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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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Heritability of nested hierarchical structural brain network.

Moo K Chung, Zhan Luo, Nagesh Adluru

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 17, 2018

Summary

This summary is machine-generated.

We developed a new method for building brain networks that avoids scale-dependent changes. This approach revealed robust genetic influences on brain structure across all network scales.

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Area of Science:

Neuroscience
Network Science
Computational Biology

Background:

Brain network topology is sensitive to the scale of existing parcellation methods.
Scale dependency can obscure true network properties and genetic influences.

Purpose of the Study:

To introduce a novel method for constructing nested hierarchical brain networks.
To overcome scale dependency in brain network analysis.
To investigate the genetic contribution to structural brain connectivity.

Main Methods:

Developed a hierarchical parcellation technique to create nested brain networks.
Applied the method to diffusion tensor imaging data from 111 twin pairs.
Analyzed the topological properties of the resulting brain networks.

Main Results:

The hierarchical network construction method successfully avoided scale dependency.
Robust genetic contributions to whole-brain structural connectivity were identified.
These genetic influences were consistent across various network scales.

Conclusions:

The proposed nested hierarchical approach provides a scale-independent framework for brain network analysis.
This method enhances the reliability of characterizing brain network topology and its genetic underpinnings.