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A Plan for Brain Connectivity Analysis John Melonakos

A Plan for Brain Connectivity Analysis John Melonakos. Schizophrenia. Kandel, Schwartz, Jessell. “Principles of Neural Science, 4 th Edition.” (2000). p.1188. The Plan. Segment brain into white matter, gray matter, and CSF

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A Plan for Brain Connectivity Analysis John Melonakos

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  1. A Plan for Brain Connectivity Analysis John Melonakos

  2. Schizophrenia Kandel, Schwartz, Jessell. “Principles of Neural Science, 4th Edition.” (2000). p.1188

  3. The Plan • Segment brain into white matter, gray matter, and CSF • Divide resulting gray matter segmentation into key anatomical regions (e.g. the DLPFC) • Grow DTI fibers from the key anatomical regions to analyze connectivity

  4. STEP 1: Find WM,GM,CSF To do this we have chose an approach based on Bayesian Segmentation Data: Probabilities generated by applying a distribution (typically Gaussian) to your data Priors: An initial guess at the solution Posteriors: The resulting probabilities Step 1

  5. The Power of Bayes’ Rule Mumford, “The Bayesian Rationale for Energy Functionals” Step 1

  6. Bayesian/Energy Relation Step 1

  7. The Algorithm • Goal: Segment Volume into 3 classes • Solution: • Create 3 Data terms • Guess at 3 Prior terms • Apply Bayes’ Rule 3 times • Find the maximum of the 3 resulting posteriors to determine the winning class • Apply a label for the winning class Haker, et al. “Knowledge-Based Segmentation of SAR Data with Learned Priors” (1999) Teo, et al. “Creating connected representations of cortical gray matter for functional MRI visualization” (1998) Teo, et al. “Anisotropic diffusion of posterior probabilities” (1997) Step 1

  8. Added Tricks • Goal: Segment Volume into ‘N’ classes • Solution: • Create ‘N’ Data terms • Guess at ‘N’ Prior terms • Apply Bayes’ Rule ‘N’ times • Find the maximum of the ‘N’ resulting posteriors to determine the winning class • Apply a label for the winning class Iterate multiple times to refine the data and prior terms Smooth posteriors before finding the maximum Step 1

  9. Project Status • Fully implemented in ITK code thanks to the Programming Week • Currently writing a paper for the Insight journal detailing the open source nature of the ITK code (i.e. was able to use code from 14 separate ITK filters) • Finishing touches still in progress Step 1

  10. Some Pictures Raw Result Step 1

  11. STEP 2: Subdivide GM • Work with Jim Fallon @ UCI (Core 3) Step 2

  12. More Sketches Step 2

  13. Semi-Automated • Work with Ramsey Al-Hakim on DLPFC Slicer project • Writing code to wrap the ITK Bayesian filter in VTK for use in our DLPFC Slicer Module Step 2

  14. STEP 3: DTI Fibers • Work with Eric and Xavier Step 3

  15. DTI: Artistic Rendition Step 3

  16. DTI: More Art Step 3

  17. The Centrum Ovale Problem Step 3

  18. DTI Reading Selected Readings • Eric Pichon’s HBJ approach • Basser & LiBihan’s early tensor work • Dave Tuch’s Q-Ball work • Isabelle Corouge’s DTI shape models • and more … (currently taking suggestions) Step 3

  19. Questions?

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