Multi-Level Integration for Predictive Inference: mPFC Connectivity in Action Anticipation Across Representational Profiles

This dataset contains behavioral and neuroimaging data from a table tennis serve anticipation study investigating how different training pathways (observation-based vs. execution-based) shape neural connectivity architecture supporting predictive inference.

Behavioral data includes pre-post training accuracy, kinematic encoding model outputs (overlap and alignment coefficients), and raw kinematic feature data for individual participants across point-light and full-body video conditions.

fMRI data comprises first-level contrast images and second-level statistical maps from whole-brain activation analyses (including conjunction and interaction effects), alongside Dynamic Causal Modeling (DCM) results with ROI masks, extracted time-series, and estimated connectivity parameters for all three groups.

Analysis code provides Python scripts for behavioral modeling (kinematic encoding/readout models, statistical analyses, and visualization) and MATLAB scripts for fMRI analyses (second-level GLM, group comparisons, DCM model specification and estimation).

All data are organized by analysis type (Behavioral data/code, fMRI data/code) to facilitate reproducibility of the reported findings.