Neuroimaging and mathematical modelling Lesson 2: Voxel Based Morphometry
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1 Neuroimaging and mathematical modelling Lesson 2: Voxel Based Morphometry Nivedita Agarwal, MD
2 Volume and surface morphometry Brain volume White matter Grey matter Volume of specific brain structures hippocampus (Alzheimer s disease, memory loss states) frontal lobe (intelligence!, analytical brain) basal ganglia (Parkinson s disease, metabolic disorders)
3 Examples applications of VBM! Many scientifically or clinically interesting questions might relate to the local volume of regions of the brain! For example, whether (and where) local patterns of brain morphometry help to:! Distinguish groups (e.g. schizophrenics and healthy controls)! Explain the changes seen in development and aging! Understand plasticity, e.g. when learning new skills! Find structural correlates (scores, traits, genetics, etc.)
4 Volume and surface morphometry This is where mathematicians and physicists come in to the scene! Lots of online free tools to study volume of brain structures and identify surface anatomy
5
6 VBM in pictures Segment Normalise
7 VBM in pictures Segment Normalise Modulate Smooth
8 Smoothing Each voxel after smoothing effectively becomes the result of applying a weighted region of interest (ROI). Before convolution Convolved with a circle Convolved with a Gaussian
9 VBM in pictures Segment Normalise Modulate! # # # # "# a1xyz a2xyz! anxyz $ & & & =Y=X! xyz +e xyz & %& e ~ N(0, σ xyz 2 xyzv ) Smooth Voxel-wise statistics X =
10 VBM in pictures beta_0001 con_0001 Segment Normalise Modulate Smooth Voxel-wise statistics ResMS FWE < 0.05 spmt_0001
11 Original Warped Template
12 Smoothing as a locally weighted ROI! VBM > ROI: no subjective (or arbitrary) boundaries! VBM < ROI: harder to interpret blobs & characterise error
13 Interpreting findings Thinning Mis-register Mis-classify Thickening Mis-register Contrast Folding
14 Simultaneous registration of GM to GM and WM to WM Subject 1 Grey matter White matter Grey matter Subject 3 White matter Grey matter Grey matter White matter Subject 2 White matter Template Grey matter White matter Subject 4
15 Template Initial Average Iteratively generated from 471 subjects Began with rigidly aligned tissue probability maps Used an inverse consistent formulation After a few iterations Final template
16 Grey matter average of 452 subjects affine
17 Grey matter average of 471 subjects
18 Initial GM images
19 Warped GM images
20 471 Subject Average
21 471 Subject Average
22 471 Subject Average
23 SPM for Anatomical statistics MRI Anatomical MRI Group-wise Preprocessing Spatially Normalised spm T Grey Matter Image Anatomical MRI Preprocessing Spatially Normalised Grey Matter Image Anatomical MRI Preprocessing Spatially Normalised Grey Matter Image
24 Some References! Wright, McGuire, Poline, Travere, Murray, Frith, Frackowiak & Friston. A voxel-based method for the statistical analysis of gray and white matter density applied to schizophrenia. Neuroimage 2(4): (1995).! Ashburner & Friston. Voxel-based morphometry-the methods. Neuroimage 11(6): , (2000).! Mechelli et al. Voxel-based morphometry of the human brain Current Medical Imaging Reviews 1(2) (2005).! Ashburner & Friston. Unified Segmentation. NeuroImage 26: , 2005.! Ashburner. A Fast Diffeomorphic Image Registration Algorithm. NeuroImage 38: (2007).! Ashburner & Friston. Computing Average Shaped Tissue Probability Templates. NeuroImage 45: (2009).! Klein et al. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46(3): (2009).! Ashburner. Computational Anatomy with the SPM software. Magnetic Resonance Imaging 27(8): (2009).! Ashburner & Klöppel. Multivariate models of inter-subject anatomical variability. NeuroImage 56(2): (2011).
25 Interpreting findings VBM is sometimes described as unbiased whole brain volumetry Regional variation in registration accuracy Segmentation problems, issues with analysis mask Intensity, folding, etc. But significant blobs probably still indicate meaningful systematic effects!
26 Summary! VBM performs voxel-wise statistical analysis on smoothed (modulated) normalised tissue segments! SPM performs segmentation and spatial normalisation in a unified generative model! Based on Gaussian mixture modelling, with warped spatial prior probability maps, and multiplicative bias field
27 End of the second lecture! Next time (29 Nov. 2013): Basics of Diffusion tensor imaging
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