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Record W2261240187 · doi:10.1073/pnas.1522150112

Rapid increases in immature synapses parallel estrogen-induced hippocampal learning enhancements

2015· article· en· W2261240187 on OpenAlexafffund
Anna Phan, Sarah Suschkov, Luke Molinaro, Kathryn E. Reynolds, Jennifer Lymer, Craig D. C. Bailey, Lee‐Ming Kow, Neil J. MacLusky, Donald W. Pfaff, Elena Choleris

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsHippocampal formationDendritic spineNeuroscienceAMPA receptorExcitatory postsynaptic potentialHippocampusEstrogenInternalizationAgonistSilent synapseSynapseChemistryBiologyReceptorGlutamate receptorEndocrinology

Abstract

fetched live from OpenAlex

Dramatic increases in hippocampal spine synapse density are known to occur within minutes of estrogen exposure. Until now, it has been assumed that enhanced spinogenesis increased excitatory input received by the CA1 pyramidal neurons, but how this facilitated learning and memory was unclear. Delivery of 17β-estradiol or an estrogen receptor (ER)-α (but not ER-β) agonist into the dorsal hippocampus rapidly improved general discrimination learning in female mice. The same treatments increased CA1 dendritic spines in hippocampal sections over a time course consistent with the learning acquisition phase. Surprisingly, estrogen-activated spinogenesis was associated with a decrease in CA1 hippocampal excitatory input, rapidly and transiently reducing CA1 AMPA activity via a mechanism likely reflecting AMPA receptor internalization and creation of silent or immature synapses. We propose that estrogens promote hippocampally mediated learning via a mechanism resembling some of the broad features of normal development, an initial overproduction of functionally immature connections being subsequently "pruned" by experience.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.139
GPT teacher head0.381
Teacher spread0.243 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations111
Published2015
Admission routes2
Has abstractyes

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