Evidence map›Paper›PMID 36069117›Full record

ArticleAnnals of the New York Academy of Sciences2022

The pale spear-nosed bat: A neuromolecular and transgenic model for vocal learning.

Sonja C Vernes, Paolo Devanna, Stephen Gareth Hörpel, Ine Alvarez van Tussenbroek, Uwe Firzlaff, Peter Hagoort, Michael Hiller, Nienke Hoeksema, Graham M Hughes, Ksenia Lavrichenko and 3 more

Abstract read
In one paragraph

Article in Annals of the New York Academy of Sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Neuronal activity underlying vocal production in bats.Annals of the New York Academy of Sciences · 2025
    Review
  3. Bats as instructive animal models for studying longevity and aging.Annals of the New York Academy of Sciences · 2024
    Review
  4. Auditory cortical regions show resting-state functional connectivity with the default mode-like network in echolocating bats.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  5. Review
  6. What do mammals have to say about the neurobiology of acoustic communication?Molecular psychology : brain, behavior, and society · 2023
    Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Sonja C VernesSchool of Biology, University of St Andrews, St Andrews, UK.
Paolo DevannaSchool of Biology, University of St Andrews, St Andrews, UK.
Stephen Gareth HörpelSchool of Biology, University of St Andrews, St Andrews, UK.
Ine Alvarez van TussenbroekSchool of Biology, University of St Andrews, St Andrews, UK.
Uwe FirzlaffTUM School of Life Sciences, Technical University of Munich, Freising, Germany.
Peter HagoortNeurobiology of Language Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
Michael HillerLOEWE Centre for Translational Biodiversity Genomics, Faculty of Biosciences, Senckenberg Research Institute, Goethe-University, Frankfurt, Germany.
Nienke HoeksemaNeurogenetics of Vocal Communication Group, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
Graham M HughesSchool of Biology and Environmental Science, University College Dublin, Belfield, Ireland.
Ksenia LavrichenkoNeurogenetics of Vocal Communication Group, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
Janine MengedeNeurogenetics of Vocal Communication Group, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
Ariadna E MoralesLOEWE Centre for Translational Biodiversity Genomics, Faculty of Biosciences, Senckenberg Research Institute, Goethe-University, Frankfurt, Germany.
Maximilian WiesmannDepartment of Medical Imaging, Anatomy, Radboud University Medical Center, Donders Institute for Brain, Cognition & Behavior, Center for Medical Neuroscience, Preclinical Imaging Center PRIME, Radboud Alzheimer Center, Nijmegen, The Netherlands.

Funding

Medical Research Council MR/T021985/1
6 · The paper itself

Abstract

Vocal learning, the ability to produce modified vocalizations via learning from acoustic signals, is a key trait in the evolution of speech. While extensively studied in songbirds, mammalian models for vocal learning are rare. Bats present a promising study system given their gregarious natures, small size, and the ability of some species to be maintained in captive colonies. We utilize the pale spear-nosed bat (Phyllostomus discolor) and report advances in establishing this species as a tractable model for understanding vocal learning. We have taken an interdisciplinary approach, aiming to provide an integrated understanding across genomics (Part I), neurobiology (Part II), and transgenics (Part III). In Part I, we generated new, high-quality genome annotations of coding genes and noncoding microRNAs to facilitate functional and evolutionary studies. In Part II, we traced connections between auditory-related brain regions and reported neuroimaging to explore the structure of the brain and gene expression patterns to highlight brain regions. In Part III, we created the first successful transgenic bats by manipulating the expression of FoxP2, a speech-related gene. These interdisciplinary approaches are facilitating a mechanistic and evolutionary understanding of mammalian vocal learning and can also contribute to other areas of investigation that utilize P. discolor or bats as study species.

Indexed as

ChiropteraAcousticsAnimalsBrainSpeechVocalization, AnimalbatsgenomelanguageMRIPhyllostomus discolorspeechtracingvocal production learning

Identifiers

PMID36069117
PMCPMC9826251

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.