Anatomical MRI mod-ule

MNTP Trainee: Georgina Vinyes Junque, Chi Hun KimProf. James T. BeckerCyrus Raji, Leonid Teverovskiy, and Robert Tamburo

Voxel-Based Morphometry (VBM)

Structural differences based on Voxel-wise com-parision

Advantages Automated, Un-biased, Whole brain analysis compared

to Manual ROI tracing Well established and Widely used over the past decade Results are biologically plausible and replicable We know the LIMITATIONS

Overview

Voxel-Based Mor-phometry Bias Field Correction Skull Stripping Spatial Normalization to

Template Tissue Segmentation Modulation SmoothingVoxel-wise statistical tests

Preprocessing

Methods

MRI sequence T1 (MPRAGE) 3T Siemens TrioTim Slices: 160; thickness 1.2mm Voxel size: 1 x 1 x 1.2 mm TE: 2.98; TR: 2300

Software SPM2 & SPM5 (Wellcome Trust

Centre for Neuroimaging) VBM2 toolbox (Gaser et al,

http://dbm.neuro.uni-jena.de/) N3 algorithm Brain Extraction Tool in FSL Watershed algorithm in

FreeSurfer

Subjects Multicenter AIDS Cohort

Study (MACS) 53 males Age: 50.2 +- 4.4

Statistical Analysis Gray matter Volume dif-

ferences in Drug users vs. Non-

Drug users

MRI Bias Field Correc-tion

Origi-nalImage

Cor-rectedImage Corrected Bias field

= Original – Corrected image

Software: N3 (Nonparametric Nonuniform intensity Normalization)

N3

Experiment 1. Adding ’Known’ Bias Field

Known Bias Field

+

Successful Removal ofKnown Bias field

N3

Experiment 2. ’Repetition’ of Bias Field Correction

Original im-age

Corrected imageAfter 5th repetition

< Amount of Corrected Bias Field over N3 Repetition >

1 2 3 4 50

1

2

3

4

5

6

7

Mean

# of repetition

Mea

n S

igna

l Int

ensi

ty o

f C

orre

cted

Bia

s F

ield

N3

N3

N3

N3

N3

Skull Stripping

Software Brain Extraction Tool (BET; v2.1 in FSL software package) Watershed algorithm in FreeSurfer software package v5.1.0

BET default setting (1 min) Watershed default setting (30 min)

Optimization of Parameters (2min)

Skull Stripping: Brain Extraction via Deformable Registration

Teverovskiy, 2011, OHBM, Poster Presentation

Spatial Normalization to Template

1. Customized template Recommended in special populations

(Eg: babies or the elderly).

2. Standardized template Better comparison with similar studies

using the same template. Eg. MNI: 152 brains, mean age 25, female 43%

http://dbm.neuro.uni-jena.de/vbm/vbm2-for-spm2/creating-custom-ized-template/

Fitting each individual brain into the same brain template, To compare regional differ-ences between groups

Effect of 3 Different Tem-plate on Statistical ResultsMACS template Default-MNI templateCustomized template

Glass brains, showing reduced grey matter volume in drug users compared to non-drug users, at 0.01 Uncorrected level

Segmentation into 3 Tissue Types

2. Tissue Probability Map

http://dbm.neuro.uni-jena.de/vbm/segmen-tation/

1. Signal Intensity of Voxel

Grey Mater Segmen-tation

CSFSegmenta-

tion

White Mater

Segmenta-tion

Modulation

It’s recommended if you are more interested in volume changes than

differences in concentration (or density)

http://dbm.neuro.uni-jena.de/vbm/segmentation/modu-lation/

Recovering volume information which was lost by spatial normalization process.

It can be thought as atrophy correction.

Effects of Modulation on Results

Modulated:Changes in GM volume

Unmodulated:Changes in GM density

Glass brains showing reduced grey matter in drug users compared to non-drug users, at 0.01 Uncorrected level

Smoothing

Intensity of every voxel is replaced by the weighted average of the surrounding vox-els. Larger kernel size, more surrounding voxels

Make distribution closely to Gaussian field model

Increase the sensitivity of tests by reduc-ing the variance across subjects

Reduce the effect of misregistration

Effect of Different Smoothing Kernels

Glass brains showing reduced grey matter volume in drug users compared to non-drug users, at 0.01 Uncorrected level

5 mm 10 mm 15 mm

Conclusion

There’s a lot of options in processing that can affect data and results.

We have to undertand what we are doing in every step to better adjust options to our sample study.

Since these techniques have several pitfalls, we have to carefully inter-pret published results.

Thank You

Prof. James T. BeckerTA: Cyrus Raji, Leonid Teverovskiy,

Robert TamburoProf. Seong-Gi Kim & Prof. Bill EddyTomika Cohen, Rebecca ClarkFellow MNTPers!