Skip to main content

Enabling Factor Analysis on Thousand-Subject Neuroimaging Datasets

Author(s): Anderson, Michael J.; Capotă, Mihai; Turek, Javier S.; Zhu, Xia; Willke, Theodore L.; et al

To refer to this page use:
Abstract: The scale of functional magnetic resonance image data is rapidly increasing as large multi-subject datasets are becoming widely available and high-resolution scanners are adopted. The inherent low-dimensionality of the information in this data has led neuroscientists to consider factor analysis methods to extract and analyze the underlying brain activity. In this work, we consider two recent multi-subject factor analysis methods: the Shared Response Model and Hierarchical Topographic Factor Analysis. We perform analytical, algorithmic, and code optimization to enable multi-node parallel implementations to scale. Single-node improvements result in 99x and 1812x speedups on these two methods, and enables the processing of larger datasets. Our distributed implementations show strong scaling of 3.3x and 5.5x respectively with 20 nodes on real datasets. We also demonstrate weak scaling on a synthetic dataset with 1024 subjects, on up to 1024 nodes and 32,768 cores.
Publication Date: 2016
Electronic Publication Date: 2016
Citation: Anderson, Michael J, Capotă, Mihai, Turek, Javier S, Zhu, Xia, Willke, Theodore L, Wang, Yida, Chen, Po-Hsuan, Manning, Jeremy R, Ramadge, Peter J, Norman, Kenneth A. (Enabling Factor Analysis on Thousand-Subject Neuroimaging Datasets. 10.1109/BigData.2016.7840719
DOI: doi:10.1109/BigData.2016.7840719
Type of Material: Journal Article
Journal/Proceeding Title: 2016 IEEE International Conference on Big Data (Big Data)
Version: Author's manuscript

Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.