Abstract

Sequence learning can be investigated by serial reaction-time (SRT) paradigms. Explicit learning occurs when subjects have to recognize a test sequence and has been shown to activate the frontoparietal network in both contralateral and ipsilateral hemispheres. Thus, the left and right superior longitudinal fasciculi (SLF), connecting the intra-hemispheric frontoparietal circuits, could have a role in explicit unimanual visuomotor learning. Also, as both hemispheres are involved, we could hypothesize that the corpus callosum (CC) has a role in this process. Pathological damage in both SLF and CC has been detected in patients with Multiple Sclerosis (PwMS), and microstructural alterations can be quantified by Diffusion Tensor Imaging (DTI). In light of these findings, we inquired whether PwMS with minimal disability showed impairments in explicit visuomotor sequence learning and whether this could be due to loss of white matter integrity in these intra- and inter-hemispheric white matter pathways. Thus, we combined DTI analysis with a modified version of SRT task based on finger opposition movements in a group of PwMS with minimal disability. We found that the performance in explicit sequence learning was significantly reduced in these patients with respect to healthy subjects; the amount of sequence-specific learning was found to be more strongly correlated with fractional anisotropy (FA) in the CC (r=0.93) than in the left (r=0.28) and right SLF (r=0.27) (p for interaction=0.005 and 0.04 respectively). This finding suggests that an inter-hemispheric information exchange between the homologous areas is required to successfully accomplish the task and indirectly supports the role of the right (ipsilateral) hemisphere in explicit visuomotor learning. On the other hand, we found no significant correlation of the FA in the CC and in the SLFs with nonspecific learning (assessed when stimuli are randomly presented), supporting the hypothesis that inter-hemispheric integrity is specifically relevant for explicit sequence learning.

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