems view which proposes that each movement is controlled by a subpopulation of neurons that form a dynamical network (Laje and Buonomano, 2013; Shenoy et al., 2013). Instead of representing movement features separately, these networks are assumed to produce complex movement patterns based on a neural state-space trajectory, which is determined by the internal connectivity and external input to the circuitry (Shenoy et al., 2013). Accordingly, for each unique spatio-temporal sequence a slightly different distribution of neurons is activated in M1 which in turn cause distinct voxel activity patterns for each of the studied sequence combinations (Kamitani and Tong, 2005; Kriegeskorte et al., 2006). This integrated encoding in M1 is in line with our model, which suggests that the temporal and spatial sequence features are integrated non-linearly in the nervous system (Kornysheva et al., 2013).

While adequate for learning and producing specific spatio-temporal sequences, integrated encoding such as found in M1 alone would not allow the system to use learned spatial or temporal features independently. However, subjects showed behavioural advantages for untrained sequence in which only one of the trained features (spatial or temporal) was retained. With spatial and temporal features of movement trajectories emerging from the same local circuits, an integrated representation as proposed in dynamical systems models (Shenoy et al., 2013) cannot explain the flexible transfer or independent adaptation of spatial and temporal features reported here and in previous studies (Ullén and Bengtsson, 2003; Ali et al., 2013; Kornysheva et al., 2013). In contrast, our results indicate that higher order motor areas (lateral and medial premotor cortices) parsed the sequences into the two constituent features, in line with the modularity and flexibility we observed in behaviour. The independent albeit partly overlapping spatial and temporal encoding suggests a modular feature-separating storage for movement production.

Although our experiment provides both behavioural and imaging evidence for the independent representation of temporal and spatial features of movements, it is possible that not all classes of movements lend themselves to such a separation. For example, the temporal profile of a force perturbation during a reaching movement is learned inseparably from the whole spatio-temporal trajectory (Conditt and Mussa-Ivaldi, 1999). One critical factor that distinguishes these classes may be whether movement kinematics are continuous as in reaching movements or fall into discrete phases, as induced by the current task (Ivry et al., 2002). In contrast, for discrete sequential movements, as studied here, the evidence for an independent representation of the temporal structure is compelling. Importantly, our behavioural finding is unlikely to be an effect artificially induced by the orthogonal design, in which all three spatial sequence features were crossed with three temporal features, since flexibility in independently adapting spatial or temporal features of sequences has also been observed in a previous study which involved the training of only one particular spatio-temporal combination (Kornysheva et al., 2013).

Previous data demonstrated that the premotor cortex modulates its activity once movement sequences are made more complex in either their spatial or temporal structure (Bengtsson et al., 2004), or in the presence of both features (Sakai et al., 2002; Brown et al., 2013). However, these studies did not establish how temporal and spatial features were encoded. Here, we address this issue, starting from a recent study that showed that voxel pattern analysis can uncover sequence representations in motor and premotor areas, which are specifically enhanced for trained sequences (Wiestler and Diedrichsen, 2013). This classification analysis was now extended to identify regions with voxel patterns related to spatial and temporal sequence features that were represented independently from the respective other feature. Our representational analysis allowed us for the first time to distinguish between integrated vs independent representations, even if the independent spatial and temporal representations overlapped spatially. While we validated this approach by voxel pattern simulations, the analysis hinges critically on the assumption that activity in independent neuronal populations within a single voxel combine additively in the observed BOLD signal. Is this assumption justified? Functional imaging signals show clear non-linearities when studied over a large dynamic range (Logothetis et al., 2001). However, the activity of single voxels only varied very slightly between different sequences in our paradigm. For this restricted range, therefore, we can be relatively confident that a linear approximation is reasonable (Diedrichsen et al., 2013a). Furthermore, because any non-linearity between neural signal and hemodynamic response should be relatively homogenous across adjacent cortical motor areas, the distinct representational dissociation found between M1 and PMd is likely to reflect neural rather than hemodynamic differences.

How can the independent encoding of spatial and temporal features be implemented on a single cell level? Neurons in primate premotor cortices have been shown to be tuned to specific spatial transitions between movement elements and whole sequences of movements increasing their firing rate prior to a movement based on its sequential context (Mushiake et al., 1991; Tanji and Shima, 1994; Shima and Tanji, 2000). Such units have been suggested to be an important component in the organisation of skilled sequential behaviours, however whether timing between movements modulates the same neurons has not been systematically explored. Our findings of independent representations of the spatial and temporal features predict that the representation of movement order in space first reported by Shima and Tanji in the SMA (Tanji and Shima, 1994) is likely to be independent of the exact timing of individual movements in the sequence. In other words, changing the temporal intervals between movements in a sequence should not interfere with the tuning of the neuron to a specific spatial movement transition or sequence.

At the same time our findings predict independent encoding of the temporal features of movement sequences within the same areas as the spatial features. In the medial premotor cortex of monkeys, Merchant et al. (2013) found evidence for the encoding of sub-second intervals sensitive to the sequential context within a synchronization-continuation task. In addition to neurons tuned to interval duration, the authors found cells that are tuned to both a specific interval between movements and a specific position in a sequence, for example, a neuron increased its firing rate only to the 850 ms interval between the fifth and sixth lever push. This type of encoding would be detectable with our method since in the trained sequences the individual digits and temporal intervals occurred at unique positions. However, the intervals tested in each sequence were isochronous, and it is currently unknown whether this type of encoding would generalize to a sequential temporal pattern similar to the one employed in the current task.

Furthermore, the timing of a motor responses can be related to the firing of neural units in the cerebellum, as for instance in the case of cerebellar Purkinje cells which decrease simple spike activity in a timed fashion depending on the trained interval between the conditioning stimulus and the conditioned response following training (Jirenhed and Hesslow, 2011a, 2011b). Yet, whether this type of encoding can also be modulated by the sequential context is not known. In contrast to our findings in the cortex, the cerebellum did not yield significant results with regard to temporal encoding, although there was a clear significant overall encoding of the sequences in the ipsilateral lobule VI which forms reciprocal connections with contralateral premotor areas (Buckner et al., 2011; Bostan et al., 2013). This null result could be related to higher noise levels for subtentorial structures (Wiestler et al., 2011) or the more complex folding structure of the cerebellar cortex, which may push the informative signal variations below the threshold of effective spatial resolution.

Despite partly overlapping temporal and spatial representations, we found a gradient with spatial features represented more in dorsal and temporal features more in ventral aspects of the lateral premotor cortex similar to studies involving spatial and temporal production (Bengtsson et al., 2004) and prediction (Schubotz and von Cramon, 2001). This regional distribution raises the possibility that the temporal representation uncovered here may not be an abstract representation of the temporal structure of sequences, but rather a representation of an additional effector system. Indeed, most subjects' introspective reports suggests subvocal rehearsal at the beginning of their training, albeit less so towards the end of training and during fMRI. However, a simple mapping of the temporal feature representations to the control of vocal sequencing and timing is unlikely. There was no direct involvement of more posterior and perisylvian primary vocalization centres. Moreover, cross-sections through the cortical Brodmann area 6 suggest that temporal encoding largely overlapped with spatial encoding along the premotor cortex, instead of being restricted to ventral-most premotor areas recruited for rhythmically structured vocal rehearsal (Riecker et al., 2002). Instead, the nervous system may build on specialised processes in premotor areas for temporal control, which have originally evolved for the sequencing of the oro-facial and laryngeal musculature in speech production (Schubotz, 2007). Such a temporal representation in the premotor cortex could modulate the finger motor system at the level of M1 by interacting with premotor input from the spatial representation in a non-linear manner, as suggested in our multiplicative model (Kornysheva et al., 2013), thereby modulating the integrated dynamical systems representation of the sequences in M1.

The presence of multiple temporal and spatial representations across the cortex suggests parallel computations related to the same sequential features. These may have complimentary functions. The medial vs lateral premotor cortex encoding of sequence features may be related to the concurrent encoding of both internally and externally (visually) driven sequential movements (Goldberg, 1985; Mushiake et al., 1991). Within the lateral premotor cortex, temporal processing has been hypothesized to be associated with the degree of motor involvement; Chen et al. (2009) have shown that the ventral premotor cortex enables direct action-related encoding of temporal structure while the dorsal premotor cortex facilitates higher order temporal organisation of sequences, in line with the direct vs indirect transformation hypothesis by Hoshi and Tanji (2007). Finally, multiple spatial representations may reflect different spatial sequence reference frames—such as movement sequences in extrinsic spatial coordinates in the rostral PMd and the posterior parietal cortex (Brown et al., 2013; Wiestler et al., 2014) and in intrinsic spatial coordinates in the caudal PMd (Wiestler et al., 2014). Taken together, such a diversity of spatial and temporal sequence representations across the network of premotor and parietal areas may enable flexible control of skilled behaviour that can adapt to the situational task requirements.

Overall, the independent encoding of the spatial and temporal features of movement sequences in premotor areas endows the nervous system with the ability for adjustments of individual movement parameters—which would not be possible with a fixed integrated representation such as in M1 alone. For example, this separate encoding may explain why a pianist who learned a particular passage can effortlessly produce the same sequence of finger movements with a novel rhythmic structure—or combine the same rhythm with new variations of the sequence of notes.

Finally, the decomposition into features in the premotor cortex provides a computational solution for representing longer and more complex sequences of actions. If the system utilized integrated encoding alone, such as in the primary motor cortex, it would have to represent all relevant permutations of spatial, temporal and other relevant parameters (e.g., amplitude and direction), which very quickly would lead to a combinatorial explosion. Instead, the premotor cortex may dedicate its resources to representing these features separately, creating a more compact and flexible representation. This special feature may explain why evolution endowed us with premotor areas, rather than simply with a larger primary motor cortex. This architecture has parallels in the ventral visual stream, in which lower visual areas encode specific combinations of simple features at specific spatial locations, whereas higher visual areas represent more complex visual arrangements—such as body parts and scenes—independently of their orientation or location of the stimulus (Freeman and Simoncelli, 2011). Thus, the decomposition of movements into features may be the cardinal function of premotor areas, and endow the system both with behavioural flexibility and the capacity to store long, complex sequences of movements.

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Author details

1. Katja Kornysheva

   1. Institute of Cognitive Neuroscience, University College London, London, United Kingdom
   2. Department of Neuroscience, Erasmus Medical Centre, Rotterdam, Netherlands

   Contribution

   KK, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   For correspondence

   k.kornysheva@ucl.ac.uk

   Competing interests

   The authors declare that no competing interests exist.

2. Jörn Diedrichsen

   Institute of Cognitive Neuroscience, University College London, London, United Kingdom

   Contribution

   JD, Conception and design, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

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