Diffusion MRI Analysis in
the LBC1936 Study: How
to Analyse Data from 1000
Subjects in < 25 Years
Susana Muñoz Maniega(1) and
Mark Bastin(2)
(1)Department
of Clinical Neurosciences,(2)Medical Physics
and
SFC Brain Imaging research Centre
University of Edinburgh
MRI Analysis: Role in the
Disconnected Mind Project
• White matter – the brain’s wiring – may play a key
role in the cognitive decline seen in normal ageing
• The Disconnected Mind project aims to understand
how changes in white matter structure contribute to
cognitive ageing
• The human imaging component of this major study
involves scanning 1000+ subjects from the Lothian
Birth Cohort 1936 (LBC1936)
Cohort with narrow age range (~70 years)
Information on prior and current mental ability
Subjects
• All subjects born in 1936
• Underwent IQ test at age 11 as part of
the Scottish Mental Survey of 1947
• Subjects are currently undergoing repeat
cognitive testing and brain MRI at the
age of 71 ~ 72 years
Structural MRI (T1, T2, T2* and FLAIR)
Diffusion tensor MRI
Magnetization transfer MRI
Diffusion MRI: Background
• Diffusion is the random translational motion (Brownian
motion) of molecules due to thermal energy
• In vivo, diffusion is affected by the local cellular
environment
Diffusion MRI: Background
Mean diffusivity
Fractional Anisotropy
⟨D⟩
FA
right/left
superior/inferior
anterior/posterior
Diffusion MRI: Background
Segmentation of white matter tracts
right/left
superior/inferior
anterior/posterior
Pre-processing of DT-MRI data
Diffusion data consists of 7×T2W and 64×DWI volumes of 72 brain
slices each = 5112 image files – acquisition time ≈ 20 mins
Pre-processing steps:
1. Conversion of raw data into ANALYZE format – 30 min
2. Distortion correction & registration – 1 hour
3. Brain extraction – 1 min (interactive)
4. Fitting of diffusion tensor and estimation of diffusion parameters – 1 min
Analysis
•
Options:
– Standard ROI Analysis to study specific brain regions, e.g. frontal white
matter
– Study of the whole brain diffusion parameters to find possible
correlations with measures of general cognitive performance, such as
IQ : Voxel-Based Analysis (VBA), Histogram Analysis
– Extract the FA and ⟨D⟩ of a particular white matter tract to investigate
possible correlations with specific aspects of cognitive performance
associated to that tract: Tractography
• e.g. is the FA in the cingulum bundles correlated with memory performance?
A Voxel-Based Analysis Approach
•
•
We can look for correlations of FA with cognitive parameters in a
hypothesis-free manner looking at the whole brain white matter
Tract-based spatial statistics (TBSS) is a voxel-based analysis approach
customised for the study of diffusion parameters in white matter
d
ne
ig
Al
Averaged
Thinned
FA projected into
skeleton
Stats
TBSS
•
•
•
In VBA the accurate registration is crucial – usually all brains are registered
to a brain template
For a cohort of older subjects we cannot use templates (created from
younger brains) so we chose a registration target from the database itself as
the most typical.
This minimises the registration errors, but at the cost of time
TBSS preprocessing requires N ×N
registrations each taking ~ 5 min
Tractography
•
We use probabilistic diffusion tractography (Bedpostx/Probtrackx)
with a model for fitting 2 fibre orientations in each voxel (Behrens et
al. Neuroimage 2007 34:144-155)
•
To achieve segmentation of the same tracts in all subjects we use
probabilistic Neighbourhood Tractography (NT)
– NT models the variability in shape and length of a particular tract and
finds the tract that best matches the model from a set of candidates
(Clayden et al IEEE TMI 2007 26:1555-1561)
Neighbourhood Tractography
•
NT selects the seed point using a reference tract as a guide to the expected topology
of the segmented tract – these are created from an anatomical atlas
•
A seed point is chosen in the standard brain for each reference tract, which is
registered into each subjects brain, and 343 candidate tracts created from a
“neighbourhood” of 7 × 7 × 7 seed points around this seed point
Neighbourhood Tractography
•
NT then selects the seed point that produces the best match to the
reference tract from the 343 candidate tracts
•
Using this tract as a mask we extract values of FA and ⟨D⟩
Diffusion MR Imaging in LBC1936
so far…
• Data from 230 participants have been preprocessed and
bedposted
• Tractography experiments done for 195 datasets in
– Left and right cingulum bundles
– Left and right arcuate fasciculi
– Left and right uncinate fasciculi
– Left and right anterior thalamic radiation
– Corpus callosum genu and splenium
• TBSS preprocessing done in 130 datasets
• BUT…
… on a single computer this would
have taken
– Preprocessing ~ 1.5 h per dataset ≈ 14 days
– Bedpostx ~ 3 days per dataset ≈ 23 months
– NT modelling ~ 6 h per dataset per tract x 10 tracts ≈ 16 months
– TBSS registration 130 × 130 × 5min ≈ 2 months
– TBSS stats ~ 5 days per contrast ≈ 10 days
Total ≈ 3.5 years
… extended to a study of 1000
datasets
• On a single computer:
– Preprocessing ≈ 2 months
– Bedpostx ≈ 8.2 years
– NT modelling ≈ 6.8 years
– TBSS registration ≈ 9.5 years
– TBSS stats ≈ 3 months
Total ≈ 25 years!
Analysing DT-MRI data in a study
of 1000 subjects
• Using ECDF (Eddie)
– Processing time decreased by various orders of
magnitude
• e.g. 20 datasets bedposted in 1 day instead of 2 months!
• NT modelling and TBSS stats are currently still being
optimised to run in parallel
– We have just purchased 1TB of data storage in Eddie
to allow us run all our processing in parallel…
– …so it will be possible to analyse the 1000 datasets
in a reasonable timescale ☺
Acknowledgements
•
This work has made use of the resources provided by the Edinburgh
Compute and Data Facility (ECDF). (http://www.ecdf.ed.ac.uk/). The
ECDF is partially supported by the eDIKT initiative
(http://www.edikt.org)
•
Bedpostx/Probtrackx, TBSS and registration tools are part of the
Oxford Centre for Functional MRI of the Brain (FMRIB) Software
Library (FSL) (http://www.fmrib.ox.a.uk/fsl)
•
Preprocessing wrappers and Neighbourhood Tractography are part of
the Tractography with R (TractoR) package, created and maintained by
Dr Jonathan Clayden (http://code.google.com/p/tractor)