Evidence map›Paper›PMID 42680907›Full record

ArticleNature neuroscience2026

State-dependent signal flow hierarchy in the human cerebral cortex.

Younghyun Oh, Yejin Ann, Jae-Joong Lee, Takuya Ito, Sean Froudist-Walsh, Casey Paquola, Michael Milham, R Nathan Spreng, Daniel Margulies, Boris Bernhardt and 2 more

Abstract read
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In one paragraph

Article in Nature neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

  • Update of
    2025
5 · Who and what money

Authors and funding

12 authors.

Younghyun OhCenter for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea.ORCID http://orcid.org/0009-0000-1067-7793
Yejin AnnCenter for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea.
Jae-Joong LeeCenter for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea.ORCID http://orcid.org/0000-0002-7353-8683
Takuya ItoThomas J. Watson Research Center, IBM Research, Yorktown Heights, NY, USA.ORCID http://orcid.org/0000-0002-2060-4608
Sean Froudist-WalshOxford Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences and Department of Experimental Psychology, University of Oxford, Oxford, UK.
Casey PaquolaInstitute of Neuroscience and Medicine, Forschungszentrum Jülich, Jülich, Germany.
Michael MilhamNathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.ORCID http://orcid.org/0000-0003-3532-1210
R Nathan SprengDepartment of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.ORCID http://orcid.org/0000-0003-1530-8916
Daniel MarguliesCentre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-8880-9204
Boris BernhardtDepartment of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.ORCID http://orcid.org/0000-0001-9256-6041
Choong-Wan WooCenter for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea. waniwoo@skku.edu.ORCID http://orcid.org/0000-0002-7423-5422
Seok-Jun HongCenter for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea. hongseokjun@skku.edu.ORCID http://orcid.org/0000-0002-1847-578X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.