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 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.

Modeling venous bias in resting state functional MRI metrics

“…The dataset was acquired in the context of a longitudinal study, where the participants learned to perform a motor sequence. Descriptions of this data have previously been published (Huck et al, 2019;Jäger et al, 2021;Tremblay et al, 2021). Briefly, participants were trained on a motor task over a time period of 17 days.…”

Touchscreen-Based Cognitive Training Alters Functional Connectivity Patterns in Aged But Not Young Male Rats

“…While increasing scan duration can reduce some of this intraindividual variability, it is also well documented that behavioral variabilities can alter resting state functional connectivity. Motor sequence learning is associated with decreased functional connectivity across motor networks over days in normal adults ( Jäger et al, 2022 ). Two months of meditation training leads to changes in resting state functional connectivity between the default mode and dorsal attention networks ( Zhang et al, 2021 ).…”

Decreased long‐range temporal correlations in the resting‐state functional magnetic resonance imaging blood‐oxygen‐level‐dependent signal reflect motor sequence learning up to 2 weeks following training

“…The present data has been published in previous studies, which focused on measures of functional connectivity and white matter changes, respectively (Jäger et al, 2022;Tremblay et al, 2021). The majority of the methods have previously been described in detail in (Jäger et al, 2022) and will briefly be presented here.…”

“…Afterward, the time series' variance was normalized, bandpass filtered between 0.01 and 0.1 Hz and smoothed with a 3.6 mm Gaussian kernel (Poldrack et al, 2011) before DFA computation for HE maps generation in native space. To bring these maps into montreal neurological institute (MNI) space for group analysis, Jäger et al's (2022) longitudinal co‐registration pipeline were used, which included 12 d of linear transformation nonlinear SYN transformations conducted with advanced normalization tools (Avants et al, 2009) and implemented with the CBS Tools (Bazin et al, 2014). Intermediate procedures such as tissue segmentation, masking, or generating transformation maps between native/group/MNI spaces were also conducted with CBS Tools.…”

“…The measurements took place inside the MRI scanner on five of the seven training days (d0, d1, d2, d5, and d17), whereas two sessions took place inside a testing room outside of the scanner (d3 and d4). We included three of these MRI measurements in the current analyses (d1, d5, and d17) representing the overall learning period as well as retention (Jäger et al, 2022). We chose those three timepoints because the behavioral analysis and our previous results…”