TY - JOUR T1 - Motor Sequences - Separating The Sequence From The Motor. A longitudinal rsfMRI Study JF - bioRxiv DO - 10.1101/2021.02.09.430495 SP - 2021.02.09.430495 AU - ATP Jäger AU - JM Huntenburg AU - SA Tremblay AU - U Schneider AU - S Grahl AU - J Huck AU - CL Tardif AU - A Villringer AU - CJ Gauthier AU - PL Bazin AU - CJ Steele Y1 - 2021/01/01 UR - http://biorxiv.org/content/early/2021/07/25/2021.02.09.430495.abstract N2 - 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 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 SMAthat has previously been identified in online task-based learning studies, and extends it to resting state network changes after sequence-specific MSL.Competing Interest StatementThe authors have declared no competing interest. ER -