Abstract

SummaryFunctionally related brain networks are engaged even in the absence of an overt behavior. The role of this resting state activity, evident as low-frequency fluctuations of BOLD (see [1] for review, [2–4]) or electrical [5, 6] signals, is unclear. Two major proposals are that resting state activity supports introspective thought or supports responses to future events [7]. An alternative perspective is that the resting brain actively and selectively processes previous experiences [8]. Here we show that motor learning can modulate subsequent activity within resting networks. BOLD signal was recorded during rest periods before and after an 11 min visuomotor training session. Motor learning but not motor performance modulated a fronto-parietal resting state network (RSN). Along with the fronto-parietal network, a cerebellar network not previously reported as an RSN was also specifically altered by learning. Both of these networks are engaged during learning of similar visuomotor tasks [9–22]. Thus, we provide the first description of the modulation of specific RSNs by prior learning—but not by prior performance—revealing a novel connection between the neuroplastic mechanisms of learning and resting state activity. Our approach may provide a powerful tool for exploration of the systems involved in memory consolidation.

Highlights

  • Motor Performance and Motor Learning To measure the modulation of resting state activity after a short period of sensorimotor learning, we exposed two groups of participants to one of two versions of a visuomotor ‘‘centerout’’ tracking task [23] (Figure 1A; see Supplemental Experimental Procedures available online)

  • We contrasted the engagement of these networks identified by Probabilistic independent components analysis (PICA) before (REST1) and after (REST2) the visuomotor task

  • Baseline Analysis To first check comparable baseline activity in the two groups, REST1 data for both groups were combined in a single PICA analysis with a between-groups contrast

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Summary

Introduction

Motor Performance and Motor Learning To measure the modulation of resting state activity after a short period of sensorimotor learning, we exposed two groups of participants to one of two versions of a visuomotor ‘‘centerout’’ tracking task [23] (Figure 1A; see Supplemental Experimental Procedures available online). Model-Free Whole-Brain Probabilistic Independent Components Analysis Probabilistic independent components analysis (PICA) of the BOLD signal allowed us to identify the networks evident during rest [26] and to measure changes in these components after motor learning (test group, n = 12) or motor performance (control group, n = 12).

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