BRAINSTORM Towards Clinically and Scientifically Useful NeuroImaging Analytics
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1
Johns Hopkins University, United States
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2
Nathan Klein Institute, United States
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3
Child Mind Institute, United States
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4
Mind Research Network, United States
We desire to transform clinical psychiatric practice to take advantage of the vast technological strides in contemporary neuroimaging. We propose three complementary steps will help facilitate this transformation. First, the construction of a computing platform to store and process large datasets. Second, methods to calibrate measurements across individuals and instruments. Third, tools to convert such measurements into clinically useful analytics. We are developing BRAINSTORM to address these three concerns.
First, a high-performance compute cluster and associated scientific database, called "BrainCloud", for storing, managing, and efficiently querying both multi-modal neuroimaging and rich phenotypic data. BrainCloud will be seeded with data already available from the International NeuroImaging Data Initiative [1] as well as the Mind Research Network [2]. Moreover, BrainCloud will include a simple one-click upload interface so that additional research and clinical facilities can contribute to the growing data corpus.
Second, a robust pipeline optimized to pre-process multimodal image data to infer multi-modal attributed connectomes (MACs). We are developing a highly configurable pipeline [3] that enables us to search for an optimal representation of data for subsequent inference via non-parametric reliabilities estimates.
Third, streaming decision theoretic manifold learning algorithms [4] that yield clinically useful outputs, as well as provide insight into brain/behavior relationships. To date, most statistical and machine learning algorithms natively operate on vector valued data; but our data are far more complex: responses to psychological instruments and multimodal images. We are developing complementary tools that natively operate on non-Euclidean data and "stream", meaning that they continue to learn as new data becomes available.
[1] http://fcon_1000.projects.nitrc.org
[2] Scott, A et al. Front. NeuroInf., 2011
[3] Sikka, S. Resting-State, 2012
[4] Priebe, CE. arXiv:1112.5510
Keywords:
Neuroimaging,
Imaging Technologies,
data storage,
method development,
Software Development
Conference:
5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012.
Presentation Type:
Poster
Topic:
Neuroinformatics
Citation:
Vogelstein
J,
Sikka
S,
Cheung
B,
Khanuja
R,
Li
Q,
Chao-Gan
Y,
Priebe
C,
Calhoun
V,
Vogelstein
R,
Milham
M and
Burns
R
(2014). BRAINSTORM Towards Clinically and Scientifically Useful NeuroImaging Analytics.
Front. Neuroinform.
Conference Abstract:
5th INCF Congress of Neuroinformatics.
doi: 10.3389/conf.fninf.2014.08.00057
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Received:
21 Mar 2013;
Published Online:
27 Feb 2014.
*
Correspondence:
Dr. Joshua Vogelstein, Johns Hopkins University, Baltimore, United States, jovo@jhu.edu