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Record W1594463122 · doi:10.1002/cne.23771

Amplified somatosensory and visual cortical projections to a core auditory area, the anterior auditory field, following early‐ and late‐onset deafness

2015· article· en· W1594463122 on OpenAlexafffund
Carmen Wong, Nicole Chabot, Melanie A. Kok, Stephen G. Lomber

Bibliographic record

VenueThe Journal of Comparative Neurology · 2015
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsBiologySomatosensory systemNeuroscienceAuditory cortexAudiologyAnatomy

Abstract

fetched live from OpenAlex

Cross-modal reorganization following the loss of input from a sensory modality can recruit sensory-deprived cortical areas to process information from the remaining senses. Specifically, in early-deaf cats, the anterior auditory field (AAF) is unresponsive to auditory stimuli but can be activated by somatosensory and visual stimuli. Similarly, AAF neurons respond to tactile input in adult-deafened animals. To examine anatomical changes that may underlie this functional adaptation following early or late deafness, afferent projections to AAF were examined in hearing cats, and cats with early- or adult-onset deafness. Unilateral deposits of biotinylated dextran amine were made in AAF to retrogradely label cortical and thalamic afferents to AAF. In early-deaf cats, ipsilateral neuronal labeling in visual and somatosensory cortices increased by 329% and 101%, respectively. The largest increases arose from the anterior ectosylvian visual area and the anterolateral lateral suprasylvian visual area, as well as somatosensory areas S2 and S4. Consequently, labeling in auditory areas was reduced by 36%. The age of deafness onset appeared to influence afferent connectivity, with less marked differences observed in late-deaf cats. Profound changes to visual and somatosensory afferent connectivity following deafness may reflect corticocortical rewiring affording acoustically deprived AAF with cross-modal functionality.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.108
GPT teacher head0.364
Teacher spread0.257 · 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

Citations63
Published2015
Admission routes2
Has abstractyes

Explore more

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