1. MNTP Trainee: Georgina Vinyes Junque, Chi Hun Kim Prof. James T. Becker Cyrus Raji, Leonid Teverovskiy, and Robert Tamburo

2.  Structural differences based on Voxel-wise comparision  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

3.  Voxel-Based Morphometry Bias Field Correction Skull Stripping Spatial Normalization to Template Tissue Segmentation Modulation Smoothing Voxel-wise statistical tests Preprocessing

4.  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/) 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 differences  in Drug users vs. Non-Drug users

5. MRI Bias Field Correction Original Image Corrected Image Corrected Bias field = Original – Corrected image Software: N3 (Nonparametric Nonuniform intensity Normalization) N3

6. Experiment 1. Adding ’Known’ Bias Field Known Bias Field + Successful Removal of Known Bias field N3

7. Experiment 2. ’Repetition’ of Bias Field Correction Original image Corrected image After 5 th repetition < Amount of Corrected Bias Field over N3 Repetition > # of repetition Mean Signal Intensity ofCorrected Bias Field N3

8. 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)

9. Teverovskiy, 2011, OHBM, Poster Presentation

10. 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-customized-template/  Fitting each individual brain into the same brain template, To compare regional differences between groups

11. MACS 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

12. 2. Tissue Probability Map http://dbm.neuro.uni-jena.de/vbm/segmentation/ 1. Signal Intensity of Voxel Grey Mater Segmentation CSF Segmentation White Mater Segmentation

13.  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/modulation/  Recovering volume information which was lost by spatial normalization p rocess.  It can be thought as atrophy correction.

14. 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

15.  Intensity of every voxel is replaced by the weighted average of the surrounding voxels. Larger kernel size, more surrounding voxels  Make distribution closely to Gaussian field model  Increase the sensitivity of tests by reducing the variance across subjects  Reduce the effect of misregistration

16. 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 mm15 mm

17.  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 interpret published results.

18.  Prof. James T. Becker  TA: Cyrus Raji, Leonid Teverovskiy, Robert Tamburo  Prof. Seong-Gi Kim & Prof. Bill Eddy  Tomika Cohen, Rebecca Clark  Fellow MNTPers!