Evidence map›Paper›PMID 25068736›Full record

ArticleNature methods2014

Mapping brain activity at scale with cluster computing.

Jeremy Freeman, Nikita Vladimirov, Takashi Kawashima, Yu Mu, Nicholas J Sofroniew, Davis V Bennett, Joshua Rosen, Chao-Tsung Yang, Loren L Looger, Misha B Ahrens

Abstract read
PubMed Publisher
In one paragraph

Article in Nature methods, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 133 papers.

0numbers the graph read from it
0cells of the map it votes in
133citing 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

133 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

73 more citing papers are in PubMed but not listed here.

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

10 authors.

Jeremy FreemanHoward Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia, USA.
Nikita VladimirovHoward Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia, USA.
Takashi KawashimaHoward Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia, USA.
Yu MuHoward Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia, USA.
Nicholas J SofroniewHoward Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia, USA.
Davis V BennettHoward Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia, USA.
Joshua RosenUniversity of California Berkeley, Computer Science Division, Berkeley, California, USA.
Chao-Tsung YangHoward Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia, USA.
Loren L LoogerHoward Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia, USA.
Misha B AhrensHoward Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia, USA.

Funding

Howard Hughes Medical Institute
6 · The paper itself

Abstract

Understanding brain function requires monitoring and interpreting the activity of large networks of neurons during behavior. Advances in recording technology are greatly increasing the size and complexity of neural data. Analyzing such data will pose a fundamental bottleneck for neuroscience. We present a library of analytical tools called Thunder built on the open-source Apache Spark platform for large-scale distributed computing. The library implements a variety of univariate and multivariate analyses with a modular, extendable structure well-suited to interactive exploration and analysis development. We demonstrate how these analyses find structure in large-scale neural data, including whole-brain light-sheet imaging data from fictively behaving larval zebrafish, and two-photon imaging data from behaving mouse. The analyses relate neuronal responses to sensory input and behavior, run in minutes or less and can be used on a private cluster or in the cloud. Our open-source framework thus holds promise for turning brain activity mapping efforts into biological insights.

Indexed as

Models, NeurologicalSoftwareAction PotentialsAnimalsBrainBrain MappingComputer SimulationComputing MethodologiesDatabase Management SystemsDatabases, FactualData Interpretation, StatisticalHumansInformation Storage and RetrievalNerve NetNeuronsProgramming Languages

Identifiers

PMID25068736

What OpenQuestion holds

Textmetadata
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.