Difference between revisions of "2010 Winter Project Week MultiscaleLupusAnalyses"
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<h3>Objective</h3> | <h3>Objective</h3> | ||
+ | To combine the multiple types of available neuroimaging data in to a multiscale analysis of a lupus patient. | ||
Perform a multiscale analysis using sMR, DWI, ASL, and an fMR rest task on a lupus patient | Perform a multiscale analysis using sMR, DWI, ASL, and an fMR rest task on a lupus patient | ||
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<h3>Approach, Plan</h3> | <h3>Approach, Plan</h3> | ||
+ | Use various existing NAMIC analysis pipelines to process each type of neuroimaging data and then combine all resulting features into a single statistical analysis. | ||
+ | Implement any functionality that doesn't currently exist in the NAMIC kit. | ||
</div> | </div> | ||
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<h3>Progress</h3> | <h3>Progress</h3> | ||
+ | '''1.''' Experimented with multiple DWI pipelines and started processing data using GTRACT | ||
+ | '''2.''' Implemented a module to perform cross-correlation of the signals created by averaging all voxels within an anatomical region per TR using a rest task during fMRI. | ||
+ | |||
+ | '''3.''' Examine multiple projects such as the Cortical Thickness and Stochastic Tractography modules and apply to our test data. | ||
+ | |||
+ | '''4.''' Perform a simple statistical analysis of the resulting features. | ||
</div> | </div> |
Revision as of 04:15, 4 January 2010
Home < 2010 Winter Project Week MultiscaleLupusAnalysesKey Investigators
- MRN: Mark Scully, Jeremy Bockholt
- BWH: Steve Pieper
Objective
To combine the multiple types of available neuroimaging data in to a multiscale analysis of a lupus patient. Perform a multiscale analysis using sMR, DWI, ASL, and an fMR rest task on a lupus patient
Approach, Plan
Use various existing NAMIC analysis pipelines to process each type of neuroimaging data and then combine all resulting features into a single statistical analysis.
Implement any functionality that doesn't currently exist in the NAMIC kit.
Progress
1. Experimented with multiple DWI pipelines and started processing data using GTRACT
2. Implemented a module to perform cross-correlation of the signals created by averaging all voxels within an anatomical region per TR using a rest task during fMRI.
3. Examine multiple projects such as the Cortical Thickness and Stochastic Tractography modules and apply to our test data.
4. Perform a simple statistical analysis of the resulting features.