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

Parcellation - what for?

- connectivity “fingerprints” - allows us to separate brain areas based on their WM connections

Behrens, Johansen-Berg et al, Nature Neuroscience, 2003

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

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)

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: 0 0 0 0 0 0 0 0 1000 1000 1000 1000 1000 1000 1000 1000 1000 …

bvals

weights for each volume (0 = T2, 1000 = diffusion)

… 1000 1000 1000 1000 1000 1000

bvecs

0 0 0 0 0 0 0 0 -0.834348 0.9726 -0.169126 -0.10601 -0.830245 …

… -0.764557 0.397774 -0.723579

gradient directions

nr directions : restricts tractography, e.g. crossing fibers can not be modelled with 36 (bedpost)

The software/toolbox

FSL - FDT: FMRI Diffusion Toolbox - Linux platform - bash-based - user interface/command line (copy-pasted from Olivier:) http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/ IRM fonctionnelle IRM anat, segmentation, VBM IRM diffusion : FMRIB’s diffusion Toolbox: FDT Tract-Based Spatial Statistics: TBSS Deux tutoriaux: http://fsl.fmrib.ox.ac.uk/fslcourse/lectures/practicals/fdt http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide

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

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)

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)

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

Estimate diffusion tensors: bedpostX

Pdd : principle diffusion direction

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

Estimate diffusion tensors : bedpostX

Tractography

Tractography Streamline (deterministic tractography)

Principle diffusion direction

If we would have no noise and perfect data we could use deterministic, but we don't...

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)

Tractography : probtrackX the dorso-medial mask

Target is the (subsampled) whole-brain seed in anat space, not diffusion space

Tractography : probtrackX

Runs for 30 min – 2h

output we need : fdt_matrix2.dot

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

Results : Group

Parcels

n=30

Tracts y = -8

y = 20

SMA preSMA threshold nr subjects

Individuals - differences

crosshair at [-4 0 0]

Limitations/Troubleshooting Clusters are sometimes not clear cut

Limitations/Troubleshooting Different algorithms different results

kmeans 2 cluster

kmeans 2 cluster

kmeans 3 cluster

kmeans 3 cluster

spectral

spectral

Useful links/sources Many images from: http://fsl.fmrib.ox.ac.uk/fslcourse/

(lecture/practical)

Our scripts + a (Marseille specific) howto available at: http://wiki.irmfmrs.free.fr/doku (questions to [email protected] or [email protected]) Useful links: http://imaging.mrc-cbu.cam.ac.uk/imaging/DTItutorial http://dbic.dartmouth.edu/wiki/index.php/Diffusion_Tensor_Imaging_Analysis FSL course 31.03 - 04.04 : http://fsl.fmrib.ox.ac.uk/fslcourse/current.html