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Anatomical MRI module

MNTP Trainee: Georgina Vinyes Junque , Chi Hun Kim Prof. James T. Becker Cyrus Raji , Leonid Teverovskiy , and Robert Tamburo. Anatomical MRI module. Voxel -Based Morphometry (VBM). Structural differences based on Voxel -wise comparision Advantages

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Anatomical MRI module

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  1. MNTP Trainee: Georgina VinyesJunque, Chi Hun Kim Prof. James T. Becker Cyrus Raji, Leonid Teverovskiy, and Robert Tamburo Anatomical MRI module

  2. Voxel-Based Morphometry (VBM) • 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. Overview • Voxel-Based Morphometry Bias Field Correction Skull Stripping Spatial Normalization to Template Tissue Segmentation Modulation Smoothing Preprocessing Voxel-wise statistical tests

  4. 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 differences • in Drug users vs. Non-Drug users

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

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

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

  8. Skull Stripping Watersheddefault setting (30 min) BETdefault setting (1 min)  Optimization of Parameters (2min) • Software • Brain Extraction Tool (BET; v2.1 in FSL software package) • Watershed algorithm in FreeSurfer software package v5.1.0

  9. Skull Stripping: Brain Extraction via Deformable Registration Teverovskiy, 2011, OHBM, Poster Presentation

  10. Spatial Normalization to Template • Fitting each individual brain into the same brain template, To compare regional differences between groups • Customized template • Recommended in special populations (Eg: babies or the elderly). • 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/

  11. Effect of 3 Different Template on Statistical Results Customized template Default-MNI template MACS template Glass brains,showing reduced grey matter volume in drug users compared to non-drug users, at 0.01 Uncorrected level

  12. Segmentation into 3 Tissue Types 2. Tissue Probability Map 1. Signal Intensity of Voxel Grey Mater Segmentation White Mater Segmentation CSF Segmentation http://dbm.neuro.uni-jena.de/vbm/segmentation/

  13. Modulation • Recovering volume information which was lost by spatial normalization process. • It can be thought as atrophy correction. • 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/

  14. Effects of Modulation on Results Unmodulated: Changes in GM density Modulated: Changes in GM volume Glass brains showing reduced grey matter in drug users compared to non-drug users, at 0.01 Uncorrected level

  15. Smoothing • 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 5 mm 10 mm 15 mm Glass brains showing reduced grey matter volume in drug users compared to non-drug users, at 0.01 Uncorrected level

  17. 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 interpret published results.

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

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