Motor sequences; separating the sequence from the motor. A longitudinal rsfMRI study

Anna-Thekla P Jäger,Christopher J Steele,Pierre-Louis Bazin,Julia Huck,Julia M Huntenburg,Arno Villringer,Sophia Grahl,Uta Schneider,Claudine J Gauthier,Stefanie A Tremblay,Christine L Tardif

doi:10.1007/s00429-021-02412-7

Abstract

In motor learning, sequence specificity, i.e. the learning of specific sequential associations, has predominantly been studied using task-based fMRI paradigms. However, offline changes in resting state functional connectivity after sequence-specific motor learning are less well understood. Previous research has established that plastic changes following motor learning can be divided into stages including fast learning, slow learning and retention. A description of how resting state functional connectivity after sequence-specific motor sequence learning (MSL) develops across these stages is missing. This study aimed to identify plastic alterations in whole-brain functional connectivity after learning a complex motor sequence by contrasting an active group who learned a complex sequence with a control group who performed a control task matched for motor execution. Resting state fMRI and behavioural performance were collected in both groups over the course of 5 consecutive training days and at follow-up after 12 days to encompass fast learning, slow learning, overall learning and retention. Between-group interaction analyses showed sequence-specific decreases in functional connectivity during overall learning in the right supplementary motor area (SMA). We found that connectivity changes in a key region of the motor network, the superior parietal cortex (SPC) were not a result of sequence-specific learning but were instead linked to motor execution. Our study confirms the sequence-specific role of SMA that has previously been identified in online task-based learning studies, and extends it to resting state network changes after sequence-specific MSL.

Highlights

Motor learning-induced brain plasticity has been typically studied using magnetic resonance imaging (MRI), from immediate online functional changes (Coynel et al 2010; Karim et al 2017; Steele and Penhune 2010; Yokoi and Diedrichsen 2019) to long-term structural effects (Gryga et al 2012; Taubert et al 2012; Bengtsson et al 2005; Scholz et al 2009)
There were no significant differences between days in the simple control condition (SMP) group, supporting the hypothesis that the SMP group was not improving in temporal accuracy
Given that the goal of this study was to identify regions that were exclusively involved in complex sequence learning, we focused on regions that were not involved in simple motor execution and not differentially recruited by the control group

Summary

Introduction

Motor learning-induced brain plasticity has been typically studied using magnetic resonance imaging (MRI) (for review, see Dayan and Cohen 2011; Krakauer et al 2019; Taubert et al 2012), from immediate online functional changes (Coynel et al 2010; Karim et al 2017; Steele and Penhune 2010; Yokoi and Diedrichsen 2019) to long-term structural effects (Gryga et al 2012; Taubert et al 2012; Bengtsson et al 2005; Scholz et al 2009). Resting state fMRI (rsfMRI) can be used to investigate functional plasticity that occurs between online functional and slower structural changes. Measured in the absence of a task, resting-state network dynamics are thought to reflect the previous co-activation of functionally connected brain regions (Biswal et al 1995; Guerra-Carrillo et al 2014). Alterations in these functional networks are thought to reflect the strengthening of the memory trace generated during practice (Albert et al 2009; Lewis et al 2009; Vahdat et al 2011). Assessing resting state networks and how they change in response to training can provide unique insight into training-related functional plasticity beyond the immediate time point of learning

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