susana muñoz maniega research fellow, disconnected mind project sfc brain imaging research centre...
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Susana Muñoz Maniega
Research Fellow, Disconnected Mind ProjectSFC Brain Imaging Research CentreDivision of Clinical Neurosciences
University of Edinburgh
From water random motion to brain's white matter fibres and the study of cognition
Overview
• Water diffusivity in the brain
• White matter integrity biomarkers
• Whole brain analysis – voxel-based
• Tractography methods
• LBC1936 – white matter and cognition
• Role of computational resources
Diffusion MRI: Background
• Diffusion is the random translational motion (Brownian motion) due to thermal energy
• In tissues, diffusivity is affected by the local cellular environment
• If the cell membranes have directional coherence, then diffusion will depend on direction – anisotropic diffusion
Robert Brown 1773 - 1858
Albert Einstein 1879 - 1955
Diffusion MRI: Background
• Diffusion is the random translational motion (Brownian motion) due to thermal energy
• In tissues, diffusivity is affected by the local cellular environment
• If the cell membranes have directional coherence, then diffusion will depend on direction – anisotropic diffusion
Diffusion MRI: Background
• Diffusion is the random translational motion (Brownian motion) due to thermal energy
• In tissues, diffusivity is affected by the local cellular environment
• If the cell membranes have directional coherence, then diffusion will depend on direction – anisotropic diffusion
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yzyyyx
xzxyxx
DDD
DDD
DDD
D
Diffusionperpendicu
larto long
axis
Diffusionparallel
to long axis
Imaging biomarkersMean diffusivity (MD = mean{λi=1,3}) ≈ magnitude of diffusion
Fractional anisotropy (FA = var{λi=1,3}/magn{D}) ≈ directional coherence:
0 indicates isotropic diffusion (CSF)
1 indicates highly anisotropic diffusion (white matter)
MD FA
• Healthy, structurally intact white matter has low MD and high FA
• Structurally compromised white matter has high MD and low FA
A Voxel-Based Analysis Approach
• We can look for correlations of FA with other 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
Aligned
Averaged Thinned
FA projected into skeleton
Stats
Smith et al. NeuroImage 2006 31:1487-1505
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
http://www.fmrib.ox.ac.uk/fsl/tbss/
White matter integrity and age
• 90 subjects 65 to 88 years old
90 ×90 registrations ~ 28 days
1-2 days in parallel
Widespread negative correlations between FA and age
p < 0.05
FA
Tractography
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DDD
DDD
DDD
D
Tractography
Reconstruct white matter tracts in 3D by piecing together voxel-based estimates of the underlying continuous fibre orientation field
Mori et al. NMR Biomed 2002 15:468-480
Behrens et al. NeuroImage 2007 34:144-155
• We use probabilistic diffusion tractography (Bedpostx/Probtrackx) with a model for fitting 2 fibre orientations in each voxel
• To perform tractography in a group study we need to automatize the process but still segmenting the tracts reliably in all subjects
Tractography
Neighbourhood Tractography
• NT selects a seed point from the set of candidates using a reference
tract as a guide to the expected topology of the segmented tract
– NT models the variability in shape and length of the tract and finds
the tract that best matches the model from a set of candidates
– An EM algorithm is used to fit the model
Clayden et al. NeuroImage 2009 45:377-385
same tract is segmented in each brain
http://code.google.com/p/tractor/
The Lothian Birth Cohort 1936 (LBC1936) comprises 1091 surviving participants of the Scottish Mental Survey 1947 (SMS1947) who now live in the Lothian area of Scotland
They were recruited at age about 70 into a follow-up study
The childhood cognitive ability data provide a baseline from which to calculate life-long cognitive changes
Deary et al. BMC Geriatrics 2007, 7:28
2007
1947
LBC1936
Using contemporary brain MRI (at age 72-73), including diffusion tensor imaging (DTI), we examined how white matter integrity relates to changes in cognition in the LBC1936
We used fractional anisotropy (FA) and mean diffusivity (MD) as markers of white matter integrity in specific tracts
White matter integrity was related to IQ (11 and 70) and general factors of cognition, speed and memory
MRI role in the DM Project
Tracts of interest
N=318
Preliminary results show association between uncinate fasciculus integrity and intelligence at ages 11 and 70
This supports the hypothesis that uncinate fasciculus is part of the neural basis for intelligence
Tracts and cognition
• Storage: – Raw data ~ 375 MB – Pre-processed tractography dataset ~ 500 MB– Pre-processed structural dataset ~ 250 MB Total × 1000 ~ 1TB
• Processing in a single computer:– Diffusion data pre-processing ~ 20 min × 1000 = 13.8
days– Data modelling for tractography (BedpostX with 2 fibre
model) > 24 h per dataset × 1000 = 2.7 years
– NT tract shape modelling ~ 2.5 h per subject per tract × 1000 = 3.5 months × 14 tracts = 4.1 years
Total: ~ 7 years
… computational issues in an imaging study of 1000 subjects
• The TractoR (Tractography with R) project includes R packages for processing, analysing and visualising magnetic resonance images (available in CRAN)
• R based scripting infrastructure and shell script frontend for running common analyses
• Facilitates running R code on parallelised systems
• Configurable with “design files” containing options, e.g. imaging datasets to analyse in parallel
• Processing of LBC1936 data in Eddie – 200 datasets processed in ~ 1-2 weeks
http://code.google.com/p/tractor/
• Disconnected mind, and particularly LBC1936, is an unique study that would give insights into normal cognitive ageing
• The size of the cohort is both a strength and a weakness
• Analysis of all the data is only possible using parallelisable processes and the Edinburgh Compute and Data Facility
Conclusions
Acknowledgements
www.disconnectedmind.ed.ac.uk
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)
Ian J. Deary Karen Horsburgh
James McCulloch
Richard Morris
John Starr Joanna Wardlaw
Mark Bastin Emma Wood Ian Marshall
Principal Investigators
Research team:Caroline Brett Robin Coltman Janie CorleyStephanie Daumas
Gail Davies Paula Davies
Ruth Deighton Tommy Dingwall Jill FowlerCatherine Gliddon
Alan Gow Sarah Harris
Ross Henderson Philip Holland Lorna HoulihanKarim Khallout Severine Launay Michelle Luciano
Kevin McGheeCatherine Murray
Alison Pattie
Lars Penke Paul Redmond Michell ReimerNatalie Royle Fiona Scott Jessica SmithAisling Spain Yanina TsenkinaMaria Valdés Hernández Susana Muñoz Maniega
SFC Brain Imaging Research Centre