Evidence map›Paper›PMID 20955931›Full record

ArticleNeuron2010

Human brain evolution: harnessing the genomics (r)evolution to link genes, cognition, and behavior.

Genevieve Konopka, Daniel H Geschwind

Abstract read
In one paragraph

Article in Neuron, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. The use of nonhuman primates in studies of noise injury and treatment.The Journal of the Acoustical Society of America · 2019
    Review
  9. Article
  10. Review
  11. Article
  12. Review
  13. Molecular networks and the evolution of human cognitive specializations.Current opinion in genetics & development · 2014
    Review
  14. Article
  15. Article
  16. Article
  17. Genomics. Behavior and the dynamic genome.Science (New York, N.Y.) · 2011
    Article
  18. 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

2 authors.

Genevieve KonopkaDepartment of Neurology, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Daniel H Geschwind

Funding

Identification and Characterization of Asymmetrically-Expressed GenesR37MH060233 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI GESCHWIND, DANIEL H · 2006 to 2014
$5.3M
FOXP2-Regulated Signaling Pathways Critical for Higher Cognitive FunctionsK99MH090238 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI KONOPKA, GENEVIEVE · 2010 to 2010
$90k
NIMH NIH HHS K99 MH090238NIMH NIH HHS K99MH090238NIMH NIH HHS R37 MH060233NIMH NIH HHS R37MH60233-06A1
6 · The paper itself

Abstract

The evolution of the human brain has resulted in numerous specialized features including higher cognitive processes such as language. Knowledge of whole-genome sequence and structural variation via high-throughput sequencing technology provides an unprecedented opportunity to view human evolution at high resolution. However, phenotype discovery is a critical component of these endeavors and the use of nontraditional model organisms will also be critical for piecing together a complete picture. Ultimately, the union of developmental studies of the brain with studies of unique phenotypes in a myriad of species will result in a more thorough model of the groundwork the human brain was built upon. Furthermore, these integrative approaches should provide important insights into human diseases.

Indexed as

Biological EvolutionGenomicsAnimalsBehaviorBrainCognitionEvolution, MolecularGene ExpressionHumansPhenotype

Identifiers

PMID20955931
PMCPMC2993319

What OpenQuestion holds

Textmetadata
LicenceTDM
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.