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Record W4394685947 · doi:10.1101/2024.04.09.588714

Unique cortical and subcortical activation patterns for different conspecific calls in marmosets

2024· preprint· en· W4394685947 on OpenAlexafffund
Azadeh Jafari, Audrey Dureux, Alessandro Zanini, Ravi S. Menon, Kyle M. Gilbert, Stefan Everling

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchCanada First Research Excellence FundGovernment of Canada
KeywordsNeuroscienceMarmosetPsychologyBiologyCommunicationPaleontology

Abstract

fetched live from OpenAlex

Abstract The common marmoset ( Callithrix jacchus ) is known for its highly vocal nature, displaying a diverse range of different calls. Functional imaging in marmosets has shown that the processing of conspecific calls activates a brain network that includes fronto-temporal cortical and subcortical areas. It is currently unknown whether different call types activate the same or different networks. Here we show unique activation patterns for different calls. Nine adult marmosets were exposed to four common vocalizations (phee, chatter, trill, and twitter), and their brain responses were recorded using event-related fMRI at 9.4T. We found robust activations in the auditory cortices, encompassing core, belt, and parabelt regions, and in subcortical areas like the inferior colliculus, medial geniculate nucleus, and amygdala in response to these conspecific calls. Different neural activation patterns were observed among the vocalizations, suggesting vocalization-specific neural processing. Phee and twitter calls, often used over long distances, activated similar neural circuits, whereas trill and chatter, associated with closer social interactions, demonstrated a closer resemblance in their activation patterns. Our findings also indicate the involvement of the cerebellum and medial prefrontal cortex (mPFC) in distinguishing particular vocalizations from others. Significance Statement This study investigates the neural processing of vocal communications in the common marmoset ( Callithrix jacchus ), a species with a diverse vocal repertoire. Utilizing event-related fMRI at 9.4T, we demonstrate that different marmoset calls (phee, chatter, trill, and twitter) elicit distinct activation patterns in the brain, challenging the notion of a uniform neural network for all vocalizations. Each call type distinctly engages various regions within the auditory cortices and subcortical areas, reflecting the complexity and context-specific nature of primate communication. These findings offer insights into the evolutionary mechanisms of primate vocal perception and provide a foundation for understanding the origins of human speech and language processing.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.027
GPT teacher head0.245
Teacher spread0.217 · 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 designObservational
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
Published2024
Admission routes2
Has abstractyes

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