MétaCan
Menu
Back to cohort
Record W2965306435 · doi:10.3389/fnsys.2019.00039

Corticofugal Augmentation of the Auditory Brainstem Response With Respect to Cortical Preference

2019· article· en· W2965306435 on OpenAlexafffund
Xiuping Liu, Oliver Zhang, Amber Chen, Kaili Hu, Günter Ehret, Jun Yan

Bibliographic record

VenueFrontiers in Systems Neuroscience · 2019
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsInferior colliculusLateral lemniscusAuditory cortexNeuroscienceBrainstemCochleaSuperior olivary complexInferior ColliculiMidbrainPsychologyCochlear nucleusBiologyCentral nervous systemNucleus

Abstract

fetched live from OpenAlex

Physiological studies documented highly specific corticofugal modulations making subcortical centers focus processing on sounds that the auditory cortex has experienced to be important. Here, we show the effects of focal conditioning of the primary auditory cortex (FCAI) on auditory brainstem response (ABR) amplitudes and latencies in house mice. FCAI significantly increased ABR peak amplitudes (peaks I – V), decreased thresholds, and shortened peak latencies in responses to the frequency tuned by conditioned cortical neurons. The amounts of peak amplitude increases and latency decreases were specific for each processing level up to the auditory midbrain. The data provide new insights in possible corticofugal modulation of inner hair cell synapses and new corticofugal effects as neuronal enhancement of processing in the superior olivary complex and lateral lemniscus. Thus, our comprehensive ABR approach confirms the role of the auditory cortex as instructor of lower auditory levels and extends this role specifically to the cochlea, superior olivary complex, and lateral lemniscus. The whole pathway from the cochlea to the inferior colliculus appears, in a common mode, instructed in a very similar way.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.736
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.037
GPT teacher head0.261
Teacher spread0.224 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in Systems NeuroscienceSame topicHearing, Cochlea, Tinnitus, GeneticsFrench-language works237,207