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BRAINSTORM Towards Clinically and Scientifically Useful NeuroImaging Analytics

Filed under:

Joshua Vogelstein (Johns Hopkins University), Sharad Sikka (Nathan Klein Institute), Brian Cheung (Nathan Klein Institute), Ranjit Khanuja (Child Mind Institute), Qinyang Li (Child Mind Institute), Yan Chao-Gan (Child Mind Institute), Carey Priebe (Johns Hopkins University), Vince Calhoun (Mind Research Network), R. Jacob Vogelstein (Johns Hopkins University), Michael Milham (Child Mind Institute), Randal Burns (Johns Hopkins University)

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 (Fig. 1) 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. 

[2] Scott, A et al. Front. NeuroInf., 2011
[3] Sikka, S. Resting-State, 2012
[4] Priebe, CE. arXiv:1112.5510

Schematic Illustration of BrainStorm.
Preferred presentation format: Poster
Topic: Neuroimaging

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Jean-Baptiste Poline
Jean-Baptiste Poline says:
May 08, 2012 09:17 AM
very ambitious - I wonder how much is already there - the figure is missing in the web interface
Andrew Davison
Andrew Davison says:
May 11, 2012 02:40 PM
Ambitious. I would like to hear more. Shame the figure is missing.