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A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation
[article]
2016
arXiv
pre-print
Finding the most effective way to aggregate multi-subject fMRI data is a long-standing and challenging problem. It is of increasing interest in contemporary fMRI studies of human cognition due to the scarcity of data per subject and the variability of brain anatomy and functional response across subjects. Recent work on latent factor models shows promising results in this task but this approach does not preserve spatial locality in the brain. We examine two ways to combine the ideas of a factor
arXiv:1608.04846v1
fatcat:2227k6htenavtf7wlfhhfhu6g4