Discovering sex and age implicator edges in the human connectome

Keresztes, László; Evelin, Szogi,; Varga, Bálint; Grolmusz, Vince

doi:10.1016/j.neulet.2022.136913

Cited by 1 publication

(1 citation statement)

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“…Using the exact methods and deep algorithms and approaches of graph theory (e.g., integer programming algorithms for computing exact solutions of NP-hard graph problems in connectomes, like minimum vertex cover or minimum bı ´section width), we have discovered numerous connectomical properties, related to the human sex differences [21][22][23][24][25], early brain development [16,[26][27][28], different lobal structures and organizations [29][30][31], and frequent edge sets in the whole brain or only those which are adjacent to the hippocampus [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Robust circuitry-based scores of structural importance of human brain areas

Hegedűs,

Grolmusz

2024

PLoS ONE

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

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