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

Recent advancements have been made in the development of cell-based in-vitroneuronal networks, or organoids. In order to better understand the networkstructure of these organoids, a super-selective algorithm has been proposed forinferring the effective connectivity networks from multi-electrode array data.In this paper, we apply a novel statistical method called spectral mirrorestimation to the time series of inferred effective connectivity organoidnetworks. This method produces a one-dimensional iso-mirror representation ofthe dynamics of the time series of the networks which exhibits a piecewiselinear structure. A classical change point algorithm is then applied to thisrepresentation, which successfully detects a change point coinciding with theneuroscientifically significant time inhibitory neurons start appearing and thepercentage of astrocytes increases dramatically. This finding demonstrates thepotential utility of applying the iso-mirror dynamic structure discovery methodto inferred effective connectivity time series of organoid networks.

Discovering a change point and piecewise linear structure in a time series of organoid networks via the iso-mirror