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Unraveling network dynamics via Riemannian filtering of network interactions

Yonatan Kleerekoper1, Kesem Shapira1, Yonatan Keselman1, Sandra Nestler2, Mohammad Kurtam1, Shai Abramson1, Dori Derdikman1, Yitzhak Schiller1, Simon Musall3 et al.

Hadas Benisty1,*

  • *Contact author: hadasbe@technion.ac.il
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Phys. Rev. Research 8, 033277 – Published 4 September, 2026

DOI: https://doi.org/10.1103/xzdr-sh34

Abstract

Functional connectivity (FC) is fundamentally nonstationary, undergoing continuous reconfigurations that track shifting behavioral and cognitive states. Despite the importance of these transitions, existing analytical frameworks struggle to reconcile the high-dimensional nature of these reconfigurations with the need for structured trajectories and mechanistic interpretability. Specifically, identifying the precise network components responsible for driving these dynamics remains a significant challenge. To bridge this gap, we introduce RONI (Riemannian filtering of network interactions), a geometry-aware, unsupervised framework that treats time-varying FC as a signal evolving on the manifold of symmetric positive semidefinite (SPSD) matrices. By defining intrinsic filtering operators along the SPSD geodesics, RONI produces a multiresolution decomposition of FC dynamics directly on the manifold. This representation allows for the isolation of “dynamic drivers”—specific network elements that dominate coherent connectivity modes across distinct temporal scales. We demonstrate the versatility of RONI by applying it to a diverse array of large-scale neural recordings, including hippocampal electrophysiology data, cortex-wide and dendritic calcium imaging, and human electroencephalography. Across these varied modalities and spatiotemporal scales, RONI identifies biologically meaningful subnetworks that shape the geometry of the connectivity trajectory, providing a unified, interpretable, and quantitative framework for studying the evolution of distributed neural interactions.

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Phys. Rev. Research 8, 033277– Published 4 September, 2026
  • Received 31 December 2025
  • Accepted 10 August 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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