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Roles of NMDA receptor NR2A and NR2B subtypes for long‐term depression in the anterior cingulate cortex

2005· article· en· W2120264644 on OpenAlexaff
Hiroki Toyoda, Ming Zhao, Min Zhuo

Bibliographic record

VenueEuropean Journal of Neuroscience · 2005
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersNational Institute of Neurological Disorders and Stroke
KeywordsLong-term potentiationNeuroscienceLong-term depressionNMDA receptorPostsynaptic potentialAnterior cingulate cortexHippocampusLTP inductionGlutamate receptorSynaptic plasticityPsychologyChemistryExcitatory postsynaptic potentialBiologyReceptorAMPA receptorInhibitory postsynaptic potentialCognitionBiochemistry

Abstract

fetched live from OpenAlex

The anterior cingulate cortex (ACC) is thought to be important for the establishment, consolidation and retrieval of permanent memory. In many brain regions, including the hippocampus, it is suggested that long-term potentiation (LTP) and long-term depression (LTD), the cellular mechanisms for learning and memory, require the activation of glutamate N-methyl-D-aspartate receptors (NMDARs). In the hippocampus, the NR2A subunit is believed to be involved in the induction of LTP, whereas the NR2B subunit contributes to the formation of LTD. However, LTD has been less well studied in the ACC as compared with the hippocampus and little is known about the role of NMDA subtype receptors in cingulate LTD. Here we show that LTD can be induced by the combination of presynaptic stimulation with postsynaptic depolarization ('pairing training') in adult mouse ACC neurons. This form of LTD is an NMDAR- and voltage-dependent mechanism and a postsynaptic Ca2+ increase is required for the induction of LTD. Furthermore, our studies provide direct physiological evidence that both NR2A and NR2B subunits are involved in the induction of LTD in the ACC.

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)0.001
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 score0.409
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.049
GPT teacher head0.342
Teacher spread0.293 · 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

Citations83
Published2005
Admission routes1
Has abstractyes

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