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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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

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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