Geo-Mamba: Geometry-informed state-space learning of functional brain organization.

Abstract

Functional magnetic resonance imaging (fMRI) derived functional connectivity (FC) is represented as graphs and as correlation or covariance matrices that live on non-Euclidean spaces, cortical graphs and the Riemannian manifold of symmetric positive-definite (SPD) matrices, thus conventional Euclidean sequence models are misspecified. To this end, we introduce Geo-Mamba, a geometric variant of Mamba formulated on Riemannian manifolds. Geo-Mamba employs a dual-path selective state-space design, (1) a stacked path performs hierarchical modeling by aggregating pyramid multi-granular features to capture short- and long-range dependencies; and (2) a distillation path combats redundancy in high-dimensional SPD inputs via progressive, geometry-aware dimensionality reduction (operating in the manifold spaces) to produce compact states without violating Riemannian constraints. Their complementary outputs are fused through the tailored GeoMix operator to yield a compact, discriminative SPD representation. Geo-Mamba is evaluated on seven public fMRI datasets, including two Alzheimer's disease cohorts, three Parkinson's disease cohorts, one Autism dataset, as well as a longitudinal single-site, single-scanner study designed for detecting subtle changes in the brain due to a season of playing contact sports. To further evaluate the cross-modal applicability and scalability of the model, we apply Geo-Mamba to three electroencephalography (EEG) datasets. Across these benchmarks, it delivers consistently competitive accuracy and robustness, supporting the value of dual-path manifold modeling for neuroimaging and its potential for clinical translation. The code is released at https://github.com/acmlab/Geo-Mamba.