Disentangling genetic and environmental contributions to the structure-function coupling
Poster No:
1193
Submission Type:
Abstract Submission
Authors:
Keshav Motwani1, Ali Shojaie1, Ariel Rokem1, Eardi Lila1
Institutions:
1University of Washington, Seattle, WA
First Author:
Co-Author(s):
Introduction:
Striking levels of inter-individual heterogeneity have been observed in the human brain, even within narrowly defined groups of young and healthy individuals (Benson et al., 2022). Brain connectivity is no exception to this, suggesting that a large variety of connectivity configurations can result in cognitively healthy individuals. Previous studies have shown that genetic factors significantly account for the variation in the connectome between individuals (Gu et al., 2021). Additionally, environmental factors, such as exposure to various pollutants, have also been shown to induce brain network changes leading to neurodegeneration. This raises a broader scientific question that we aim to explore in this work: which specific aspects of the structural and functional connectome, and their interplay, are genetically rooted, and which aspects are shaped by environmental exposures? To address this gap, we introduce a computationally efficient and accurate estimator of the covariance matrices of multidimensional genetic and environmental components, enabling the characterization of their contributions to the structure-function connectivity coupling.
·Figure 1. Illustration of the proposed approach for analyzing the relationship between individual structural and functional brain connectomes.
Methods:
We propose a novel moment-matching estimator with a positive semi-definite constraint on the covariance estimates of genetic and environmental components, which provides substantial improvement over unconstrained versions of the model (Ge et al., 2016), nearly matching estimation accuracy of the state-of-the-art multivariate restricted maximum likelihood (mREML) estimator (Zhou et al., 2014). Our estimator runs in just a few seconds in our final application, compared to an estimated runtime of over 300 years for mREML. When used to define a novel ridge regression model, it enables the investigation of systematic relationships between structural connections and functional connections through both genetic and environmental mechanisms. As illustrated in Figure 1, we apply our method to structural and functional connectivity data from 955 participants in the S1200 Human Connectome Project, using connectivity matrices computed in Kiar et al. (2018), and incorporating the associated kinship structure to disentangle genetic and environmental contributions.
Results:
Our analysis shows that when restricted to genetic mechanisms, individual functional connectomes show remarkably high levels of predictability from the structural connectome, even with a simple linear model, supporting the hypothesis that structural connectomes underpin the observed functional connections. High levels of predictability are also observed within common environmental mechanisms, though substantially smaller than those observed for genetic mechanisms. These findings suggest that the heterogeneous and unpredictable nature of the functional connectome, given the structural connectome, is predominantly the effect of unique environmental factors. For example, as shown in Figure 2, the functional connection strength between the visual (VIS) and cingulo-opercular (COP) networks at the individual level is poorly predicted by the individual-level structural connectome through a ridge regression model (R^2 = 0.02). However, predictability increases substantially (R^2 = 0.85) when the analysis is restricted to genetic mechanisms.
·Figure 2. Estimated linear regression coefficients for the relationship between the structural connections (each cell in the heatmap), used as covariates, and the VIS-COP functional connection.
Conclusions:
We propose a novel approach for characterizing the relationship between functional and structural connectomes, separating genetic, common environment, and unique environment mechanisms. Our analysis extends previous work (Gu et al 2021), which models structure-function coupling using a single subject-specific univariate coefficient and assumes that each structural pair influences only its corresponding functional pair. By contrast, our analysis allows for more complex global interaction patterns between structural and functional connectomes, revealing a high degree of coherence in structure-function coupling at a genetic level.
Genetics:
Genetic Modeling and Analysis Methods 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Keywords:
Data analysis
FUNCTIONAL MRI
Modeling
Statistical Methods
STRUCTURAL MRI
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Ge, T. et al. (2016). “Multidimensional Heritability Analysis of Neuroanatomical Shape”. In: Nature Communications
Gu, Z. et al. (2021). “Heritability and Interindividual Variability of Regional Structure-Function Coupling”. In: Nature Communications 12.1, p. 4894.
Kiar, G. et al. (2018) A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability. In: biorRxiv.
Van Essen, D. C. et al. (2012). “The Human Connectome Project: A Data Acquisition Perspective”. In: NeuroImage 62.4, pp. 2222–2231
Zhou, X. et al. (2014). “Efficient multivariate linear mixed model algorithms for genome-wideassociation studies”. In: Nature methods 11.4, pp. 407–409