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Record W4412934655 · doi:10.1016/j.isci.2025.113028

Neural encoding of auditory rhythm beyond cortical auditory areas before the age of term

2025· article· en· W4412934655 on OpenAlexaff
Ali Mashhadi, Fabrice Wallois, Mohammadreza Edalati, Florence Levé, Alexandros Stamatiadis, Christelle Chazal, Laurel J. Trainor, Sahar Moghimi

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcMaster UniversityBaycrest Hospital
FundersHorizon 2020 Framework ProgrammeAgence Nationale de la RechercheHorizon 2020Fondation Pour l'Audition
KeywordsAuditory cortexRhythmNeuroscienceTerm (time)Auditory pathwaysEncoding (memory)Auditory perceptionCognitive sciencePsychologyMedicinePerceptionPhysics

Abstract

fetched live from OpenAlex

Rhythm experience begins in fetal life, shaping neural capacities critical for language, communication, and motor skills. While rhythm processing in adults involves distributed cortical networks, including premotor and supplementary motor regions, the mechanisms in the fetal brain remain unclear. We provide evidence that premature newborns encode rhythmic beats through cortical networks extending beyond the auditory cortex into premotor and sensorimotor regions. Using high-density functional near-infrared spectroscopy, we show that auditory beats trigger distinct cortical activation patterns, indicating early involvement of an auditory-motor network, despite the absence of coordinated motor activity. Our results highlight a fundamental role for these regions in rhythm perception, forming the basis for predictive timing mechanisms. This early engagement of sensorimotor regions reveals a neural framework supporting beat perception from the fetal stage onward. These findings advance understanding of the neural architecture for rhythm processing, showing that the premature brain is already wired for complex auditory-motor interactions.

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 categoriesScience and technology studies
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 score0.632
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
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 head0.026
GPT teacher head0.291
Teacher spread0.265 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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