Big Data: Pipeline Demo Day Analysis of white matter shapes Nic - PowerPoint PPT Presentation

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Big Data: Pipeline Demo Day Analysis of white matter shapes Nic Novak NSIDP 2 nd Year, Laboratory of Neuroimaging Summary White matter morphology and Alzheimers LONI Pipeline / methodology Results A problem for Pipeline

Big Data: Pipeline Demo Day Analysis of white matter shapes Nic Novak – NSIDP 2 nd Year, Laboratory of Neuroimaging
Summary  White matter morphology and Alzheimer’s  LONI Pipeline / methodology  Results
A problem for Pipeline  Alzheimer’s disease  Modest delay of onset  significant positive impact  Importance of finding earliest markers  Classically a GM disease  Association with altered WM  The Question : How do the shapes of particular fiber bundles vary between different groups of people? Normal aging? Alzheimer’s?
The goal  Find a way to:  Isolate target fiber bundles from subjects (DTI, tractography)  Represent these bundles as geometric shapes (Triangular mesh wrapping)  Perform comparisons between subjects (multilevel modeling)  Pipeline automation
Pipeline automation • Starting point: DICOM • Tractography • Data preprocessing Bundle extraction, surface computation and visualization • Surface measurement • • Output to SPSS
Pipeline automation • Starting point: DICOM • Tractography • Data preprocessing Bundle extraction, surface computation and visualization • Surface measurement • • Output to SPSS Advanced Normalization Tools • Nonlinear registration of an atlas + ROI labels to each subject
Pipeline automation • Starting point: DICOM • Tractography • Data preprocessing Bundle extraction, surface computation and visualization • Surface measurement • • Output to SPSS • Define a portion of wholebrain tractography (“the bundle”) • Wrap the bundle with a triangular mesh • Visualize the results: 0° 90° … 180° 270°
Pipeline automation • Starting point: DICOM • Tractography • Data preprocessing Bundle extraction, surface computation and visualization • Surface measurement • • Output to SPSS • Compute level contours Within each contour, sample values of FA, • diffusivity, thickness, bending angle
Pipeline automation • Starting point: DICOM • Tractography • Data preprocessing Bundle extraction, surface computation and visualization • Surface measurement • • Output to SPSS
Results Distance from Midline L SF Gyrus R SF Gyrus Linear mixed modeling: • No interhemispheric differences • Overall decreased thickness in oldest tertile (vs youngest, p<0.001) • Significant interaction between age and location across the bundle (p<0.001)
A pipe(line) dream
Acknowledgements  Project  Yonggang Shi, PhD  Kristi Clark, PhD  Arthur T oga, PhD  Special thanks to  Jack Van Horn, PhD; David Shattuck, PhD; Ivo Dinov, PhD  Alen Zamanyan, Petros Petrosyan, Yohance Clark, Joe Franco, Jonathan Pierce, Grace Liang-Franco, Melinda Ly  Funding  This work was supported by the National Institutes of Health (NIH) and the National Center for Research Resources (NCRR) grant P41 RR013642.
Questions?
Media References  Images  http://www.ibiblio.org/rcip//images/corpuscallosum.jpg

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