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Record W4401824379 · doi:10.1038/s41467-024-51586-1

Sleep-like cortical dynamics during wakefulness and their network effects following brain injury

2024· review· en· W4401824379 on OpenAlexfundno aff
Marcello Massimini, Maurizio Corbetta, María V. Sánchez-Vives, Thomas Andrillon, Gustavo Deco, Mario Rosanova, Simone Sarasso

Bibliographic record

VenueNature Communications · 2024
Typereview
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsnot available
FundersHorizon 2020 Framework ProgrammeFondazione Regionale per la Ricerca BiomedicaMinistero dell'Università e della RicercaAgencia Estatal de InvestigaciónFundação BialEuropean CommissionCanadian Institute for Advanced ResearchMinistero della SaluteRegione LombardiaFondazione Cassa di Risparmio di Padova e Rovigo
KeywordsWakefulnessNeuroscienceElectroencephalographyNeuromodulationSleep (system call)Traumatic brain injuryBrain wavesBrain activity and meditationComputer sciencePsychologyCentral nervous system

Abstract

fetched live from OpenAlex

By connecting old and recent notions, different spatial scales, and research domains, we introduce a novel framework on the consequences of brain injury focusing on a key role of slow waves. We argue that the long-standing finding of EEG slow waves after brain injury reflects the intrusion of sleep-like cortical dynamics during wakefulness; we illustrate how these dynamics are generated and how they can lead to functional network disruption and behavioral impairment. Finally, we outline a scenario whereby post-injury slow waves can be modulated to reawaken parts of the brain that have fallen asleep to optimize rehabilitation strategies and promote recovery. In this Perspective, the authors propose that brain injury can result in sleep-like slowing of cortical EEG waves during wakefulness. The generation of these dynamics and their effects on brain networks and behavior are discussed, as well as future directions for neuromodulation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0030.003
Research integrity0.0010.007
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.043
GPT teacher head0.381
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations53
Published2024
Admission routes1
Has abstractyes

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