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Record W4304775793 · doi:10.1038/s41467-022-33536-x

A consensus statement on detection of hippocampal sharp wave ripples and differentiation from other fast oscillations

2022· review· en· W4304775793 on OpenAlexaff
Anli Liu, Simon Henin, Saman Abbaspoor, Anatol Bragin, Elizabeth A. Buffalo, Jordan S. Farrell, David J. Foster, Loren M. Frank, Tamara Gedankien, Jean Gotman, Jennifer A. Guidera, Kari L. Hoffman, Joshua Jacobs, Michael J. Kahana, Lin Li, Zhenrui Liao, Jack J. Lin, Attila Losonczy, Rafael Malach, Matthijs A. A. van der Meer, Kathryn McClain, Bruce L. McNaughton, Yitzhak Norman, Andrea Navas-Olivé, Liset Menéndez de la Prida, Jon W. Rueckemann, John J. Sakon, Ivan Skelin, Iván Soltész, Bernhard P. Staresina, Shennan A. Weiss, Matthew A. Wilson, Kareem A. Zaghloul, Michaël Zugaro, György Buzsáki

Bibliographic record

VenueNature Communications · 2022
Typereview
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsUniversity of LethbridgeMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthNational Metal and Materials Technology CenterFondation pour la Recherche MédicaleNational Institutes of HealthNational Science FoundationU.S. Department of Health and Human ServicesHoward Hughes Medical Institute
KeywordsHippocampal formationNeuroscienceComputer scienceRippleEpisodic memoryFoundation (evidence)Cognitive psychologyStatement (logic)Data scienceCognitive sciencePsychologyPhysicsHistoryCognitionEpistemology

Abstract

fetched live from OpenAlex

Decades of rodent research have established the role of hippocampal sharp wave ripples (SPW-Rs) in consolidating and guiding experience. More recently, intracranial recordings in humans have suggested their role in episodic and semantic memory. Yet, common standards for recording, detection, and reporting do not exist. Here, we outline the methodological challenges involved in detecting ripple events and offer practical recommendations to improve separation from other high-frequency oscillations. We argue that shared experimental, detection, and reporting standards will provide a solid foundation for future translational discovery.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.026
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.026
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0050.003
Science and technology studies0.0010.004
Scholarly communication0.0040.006
Open science0.0060.004
Research integrity0.0090.014
Insufficient payload (model declined to judge)0.0030.006

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 head0.110
GPT teacher head0.340
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations169
Published2022
Admission routes1
Has abstractyes

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