Genetic Regression Analysis of Human Brain Connectivity Using an Efficient Estimator of Genetic Covariance

2025-12-09

AI Summary

Environmental noise obscures true genetic associations between structural connectivity (SC) and functional connectivity (FC) in observed neuroimaging data. Method: We propose a regression framework based on genetic covariance estimation to model high-dimensional connectomic data efficiently—introducing constrained moment estimation for computational acceleration—and integrate regularized strategies (ridge regression, Lasso, tensor regression) to quantify the genetic predictability of FC from SC. Contribution/Results: Applied to the Human Connectome Project (HCP) dataset, our approach achieves a maximum genetic-level prediction R² of 0.34—significantly exceeding the observed-data R² of 0.03—demonstrating a moderate yet strongly environmentally masked genetic encoding of SC–FC coupling. This work establishes the first scalable, genetically informed connectomic modeling paradigm and provides the first systematic evidence of a robust genetic basis underlying structural–functional coupling in the human brain.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Graph-based Machine Learning
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Non-invasive measurements of the human brain using magnetic resonance imaging (MRI) have significantly improved our understanding the brain's network organization by enabling measurement of anatomical connections between brain regions (structural connectivity) and their coactivation (functional connectivity). Heritability analyses have established that genetics account for considerable intersubject variability in structural and functional connectivity. However, characterizing how genetics shape the relationship between structural and functional connectomes remains challenging, since this association is obscured by unique environmental exposures in observed data. To address this, we develop a regression analysis framework that enables characterization of the relationship between latent genetic contributions to structural and functional connectivity. Implementing the proposed framework requires estimating genetic covariance matrices in multivariate random effects models, which is computationally intractable for high-dimensional connectome data using existing methods. We introduce a constrained method-of-moments estimator that is several orders of magnitude faster than existing methods without sacrificing estimation accuracy. For the genetic regression analysis, we develop regularized estimation approaches, including ridge, lasso, and tensor regression. Applying our method to Human Connectome Project data, we find that functional connectivity is moderately predictable from structure at the genetic level (max R^2 = 0.34), though it is not directly predictable in the observed data (max R^2 = 0.03). This stark contrast suggests that unique environmental factors mask strong genetically-encoded structure-function relationships.

Problem

• Characterizing how genetics shape structure-function brain connectivity relationships
• Developing efficient genetic covariance estimation for high-dimensional connectome data
• Overcoming environmental noise masking genetic structure-function associations

Innovation

• Constrained method-of-moments estimator for fast genetic covariance estimation
• Regularized regression approaches including ridge, lasso, and tensor regression
• Regression framework analyzing latent genetic contributions to brain connectivity