Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain

arXiv:2403.06432v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) have shown promise in learning dynamic functional connectivity for distinguishing phenotypes from human brain networks. However, obtaining extensive labeled clinical data for training is often resource-intensive, making practical application difficult. Leveraging unlabeled data thus becomes crucial for representation learning in a label-scarce setting. Although generative self-supervised learning techniques, especially masked autoencoders, have shown promising results in representation learning in various domains, their application to dynamic graphs for dynamic functional connectivity remains …

abstract application arxiv autoencoder brain clinical connectivity cs.lg data dynamic embedding functional gnns graph graph neural networks however human making masked autoencoder networks neural networks practical q-bio.nc self-supervised learning supervised learning training type