Science in the cloud (SIC): A use case in MRI connectomics

Kiar, Gregory; Gorgolewski, Krzysztof J.; Kleissas, Dean; Roncal, William Gray; Litt, Brian; Wandell, Brian A.; Poldrack, Russell A.; Wiener, Martin; Vogelstein, R. Jacob; Burns, Randal; Vogelstein, Joshua T.

doi:10.1093/gigascience/gix013

Cited by 28 publications

(16 citation statements)

References 37 publications

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“…This proposed method is significant for dealing with large scale fMRI data, especially when functional networks analysis becomes an important step for discovering the underlying organization structures and meaningful dynamic patterns from the vast amount of fMRI snals (Li et al, 2016 ). We could apply this method to a distributed and parallel computing environme nt (Kiar et al, 2017 ), as the DICCCOL-based sampling of each brain can be in parallel. The dictionary learning algorithm is also parallelized in its code, and the pre-processing of DTI including DICCCOL and fMRI can be parallelized, so the processing of DTI and DICCCOL is not a time-cost problem in a distributed and parallel computing environment.…”

Section: Discussionmentioning

confidence: 99%

A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data

Jiang

et al. 2018

Front. Neuroinform.

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The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size. Therefore, how to reduce the data size but without losing important information becomes a more and more pressing issue. To address this problem, we propose a signal sampling approach for significant fMRI data reduction before performing structurally-guided dictionary learning and sparse coding of whole brain's fMRI data. We compared the proposed structurally guided sampling method with no sampling, random sampling and uniform sampling schemes, and experiments on the Human Connectome Project (HCP) task fMRI data demonstrated that the proposed method can achieve more than 15 times speed-up without sacrificing the accuracy in identifying task-evoked functional brain networks.

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Section: Discussionmentioning

confidence: 99%

A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data

Jiang

et al. 2018

Front. Neuroinform.

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“…For small-scale problems, individual software tools and pipelines which are fully portable and reproducible have been produced (e.g., [41]), but this challenge has not yet been solved at the scale of modern EM and XRM volumes.…”

Section: Existing Software Solutionsmentioning

confidence: 99%

Toward A Reproducible, Scalable Framework for Processing Large Neuroimaging Datasets

Johnson

Wilt

Rodríguez

et al. 2019

Preprint

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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.

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“…The brainlife.io platform allows running said Apps to process data available within the platform itself [66][67][68] . The concept of open service is akin to that of the Brain Imaging Data Structure Applications 69 as also introduced previously by others 70 . The brainlife.io Apps used below follow a generalized and light-weight specification as to allow usage with diverse combinations of software from multiple libraries, such as FSL 71 83 ), as well as registered project on both the NeuroImaging Tools and Resources Collaboratory (http://www.nitrc.org/projects/ brainlife_io) and scicrunch.org (RRID: SCR_016513).…”

Section: Background and Summarymentioning

confidence: 99%

The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services

Avesani¹,

McPherson²,

Hayashi³

et al. 2018

Preprint

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We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities.

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Science in the cloud (SIC): A use case in MRI connectomics

Cited by 28 publications

References 37 publications

A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data

A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data

Toward A Reproducible, Scalable Framework for Processing Large Neuroimaging Datasets

The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services

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