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Susana Muñoz Maniega

From water random motion to brain's white matter fibres and the study of cognition . Susana Muñoz Maniega. Research Fellow, Disconnected Mind Project SFC Brain Imaging Research Centre Division of Clinical Neurosciences University of Edinburgh. Overview. Water diffusivity in the brain

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Susana Muñoz Maniega

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  1. From water random motion to brain's white matter fibres and the study of cognition Susana Muñoz Maniega Research Fellow, Disconnected Mind Project SFC Brain Imaging Research Centre Division of Clinical Neurosciences University of Edinburgh

  2. 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

  3. Robert Brown 1773 - 1858 AlbertEinstein 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

  4. 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

  5. Diffusion parallel to long axis Diffusion perpendicular to long axis 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

  6. MD FA Imaging biomarkers Mean 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) • Healthy, structurally intact white matter has low MD and high FA • Structurally compromised white matter has high MD and low FA

  7. Aligned Averaged Thinned FA projected into skeleton Stats 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 Smith et al. NeuroImage 2006 31:1487-1505

  8. 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 asthe 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/

  9. White matter integrity and age • 90 subjects 65 to 88 years old Widespread negative correlations between FA and age p < 0.05 • 90 ×90 registrations ~ 28 days • 1-2 days in parallel

  10. FA Tractography

  11. 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

  12. Tractography • 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 Behrens et al. NeuroImage 2007 34:144-155

  13. 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

  14. same tract is segmented in each brain http://code.google.com/p/tractor/

  15. LBC1936 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 2007 1947 Deary et al. BMC Geriatrics 2007, 7:28

  16. MRI role in the DM Project 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

  17. Tracts of interest

  18. N=318 Tracts and cognition 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

  19. … computational issues in an imaging study of 1000 subjects • 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

  20. 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/

  21. Conclusions • 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

  22. Acknowledgements Principal Investigators www.disconnectedmind.ed.ac.uk Research team: 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)

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