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

We consider the 1015-vertex human consensus connectome computed from thediffusion MRI data of 1064 subjects. We define seven different orders on these1015 graph vertices, where the orders depend on parameters derived from thebrain circuitry, that is, from the properties of the edges (or connections)incident to the vertices ordered. We order the vertices according to theirdegree, the sum, the maximum, and the average of the fiber counts on theincident edges, and the sum, the maximum and the average length of the fibersin the incident edges. We analyze the similarities of these seven orders by theSpearman correlation coefficient and by their inversion numbers and have foundthat all of these seven orders have great similarities. In other words, if weinterpret the orders as scoring of the importance of the vertices in theconsensus connectome, then the scores of the vertices will be similar in allseven orderings. That is, important vertices of the human connectome typicallyhave many neighbors, connected with long and thick axonal fibers (wherethickness is measured by fiber numbers), and their incident edges have highmaximum and average values of length and fiber-number parameters, too.Therefore, these parameters may yield robust ways of deciding which verticesare more important in the anatomy of our brain circuitry than the others.

Robust Circuitry-Based Scores of Structural Importance of Human Brain Areas