ZFIN ID: ZDB-PUB-140730-8
Mapping brain activity at scale with cluster computing
Freeman, J., Vladimirov, N., Kawashima, T., Mu, Y., Sofroniew, N.J., Bennett, D.V., Rosen, J., Yang, C.T., Looger, L.L., Ahrens, M.B.
Date: 2014
Source: Nature Methods   11(9): 941-50 (Journal)
Registered Authors: Ahrens, Misha, Bennett, Davis, Kawashima, Takashi, Mu, Yu, Vladimirov, Nikita, Yang, Chao-Tsung
Keywords: none
MeSH Terms:
  • Action Potentials/physiology*
  • Animals
  • Brain/physiology
  • Brain Mapping/methods*
  • Computer Simulation
  • Computing Methodologies
  • Data Interpretation, Statistical
  • Database Management Systems
  • Databases, Factual
  • Humans
  • Information Storage and Retrieval/methods*
  • Models, Neurological*
  • Nerve Net/physiology*
  • Neurons/physiology*
  • Programming Languages
  • Software*
PubMed: 25068736 Full text @ Nat. Methods
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.