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Related Experiment Video

Updated: Jan 10, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
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Time-resolved functional connectivity during visuomotor graph learning.

Sophie Loman1, Lorenzo Caciagli2, Shubhankar P Patankar1

  • 1Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA.

Neuroimage
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

The brain dynamically adapts its functional organization to reflect the statistical structure of our environment. Learning complex patterns involves shifts in neural processing, moving from top-down to bottom-up strategies.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Neuroimaging

Background:

  • Humans implicitly learn statistical regularities in their environment.
  • This learning can be modeled using graph theory, where perceptual events are nodes and transitions are edges.
  • Understanding how neural dynamics change with different graph topologies is crucial.

Purpose of the Study:

  • To investigate how different graph topologies (modular vs. lattice) influence neural dynamics during statistical learning.
  • To link behavioral sensitivity to graph structure with its time-resolved neural correlates.
  • To explore the brain's adaptive mechanisms in representing and processing complex statistical environments.

Main Methods:

  • Functional magnetic resonance imaging (fMRI) data were collected during a visuomotor graph-learning task.
  • Stimuli were presented based on random walks on either modular or lattice graphs.
  • Time-resolved network analyses were applied to fMRI data.

Main Results:

  • Participants showed faster responses to modular graphs early in learning, with this advantage decreasing over time.
  • Neural activity revealed a flexible visual system and stable large-scale community structure.
  • Increased cohesiveness in dorsal attention, limbic, default-mode, and subcortical systems was observed.
  • Integration between visual and ventral-attention regions increased, while frontoparietal control coupling decreased, indicating a shift to bottom-up processing.
  • Stronger integration within specific brain systems predicted faster learning for modular graphs.

Conclusions:

  • The brain's dynamic functional organization adapts to the statistical topology of experienced environments.
  • Neural processing shifts from top-down to bottom-up strategies as learning progresses.
  • This study provides insights into how the brain represents and adapts to complex statistical structures.