Abstract
Technologies for scalable analysis of very large datasets have emerged in the domain of internet computing, but are still rarely used in neuroimaging despite the existence of data and research questions in need of efficient computation tools especially in fMRI. In this work, we present software tools for the application of Apache Spark and Graphics Processing Units (GPUs) to neuroimaging datasets, in particular providing distributed file input for 4D NIfTI fMRI datasets in Scala for use in an Apache Spark environment. Examples for using this Big Data platform in graph analysis of fMRI datasets are shown to illustrate how processing pipelines employing it can be developed. With more tools for the convenient integration of neuroimaging file formats and typical processing steps, big data technologies could find wider endorsement in the community, leading to a range of potentially useful applications especially in view of the current collaborative creation of a wealth of large data repositories including thousands of individual fMRI datasets.
Highlights
The pressure to continuously analyze fast growing datasets has led internet companies to engage in the development of specialized tools for this new field of Big Data analysis, at first strongly focused on the specific data structures used by their applications, but increasingly taking more generalized forms
Big Data technologies are not yet often employed in the analysis of neuroimaging data, though the emergence of large collaborative repositories especially in the field of fMRI provides an ideal environment for their application
We present a distributed NIfTI file reader written in Scala for Apache Spark and show applications that become possible with this framework, including graph analyses using GraphX
Summary
The pressure to continuously analyze fast growing datasets has led internet companies to engage in the development of specialized tools for this new field of Big Data analysis, at first strongly focused on the specific data structures used by their applications, but increasingly taking more generalized forms. Many data analysis applications, like iterative machine learning algorithms, need to access data multiple times, which would be very inefficient if implemented in pure MapReduce terms Addressing this issue and providing a more general framework for distributed computations on large datasets was the main motivation behind the introduction of the Spark framework (Zaharia et al, 2012; The Apache Software Foundation, 2015). The Consortium for Reliability and Reproducibility in particular has gathered a large dataset of over 5000 resting-state fMRI measurements to this end (Zuo et al, 2014), and proposes a number of computational tools for use on this database, yet these do not currently include big data tools
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