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성균관대학교, 기초과학연구원

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Nat. Neurosci., 01 September 2026 | https://doi.org/10.1038/s41593-026-02389-8 State-dependent signal flow hierarchy in the human cerebral cortex

Younghyun Oh^1,2,3, Yejin Ann^1,4, Jae-Joong Lee¹, Takuya Ito⁵, Sean Froudist-Walsh^6,7,8, Casey Paquola⁹, Michael Milham^10,11, R. Nathan Spreng¹², Daniel Margulies^13,14, Boris Bernhardt¹², Choong-Wan Woo^1,2,3,4,15 & Seok-Jun Hong^1,2,3,4,15

¹Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea.
²Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea.
³Life-inspired Neural Network for Prediction and Optimization Research Group, Suwon, South Korea.
⁴Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea.
⁵Thomas J. Watson Research Center, IBM Research, Yorktown Heights, NY, USA.
⁶Oxford Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences and Department of Experimental Psychology, University of Oxford, Oxford, UK.

⁷School of Computer Science and Statistics and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.
⁸Bristol Computational Neuroscience Unit, School of Engineering Mathematics and Technology, University of Bristol, Bristol, UK.
⁹Institute of Neuroscience and Medicine, Forschungszentrum Jülich, Jülich, Germany.
¹⁰Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
¹¹Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
¹²Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
¹³Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
¹⁴Integrative Neuroscience and Cognition Center, CNRS, Université Paris Cité, Paris, France.
¹⁵Department of Brain Science and Engineering, Sungkyunkwan University, Suwon, South Korea.

Corresponding authors
Correspondence to Choong-Wan Woo or Seok-Jun Hong.

Abstract

Understanding how information flows across distributed brain networks is central to linking brain structure, dynamics and function. Here we present a neuroimaging framework that combines integrated effective connectivity (iEC) and unconstrained signal flow mapping for data-driven identification of human cerebral functional hierarchies. Simulations and empirical validation show that iEC recovers connectome directionality and aligns with histologically defined feedforward and feedback pathways. The iEC-derived hierarchy exhibits a monotonically increasing level along the axis where the sensorimotor, association and paralimbic areas are sequentially ordered, consistent with predictions from the structural model of laminar connectivity. This hierarchy is not fixed but flexibly reorganizes across brain states; it becomes flatter during externally oriented processing and steeper during internally focused conditions, reflecting increased engagement of interoceptive regions. Our study indicates that macroscale directed functional connectivity can reveal biologically grounded, state-dependent principles of signal flow in the human brain.

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