Abstract #0126

Estimating Network Topology in Weighted and Dense Connectomes

Luis Manuel Colon-Perez¹, Michelle Couret², William Triplett³, Catherine Price³, and Thomas H Mareci³

¹Psychiatry, University of Florida, Gainesville, FL, United States, ²Medicine, Columbia University, New York, NY, United States, ³University of Florida, Gainesville, FL, United States

Brain networks are organized in a heterogeneous range of white-matter tract sizes suggesting that the brain is organized in broad range of white matter connection strengths. Studies of brain structure with a binary connection model have shown a small-world network topological organization of the brain. We developed a generalized framework to estimate the topological properties of brain networks using weighted connections, which offers a more realistic model of the brain compared to the binary connection model. In addition, this model reduces the need for thresholding to obtain topological properties in dense and weighted connectomes.

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