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Updated: Jan 27, 2026
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Published on: July 20, 2022
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Heritability Estimation of Reliable Connectomic Features.
Linhui Xie1, Enrico Amico2,3, Paul Salama1
1Department of Electrical and Computer Engineering, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA.
Summary
This study identifies highly heritable brain network measures from diffusion imaging. Network-level traits, reflecting brain integrality and resilience, show significant genetic influence, with additive genetics explaining 59-77% of variance.
Area of Science:
- Neuroscience
- Genetics
- Medical Imaging
Background:
- Brain imaging genetics explores genetic influences on brain structure and function.
- Identifying heritable neuroimaging phenotypes is crucial for genetic studies.
- Structural brain networks offer a promising avenue for such investigations.
Purpose of the Study:
- To identify highly heritable measures of structural brain networks using diffusion-weighted imaging.
- To assess the reliability and heritability of individual edge properties and network-level metrics.
- To determine the genetic contribution to brain network organization.
Main Methods:
- Utilized twin data from the Human Connectome Project (HCP).
- Evaluated reliability of fractional anisotropy, fiber length, and fiber number using intraclass correlation coefficients.
- Estimated heritability of network measures with SOLAR-Eclipse software.
Main Results:
- Approximately 5% of structural network edges showed significant heritability in fractional anisotropy, fiber length, or fiber number.
- All tested network-level measures (integrality, segregation, resilience) were highly heritable.
- Additive genetic effects explained 59-77% of the variance in network-level measures.
Conclusions:
- Structural brain network measures, particularly at the network level, are substantially heritable.
- These findings highlight the genetic underpinnings of brain network organization.
- Identified reliable and heritable metrics suitable for future brain imaging genetics research.
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