2008 Summer Project Week:fMRIconnectivity - Revision history

Ybkim: /* Key Investigators */

2008-06-27T14:15:15Z

Key Investigators

Ybkim at 20:06, 10 June 2008

2008-06-10T20:06:39Z

Ybkim at 20:03, 10 June 2008

2008-06-10T20:03:47Z

Ybkim at 19:43, 10 June 2008

2008-06-10T19:43:39Z

Ybkim: New page: ===Key Investigators=== * UNC: Isabelle Corouge, Casey Goodlett, Guido Gerig * Utah: Tom Fletcher, Ross Whitaker

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 We have applied standard fMRI preprocessing steps on the data and regressed out the effects of white matter and ventricles. We have also customized PICA and GMM tools for our analysis purpose and obtained some preliminary results.
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 ===References===
 * C. Beckmann and S. Smith. "Probabilistic independent component analysis for functional magnetic resonance imaging." IEEE Transactions on Medical Imaging, 23(2):137–152, 2004.

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 <h1>Progress</h1>
-We have applied standard fMRI preprocessing steps on the data and regressed out the effects of white matter and ventricles. We have also customized PICA for our analysis purpose and written GMM tools.
+We have applied standard fMRI preprocessing steps on the data and regressed out the effects of white matter and ventricles. We have also customized PICA and GMM tools for our analysis purpose and obtained some preliminary results.
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 ===References===
-* Fletcher, P.T., Tao, R., Jeong, W.-K., Whitaker, R.T., "A Volumetric Approach to Quantifying Region-to-Region White Matter Connectivity in Diffusion Tensor MRI," to appear Information Processing in Medical Imaging (IPMI) 2007.
+* C. Beckmann and S. Smith. "Probabilistic independent component analysis for functional magnetic resonance imaging." IEEE Transactions on Medical Imaging, 23(2):137–152, 2004.

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 <h1>Objective</h1>
-We are developing methods for analyzing diffusion tensor data along fiber tracts. The goal is to be able to make statistical group comparisons with fiber tracts as a common reference frame for comparison.
+Our objective is to study functional connectivity of schizophrenia patients versus normal with unsupervised data-driven analysis methods, such as ICA and clustering.
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 <h1>Approach, Plan</h1>
-Our approach for analyzing diffusion tensors is summarized in the IPMI 2007 reference below.  The main challenge to this approach is <foo>.
+Our approach for investigating functional connectivity of schizophrenia patients is to apply probabilistic independent component analysis (PICA) and clustering based on Gaussian mixture model (GMM).
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 <h1>Progress</h1>
-Software for the fiber tracking and statistical analysis along the tracts has been implemented. The statistical methods for diffusion tensors are implemented as ITK code as part of the [[NA-MIC/Projects/Diffusion_Image_Analysis/DTI_Software_and_Algorithm_Infrastructure|DTI Software Infrastructure]] project. The methods have been validated on a repeated scan of a healthy individual. This work has been published as a conference paper (MICCAI 2005) and a journal version (MEDIA 2006). Our recent IPMI 2007 paper includes a nonparametric regression method for analyzing data along a fiber tract.
+We have applied standard fMRI preprocessing steps on the data and regressed out the effects of white matter and ventricles. We have also customized PICA for our analysis purpose and written GMM tools.
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 ===References===
 * Fletcher, P.T., Tao, R., Jeong, W.-K., Whitaker, R.T., "A Volumetric Approach to Quantifying Region-to-Region White Matter Connectivity in Diffusion Tensor MRI," to appear Information Processing in Medical Imaging (IPMI) 2007.

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 ===Key Investigators===
-* UNC: Isabelle Corouge, Casey Goodlett, Guido Gerig
+* MIT: Bryce Kim, Polina Golland
-* Utah: Tom Fletcher, Ross Whitaker
+* BWH: Jungsu Oh, Marek Kubicki
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