ArticleConnectomics in neuroImaging : second international workshop, CNI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018 : proceedings. CNI (Workshop) (2nd : 2018 : Granada, Spain)2018
Heritability Estimation of Reliable Connectomic Features.
Article in Connectomics in neuroImaging : second international workshop, CNI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018 : proceedings. CNI (Workshop) (2nd : 2018 : Granada, Spain), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- TOPOLOGICAL LEARNING FOR BRAIN NETWORKS.The annals of applied statistics · 2023Article
- The expanding horizons of network neuroscience: From description to prediction and control.NeuroImage · 2022Review
- Genetic Influence Underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts.Frontiers in genetics · 2021Article
- Rapid Acceleration of the Permutation Test via Transpositions.Connectomics in neuroImaging : third International Workshop, CNI 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings. CNI (Workshop) (3rd : 2019 : Shenzhen Shi, China) · 2019Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Brain imaging genetics is an emerging research field to explore the underlying genetic architecture of brain structure and function measured by different imaging modalities. However, not all the changes in the brain are a consequential result of genetic effect and it is usually unknown which imaging phenotypes are promising for genetic analyses. In this paper, we focus on identifying highly heritable measures of structural brain networks derived from diffusion weighted imaging data. Using the twin data from the Human Connectome Project (HCP), we evaluated the reliability of fractional anisotropy measure, fiber length and fiber number of each edge in the structural connectome and seven network level measures using intraclass correlation coefficients. We then estimated the heritability of those reliable network measures using SOLAR-Eclipse software. Across all 64,620 network edges between 360 brain regions in the Glasser parcellation, we observed ~5% of them with significantly high heritability in fractional anisotropy, fiber length or fiber number. All the tested network level measures, capturing the network integrality, segregation or resilience, are highly heritable, with variance explained by the additive genetic effect ranging from 59% to 77%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.