Multimodal branched transport infers anatomically aligned brain reaction maps

TL;DR

This paper introduces a novel multimodal approach to infer anatomically aligned brain reaction maps by combining various neuroimaging data and variational optimization to model branched neural transport architecture.

Contribution

It presents a new method that infers brain signal routing maps from multimodal data, incorporating anisotropic costs and stochastic dynamics, advancing understanding of neural propagation architecture.

Findings

Multimodal data generate anatomically aligned brain reaction maps.

Anisotropic costs reshape routing backbones compared to isotropic baselines.

Hybrid geometric--dynamical optimization reveals non-trivial rank reversals across branching regimes.

Abstract

How external stimulation is transformed into distributed reaction patterns remains unresolved at the level of propagation architecture. Existing large-scale control models quantify transition costs on prescribed networks but do not infer the routing map itself from source and target activity. Here we combine task-related blood-oxygen-level-dependent responses, source-reconstructed electrophysiology and tractography-derived anisotropy to estimate stimulation and reaction measures, define an anatomical transport cost, and infer a branched propagation architecture by variational optimisation. Unlike standard transport formulations, branched transport favours aggregation of signal into shared neural highways before redistribution. We further attach a stochastic graph-induced dynamics to the inferred map and quantify the trade-off between geometric efficiency and dynamical controllability. We show that multimodal data generate anatomically aligned brain reaction maps, that anisotropic costs qualitatively reshape routing backbones relative to isotropic baselines, and that hybrid geometric--dynamical optimisation reveals non-trivial rank reversals across branching regimes.