Latent-Space Causal Discovery from Indirect Neuroimaging Observations

TL;DR

This paper introduces INCAMA, a physics-aware method for causal discovery from indirect neuroimaging data, demonstrating significant improvements in directed-structure recovery and meaningful brain pathway estimates.

Contribution

It formalizes a new framework for causal inference from indirect neuroimaging signals and proposes INCAMA, combining physics-aware inversion with delay-aware encoding.

Findings

INCAMA improves directed-structure recovery by 2-3x in F1 score over baselines.

On HCP fMRI data, INCAMA yields sparse, meaningful directed estimates in visuo-motor pathways.

Controlled simulations validate the quantitative advantages of INCAMA.

Abstract

Neuroimaging does not observe causal variables directly: hemodynamics and volume conduction distort signals so that statistical dependence need not reflect latent neural influence. Before estimating graphs, one must specify under what assumptions delayed directed structure can be studied from such indirect observations. We formalize a conditional setting - recoverable inversion under modality physics together with nonstationary latent dynamics - and derive an inversion-error propagation bound under explicit assumptions. Building on this framing, we propose INCAMA (INdirect CAusal MAmba): physics-aware inversion coupled with a delay-aware Mamba encoder that uses mechanism shifts as informative variation for directed graph scoring. We use controlled simulations for quantitative validation and HCP motor-task fMRI as a zero-shot external transfer check based on anatomical and task-network consistency. Across TVB simulations, INCAMA improves directed-structure recovery by 2-3x in F1 over observation-space and two-stage baselines, and on HCP motor-task fMRI it produces sparse directed estimates concentrated in canonical visuo-motor pathways.