Jun 18, 2020

Representation Learning of Resting State fMRI with Variational Autoencoder

BioRxiv : the Preprint Server for Biology

Abstract

Resting state functional magnetic resonance imaging (rs-fMRI) data exhibits complex but structured patterns. However, the underlying origins are unclear and entangled in rs-fMRI data. Here we establish a variational auto-encoder, as a generative model trainable with unsupervised learning, to disentangle the unknown sources of rs-fMRI activity. After being trained with large data from the Human Connectome Project, the model has learned to represent and generate patterns of cortical activity and connectivity using latent variables. Of the latent representation, its distribution reveals overlapping functional networks, and its geometry is unique to each individual. Our results support the functional opposition between the default mode network and the task-positive network, while such opposition is asymmetric and non-stationary. Correlations between latent variables, rather than cortical connectivity, can be used as a more reliable feature to accurately identify subjects from a large group, even if only a short period of data is available per subject.

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Mentioned in this Paper

Microelectrodes
Positioning Attribute
Size
CNTF gene
Extracellular
Entire Thalamic Reticular Nucleus
Hydra Polyps
Structure of Thalamic Reticular Nucleus
Neural Tissue Damage
Brain

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