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Environmental enrichment enhances auditory takeover of the occipital cortex in anophthalmic mice

2004· article· en· W2015255442 on OpenAlexafffund
Mathieu Piché, Stéphane Robert, D. Miceli, Gilles Bronchti

Bibliographic record

VenueEuropean Journal of Neuroscience · 2004
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersFondation de l’UQTRFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsNeuroscienceSensory systemThalamusInferior colliculusAuditory cortexStimulus modalityStimulationPremovement neuronal activitySuperior colliculusSensory stimulation therapyCortex (anatomy)Sensory deprivationNeuroplasticityEnvironmental enrichmentPsychologyVisual cortexNucleus

Abstract

fetched live from OpenAlex

Enrichment of the environment is an effective means of enhancing neuronal development and plasticity but its effect on the cross-modal compensation resulting from sensory deprivation has never been investigated. The present study used c-Fos immunohistochemistry and dextran-biotin neuronal tracing to examine the reorganization of sensory modalities in the brain of anophthalmic mutant mice (ZRDCT/An) raised in either enriched or standard environments. Auditory stimulation was found to elicit strong neuronal activation in thalamic and cortical structures that are normally visual. An important finding was that the latter auditory-evoked cortical activity was considerably enhanced in blind mice raised in the enriched environment. The axonal tracing study demonstrated auditory inputs from the inferior colliculus to the visual thalamus. This animal model will be useful for understanding neuronal mechanisms underlying some cross-modal sensory phenomena observed in blind or deaf humans.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.287
Teacher spread0.259 · 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 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

Citations52
Published2004
Admission routes2
Has abstractyes

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