Abstract
Repeated practice is fundamental to the acquisition of skills, which is typically accompanied by increasing reliability of neural representations that manifested as more stable activation patterns for the trained stimuli. However, large-scale neural pattern induced by learning has been rarely studied. Here, we investigated whether global connectivity patterns became more reliable as a result of motor learning using a novel analysis of the multivariate pattern of functional connectivity (MVPC). Human participants were trained with a finger-tapping motor task for five consecutive days and went through Functional magnetic resonance imaging (fMRI) scanning before and after training. We found that motor learning increased the whole-brain MVPC stability of the primary motor cortex (M1) when participants performed the trained sequence, while no similar effects were observed for the untrained sequence. Moreover, the increase of MVPC stability correlated with participants’ improvement in behavioral performance. These findings suggested that the acquisition of motor skills was supported by the increased connectivity pattern stability between the M1 and the rest of the brain. In summary, our study not only suggests global neural pattern stabilization as a neural signature for effective learning but also advocates applying the MVPC analysis to reveal mechanisms of distributed network reorganization supporting various types of learning.
Highlights
Learning requires adapting brain functions to achieve mastery
We found a significant increase of multivariate pattern of functional connectivity (MVPC) stability when participants performed the trained sequence (one-tailed one-sample t-test: t(9) = 3.031, p = 0.007), while there was no changes in MVPC stability when performing the untrained sequence (one-tailed one-sample t-test: t(9) = −1.117, p = 0.147)
The results indicated that motor learning modulated global connectivity patterns by increasing the stability of the functional connectivity (FC) pattern between M1 and other brain regions, and the improvement was specific to the trained sequence
Summary
Extensive neuroimaging studies have demonstrated learning-induced plasticity in regional activation and inter-regional connectivity in the human brain (Schoups et al, 2001; Op de Beeck et al, 2006; Sun et al, 2006; Lewis et al, 2009; Song et al, 2010). Recent studies using multivariate pattern analysis (MVPA) on regional activation have revealed increased activation pattern stability induced by various types of learning tasks (Xue et al, 2010; Visser et al, 2011; Huang et al, 2013; Wiestler and Diedrichsen, 2013; Bi et al, 2014). Learning Improves Connectivity Pattern Stability (Wiestler and Diedrichsen, 2013). The increased activation pattern stability after learning possibly reflects a more reliable and refined neural representation for trained stimuli at the regional level, suggesting neural stabilization as a critical mechanism underlying learning
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
