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The constitutive differential transcriptome of a brain circuit for vocal learning

Overview of attention for article published in BMC Genomics, April 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

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17 tweeters

Citations

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16 Dimensions

Readers on

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40 Mendeley
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Title
The constitutive differential transcriptome of a brain circuit for vocal learning
Published in
BMC Genomics, April 2018
DOI 10.1186/s12864-018-4578-0
Pubmed ID
Authors

Peter V. Lovell, Nicole A. Huizinga, Samantha R. Friedrich, Morgan Wirthlin, Claudio V. Mello

Abstract

The ability to imitate the vocalizations of other organisms, a trait known as vocal learning, is shared by only a few organisms, including humans, where it subserves the acquisition of speech and language, and 3 groups of birds. In songbirds, vocal learning requires the coordinated activity of a set of specialized brain nuclei referred to as the song control system. Recent efforts have revealed some of the genes that are expressed in these vocal nuclei, however a thorough characterization of the transcriptional specializations of this system is still missing. We conducted a rigorous and comprehensive analysis of microarrays, and conducted a separate analysis of 380 genes by in situ hybridizations in order to identify molecular specializations of the major nuclei of the song system of zebra finches (Taeniopygia guttata), a songbird species. Our efforts identified more than 3300 genes that are differentially regulated in one or more vocal nuclei of adult male birds compared to the adjacent brain regions. Bioinformatics analyses provided insights into the possible involvement of these genes in molecular pathways such as cellular morphogenesis, intrinsic cellular excitability, neurotransmission and neuromodulation, axonal guidance and cela-to-cell interactions, and cell survival, which are known to strongly influence the functional properties of the song system. Moreover, an in-depth analysis of specific gene families with known involvement in regulating the development and physiological properties of neuronal circuits provides further insights into possible modulators of the song system. Our study represents one of the most comprehensive molecular characterizations of a brain circuit that evolved to facilitate a learned behavior in a vertebrate. The data provide novel insights into possible molecular determinants of the functional properties of the song control circuitry. It also provides lists of compelling targets for pharmacological and genetic manipulations to elucidate the molecular regulation of song behavior and vocal learning.

Twitter Demographics

The data shown below were collected from the profiles of 17 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 30%
Student > Master 9 23%
Researcher 6 15%
Student > Bachelor 3 8%
Professor > Associate Professor 2 5%
Other 5 13%
Unknown 3 8%
Readers by discipline Count As %
Neuroscience 15 38%
Agricultural and Biological Sciences 8 20%
Biochemistry, Genetics and Molecular Biology 7 18%
Nursing and Health Professions 2 5%
Linguistics 1 3%
Other 4 10%
Unknown 3 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 January 2019.
All research outputs
#2,344,980
of 15,604,031 outputs
Outputs from BMC Genomics
#1,080
of 8,765 outputs
Outputs of similar age
#60,794
of 280,136 outputs
Outputs of similar age from BMC Genomics
#4
of 23 outputs
Altmetric has tracked 15,604,031 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,765 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 280,136 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.