Motor Sequences - Separating The Sequence From The Motor. A longitudinal rsfMRI Study

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 increases in functional connectivity during fast learning in the sensorimotor territory of the internal segment of right globus pallidus (GPi), and sequence-specific decreases in right supplementary motor area (SMA) in overall learning. We found that connectivity changes in key regions of the motor network including the superior parietal cortex (SPC) and primary motor cortex (M1) were not a result of sequence-specific learning but were instead linked to motor execution. Our study confirms the sequence-specific role of SMA and GPi that has previously been identified in online task-based learning studies in humans and primates, and extends it to resting state network changes after sequence-specific MSL. Finally, our results shed light on a timing-specific plasticity mechanism between GPi and SMA following MSL.

White matter microstructural changes in short-term learning of a continuous visuomotor sequence

“…com/ AthSc hmidt/ MMPI/ tree/ master/ prepr ocess ing. Preprocessing of rs-fMRI data is explained in more details in Jaeger et al (2021).…”

“…Significant changes in both groups were defined as non-sequence specific and interpreted as related to motor execution of the SPFT. Similar voxel-wise interaction analyses were conducted on rs-fMRI centrality metrics with Group and Scan as factors, allowing to identify clusters of changes in functional connectivity (Jaeger et al 2021), which were used for ROI generation.…”

“…Work from our group focusing on the rs-fMRI data in the same cohort identified functional reorganization of the networks involved in MSL (Jaeger et al 2021). These regions were used to define ROIs in WM tracts associated with these task-relevant functional changes.…”

“…5d) in both groups (mean ΔFA in all subjects = 0.119, mean ΔFA in LRN = 0.147, Fig. 3 Regions of interest (ROIs) in white matter (blue) created from grey matter ROIs (red) where sequence-specific changes in functional connectivity were found (Jaeger et al 2021). ROIs are displayed on the MNI152 template and WM ROIs are overlaid on a white matter mask created from the group average of FA maps thresholded at 0. mean ΔFA in SMP = 0.093).…”

“…The DTI model used to derive diffusivity metrics from DWI data yields measures that are highly sensitive to changes in WM microstructure, although physiologically non-specific (Riffert et al 2014). Moreover, we investigated WM plasticity in regions of interest (ROIs) near areas of change in functional connectivity, assessed with resting-state fMRI (rs-fMRI) data in the same cohort (Jaeger et al 2021).…”