Off-line consolidation of motor sequence learning results in greater integration within a cortico-striatal functional network

Highlights

• Functional connectivity associated with motor sequence consolidation was measured.

• Both hypothesis- and data-driven approaches were used.

• Sleep, as opposed to daytime, results in increased levels of connectivity.

• Solely the cortico-striatal system showed sleep-dependent changes in connectivity.

• Sequence consolidation is associated with increased integration within that system.

Abstract

The consolidation of motor sequence learning is known to depend on sleep. Work in our laboratory and others have shown that the striatum is associated with this off-line consolidation process. In this study, we aimed to quantify the sleep-dependent dynamic changes occurring at the network level using a measure of functional integration. We directly compared changes in connectivity before and after sleep or the simple passage of daytime. As predicted, the results revealed greater integration within the cortico-striatal network after sleep, but not an equivalent daytime period. Importantly, a similar pattern of results was also observed using a data-driven approach; the increase in integration being specific to a cortico-striatal network, but not to other known functional networks. These findings reveal, for the first time, a new signature of motor sequence consolidation: a greater between-regions interaction within the cortico-striatal system.

Introduction

Contemporary theories of motor skill learning advocate that following encoding of a new motor ability, the memory undergoes “off-line” transformations allowing the initially labile trace to become somewhat fixed into the physical structure of the brain through a cascade of events occurring at both cellular and systems levels: a phase called “memory consolidation” (Dudai, 2004). A large number of studies have now convincingly demonstrated that sleep, during nighttime or daytime, plays a critical role in the off-line consolidation of some, but not all types of motor skills (see Diekelmann et al., 2009, Born and Wilhelm, 2012 for reviews). Indeed, sleep-dependent consolidation has particularly been observed following the acquisition of a new sequence of movements, as opposed to tasks requiring subjects to adapt to visuomotor changes in the environment (Albouy et al., 2013c, Doyon et al., 2009b). This mnemonic process has also been reported in conditions where motor sequences were acquired explicitly (Fischer et al., 2002, Walker et al., 2002) rather than when they are learned implicitly (Robertson et al., 2004), and more so for the allocentric (spatial) compared to the egocentric (motor) representation of a newly learned sequence of movements (Albouy et al., 2013a, Cohen et al., 2005, Witt et al., 2010).

Up to now, functional neuroimaging studies in the field have mainly attempted to identify the specific brain regions mediating motor sequence learning (MSL) and consolidation. While the results have corroborated the contribution of both cortico-striatal and cortico-cerebellar systems in the acquisition of such skilled behaviours (Doyon and Benali, 2005, Doyon et al., 2009a, Floyer-Lea and Matthews, 2005), the off-line consolidation phase has been associated with increased activity in the striatum, and the putamen in particular (Debas et al., 2010), the hippocampus (Albouy et al., 2008, Walker et al., 2005), the cerebellum (Steele and Penhune, 2010, Walker et al., 2005) as well as other cortical regions including the primary motor (Steele and Penhune, 2010) and the medial prefrontal cortices (Walker et al., 2005). Interestingly, however, recent neuroimaging work has also begun to characterize the dynamic learning-dependent functional changes between cerebral regions through connectivity analyses, which are based upon correlations between time courses of brain areas (Friston et al., 1993, Marrelec et al., 2008). Most of these studies have used hypothesis-driven, predefined motor networks or specific seed regions in order to identify the connectivity changes within or between networks during learning. Hence, MSL has been associated with greater connectivity between motor-related regions in the early learning phase of a new sequence of movements, followed by stabilization within the 2nd and 4th weeks of the acquisition process (Ma et al., 2010). Similarly, Sun et al. (2007) have also found greater connectivity between the sensorimotor, premotor and the supplementary motor areas (SMA) within and between hemispheres during early learning of a bimanual motor sequence task. Yet, although very informative, such an a priori approach is limited by the fact that the pattern of changes in connectivity during learning can vary a great deal depending on the seed motor areas chosen within a given network. For example, Coynel et al. (2010) have reported decreases of integration within the associative, but not within the sensorimotor cortico-striatal network across 28 days of acquisition of an explicit sequence, hence demonstrating that the choice of motor regions within a motor network does have a critical effect on pattern of connectivity changes observed with MSL. To overcome this limitation, some authors have employed data-driven approaches. For example, using a graph-theoretical network analysis strategy in groups of young healthy subjects who learned to execute a bimanual learning task, Heitger et al. (2012) have reported that improved motor performance over a 5-day period was associated with increased functional network connectivity metrics. Using a multi-variate model-free method to analyse changes in functional networks related to practice of a motor sequence, another study has revealed an increase in connectivity within a network comprising the premotor and posterior parietal cortices within a first training session (Tamas Kincses et al., 2008). Yet very little is known with respect to the change in functional connectivity related to the consolidation process of a newly learned sequential motor skill (see Dayan and Cohen, 2011).

Task-related changes in connectivity between motor brain regions before and after sleep have previously been measured in order to gain insight into their interaction in association with motor memory consolidation. For instance, a dynamic interplay between the hippocampus and the striatum during MSL training has been shown to predict overnight gains in performance (Albouy et al., 2008, Albouy et al., 2013b). While activity in the dorsal premotor cortex, posterior parietal cortex and pre-SMA was significantly correlated during REM sleep following sequence learning, no correlation was observed between these structures in a group that had not learned the motor sequence (Laureys et al., 2001). Despite such advances, however, it remains unclear whether such increase in connectivity (possibly reflecting the consolidation process following sequence learning) was strictly dependent on sleep, as no daytime control condition was used in both of these investigations. Furthermore, considering that the latter researchers analysed connectivity changes through the use of specific seed regions based on a priori hypothesis, it is thus unknown whether the sleep-related changes in brain connectivity described above are specific to the motor regions mediating the learning process in the first place, or whether they can be observed in other brain networks. Finally, no study, to our knowledge, has described connectivity changes following sleep at the systemic level using a data-driven approach.

The aim of the present study was thus to compare directly the changes in functional connectivity related to the consolidation process of a motor memory trace in two groups of young adults who participated in a test–retest paradigm, where motor sequence learning was measured before and after a 12-hour delay filled with either night sleep or the simple passage of daytime. Participants belonging to the Day/awake group were scanned in the morning and evening using functional magnetic resonance imaging (fMRI) while they executed a 5-element version of the finger sequence learning task (Karni et al., 1995), whereas those in the Night/sleep group were first tested on the same task in the evening, and then retested the following morning. Two different approaches in connectivity analysis were applied: First, similar to studies summarized above, we used a hypothesis-driven method including motor brain regions known to contribute to MSL and/or motor sequence consolidation. Based on Doyon and colleagues' model, which predicts that the cortico-striatal system contributes to the consolidation process of a new sequence of movements (Doyon and Benali, 2005, Doyon et al., 2009a, Doyon and Ungerleider, 2002), we hypothesized that subjects in the night group would show increased correlations (i.e., greater integration) between the learning-dependent motor regions of that system when compared to subjects in the day group. Second, we used a data-driven approach that allowed functional network reorganization to be quantified without a priori assumptions. This method permitted us to measure the change of integration not only within the cortico-striatal system before and after a night of sleep, but also within other large-scale, functionally distinct networks extracted through an independent component analysis (ICA). It was predicted that the changes in integration after sleep, associated with off-line consolidation, would be spatially specific as it would only be observed within the cortico-striatal system, and not within the other extracted networks.