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Gene Gradients Reveal Directed Structural Connectivity Across Species
Benjamin Snow Sipes1, Srikantan Nagarajan2, Ashish Raj1
1University of California, San Francisco.
Biorxiv : the Preprint Server for Biology
|May 18, 2026
Summary
This study introduces a new computational framework to infer the directionality of brain connections using diffusion MRI (dMRI) and gene expression. The method reveals a directional brain network architecture and a novel measure of directed functional connectivity called angular flow (AF).
Area of Science:
- Neuroscience
- Computational Biology
- Biophysics
Background:
- Diffusion MRI (dMRI) tractography estimates structural connectivity (SC) but lacks directional information in white matter pathways.
- Understanding the directionality of neural connections is crucial for comprehending brain function and dysfunction.
- Existing methods for inferring connection directionality are limited, especially at a large scale in humans.
Purpose of the Study:
- To develop and validate a computational framework for inferring directional structural connectivity (dSC) from dMRI data.
- To investigate the brain's network architecture and functional connectivity using the inferred dSC.
- To introduce and characterize a novel measure of directed functional connectivity, termed angular flow (AF).
Main Methods:
- Combined dMRI-derived SC with gene co-expression gradients.
- Fitted a structure-function model using the Lyapunov equation to infer directionality.
- Validated the model against ground-truth synaptic connectivity in *C. elegans* and tracer data in rodents and primates.
- Applied the framework to Human Connectome Project (HCP) data from 770 healthy adults.
- Developed and analyzed the angular flow (AF) measure based on dSC.
Main Results:
- The computational framework successfully predicted ground-truth directional connectivity in model organisms and animal studies.
- Inferred dSC in humans revealed an interdigitated sink/source network architecture and plausible feedback/feedforward pathways in sensory areas.
- The novel AF measure correlated with causal functional connectivity metrics.
- AF explained the principal gradient of undirected functional connectivity as net flow from sensory to multimodal areas.
Conclusions:
- The developed framework provides a robust method for inferring directional structural connectivity in the human brain.
- The findings reveal a directional brain network architecture and highlight the significance of directed pathways in brain function.
- Angular flow (AF) offers a new perspective on directed functional connectivity, linking structural directionality to functional organization.
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