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Record W2105094581 · doi:10.1503/jpn.100015

Continuing the search for the engram: examining the mechanism of fear memories

2010· review· en· W2105094581 on OpenAlexafffundvenue
Sheena A. Josselyn

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2010
Typereview
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsSickKids FoundationUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsAmygdalaCREBNeurosciencePsychologyFear processing in the brainEngramAnxietyFear conditioningMechanism (biology)Memory consolidationTranscription factorHippocampusBiologyPsychiatryGeneticsPhysics

Abstract

fetched live from OpenAlex

The goal of my research is to gain insight using rodent models into the fundamental molecular, cellular and systems that make up the base of memory formation. My work focuses on fear memories. Aberrant fear and/or anxiety may be at the heart of many psychiatric disorders. In this article, I review the results of my research group; these results show that particular neurons in the lateral amygdala, a brain region important for fear, are specifically involved in particular fear memories. We started by showing that the transcription factor CREB (cAMP/Ca(2+) response element binding protein) plays a key role in the formation of fear memories. Next, we used viral vectors to overexpress CREB in a subset of lateral amygdala neurons. This not only facilitated fear memory formation but also "drove" the memory into the neurons with relatively increased CREB function. Finally, we showed that selective ablation of the neurons overexpressing CREB in the lateral amygdala selectively erased the fear memory. These findings are the first to show disruption of a specific memory by disrupting select neurons within a distributed network.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0000.002
Scholarly communication0.0010.004
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.002

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 head0.173
GPT teacher head0.382
Teacher spread0.210 · 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

Citations114
Published2010
Admission routes3
Has abstractyes

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