ArticleFrontiers in neuroscience2015
Big Data Approaches for the Analysis of Large-Scale fMRI Data Using Apache Spark and GPU Processing: A Demonstration on Resting-State fMRI Data from the Human Connectome Project.
Article in Frontiers in neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Hierarchical Storage Management in User Space for Neuroimaging Applications.Neuroinformatics · 2025Article
- Analysis of Inducing Factors of Chronic Pulmonary Heart Disease Caused by Chronic Obstructive Pulmonary Disease at High Altitude through Epidemiological Investigation under Intelligent Medicine and Big Data.Journal of healthcare engineering · 2022Article
- Large-scale comparative neuroimaging: Where are we and what do we need?Cortex; a journal devoted to the study of the nervous system and behavior · 2019Review
- Functional Neuroimaging in the New Era of Big Data.Genomics, proteomics & bioinformatics · 2019Review
- Statistical Challenges of Big Brain Network Data.Statistics & probability letters · 2018Article
- PAGANI Toolkit: Parallel graph-theoretical analysis package for brain network big data.Human brain mapping · 2018Article
- A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data.Frontiers in neuroinformatics · 2018Article
- Processing Time Reduction: an Application in Living Human High-Resolution Diffusion Magnetic Resonance Imaging Data.Journal of medical systems · 2016Article
- Imaging Informatics: 25 Years of Progress.Yearbook of medical informatics · 2016Article
- Resting-State Functional Connectivity in the Human Connectome Project: Current Status and Relevance to Understanding Psychopathology.Harvard review of psychiatryReview
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.