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Record W2144446385 · doi:10.1002/syn.21685

Effects of repeated electroconvulsive shocks on catecholamine systems: Electrophysiological studies in the rat brain

2013· article· en· W2144446385 on OpenAlexafffund
Peter Tsen, Mostafa El Mansari, Pierre Blier

Bibliographic record

VenueSynapse · 2013
Typearticle
Languageen
FieldMedicine
TopicElectroconvulsive Therapy Studies
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsElectrophysiologyNeuroscienceCatecholamineElectroconvulsive ShockPsychologyAnesthesiaMedicine

Abstract

fetched live from OpenAlex

Electroconvulsive therapy (ECT) treats depression by repeated administration of seizure-inducing electrical stimuli. To assess the effects of repeated electroconvulsive shocks (ECSs), an animal model of ECT on monoamine transmission, Sprague-Dawley rats were administered 6 ECS over 2 weeks and in vivo single-unit extracellular electrophysiological recordings were obtained 48 h after the final ECS. Overall firing activity of dopamine (DA) neurons in the ventral tegmental area was unchanged following repeated ECS. In the locus coeruleus (LC), the burst activity of norepinephrine (NE) neurons was increased while population activity was decreased after ECS. In the substantia nigra pars compacta (SNc), there were more spontaneously active neurons, suggesting greater DA tone in the nigrostriatal motor pathway, which may contribute to an alleviation of motor retardation. In the facial motor nucleus (FMN), facilitation of electrophysiological activity by serotonin (5-HT), and NE was determined to be through the 5-HT2C receptor and α1 -adrenoceptor, respectively. Locally administered NE, but not 5-HT, facilitated glutamate-induced firing following repeated ECS, which may contribute to improved motor function. These results showed that repeated ECS enhance DA activity in the SNc and NE transmission in the FMN, which could be a part of the mechanism behind the alleviation of depressive symptoms, including motor retardation, by ECT.

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)0.000
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 score0.099
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.303
Teacher spread0.285 · 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

Citations14
Published2013
Admission routes2
Has abstractyes

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