Automated Amygdala Surface Modeling Pipeline
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Automated Amygdala Surface Modeling Pipeline Moo K. Chung Department of Biostatistics and Medical Informatics Waisman Laboratory for Brain Imaging and Behavior University of Wisconsin-Madison www.stat.wisc.edu/~mchung/research/amygdala* atlab-based image processing/analysis/visualization too
description
Automated Amygdala Surface Modeling Pipeline. Moo K. Chung Department of Biostatistics and Medical Informatics Waisman Laboratory for Brain Imaging and Behavior University of Wisconsin-Madison www.stat.wisc.edu/~mchung/research/amygdala*. - PowerPoint PPT Presentation
Transcript of Automated Amygdala Surface Modeling Pipeline
Automated Amygdala Surface Modeling Pipeline
Moo K. Chung
Department of Biostatistics and Medical InformaticsWaisman Laboratory for Brain Imaging and Behavior
University of Wisconsin-Madison
www.stat.wisc.edu/~mchung/research/amygdala*
*Matlab-based image processing/analysis/visualization tools
Acknowledgments
Brendon, M. Nacewicz, Anqi Qiu, Shubing Wang, Kim M. Dalton, Jamie Hanson, Seth Pollak, Richard J. Davidson
Waisman laboratory for brain imaging and behavior
University of Wisconsin-Madison
Amygdala manual segmentation
Left amygdala of subject 001
FreeSurfer can be used to automatically segment amygdala and hippocampus. Publications coming out in NeuroImage using FreeSurfer segmentation.
Traditional Volumetry There is no volume difference in autism vs. control (study 1 (n=24) + study 3 (n=23) combined):
Left (p=0.64)Right (p=0.81)
Can we still have localized difference?
Step1
3D model of left amygdala of subject 001
left
front middle
top
bottom
back
Orientation
front
3D model of left amygdala of subject 001
back
left
middle backfront left
middle
top
bottom
top
bottom
Spherical coordinate system for amygdala surface
Analysis & surface registration will be done on a sphere and the result will be back projected onto the average amygdala surface.
Step 2
Left Rightfront
back
middleleft
Hotelling’s T-square test on group difference
Origami representation
rightmiddle
Keith Worsley’s SurfStat MATLAB package
slm = SurfStatLinMod(disp, Brain + Age + Group,avsurf);slm = SurfStatT(slm, group);
Testing Group difference controlling for Brain size and Age
>pvalue = [0.001 0.005 0.01 0.05 0.1]>threshold=randomfield_threshold(slm, pvalue)
pvalue = 0.0010 0.0050 0.0100 0.0500 0.1000
threshold = 6.8058 6.2154 5.9564 5.3398 5.0635
Corrected P-value thresholding using the random field theory
Left Rightfront
back
middleleft rightmiddle
T-stat.
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Max T = 3.7970Random field thresholding at 0.05 level = 5.3398
3.7
Max T = 3.6687Random field thresholding at 0.05 level = 5.3200
Significance of group difference controlling for Brain size and Age
