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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The problem of jointly analysing functional connectomics and behavioral datais extremely challenging owing to the complex interactions between the twodomains. In addition, clinical rs-fMRI studies often have to contend withlimited samples, especially in the case of rare disorders. This data-starvedregimen can severely restrict the reliability of classical machine learning ordeep learning designed to predict behavior from connectivity data. In thiswork, we approach this problem from the lens of representation learning andbayesian modeling. To model the distributional characteristics of the domains,we first examine the ability of approaches such as Bayesian Linear Regression,Stochastic Search Variable Selection after performing a classical covariancedecomposition. Finally, we present a fully bayesian formulation for jointrepresentation learning and prediction. We present preliminary results on asubset of a publicly available clinical rs-fMRI study on patients with AutismSpectrum Disorder.

Bayesian Models of Functional Connectomics and Behavior