Multi-source fMRI transfer learning on 23 HCP tasks reveals motor clusters with limited cross-paradigm transfer and uses BIP to prioritize working-memory states for direct supervision under budget constraints.
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A Bayesian method constructs credible hyperrectangles from posteriors to compare high-dimensional correlation matrices in brain connectivity analysis, with theoretical guarantees under the inverse-Wishart model.
HCP data analysis clusters individuals by social profiles into two groups where the more socially beneficial cluster scores higher on positive mental health measures and shows lower interconnectivity especially in the default mode network.
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Beyond Single-Source Cognitive Taskonomy:Multi-Source Task Relations through fMRI Transfer Learning
Multi-source fMRI transfer learning on 23 HCP tasks reveals motor clusters with limited cross-paradigm transfer and uses BIP to prioritize working-memory states for direct supervision under budget constraints.