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

Functional connectivity (FC) has become a primary means of understandingbrain functions by identifying brain network interactions and, ultimately, howthose interactions produce cognitions. A popular definition of FC is bystatistical associations between measured brain regions. However, this could beproblematic since the associations can only provide spatial connections but notcausal interactions among regions of interests. Hence, it is necessary to studytheir causal relationship. Directed acyclic graph (DAG) models have beenapplied in recent FC studies but often encountered problems such as limitedsample sizes and large number of variables (namely high-dimensional problems),which lead to both computational difficulty and convergence issues. As aresult, the use of DAG models is problematic, where the identification of DAGmodels in general is nondeterministic polynomial time hard (NP-hard). To thisend, we propose a $\psi$-learning incorporated linear non-Gaussian acyclicmodel ($\psi$-LiNGAM). We use the association model ($\psi$-learning) tofacilitate causal inferences and the model works well especially forhigh-dimensional cases. Our simulation results demonstrate that the proposedmethod is more robust and accurate than several existing ones in detectinggraph structure and direction. We then applied it to the resting state fMRI(rsfMRI) data obtained from the publicly available PhiladelphiaNeurodevelopmental Cohort (PNC) to study the cognitive variance, which includes855 individuals aged 8-22 years. Therein, we have identified three types of hubstructure: the in-hub, out-hub and sum-hub, which correspond to the centers ofreceiving, sending and relaying information, respectively. We also detected 16most important pairs of causal flows. Several of the results have been verifiedto be biologically significant.

Causal inference of brain connectivity from fMRI with $\psi$-Learning Incorporated Linear non-Gaussian Acyclic Model ($\psi$-LiNGAM)