Anatomic parcellation based on DTI data with FSL taking the example of SMA/preSMA

Transcription

1 Groupe de Travail IRMf/MEG 02/2014 Anatomic parcellation based on DTI data with FSL taking the example of SMA/preSMA Magdalena Wutte, Lucile Brun & Boris Burle SMA/preSMA

2 Parcellation - what for? - connectivity fingerprints - allows us to separate brain areas based on their WM connections Behrens, Johansen-Berg et al, Nature Neuroscience, 2003

3 Different types of parcellation - with targets: parcel an area based on its differential connection to predefined regions (e.g. thalamus Behrens2003, cingulate cortex Beckmann2009) - blind: parcel an area based on its differential connection to the whole brain SMA/preSMA Johansen-Berg2004 Operculum (Broca area) Klein2007

4 Principle of blind parcellation Seed mask : dorso-medial frontal cortex Target mask : whole brain Estimate diffusion tensors Preprocessing (movement correction etc) Split seed mask according to those fingerprints Tractography Search for differential connectivity fingerprints in seed mask (cross correlation/clustering)

5 Data from fmri center (copy-pasted from Olivier:) sujet/ anat01/ sujet_anat01.nii (1x1x1mm3) sujet_diffusion/ sujet_dwi.nii (1.89x1.89x2.33mm3) anatomie, T1 diffusion, 36 directions + 8 images T2 (b=0) (4D image) for all subjects the same: bvals weights for each volume (0 = T2, 1000 = diffusion) bvecs gradient directions nr directions : restricts tractography, e.g. crossing fibers can not be modelled with 36 (bedpost)

6 The software/toolbox FSL - FDT: FMRI Diffusion Toolbox - Linux platform - bash-based - user interface/command line (copy-pasted from Olivier:) IRM fonctionnelle IRM anat, segmentation, VBM IRM diffusion : FMRIB s diffusion Toolbox: FDT Tract-Based Spatial Statistics: TBSS Deux tutoriaux:

7 Overview of analysis steps Create masks Preprocessing fslview Brainvisa FSL preprocessing tools (fslreorient, bet, flirt) FSL FDT toolbox (eddy_correct) Estimate diffusion tensors FSL FDT toolbox Tractography Correlation/Clustering [the complete pipeline/commands in wiki] Matlab

8 Create mask Seed mask : Target mask : - one slice at x: -4 - y: -22 to y 30 - z: superior of cingulate sulcus - in MNI space - then transformed to individual spaces - goal was to determine border SMA/preSMA (around y 0, vertical line From AC) - subsample to 4mm voxels (fslmaths) (due to processing time)

9 Preprocessing 1) conversion to nifti: Brainvisa 2) get all images in the same orientation (fslreorient2std) T1 DTI 3) extraction of B0 images from *_dwi.nii ('nodif.nii'; as registration target in flirt and as reference volume in eddy_correct) 4) brain extraction/skull stripping of T1 image (bet) 5) transformation matrices from one space to the other (individual > MNI, indivt1 > indivdti; flirt)

10 Preprocessing 6) Apply those matrices to the seed mask (MNI>indivT1) 7) Realignment (motion correction; FDT diffusion > eddy_correct) uses co-registration of each dti volume to nodif image Data for next steps: - skull stripped anatomy (*_brain.nii) - preprocessed dti data (data.nii) - dti brain mask (nodif.nii) - subsampled target brain mask - seed mask

11 Estimate diffusion tensors: bedpostx Pdd : principle diffusion direction

12 Estimate diffusion tensors : bedpostx Input : data.nii.gz, nodif_brain_mask.nii.gz, bvals, bvec Output : dti01.bedpostx/ Here : only 1 fiber estimated because we have only 36 directions Processing time : ~2-3 h Does the same as dtifit used for TBSS; Difference : 1) can estimate crossing fibers ; 2) quantifies uncertainty of pdd (bayesian modelling) needed for probabilistic tractography (probtrackx needs its output) Different nomenclature : e.g. dyads1.nii.gz == dti_fa.nii.gz

13 Estimate diffusion tensors : bedpostx

14 Tractography

15 Tractography Streamline (deterministic tractography) Principle diffusion direction If we would have no noise and perfect data we could use deterministic, but we don't...

16 Tractography : probtrackx How confident are we? Measure of uncertainty. Probabilistic rather than deterministic tractography > from bedpostx : distribution of directions per voxel > run n streamlines from seed randomly choosing orientation from voxel > spatially overlap results : probabilistic map (number of lines)

17 Tractography : probtrackx the dorso-medial mask Target is the (subsampled) whole-brain seed in anat space, not diffusion space

18 Tractography : probtrackx Runs for 30 min 2h output we need : fdt_matrix2.dot

19 Principle of blind parcellation with Matlab: Cross-correlation of connectivity profiles number of fibers fdt_matrix2.dot native cross correlation matrix c dorso-medial dorso-medial orr e whole brain lati on [0.3] dorso-medial Clustering of the cross correlation matrix Split mask according to those clusters

20 Results : Group Parcels n=30 Tracts y = -8 y = 20 SMA presma threshold nr subjects

21 Individuals - differences crosshair at [-4 0 0]

22 Limitations/Troubleshooting Clusters are sometimes not clear cut

23 Limitations/Troubleshooting Different algorithms different results kmeans 2 cluster kmeans 2 cluster kmeans 3 cluster kmeans 3 cluster spectral spectral

24 Useful links/sources Many images from: (lecture/practical) Our scripts + a (Marseille specific) howto available at: (questions to magdalena.wutte@univ-amu.fr or lucile.brun@univ-amu.fr) Useful links: FSL course :