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Mapping brain activity at scale with cluster computing

Nature Methods volume 11, pages 941–950 (2014)Cite this article

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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.

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Figure 1: A platform for large-scale neural analytics.
Figure 2: Performance benchmarks for Thunder.
Figure 3: Maps of in vivo two-photon calcium imaging data from mouse cortex revealing response modulation by locomotion.
Figure 4: Direction tuning maps of whole-brain neural activity measured in a larval zebrafish with light-sheet microscopy while zebrafish were presented visual stimuli.
Figure 5: Maps of sensorimotor responses in larval zebrafish.
Figure 6: Dynamical portraits of larval zebrafish whole-brain activity via dimensionality reduction.
Figure 7: Analysis of whole-brain spontaneous activity by ICA reveals functionally defined networks.

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Acknowledgements

We thank K. Carlisle and R. Lines for help installing and running Spark on the Janelia Farm Research Campus Compute Cluster, D. Ganguli and M. Zaharia for advice on using Spark, G. Merlino for advice on benchmarking, C. Ziemba, C. Stock and T.J. Florence for help testing EC2 installation procedures, P. Keller for his help and advice in building the light-sheet microscope, B. Coop and T. Tabachnik for their help with hardware design, M. Coleman for writing the light-sheet microscope control software Zebrascope and continuing support, S. Narayan for help with zebrafish experiments, K. Svoboda and S. Peron for help setting up the mouse two-photon imaging, B. MacLennan for help with mouse surgeries, D.G.C. Hildebrand and M. Koyama for discussions, the Janelia Farm Research Campus vivarium staff for fish and mouse husbandry, and V. Jayaraman, G. Murphy, K. Svoboda and P. Keller for comments on an earlier draft of the manuscript. This work was supported by the Howard Hughes Medical Institute.

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Authors and Affiliations

  1. Howard Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia, USA

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

  2. Computer Science Division, University of California Berkeley, Berkeley, California, USA

    Joshua Rosen

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Contributions

J.F. and M.B.A. conceived of the project. J.F. developed the analysis library and analyzed the data. N.V., M.B.A. and T.K. developed the zebrafish light-sheet imaging experimental preparation. N.V., M.B.A., Y.M., T.K. and J.F. collected the zebrafish data. N.J.S. developed the mouse experimental preparation, collected the data reported in Figure 3 and helped develop the analysis of those data. D.V.B. contributed to zebrafish experiments. J.R. contributed code to the analysis library. C.-T.Y. and L.L.L. developed the Tg(elavl3:H2B-GCaMP6s)jf5 transgenic fish line. J.F. and M.B.A. wrote the paper, with input from all authors.

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Correspondence to Jeremy Freeman or Misha B Ahrens.

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Supplementary information

Supplementary Text and Figures

Supplementary Protocol (PDF 118 kb)

Direction tuning, planes.

Maps of direction tuning from individual planes (MOV 1188 kb)

Direction tuning, volume.

Volumetric rendering of direction tuning maps (MOV 2206 kb)

Principal component analysis, planes.

Maps of sensorimotor responses from individual planes (MOV 351 kb)

Swimming-related responses, volume.

Volumetric rendering of sensorimotor response maps (MOV 1148 kb)

Swimming-related responses, planes.

Maps of swimming-related responses from individual planes (MOV 3449 kb)

Swimming-related responses, volume.

Volumetric rendering of swimming-related response maps (MOV 2840 kb)

Swim-related trajectories.

State-space trajectories showing how neural activity on individual trials evolves through a low-dimensional space. Each trace is a trial. Size of dot indicates strength of swimming. (MOV 757 kb)

Direction-related trajectories.

State-space trajectories showing how neural activity on individual trials evolves through a low-dimensional space recovered to capture variability related to different stimulus directions. Each trace is a trial. Color indicates stimulus direction. (MOV 3277 kb)

Independent component analysis, planes.

Maps of spontaneous functional networks from individual planes. (MOV 486 kb)

Independent component analysis, volume.

Volumetric rendering of spontaneous functional network maps. (MOV 1148 kb)

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Freeman, J., Vladimirov, N., Kawashima, T. et al. Mapping brain activity at scale with cluster computing. Nat Methods 11, 941–950 (2014). https://doi.org/10.1038/nmeth.3041

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