Effective motor behavior relies on the central nervous system’s ability to adapt to the environment by learning new mappings between motor commands and sensory outcomes. For example, an inexperienced youth hockey player, when walking from the change-room onto the ice surface, has to learn to adapt their stride in response to the drastic change in surface properties. This sensorimotor adaptation is known to rely partly on implicit learning processes, driven by sensory prediction errors reflecting the difference between the expected and sensed consequences of movement (Wolpert et al., 1995; Wolpert and Ghahramani, 2000). In addition, there is evidence that explicit (declarative) learning processes, such as cognitive strategies, also help guide behavioral adjustments, and that these operate synergistically with implicit processes (see Krakauer and Mazzoni, 2011; McDougle et al., 2016; Wolpert et al., 2011; Miyamoto et al., 2020). Recent behavioral and computational work (McDougle et al., 2015) has suggested that these two processes operate on different timescales, with implicit and explicit learning leading to slow and fast adaptation and de-adaptation, respectively.

Although the neural systems supporting these two learning processes are not perfectly understood, there is considerable evidence that implicit adaptation relies, in part, on sensory prediction errors computed in the cerebellum (Izawa et al., 2012; Taylor et al., 2014), which are used to drive adaptation in frontal and parietal sensorimotor regions in the neocortex (McDougle et al., 2015). Explicit learning, by contrast, has been linked to higher-level working memory and inhibitory control processes (Keisler and Shadmehr, 2010; Anguera et al., 2010; Christou et al., 2016; Drummond et al., 2015), and thus has been associated with a wide array of higher-order association areas across the neocortex (Anguera et al., 2007; Dayan and Cohen, 2011; Doyon et al., 2009; Hardwick et al., 2013). There is also evidence that explicit learning is responsible for ‘savings’, or faster relearning upon reexposure to the task (Haith et al., 2015; Morehead et al., 2015). Moreover, emerging behavioral evidence indicates that individual differences in rates of adaptation and savings is largely driven by subject-level differences in the contribution of explicit processes to learning (de Brouwer et al., 2018; Fernandez-Ruiz et al., 2011). While the neural bases that underlie these subject-level differences in learning performance are likewise poorly understood, one hypothesis that follows from this prior work is that fast and slow learners are likely to differ in the relative degree to which they recruit brain networks associated with these distinct learning systems. These differences in the engagement of sensorimotor networks on the one hand, and higher-order cognitive regions on the other, may then reflect the differential contribution of explicit cognitive processes to their learning performance.

At the neural level, learning requires that the brain generates new patterns of activity. These activity patterns are presumably constrained by the underlying neural circuitry and shaped by the information processing functions of single neurons or individual brain regions. This architecture results in correlations between the activity of neurons (and between brain regions), resulting in patterns of activation that primarily occupy a low-dimensional subspace or manifold (Gallego et al., 2017; Sadtler et al., 2014; Shenoy et al., 2013). In neurophysiological studies, this manifold structure has been observed both at the level of whole-brain functional networks (via functional MRI [fMRI]; Shine et al., 2019) and at the level of local neural populations (Sadtler et al., 2014; Gallego et al., 2017). Recent work has suggested that some types of learning are associated with a reconfiguration of this low-dimensional structure, allowing the brain to generate new patterns of off-manifold activity (Shine et al., 2019; Oby et al., 2019). Here, we wondered whether differences in the rate of learning and relearning during sensorimotor adaptation could be explained by differences in the generation of off-manifold activity in cognitive and sensorimotor networks; and, if so, whether differences in cognitive and sensorimotor excursion are associated with distinct patterns of subject performance.

We used fMRI to study changes in intrinsic manifold structure during sensorimotor adaptation on 2 consecutive days. Analysis of subject behavior revealed three broad clusters of learners: Subjects who adapted and dea-dapted rapidly on both days; subject who adapted and de-adapted slowly on both days; and a third group of subjects who learned slowly on day 1, but de-adapted rapidly and relearned rapidly on day 2. We then examined excursions from intrinsic manifold structure in cognitive and sensorimotor networks (i.e., off-manifold activity) during learning, and found that the degree of excursion in these networks is related to differences in these group’s patterns of learning and relearning across days. Finally, to further characterize these changes, we analyzed functional network structure across learning epochs by embedding networks from each subject and learning phase of the task into a common space. Together, our findings suggest that excursion from intrinsic manifold structure provides an index of the relative engagement of distributed brain networks during learning, and that fast versus slow patterns of learning across days are associated with different reconfiguration processes of cognitive and sensorimotor systems.

To study adaptation learning, subjects (N = 32) performed a visuomotor rotation (VMR) task on 2 consecutive days, requiring them to launch a cursor to intercept a stationary target by applying a brief, isometric directional force pulse on an MRI-compatible force sensor. The task structure on each day was identical: After a series of baseline trials (baseline epoch), the mapping between force and cursor motion was rotated clockwise by 45°, such that subjects had to learn to counteract the rotation in order to successfully hit the target. Following this learning epoch, the rotation was removed (washout epoch) to allow subjects to de-adapt before relearning on the second day (see Figure 1a for trial structure).

Figure 1

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Here, we took two approaches to study neural changes associated with learning. First, we examined learning-induced changes in cognitive and sensorimotor network structure (i.e., following the onset of the VMR) using a neural manifold approach inspired by the works of Sadtler et al., 2014, and Shine et al., 2019. This allowed us to study the degree to which activity in each network during learning deviates from its intrinsic manifold structure, assessed during a prior resting-state scan — a measure which we will hereby refer to as excursion (Figure 1b). We then related the degree of this neural excursion in the cognitive and sensorimotor brain networks to subjects’ patterns of learning performance within and across days. Second, we examined changes in functional network structure during learning by estimating covariance networks from the cognitive and sensorimotor regions during the baseline, early, and late learning epochs (Figure 1c). We then derive a low-dimensional embedding of these networks, which allows us to directly characterize subject differences in network structure across each of these epochs of the task, as well as across days (Figure 1d).

Clustering of behavior reveals qualitatively distinct groups of learners across days

Mean error curves across subjects for each day are shown in Figure 2a (top), confirming that subjects successfully reduced their error over the course of the learning epoch. Since a full characterization of subject performance would require summarizing rates of learning, unlearning, and the total adaptation on both days (along with other features of performance, e.g., reaction times, which have been linked to distinct cognitive or motor processes), here we sought an interpretable, low-dimensional summary amenable to simple statistical analysis. This approach was motivated by our observation that standard summary measures generally failed to capture overall performance patterns across both days (see also Standage et al., 2020). For example, ‘savings’, the difference in early learning performance across days, failed to distinguish between subjects who learned the rotation rapidly on both days versus those who learned and relearned slowly. Similarly, early learning did not distinguish between subjects who adapted and de-adapted slowly on the first day versus those who learned slowly but showed performance features similar to fast learners by the end of the first day. In order to properly profile these differences in performance, we clustered individual subjects by early error on days 1 and 2 (defined as the mean error over the first 4 eight-trial blocks of the rotation phase), along with savings (the difference in early error across days). This clustering revealed three distinct subgroups of learners (Figure 2b), which corresponded to subjects who exhibited fast learning on both days (FF, N = 16), slow learning on both days (SS, N = 9), and slow learning on day 1 but fast learning on day 2 (SF, N = 7; Figure 2c). We evaluated this cluster solution using the procedure described by Kimes et al., 2017. For numbers of clusters between 2 and 8, we computed the ratio of within/between cluster sums of squares and compared this to a null model of a multivariate normal distribution with covariance equal to the observed covariance between variables. Based on these simulations, we judged that a three cluster solution best explained the data (Figure 2b, bottom). This clustering solution had the dual benefit of both capturing the dominant patterns of variability in subject performance, and of producing a simple performance measure that could be compared to each subject’s neural data.

Figure 2

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The rapid adaptation and de-adaptation shown by the FF group on both days, and by the SF group on day 2, are considered signatures of explicit learning (Benson et al., 2011; de Brouwer et al., 2018). In the context of the VMR task, this includes the use of a re-aiming strategy, in which a subject aims so as to counteract the rotation applied to the cursor (Taylor et al., 2014). One possible interpretation of the SF group’s performance is that they adopt an explicit strategy early during day 2, after learning largely implicitly on day 1. Another alternative is that this group learns explicitly, even on day 1, but employs an explicit strategy either poorly or too late to reduce their early error. This second possibility is supported by similarity between the FF and SF groups later on day 1 (Figure 2d): both groups show comparable performance during late learning and washout (Figure 2c). The FF and SF groups also display relatively long reaction times compared to the SS group (Figure 2d), which have been associated with explicit strategy use (e.g., Fernandez-Ruiz et al., 2011; Haith et al., 2015).

As explicit learning is believed to be highly cognitive, we may expect that the rapid learning exhibited by the FF group would be associated with the greater recruitment of cognitive brain regions outside of traditional sensorimotor networks, commonly associated with adaptation to sensorimotor errors. Moreover, if the late performance of the SF group on day 1 is indeed reflective of explicit strategy use, we might expect to see similar patterns of neural recruitment between the FF and SF groups.

FF and SF groups show greater excursion from intrinsic manifold structure at the onset of learning

In order to quantify the engagement of cognitive and sensorimotor systems during learning (and, in particular, at the onset of the VMR), we developed a novel measure, called excursion, quantifying the degree to which the functional organization of these networks deviates from the structure present during rest. Our approach is motivated by recent neural recording work (Sadtler et al., 2014) investigating whether deviations from low-dimensional manifold structure are related to the capacity for learning, as well as fMRI work investigating how these deviations relate to task performance (Shine et al., 2019).

We first defined cognitive and sensorimotor networks using the whole-brain parcellation proposed by Seitzman et al., 2020, comprising 300 regions of interest (ROIs) across the cortex, basal ganglia, and cerebellum, and assigned functional classifications according to the parcellation of Power et al., 2011. Given that nearly every part of the brain can be said to be involved in ‘cognition’ in some capacity, we simplified our division of motor versus cognitive networks such that it distinguished between higher-order association areas implicated in attention and cognitive control (frontoparietal, dorsal attention, and ventral attention networks, FP, DA and VA, respectively; 62 ROIs), and regions primarily involved in the control and generation of movement (somatomotor dorsal and somatomotor lateral networks, SMD and SML, respectively; 51 ROIs). We note that our cognitive network comprises a large part of what has been characterized as the ‘multiple-demand system’ (Duncan, 2010) in frontal and parietal cortex, which has been associated with diverse cognitive processes and the implementation of strategies. The DA and VA networks in particular have been implicated in motor inhibition (Hsu et al., 2020) and in the reorienting of spatial attention (Simpson et al., 2011; Corbetta and Shulman, 2002), both processes of which are involved in explicit strategy use during VMR learning (de Brouwer et al., 2018). A more exploratory analysis, comprising the individual functional networks making up the Seitzman parcellation, is presented in Figure 3—figure supplement 1.

We used principal component analysis (PCA) to estimate a low-dimensional subspace capturing the dominant patterns of BOLD signal covariance in each network during each subject’s resting-state scan. We then examined excursions from this intrinsic structure during the VMR task, defined as the proportion of the BOLD signal activation vector which lies off of the subject’s intrinsic manifold. This measure provides an index of the degree to which activation within the cognitive and sensorimotor networks deviate from the structure present during rest. For each subject, we computed this neural excursion at each timepoint (imaging volume) in the cognitive and sensorimotor networks on each day. We then baseline-centered these excursion curves in order to isolate changes in excursion during the learning phase (see Materials and methods for a more detailed description).

Figure 3a shows the mean excursion across subjects in the cognitive and sensorimotor networks on each day. Notably, we observed a sharp peak in excursion in the cognitive network immediately after the onset of rotation, which is most pronounced on day 1, but also prominent on day 2. We defined early excursion to be the mean excursion during the first 4 eight-trials blocks – equivalent to our behavioral definition of early error – and compared excursion across networks and days using a 2 × 2 factorial analysis of variance (ANOVA) with day and network as within-subjects effects. After correcting for multiple comparisons (Benjamini and Hochberg, 1995), we observed significant main effects of day ($F_{1,29}=10.197$, ${\displaystyle \mathrm{p}=.002}$, ${\displaystyle \mathrm{q}=.003}$) and of network ($F_{1,29}=25.766$, ${\displaystyle \mathrm{p}<.001}$, ${\displaystyle \mathrm{q}<.001}$), but no day by network interaction ($F_{1,29}=3.691$, ${\displaystyle \mathrm{p}=.06}$, ${\displaystyle \mathrm{q}=.06}$). Figure 3b shows the post-rotation peak broken down by behavioral group, revealing what appears to be greater peak magnitude in the FF and SF groups (compared to SS) in the cognitive network on day 1, and a greater peak in the SF group on day 2, where these subjects succeed in reducing their early error in a similar fashion to FF subjects.

Figure 3 with 1 supplement see all

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Due to the complexity of these data – which comprise excursion at each timepoint, in each network, and across 2 days – we sought an interpretable, low-dimensional representation of the patterns of excursion across days and across networks in order to reduce the total number of possible comparisons. To this end, we used multivariate functional principal component analysis (mfPCA) to derive a low-dimensional summary of the main patterns of excursion across individual subjects during learning. Just as standard PCA finds a set of components capturing the dominant patterns of variability of data lying in ordinary Euclidean space ${ℝ}^n$, fPCA attempts to find a basis of component functions capturing the dominant patterns of variability in functional data (see Figure 4a for an example illustration). As each subject contributes four excursion curves – one for each network and each day – we used mfPCA (Happ and Greven, 2018) to summarize the dominant patterns of excursion across days and networks. Based on variance explained by each component, we retained three components for analysis, which together describe 77% of the variance across all excursion curves. Each of these components describes a characteristic pattern of excursion, represented as a deviation from the overall mean. Note that the identification of these components was done over all subjects, and thus is agnostic to group membership (i.e., FF versus SS versus SF subjects).

Figure 4

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The first of these components is shown in Figure 4b. In the left panel, the black lines denote the mean excursion across all subjects, while the colored bands denote the modes of variation encoded by the component, with red (resp. blue) denoting the effects of positive (resp. negative) scores. The right panel shows mean scores on this component for each of the three groups. In this case, the first component appears to describe overall excursion on day 1, with positive scores denoting greater excursion than average in both networks. Positive scores on the second component appear to encode greater excursion in the cognitive network on day 2. Finally, component 3 appears to encode cognitive excursion on day 1, with a slight decrease in motor excursion on both days.

Notably, we observed significant group differences in the expression of the first (Kruskal-Wallis, ${\chi }_2^2=6.03$, ${\displaystyle \mathrm{p}=.04}$) and the second components (${\chi }_2^2=10.16$, ${\displaystyle \mathrm{p}=.006}$). For each of these components, we conducted pairwise Conover-Iman post hoc tests (Conover and Iman, 1979) between groups, correcting for multiple comparisons (Benjamini and Hochberg, 1995). For component 1, scores were significantly higher in the FF group than in the SS group ($z=2.62$, ${\displaystyle \mathrm{p}=.02}$), as well as higher in the SF group than in the SS group ($z=2.25$, ${\displaystyle \mathrm{p}=.02}$). For component 2, scores were significantly higher in the SF group than in the FF group ($z=3.29$, ${\displaystyle \mathrm{p}=.004}$), as well as higher in the SS group than in the FF group ($z=2.35$, ${\displaystyle \mathrm{p}=.02}$). Finally, for component 3, we found no significant effect of group.

In summary, interpreting both the results of the mfPCA and the mean excursion shown in Figure 3a, both the FF and SF groups show a large degree of excursion in the cognitive network on the first day, with the SF group likewise showing a spike in excursion on day 2. These results are consistent with the idea that the rapid learning exhibited by the FF group involves the recruitment of cognitive brain regions outside the sensorimotor cortices. The fact that the SF group expresses a similar excursion profile on day 1 lends evidence to the interpretation that the SF does indeed engage cognitive learning processes on day 1, as reflected in their late performance, but merely fails to learn a strategy early enough to bring down their early error.

Covariance centering reveals task-related changes in network structure

Previous work (e.g., Gratton et al., 2018) has found that subjects exhibit strong individual differences in functional connectivity, which are likely to obscure learning- or task-related changes in network structure. To remove these subject-specific differences prior to analysis, we centered each subject’s task-related covariance matrices with respect to their resting-state covariance matrix using a procedure modelled after the approach of Zhao et al., 2018 (a full description is given in the Materials and methods). To illustrate the effect of this centering procedure – and why it is critical for investigating any learning-related effects here – we used uniform manifold approximation (UMAP; McInnes et al., 2018) to project the individual covariance matrices into two dimensions, both before and after centering. As can be seen in Figures 5b and 6b, prior to the centering, the network structure is completely dominated by subject-level clustering, with no hint of any task-related structure (i.e., separation of baseline, early, and late learning). However, following the centering procedure, a clear task-related structure is now apparent, particularly between the baseline epoch and the two learning epochs (early and late). Notably, this task-related structure is present in both the cognitive and sensorimotor networks.

Figure 5 with 1 supplement see all

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Figure 6

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Next, we represented these centered covariance matrices as tangent vectors describing differences in functional connectivity (for each task epoch and day) with respect to the mean resting-state scan (see Varoquaux et al., 2010). This approach is beneficial, as it has the effect of removing static features of a subject’s network structure which do not change across task epochs (and thus are not of interest in the context of learning). We then embedded the ROIs within the cognitive and sensorimotor networks separately into two dimensions using the model of Wang et al., 2019, in order to identify components describing patterns of changes in functional connectivity relative to rest. In this embedded space, two ROIs are located close together if they exhibit similar changes in functional connectivity with respect to rest.

Given these components ${\mathbf{h}}_k$, every tangent vector ${\mathbf{T}}_i$ is then approximated by ${\mathbf{T}}_i={\sum }_k{\mathbf{h}}_k^{\top }{\mathbf{h}}_k{\lambda }_{ik}$. The matrices ${\mathbf{h}}_k^{\top }{\mathbf{h}}_k$ can be interpreted as patterns of changes in functional connectivity relative to rest, while the score ${\lambda }_{ik}$ is the degree to which that pattern is expressed in the ith observation. By examining changes in the component scores across group, day, and task epoch, we are able to characterize learning-related changes in cognitive and sensorimotor network structure.

Prior work has shown that some neural activity patterns are more difficult to generate than others, and that this constrains learning Sadtler et al., 2014. Here, using human fMRI and by investigating changes in cognitive and sensorimotor network structure during sensorimotor adaptation, we asked whether similar principles might also account for individual differences in learning. Clustering of subject learning data revealed distinct profiles of learning, including a group which adapted rapidly on both days, achieving near-complete reduction of error during late learning, as well as rapid de-adaptation (FF), and a group which adapted and de-adapted slowly on both days (SS). The performance patterns exhibited by the FF group, including rapid learning, unlearning, and relearning, are consistent with the use of an explicit cognitive strategy (Leow et al., 2017; de Brouwer et al., 2018). Notably, we also observed a third group of subjects (SF), who displayed large early errors and slow adaptation during the first day, but also showed comparable rates of de-adaptation to the FF group, and rapid readaptation on the second day. We interpreted this as evidence that the SF group may have been attempting the use of cognitive re-aiming strategies during initial learning, but that it took them longer to figure out the appropriate strategy. Consistent with this idea, the embedding of subject cognitive networks revealed that network structure in the FF and SF groups were highly similar during learning across both days (Figure 5e). In particular, both groups showed high expression of a component encoding an increase in connectivity within and between the FP and DA networks. Our excursion analysis further revealed that these groups’ cognitive networks exhibited greater excursion from intrinsic network structure during the learning phase, after onset of the VMR. Taken together, these results suggest that both the FF and SF groups, who display behavioral characteristics consistent with the use of explicit, cognitive strategy use, engage their cognitive brain networks in a manner that is distinct from gradual learners, and to a greater degree.

All behavioral and imaging data (including T1w and functional scans) are hosted in a repository at OpenNeuro (Standage et al., 2022, accession number ds004021). Processed behavioral data and ROI timecourses are available in a Dryad repository (https://doi.org/10.5061/dryad.h18931znq). Imaging data were preprocessed using fmriPrep 1.4.0, which is open source and freely available. Operations on covariance matrices, including estimation and centering, were performed using the R package spdm (Areshenkoff, 2022b), which is freely available in a repository at https://github.com/areshenk-rpackages/spdm, (copy archived at swh:1:rev:bbb9ea0419092f9cb5bdeaf289d5a691233d8053). Tutorial code for implementing the centering procedure, excursion analysis, and embedding are hosted in a GitHub repository at https://github.com/areshenk-opendata/2022-vmr-neuralexcursions, (copy archived at swh:1:rev:96c15068e902ecd58646e1d7ba05899a5f8feaa7, Areshenkoff, 2022a).

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Author details

1. Corson Areshenkoff

   1. Centre for Neuroscience Studies, Queen's University, Kingston, Canada
   2. Department of Psychology, Queen's University, Kingston, Canada

   Contribution

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

   For correspondence

   areshenk@protonmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9072-9185

2. Daniel J Gale

   Centre for Neuroscience Studies, Queen's University, Kingston, Canada

   Contribution

   Conceptualization, Data curation, Methodology, Software

   Competing interests

   No competing interests declared

3. Dominic Standage

   School of Psychology, Centre for Computational Neuroscience and Cognitive Robotics, University of Birmingham, Birmingham, United Kingdom

   Contribution

   Conceptualization, Investigation, Methodology

   Competing interests

   No competing interests declared

4. Joseph Y Nashed

   Centre for Neuroscience Studies, Queen's University, Kingston, Canada

   Contribution

   Data curation, Investigation, Software

   Competing interests

   No competing interests declared

5. J Randall Flanagan

   1. Centre for Neuroscience Studies, Queen's University, Kingston, Canada
   2. Department of Psychology, Queen's University, Kingston, Canada

   Contribution

   Conceptualization, Project administration, Resources, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

6. Jason P Gallivan

   1. Centre for Neuroscience Studies, Queen's University, Kingston, Canada
   2. Department of Psychology, Queen's University, Kingston, Canada
   3. Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Canada

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

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