Preprocessing

Question 1

How are the terms session, run, volume, slice, voxel, in-plane resolution, field of view, slice thickness related?

Answer 1

• one session consists of multiple runs
• one run consists of multiple volumes
• one volume consists of multiple slices
• one slice consists of multiple voxels
• one slice is characterized by matrix size (amount of voxels) and in-plane resolution
• field of view = matrix size * in-plane resolution
• voxel size = in-plane resolution and slice thickness

Question 2

What are the 6 steps of preprocessing

Answer 2

• slice timing
• realignment
• coregistration
• segmentation
• normalization
• smoothing

Question 3

slice timing

Answer 3

• different slices are acquired during different time points
• measured signal of each voxel is Fourier-transformed, resulting in a frequency representation of the signal from which it can be reconstructed
• Fourier-transformed signal is phase-shifted to reference slice and back-transformed into signal space
• different reference slices give different statistical results

Question 4

realignment - why?

Answer 4

• head motion shifts measured signal between voxels -> failure to detect local activations (reduced sensitivity)
• motion can be correlated with experimental paradigm -> findings of spurious activations (reduced specificity)

Question 5

realignment - how?

Answer 5

• estimation/registration: determine rigid-body transformation from each acquired image to first (or mean) scan (3 translations and 3 rotations, reference volume - volume to realign = min)
• reslicing/resampling: apply estimated transformation to correct whole series of scans

Question 6

coregistration

Answer 6

• structural MRI needs to be in alignment with functional MRI before mapping to standard space (-> normalization)
• allows more accurate anatomical localization of activations
• practically, using the same algorithm as used for realignment
• using mean image from realignment

Question 7

segmentation - why?

Answer 7

• differentiation of structural MR image into tissue types can improve mapping to standard space (-> normalization)
• in SPM, segmentation and normalization are actually
  performed using one model (“unified segmentation”)
• brain tissue types: gray matter, white matter, CSF, meninges, skull, air

Question 8

segmentation - what?

Answer 8

• tissue probability maps
• normalization parameters used for warping the subject to standard space

Question 9

normalization - why?

Answer 9

• substantial inter-individual differences in brain anatomy
• increase sensitivity in analyses with multiple subjects by matching them to a standard brain -> anatomical template
• make results from different studies comparable by bringing them into a standard coordinate system -> MNI space

Question 10

normalization - how?

Answer 10

• linear registration: adjustment of global differences using an affine transformation with 4*3 = 12 parameters (translations, rotations, zooms, sheers)
• non-linear registration: adjustment of local differences using deformation fields based on smooth basis functions

Question 11

circular relationshipt between segmentation and normalization

Answer 11

• knowing which tissue type a voxel belongs to helps normalization
• knowing where a voxel is in standard space helps segmentation

solution: build generative model which accounts for both
- model how voxel intensities result from mixture of tissue type distributions
- model how tissue types have to be spatially deformed to match those of template

Question 12

normalization - best approach

Answer 12

• calculate mean functional image during realignment
• coregister structural image to mean functional image
• normalize coregistered mean functional image to anatomical template
• use resulting normalization parameters to normalize functional scans

Question 13

smoothing - why?

Answer 13

• residual anatomical mismatch, even after normalization
• reduce measurement artifacts, increase signal-to-noise ratio
• required by random field theory (RFT) -> statistical analysis

Question 14

smoothing - how?

Answer 14

• convolution with 3D Gaussian kernel defined by its full width at half maximum (FWHM)
• FWHM is directly related to standard deviation of a (multivariate) normal distribution (FWHM = 2 * sqrt(2 * ln(2 * sd))
• after smoothing, each voxel effectively becomes the result of applying a weighted region of interest

Question 15

When to normalize/smooth?

Answer 15

• to ensure that assumptions of random field theory (RFT) are met, use at least 2 x [normalized voxel size] as smoothing FWHM
• when using univariate approaches (e.g. voxel wise GLM), normalize and smooth before statistical analysis
• when using multivariate approaches (multi voxel pattern analysis), normalize and smooth after statistical analysis

Question 16

masking

Answer 16

• restricting data to particular voxels, e.g. GM
• thresholding TPM and multiplying it with the fMRI scans

Question 17

temporal filtering

Answer 17

• removal of low-frequency drifts/trends
• in SPM, performed during statistical model estimation