title: A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets.
abstract: In this paper we propose a web-based approach for quick visualization of big data from brain magnetic resonance imaging (MRI) scans using a combination of an automated image capture and processing system, nonlinear embedding, and interactive data visualization tools. We draw upon thousands of MRI scans captured via the COllaborative Imaging and Neuroinformatics Suite (COINS). We then interface the output of several analysis pipelines based on structural and functional data to a t-distributed stochastic neighbor embedding (t-SNE) algorithm which reduces the number of dimensions for each scan in the input data set to two dimensions while preserving the local structure of data sets. Finally, we interactively display the output of this approach via a web-page, based on data driven documents (D3) JavaScript library. Two distinct approaches were used to visualize the data. In the first approach, we computed multiple quality control (QC) values from pre-processed data, which were used as inputs to the t-SNE algorithm. This approach helps in assessing the quality of each data set relative to others. In the second case, computed variables of interest (e.g., brain volume or voxel values from segmented gray matter images) were used as inputs to the t-SNE algorithm. This approach helps in identifying interesting patterns in the data sets. We demonstrate these approaches using multiple examples from over 10,000 data sets including (1) quality control measures calculated from phantom data over time, (2) quality control data from human functional MRI data across various studies, scanners, sites, (3) volumetric and density measures from human structural MRI data across various studies, scanners and sites. Results from (1) and (2) show the potential of our approach to combine t-SNE data reduction with interactive color coding of variables of interest to quickly identify visually unique clusters of data (i.e., data sets with poor QC, clustering of data by site) quickly. Results from (3) demonstrate interesting patterns of gray matter and volume, and evaluate how they map onto variables including scanners, age, and gender. In sum, the proposed approach allows researchers to rapidly identify and extract meaningful information from big data sets. Such tools are becoming increasingly important as datasets grow larger.
authors.affiliation: The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA.
authors.forename: Sandeep R
authors.initials: SR
authors.lastname: Panta
authors.affiliation: The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA.
authors.forename: Runtang
authors.initials: R
authors.lastname: Wang
authors.affiliation: The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA.
authors.forename: Jill
authors.initials: J
authors.lastname: Fries
authors.affiliation: The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA.
authors.forename: Ravi
authors.initials: R
authors.lastname: Kalyanam
authors.affiliation: Intermountain Neuroimaging Consortium, University of Boulder Colorado Boulder, CO, USA.
authors.forename: Nicole
authors.initials: N
authors.lastname: Speer
authors.affiliation: Intermountain Neuroimaging Consortium, University of Boulder Colorado Boulder, CO, USA.
authors.forename: Marie
authors.initials: M
authors.lastname: Banich
authors.affiliation: The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA; Department of Psychology, University of New MexicoAlbuquerque, NM, USA.
authors.forename: Kent
authors.initials: K
authors.lastname: Kiehl
authors.affiliation: The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA.
authors.forename: Margaret
authors.initials: M
authors.lastname: King
authors.affiliation: The Child Mind Institute and The Nathan Kline Institute New York, NY, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Milham
authors.affiliation: Intermountain Neuroimaging Consortium, University of Boulder Colorado Boulder, CO, USA.
authors.forename: Tor D
authors.initials: TD
authors.lastname: Wager
authors.affiliation: Department of Psychology, Georgia Tech University Atlanta, GA, USA.
authors.forename: Jessica A
authors.initials: JA
authors.lastname: Turner
authors.affiliation: The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA; Department of Electrical & Computer Engineering, University of New MexicoAlbuquerque, NM, USA.
authors.forename: Sergey M
authors.initials: SM
authors.lastname: Plis
authors.affiliation: The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA; Department of Electrical & Computer Engineering, University of New MexicoAlbuquerque, NM, USA.
authors.forename: Vince D
authors.initials: VD
authors.lastname: Calhoun
keywords: big data; data sharing; magnetic resonance imaging (MRI); neuroscience; t-SNE (t-distributed stochastic neighbor embedding); visualization