White matter microstructure associated with functional connectivity changes following short-term learning of a visuomotor sequence

Themes:
AdultsNeuroscienceBrain Plasticity
What:
Poster
Where:
  Virtual session

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Efficient neural transmission is crucial for optimal brain function, yet the plastic potential of white matter (WM) has long been overlooked. Growing evidence now shows that modifications to axons and myelin occur not only as a result of long-term learning, but also after short training periods [1]. Motor sequence learning (MSL) has been shown to occur in overlapping learning stages where different neural circuits are involved at each stage [2]. However, most studies investigating short-term WM plasticity have used a pre-post design, in which the temporal dynamics of changes across learning stages cannot be assessed [1,3]. In this study, we used multiple magnetic resonance imaging (MRI) scans at 7 Tesla to investigate changes in WM in a group learning a complex visuomotor sequence (LRN) and in a control group (SMP) performing a simple sequence, for 5 consecutive days. Consistent with behavioural results, where most improvements occurred between the two first days, structural changes in WM were observed only in the early phase of learning (d1-d2), and in overall learning (d1-d5). In LRNs, WM microstructure was altered in the tracts underlying the primary motor and sensorimotor cortices. Moreover, our structural findings in WM were related to changes in functional connectivity, assessed with resting-state functional MRI data in the same cohort. Significant changes in WM microstructure were found in a region of interest underlying the right supplementary motor area, where a decrease in functional connectivity was also found [4]. Together, our findings provide evidence for highly dynamic WM plasticity in the sensorimotor network during short-term MSL, where the SMA would play a key role in linking the spatial and motor aspects of MSL [2,5]. A better understanding of how learning can structurally shape neural networks could have important implications in other fields of research such as in stroke rehabilitation, to optimize interventions through motor learning.

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Poster (1242.68KB)