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The code including in this dataset creates all the main figures and analyses for the manuscript. If you download the whole folder and run the "Main_Behavioral2rsFC.m" out of the downloaded folder, it will recreate figures 2,3,4,8,9. The model pipeline used for linear regression analysis (reported in figures7 and the tables 1-2) is included at the bottom of this code. "Main_Behavioral2rsFC_RF_group.m" creates figure 5 and performs all associated analyses.We also include the CONN .mat files of the rsFC between all ROIs. Paper abstract: Dynamic adaptation is an error-driven process of adjusting planned motor actions to changes in task dynamics (Shadmehr, 2017). Adapted motor plans are consolidated into memories that contribute to better performance on re-exposure.Consolidation begins within 15 min following training (Criscimagna-Hemminger and Shadmehr, 2008), and can be measuredvia changes in resting state functional connectivity (rsFC). For dynamic adaptation, rsFC has not been quantified on thistimescale, nor has its relationship to adaptative behavior been established. We used an functional magnetic resonance imaging(fMRI)-compatible robot, the MR-SoftWrist (Erwin et al., 2017), to quantify rsFC specific to dynamic adaptation of wristmovements and subsequent memory formation in a mixed-sex cohort of human participants. We acquired fMRI during amotor execution and a dynamic adaptation task to localize brain networks of interest, and quantified rsFC within these networkin three 10-min windows occurring immediately before and after each task. The next day, we assessed behavioralretention. We used a mixed model of rsFC measured in each time window to identify changes in rsFC with task performance,and linear regression to identify the relationship between rsFC and behavior. Following the dynamic adaptation task, rsFCincreased within the cortico-cerebellar network and decreased interhemispherically within the cortical sensorimotor network.Increases within the cortico-cerebellar network were specific to dynamic ... |