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

In many human brain network studies, we do not have sufficient number (n) ofimages relative to the number (p) of voxels due to the prohibitively expensivecost of scanning enough subjects. Thus, brain network models usually suffer thesmall-n large-p problem. Such a problem is often remedied by sparse networkmodels, which are usually solved numerically by optimizing L1-penalties.Unfortunately, due to the computational bottleneck associated with optimizingL1-penalties, it is not practical to apply such methods to constructlarge-scale brain networks at the voxel-level. In this paper, we propose a newscalable sparse network model using cross-correlations that bypass thecomputational bottleneck. Our model can build sparse brain networks at thevoxel level with p > 25000. Instead of using a single sparse parameter that maynot be optimal in other studies and datasets, the computational speed gainenables us to analyze the collection of networks at every possible sparseparameter in a coherent mathematical framework via persistent homology. Themethod is subsequently applied in determining the extent of heritability on afunctional brain network at the voxel-level for the first time using twin fMRI.

Mapping Heritability of Large-Scale Brain Networks with a Billion Connections {\em via} Persistent Homology