automated amygdala surface modeling pipeline

Post on 22-Jan-2016

47 Views

Category:

Documents

0 Downloads

Preview:

Click to see full reader

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

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.

0

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

top related