Evidence map›Paper›PMID 39677733›Full record

ArticlebioRxiv : the preprint server for biology2024

Proximity to Explosive Synchronization Determines Network Collapse and Recovery Trajectories in Neural and Economic Crises.

UnCheol Lee, Hyoungkyu Kim, Minkyung Kim, Gabjin Oh, Ayoung Park, Pangyu Joo, Dinesh Pal, Irene Tracey, Catherine E Warnaby, Jamie Sleigh and 1 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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

5 · Who and what money

Authors and funding

11 authors.

UnCheol LeeDepartment of Anesthesiology, Center for Consciousness Science, Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, Michigan, USA.ORCID 0000-0001-7858-0381
Hyoungkyu KimDepartment of Anesthesiology, Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, Michigan, USA.
Minkyung KimDepartment of Anesthesiology, Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, Michigan, USA.
Gabjin OhDivision of Business Administration, College of Business, Chosun University, Gwangju, Republic of Korea.
Ayoung ParkDivision of Business Administration, College of Business, Chosun University, Gwangju, Republic of Korea.
Pangyu JooDepartment of Anesthesiology, Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, Michigan, USA.
Dinesh PalDepartment of Anesthesiology, University of Michigan Medical School, Ann Arbor, Michigan, USA.
Irene TraceyNuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom.
Catherine E WarnabyWellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Nuffield Division of Anaesthetics, University of Oxford, Oxford, United Kingdom.
Jamie SleighDepartment of Anesthesiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
George A MashourDepartment of Anesthesiology, Center for Consciousness Science, Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, Michigan, USA.

Funding

Brain Network Mechanism of Fast and Slow Recoveries in Pharmacologically and Pathologically Induced UnconsciousnessR21GM143521 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LEE, UNCHEOL, PAL, DINESH · 2021 to 2022
$429k
NIGMS NIH HHS R21 GM143521
6 · The paper itself

Abstract

When complex systems move away from criticality-a balance between order and chaos-they are no longer optimized. Furthermore, when criticality is lost too quickly, or recovery is delayed, system damage can result. However, the mechanism for these abnormally fast or slow critical transitions remains unknown. Here, we show that the proximity of a complex network to explosive synchronization (ES), a first-order phase transition, determines the trajectories of criticality loss and recovery after perturbations. Our computational models revealed characteristic dynamics based on network proximity to ES, enabling us to infer network phase transition types from empirical data and predict criticality transition patterns. We validated our predictions using empirical data from the human brain under anesthesia and the stock market during an economic crisis, demonstrating that early and prolonged recoveries can be systematically predicted. This study has implications for designing resilient networks that withstand perturbations and recover quickly.

Identifiers

PMID39677733
PMCPMC11642761

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LicenceCC BY-NC-ND
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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.