Evidence map›Paper›PMID 41843677›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2026

Hierarchical whole-brain modeling of critical synchronization dynamics in the human brain.

Vladislav Myrov, Alina Suleimanova, Samanta Knapič, Paula Partanen, Maria Vesterinen, Wenya Liu, Satu Palva, J Matias Palva

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Cell division sets a universal flow geometry in cell layers.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Symmetry breaking and avalanche shapes in modular neural networks.Frontiers in computational neuroscience · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Vladislav MyrovDepartment of Neuroscience and Biomedical Engineering, Aalto University, Espoo FI-00076, Finland.ORCID 0000-0001-5147-2727
Alina SuleimanovaDepartment of Neuroscience and Biomedical Engineering, Aalto University, Espoo FI-00076, Finland.
Samanta KnapičDepartment of Neuroscience and Biomedical Engineering, Aalto University, Espoo FI-00076, Finland.
Paula PartanenNeuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki FI-00014, Finland.
Maria VesterinenNeuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki FI-00014, Finland.ORCID 0009-0000-2841-369X
Wenya LiuDepartment of Neuroscience and Biomedical Engineering, Aalto University, Espoo FI-00076, Finland.
Satu PalvaNeuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki FI-00014, Finland.ORCID 0000-0001-9496-7391
J Matias PalvaDepartment of Neuroscience and Biomedical Engineering, Aalto University, Espoo FI-00076, Finland.

Funding

Research Council of Finland (AKA) 296304Sigrid Juséliuksen Säätiö (Sigrid Jusélius Stiftelse) 240156
6 · The paper itself

Abstract

The brain operates at the critical transition between order and disorder which supports optimal information processing. Whole-brain computational modeling is a powerful tool for uncovering the system-level mechanisms behind large-scale brain activity in both healthy and pathological states. However, most previous approaches have focused on either functional connectivity or criticality, making it difficult to capture both aspects simultaneously. Here, we introduce a method based on a Hierarchical Kuramoto model that incorporates two levels of hierarchy. In our model, each node contains a large number of coupled oscillators, which allows us to examine both local synchronization and long-distance interactions between brain regions. The model produces critical-like dynamics marked by emergent long-range temporal correlations (LRTCs) and both interareal phase synchronization and amplitude cross-correlations (CC) during the transition from asynchronous to synchronous states. Notably, structure-function coupling shows distinct patterns: correlations with structural connectivity peak at criticality for LRTCs and CC, but decay for local and interareal phase synchronization. Comparisons with human resting-state magnetoencephalography (MEG) data reveal that the model's behavior most closely resembles MEG phase synchronization and multipeak power spectra on the subcritical side of an extended critical regime, supporting the hypothesis that the human brain operates in this state.

Indexed as

BrainCortical SynchronizationModels, NeurologicalComputer SimulationHumansMagnetoencephalographybrain oscillationscomputational modelingcriticalitykuramotoMEG

Identifiers

PMID41843677
PMCPMC13012088

What OpenQuestion holds

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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.