Elsevier

NeuroImage

Volume 260, 15 October 2022, 119459
NeuroImage

Reduced functional connectivity supports statistical learning of temporally distributed regularities

https://doi.org/10.1016/j.neuroimage.2022.119459Get rights and content
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open access

Highlights

  • We used fMRI to investigate functional network involved in statistical learning.

  • The superior frontal network showed the largest correlation with statistical learning.

  • The weakened connectivity of superior frontal gyrus helps statistical learning.

Abstract

Statistical learning is a powerful ability that extracts regularities from our environment and makes predictions about future events. Using functional magnetic resonance imaging, we aimed to probe how a wide range of brain areas are intertwined to support statistical learning, characterising its architecture in the whole-brain functional connectivity (FC). Participants performed a statistical learning task of temporally distributed regularities. We used refined behavioural learning scores to associate individuals’ learning performances with the FC changed by statistical learning. As a result, the learning performance was mediated by the activation strength in the lateral occipital cortex, angular gyrus, precuneus, anterior cingulate cortex, and superior frontal gyrus. Through a group independent component analysis, activations of the superior frontal network showed the largest correlation with the statistical learning performances. Seed-to-voxel whole-brain and seed-to-ROI FC analyses revealed that the FC between the superior frontal gyrus and the salience, language, and dorsal attention networks were reduced during statistical learning. We suggest that the weakened functional connections between the superior frontal gyrus and brain regions involved in top-down control processes serve a pivotal role in statistical learning, supporting better processing of novel information such as the extraction of new patterns from the environment.

Keywords

Statistical learning
fMRI
Functional connectivity
Group ICA
ASRT

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1

These authors contributed equally to this work.