Fast and scalable distributed deep convolutional autoencoder for fMRI big data analytics.

ABSTRACT: In recent years, analyzing task-based fMRI (tfMRI) data has become an essential tool for understanding brain function and networks. However, due to the sheer size of tfMRI data, its intrinsic complex structure, and lack of ground truth of underlying neural activities, modeling tfMRI data is hard and challenging. Previously proposed data modeling methods including Independent Component Analysis (ICA) and Sparse Dictionary Learning only provided shallow models based on blind source separation under the strong assumption that original fMRI signals could be linearly decomposed into time series components with corresponding spatial maps. Given the Convolutional Neural Network (CNN) successes in learning hierarchical abstractions from low-level data such as tfMRI time series, in this work we pro

SUBMITTER: Makkie M 

PROVIDER: S-EPMC6660166 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature