Search Results for author:
Found 3 papers, 0 papers with code
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.
CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs
To analyze communication, the brain is often split up into anatomical regions that each perform certain computations.
Spatio-temporally separable non-linear latent factor learning: an application to somatomotor cortex fMRI data
Our approach is evaluated on data with multiple motor sub-tasks to assess whether the model captures disentangled latent factors that correspond to each sub-task.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
