English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Recurrent neural networks (RNNs) were designed for dealing with time-seriesdata and have recently been used for creating predictive models from functionalmagnetic resonance imaging (fMRI) data. However, gathering large fMRI datasetsfor learning is a difficult task. Furthermore, network interpretability isunclear. To address these issues, we utilize multitask learning and design anovel RNN-based model that learns to discriminate between classes whilesimultaneously learning to generate the fMRI time-series data. Employing thelong short-term memory (LSTM) structure, we develop a discriminative modelbased on the hidden state and a generative model based on the cell state. Theaddition of the generative model constrains the network to learn functionalcommunities represented by the LSTM nodes that are both consistent with thedata generation as well as useful for the classification task. We apply ourapproach to the classification of subjects with autism vs. healthy controlsusing several datasets from the Autism Brain Imaging Data Exchange. Experimentsshow that our jointly discriminative and generative model improvesclassification learning while also producing robust and meaningful functionalcommunities for better model understanding.

Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI