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Found 2 papers, 1 papers with code
Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks
Based on these insights, we propose a new model, Interpretable Graph Sparsification (IGS), which enhances graph classification performance by up to 5. 1% with 55. 0% fewer edges.
Learning low-dimensional dynamics from whole-brain data improves task capture
We evaluate our approach on various task-fMRI datasets, including motor, working memory, and relational processing tasks, and demonstrate that it outperforms widely used dimensionality reduction techniques in how well the latent timeseries relates to behavioral sub-tasks, such as left-hand or right-hand tapping.
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