SPM Course

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SPM Course Zurich 2010 Voxel-Based Morphometry
Practical

• Ged Ridgway
• For SPM8, using data from www.oasis-brains.org
• Tools to help with OASIS data available
  fromwww.cs.ucl.ac.uk/staff/G.Ridgway/zurich
• Any problems, please email Ged.Ridgway_at_gmail.com

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Background

• Alzheimers Disease (AD) is a progressive
  neurodegenerative disease affecting over 24
  million people world-wide (Ferri et al, Lancet,
  2005)
• At present, researchers know of no single cause,
  nor of a cure, though prototype drugs are in
  development
• Histopathology (microscopic analysis of
  post-mortem tissue samples) reveals amyloid
  plaques and neurofibrillary tangles, with
  varying distribution through the brain, that
  changes with disease severity

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Background

• MRI has the potential to find systematic
  differences between the brains of AD patients and
  healthy elderly controls in vivo
• MRI may have potential to advance our
  understanding of the disease, to allow earlier
  diagnosis, and to track disease progression or
  drug response over time

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Introduction

• In this practical we will perform Voxel Based
  Morphometry with spm8 to determine local patterns
  of significant grey-matter differences between AD
  patients and healthy controls
• The data come from the Open Access Series of
  Imaging Studies, www.oasis-brains.org

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Preliminary SPM Setup

• Install spm8 if you have not already done so
• Extract the archive somewhere on your hard-drive
  and add the resulting spm8 directory to your
  MATLAB path with editpath
• Open the SPM interface by typing spm pet at the
  MATLAB command prompt

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Preliminary SPM Setup

• Edit spm_defaults
• defaults.stats.maxmem 229 229bytes 500MB
• This will make things much faster later
• defaults.analyze.flip 1
• LEFT/RIGHT FLIPPING IS IMPORTANT!
• SPM shows images with what it thinks is
  anatomical-right on screen-right you can check
  this is correct by comparing SPMs display with
  the www.oasis-brains.org website after browsing
  to one of the cross-sectional images

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Data

• We will consider a subset of the complete
  cross-sectional OASIS dataset (which contains
  over 400 subjects), with 30 controls and 30 AD
  patients
• There is a comma-separated-variables
  spreadsheetoasis_subset.csv which you can open
  in Excel, or in MATLAB using oasis_read_csv.m H
  C oasis_read_csv(Hn contains the nth
  column heading, Cn contains the data for the
  nth column)
• The first 30 rows are controls, the next 30 are
  patients

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Getting the data

• If you want to download some of the data, you can
  get individual subjects from www.oasis-brains.org
• browse to the cross-sectional study, and
  particular subject-ID you only need the
  RECONSTRUCTIONS (about 44MB per subject)
• To get the complete subset, you need
• The xnat tools from www.xnat.org
• The MATLAB functions oasis_read_csv.m and
  oasis_get_xnat.m
• A comma-separated variables spreadsheet, like
  oasis_subset.csv
• Note that you probably want to use oasis_get_xnat
  with a proc argument, see help oasis_get_xnat
  in MATLAB

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Reorientation

• Newly downloaded OASIS data is not correctly
  oriented for SPM (0,0,0mm is way outside the
  brain)
• The script oasis_reorient.m should fix this
• Check reg with spm8/templates/T1.nii afterwards!
• Right click the image and select reorient this
  image from the check reg context menu if you need
  to make manual adjustments (you dont need the AC
  perfectly at 0,0,0, or the PC perfectly on the
  same x and z coords, but you do need rough
  alignment of the subject and template in order
  for the segmentation step to work well

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Tissue Segmentation

• Newly downloaded data needs segmenting
• Click the segment button in the top-left window
• Select your newly downloaded t88_gfc images as
  the data
• Under output files
• Choose modulated normalised grey matter, and
  none for white matter and CSF
• Dont output bias corrected
• Dont do clean-up
• Leave the custom options. Click run (it should
  take 10-20mins per subject)

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Spatial Smoothing

• Click the smooth button in the top-left window
• For images to smooth, select the 60 mwc1 images
• Leave the data type as same
• Set the Full-Width at Half-Maximum (FWHM) of the
  Gaussian kernel to 10mm isotropic (10 10 10)
• You might like to explore different values

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Statistical Parametric Mapping

• We will now perform voxel-wise statistics on the
  segmentations (this is the essence of Voxel-based
  morphometry)
• Choose basic models from the top-left window
• Under design, select two sample t-test
• Enter the controls (smoothed smwc1 images) as
  group 1 scans, and the AD patients as group 2
• Leave independence as yes set variance to
  unequal
• Equal variance probably wont make much
  difference, but you could try if you are
  interested
• With equal variance, the resultant SPM
  t-statistic at a particular voxel would match a
  simple two-group t-test in Excel or SPSS, etc.,
  if you extracted the voxel intensity from each
  smoothed image
• Leave grand mean scaling and ANCOVA as no

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Covariates

• Under covariates, add a new covariate
• Use estimated Total Intracranial Volume (eTIV)
• This is the 10th column of the oasis_subset.csv
  spreadsheet
• You might like to test using gender instead or as
  well, or not using a covariate at all. For gender
  (or orther boolean/categorical variables) you
  need indicator variables, e.g. a binary female1,
  male0 variable (not M or F!)
• Use oasis_read_csv to get the variable in the
  workspace, then evaluate this variable as the
  vector (the values should appear in the window)
  and specify eTIV (or Female, etc.) as the name
• Leave interactions as none and centering as
  overall mean

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Masking and globals

• Threshold masking is not always wise for VBM I
  am biased, but would recommend my Masking
  toolbox, available from
• www.fil.ion.ucl.ac.uk/spm/ext/Masking
• Alternatively, use imcalc with the data matrix
  option to produce an average of your smwc1
  images, with the expression mean(X)
• Then use imcalc (without data-matrix) and the
  expression i1gt0.1 to threshold the average,
  giving a binary mask that includes all voxels
  with more than 10 probability on average of
  being GM
• (This interpretation is not completely true,
  since the data are modulated, but its close
  enough for the mask to be reasonable)
• Leave global calculation and global normalisation

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Checking the design

• Select the output directory as twogroup_tiv_s10
• (or other covariate and smoothing options)
• (SPM wont make this directory ensure it exists
  first)
• Click run (this step should only take a few
  seconds)
• A design summary will appear, and various aspects
  may be checked

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GLM Estimation

• Now we are ready to fit the model we just
  designed
• Click the estimate button from the top-left
  window, and select SPM.mat in the output
  directory you specified
• This step can take quite some time

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Results

• Once estimation has finished, click the results
  button
• Select the SPM.mat in your results directory
• Click define new contrast, enter the name as
  hcgtad and the contrast vector as 1 -1, then
  done
• Dont mask with other contrasts
• Leave the title as hcgtad
• Choose FWE correction and leave the p-value at
  0.05
• Leave the extent threshold at 0

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Glass-brain MIP

• You should see a glass-brain Maximum Intensity
  Projection of the significant voxels
• Click the whole brain button under the p-values
  tab of the bottom-left (interactive) window, this
  should add a table of results below your glass
  brain
• Click the SPM-Print button in the menu bar of the
  right-hand (graphics) window, choose other print
  file and give a meaningful name like mip.ps

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Presenting results

• Interpreting complicated 3D datasets can be
  difficult
• The glass brain display is one of several options
• We would also like to overlay the significant
  regions on a representative image
• One option is a mean mwc1 image
• Nicer (but more work) is to normalise all the
  original T1-weighted images and average them
• If you use DARTEL for the registration, you could
  use the final Templates GM volume
• While the glass brain is showing, click the
  save button near the bottom-right of the
  bottom-left window, and enter a helpful filename
  spmT_0001_fwe5

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Presenting results

• Produce a figure showing the spmT_thresh image
  overlaid on the average image
• Use SPMs check registration
• Display the average, right click and choose
  blobs -gt add coloured image from the context
  menu, then select spmT_thresh
• Use the spm-print button to store the figure with
  a filename like overlay.ps
• You might also like to investigate slover
• slover(basic_ui) will get you started

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Looking at global GM

• The function get_totals.m is a simple script to
  find the total (probabilistic) volume of a
  segmentation in ml
• Use this function on all control and patient mwc1
  images, collecting the volumes into a 60-vector
• Note that results should be almost identical if
  you used native c1 images or unsmoothed mwc1
  images, thanks to the properties of the smoothing
  kernel and thanks to the modulation process
• You might like to produce plots of GM volume
  against covariates from the oasis_subset.csv
  spreadsheet, using the MATLAB plot command
• E.g. age, eTIV, and MMSE (separately)
• You can distinguish the patients from controls
  with different colours, e.g.
• plot(age(130), totalGM(130), b, age(3160),
  totalGM(3160), r)
• Can you see any interesting correlations?
• You can use MATLABs corrcoef to quantify the
  relationships (r and p)

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Looking at global GM

• How does mean GM volume compare to mean eTIV?
  (i.e. on average what fraction of the brain is
  GM?)
• Produce a scatter plot of eTIV against gender
• plot(0, eTIV(female0), 'b', 1,
  eTIV(female1), 'ro')
• title('eTIV by gender') ylabel(eTIV (ml))
  xlim(-0.5 1.5)
• set(gca, 'Xtick', 0 1) set(gca, 'XtickLabel',
  'male', 'female')
• You might like to repeat this for GM volume, and
  for GM volume divided by eTIV, exploring how the
  sexes differ in these scatter plots

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VBM adjusted for global differences

• Repeat your previous VBM statistical analysis,
  but this time use the global GM volume you have
  just computed as a covariate instead of the eTIV
  covariate that you used before.
• Use twogroup_totalGM_s8 as the output directory
• Think about the differences between adjusting for
  eTIV or gender compared to global GM volume
• Is AD likely to cause or correlate with decreased
  skull-size? Certainly not as strongly as it
  causes global GM reduction

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Final points

• Both global volume and VBM show differences
  between controls and AD patients
• Each approach has relative advantages and
  disadvantages with regard to
• Ease of interpretation
• Potential contribution to disease understanding
• Ease of use for classifying potential disease
  carriers and for tracking disease progress over
  time
• Power to detect a range of disease progressions