RT Journal Article
SR Electronic
T1 Toward A Reproducible, Scalable Framework for Processing Large Neuroimaging Datasets
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 615161
DO 10.1101/615161
A1 Erik C. Johnson
A1 Miller Wilt
A1 Luis M. Rodriguez
A1 Raphael Norman-Tenazas
A1 Corban Rivera
A1 Nathan Drenkow
A1 Dean Kleissas
A1 Theodore J. LaGrow
A1 Hannah Cowley
A1 Joseph Downs
A1 Jordan Matelsky
A1 Marisa Hughes
A1 Elizabeth Reilly
A1 Brock Wester
A1 Eva Dyer
A1 Konrad Kording
A1 William Gray-Roncal
YR 2019
UL http://biorxiv.org/content/early/2019/04/22/615161.abstract
AB Emerging neuroimaging datasets (collected through modalities such as Electron Microscopy, Calcium Imaging, or X-ray Microtomography) describe the location and properties of neurons and their connections at unprecedented scale, promising new ways of understanding the brain. These modern imaging techniques used to interrogate the brain can quickly accumulate gigabytes to petabytes of structural brain imaging data. Unfortunately, many neuroscience laboratories lack the computational expertise or resources to work with datasets of this size: computer vision tools are often not portable or scalable, and there is considerable difficulty in reproducing results or extending methods. We developed an ecosystem of neuroimaging data analysis pipelines that utilize open source algorithms to create standardized modules and end-to-end optimized approaches. As exemplars we apply our tools to estimate synapse-level connectomes from electron microscopy data and cell distributions from X-ray microtomography data. To facilitate scientific discovery, we propose a generalized processing framework, that connects and extends existing open-source projects to provide large-scale data storage, reproducible algorithms, and workflow execution engines. Our accessible methods and pipelines demonstrate that approaches across multiple neuroimaging experiments can be standardized and applied to diverse datasets. The techniques developed are demonstrated on neuroimaging datasets, but may be applied to similar problems in other domains.