Functional MRI data preprocessing

Functional MRIFunctional MRI

data data preprocessingpreprocessing

Cyril Pernet, PhDCyril Pernet, PhD

Data have been acquired, whatData have been acquired, what’’s next?s next?

Picture credit: http://home.kpn.nl/raema005/functional_magnetic_resonance_imaging_fmri.html

No matter the design, multiple volumes (made from multiple slices) have

been acquired in time. Before getting data out, we need to make sure the signal from each voxel contains the right temporal and spatial information.

time

Slice timing andRealignment

smoothing

normalisation

Statistics

AnatomicalAnatomical

referencereference

kernelkernel

fMRI timefMRI time--seriesseries

Slice Timing CorrectionSlice Timing Correction

R. Henson, C. Buechel, O. Josephs, K. FristonThe slice-timing problem in event-related fMRI

NeuroImage, 9 (1999), p. 125-125


�� Most of the time, fMRI data are acquired using sequential Most of the time, fMRI data are acquired using sequential

2D imaging like single shot EPI. Since fMRI statistics are 2D imaging like single shot EPI. Since fMRI statistics are

about analyzing the time course of the BOLD signal, exact about analyzing the time course of the BOLD signal, exact

timing with regard to the stimulus presentation is crucial.timing with regard to the stimulus presentation is crucial.

Sladky et al. (2011) NeuroImage, 58, 588-594.

�� For instance, if you set a For instance, if you set a

TR of 2 sec and acquire TR of 2 sec and acquire

30 slices, the acquisition 30 slices, the acquisition

time of 1 slice is ~66.66 time of 1 slice is ~66.66

ms (2000/30) and STC ms (2000/30) and STC

compensates for these compensates for these

sampling differences .sampling differences .

� STC consists in shifting the signal phase by a given amount to temporally align data. It is therefore mandatory to select a reference slice. The reference slice is usually the slice acquired in the middle of the sequence (maximum interpolation of TR/2) but any slice can be used.

00.20.40.60.811.21.41.61.8

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010.21.2 0.41.4 0.61.6 0.81.8

Sequential acquisitionRef slice 6

Interleaved acquisitionRef slice 2

Data are acquired either in sequential or interleaved mode and the middle of the sequence is not the middle of the brain!



� TR of e.g. 2 sec with the middle temporal slice as reference is comparable to a dataset with a TR of 1 sec when the first (or last) slice acquired are used as a reference slice. This can be a reasonable practice, if the region of interest and putative activations are located near the first (or last) slice because it suppresses temporal interpolation effects in these areas.


Note that all regressors in the GLM also need to be adjusted for this shift in time according to the reference slice (e.g. TR / 2).


� Possible introduction of aliasing effects for signals at frequencies above the Nyquist sampling limit. Given a typical TR of 2 s (f = 0.25 Hz) a minimal inter-stimulus interval (ISI) of more than 4 s is recommended. When using the slice acquired in the middle of the acquisition period (TR) as a reference slice, the signal needs to be shifted by TR / 2, therefore reducing the suggested minimal ISI to 2 s.

� It is sometimes advocated to not do the STC especially for TR<2 sec. However, Sladky et al. showed that is always beneficial. When not performed, the reduction of parameter estimates (effects) were more pronounced for long TRs, event-related designs and designs with shorter SOA (up to 63% !).


motion correction motion correction

(realignment)(realignment)

JV. Hajnal, R. Myers, A. Oatridge, JE. Schwieso, IR. Young, GM. BydderArtifacts due to stimulus-correlated motion in functional imaging of the brain.

Magn Reson Med, 3 (1994), p283–291.

Motion artefactsMotion artefacts

�� Subjects will always move in the scanner: swallowing for Subjects will always move in the scanner: swallowing for instance lead to motion along the x axis or some instance lead to motion along the x axis or some movements may be related to the tasks performed.movements may be related to the tasks performed.

�� Motion will results in a mismatch of the location of Motion will results in a mismatch of the location of subsequent images in the timesubsequent images in the time--series. Since the sensitivity series. Since the sensitivity of the statistical analysis is determined by the amount of of the statistical analysis is determined by the amount of residual noise in the image series, mismatch of the location residual noise in the image series, mismatch of the location will add to this noise and reduce the sensitivity.will add to this noise and reduce the sensitivity.

�� This type of motion problem corresponds to wholesale This type of motion problem corresponds to wholesale movements (movements (bulkbulk--motionmotion) and is well corrected by ) and is well corrected by realignment algorithms.realignment algorithms.

Motion correction: How?Motion correction: How?

∆x=10mm

∆y=0

∆θ=10º

∆θ

Transform

co-ordinate∆x

For each voxel-centre

co-ordinateFind co-ordinate in

original image

�� Determine the rigid body transformation that minimises Determine the rigid body transformation that minimises

some cost function (a way to define the difference between some cost function (a way to define the difference between

2 images as e.g. least square (SPM) or normalized 2 images as e.g. least square (SPM) or normalized

correlation ratio (FSL)).correlation ratio (FSL)).

�� Rigid body transformation is defined by: 3 translations in X, Rigid body transformation is defined by: 3 translations in X,

Y & Z directions and 3 rotations around the X, Y & Z axes.Y & Z directions and 3 rotations around the X, Y & Z axes.

Illustration taken from Jesper Andersson

Motion correction: Quality checkMotion correction: Quality check

�� Plots the estimates from the head motion algorithmPlots the estimates from the head motion algorithm

(might be useful to change angles into degrees and also (might be useful to change angles into degrees and also

plot the 1plot the 1stst derivative derivative –– outlier detection can then be run to outlier detection can then be run to

identify identify ‘‘badbad’’ scans)scans)

�� Compute whole head distance between volumes and to the Compute whole head distance between volumes and to the

mean (mean square difference)mean (mean square difference)

�� Inspect the realigned data as a movieInspect the realigned data as a movie

Lots of tools available on websitesFor SPM users, I wrote a script to do this automaticallysee https://sourceforge.net/projects/spm-qa-tools


Plots of head motion

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scans

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Rotation

scans

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Mean square displacement

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Whole head distances

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Mean quared distance to the average

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Volume to volume mean squared distance

Motion correction: Quality checkMotion correction: Quality checkMovies

Even more motion artefactsEven more motion artefacts

�� Motion can also alter the MR signal because protons that Motion can also alter the MR signal because protons that moves into a moves into a voxelvoxel from a neighbouring slice have an from a neighbouring slice have an excitation different from that expected by the scanner and excitation different from that expected by the scanner and the signal will not reflect well the tissue in that the signal will not reflect well the tissue in that voxelvoxel. This . This spin history effectspin history effect is not corrected using motion correction is not corrected using motion correction algorithms and Independent Component Analysis (ICA) or algorithms and Independent Component Analysis (ICA) or dedicated methods must be used.dedicated methods must be used.

The spin history effect is seen as alternating bright and dark stripes with interleaved acquisitions. One can mitigate this by modelling bad images as dummy regressors and include movement parameter estimates into the

analysis.

Picture credit: http://wagerlab.colorado.edu/wiki/doku.php/help/fmri_quality_control_overview

Even more motion artefactsEven more motion artefacts

�� Physiological artefactsPhysiological artefacts

Lund et al. (2006) NeuroImage, 29, 54-66

respiratory-induced noise is dominant near the edges of the brain as well as near in the larger veins and in the ventricles.

cardiac-induced noise is dominant near larger vessels (e.g. medial cerebral artery and Circle of Willis)

�Physio monitoring�Don’t trust those regions

Spatial NormalizationSpatial Normalization

M, Holden A review of geometric transformations for nonrigid body registration.

IEEE Trans Med Imaging, 27 (2008) , p 111-128

Normalization: Why?Normalization: Why?

�� InterInter--subject averagingsubject averaging

�� extrapolate findings to the population as a wholeextrapolate findings to the population as a whole

�� increase activation signal above that obtained from increase activation signal above that obtained from

single subjectsingle subject

�� increase number of possible degrees of freedom increase number of possible degrees of freedom

allowed in statistical modelallowed in statistical model

�� Enable reporting of activations as coEnable reporting of activations as co--ordinates within a ordinates within a

known standard spaceknown standard space

�� e.g. the space described by e.g. the space described by TalairachTalairach & & TournouxTournoux, , or or

thethe MNI space MNI space (SPM, FSL)(SPM, FSL)

Normalization: How?Normalization: How?

�� Current methods works mainly on T1 weighted images Current methods works mainly on T1 weighted images

(i.e. anatomical), so in practice one uses a 3 steps (i.e. anatomical), so in practice one uses a 3 steps

approach in which one i) approach in which one i) coregistercoregister T1 and T2* (fMRI) T1 and T2* (fMRI)

data so that they are aligned and in the same space and data so that they are aligned and in the same space and

ii) normalize the anatomical image (i.e. transform it to ii) normalize the anatomical image (i.e. transform it to

match the template) and iii) apply the parameters match the template) and iii) apply the parameters

obtained to the fMRI imagesobtained to the fMRI images

Mean EPI T1-weighted Template

coregister normalize

apply normalization parameters

T1 image T1 image preprocessingpreprocessing

�� The anatomical image is 1The anatomical image is 1stst preprocessedpreprocessed, depending on , depending on

the method this includes: i) the method this includes: i) Noise reductionNoise reduction: improves : improves

local features using local smoothing (SUSAN algorithm in local features using local smoothing (SUSAN algorithm in

FSL), ii) FSL), ii) Bias correctionBias correction: from 3 : from 3 TeslasTeslas, there is often , there is often

broad intensity variations in space that need to be broad intensity variations in space that need to be

corrected, iii) corrected, iii) Brain extractionBrain extraction: remove non brain tissue, : remove non brain tissue,

iv) iv) SegmentationSegmentation: separate grey matter, white matter : separate grey matter, white matter

and CSFand CSF

In SPM, the unified segmentation approach (Ashburner and Friston (2005) NeuroImage, 26, 839-851) combines/optimizes bias correction, segmentation, and normalization all in one, such as the prior probability of

a voxel to belong to a tissue class is determined using a probabilistic atlas. In this framework 2 voxels with the same values can be classified differently using anatomical knowledge.

T1 image T1 image preprocessingpreprocessing

Noise reductionNoise reduction

Bias correctionBias correction

Brain extractionBrain extraction

SegmentationSegmentation

Original T1Original T1--weightedweighted

�� The first part of spatial normalisation is a 12 parameter The first part of spatial normalisation is a 12 parameter affine transformationaffine transformation: :

�� 3 translations3 translations

�� 3 rotations3 rotations

�� 3 scaling3 scaling

�� 3 shears3 shears

VolumeVolume--based normalizationbased normalization

} Rigid body transformation (realignment)� does not change the size or shape of images

Affine transformation: any set of points that fell on a line prior transformation will continue to fall on a line after the transformation

} Allow change in overall size and shape

VolumeVolume--based normalizationbased normalization

�� DARTEL toolbox in SPM or DARTEL toolbox in SPM or FNIRT in FSL rely on FNIRT in FSL rely on diffeomorphismdiffeomorphism, that is the , that is the transformation from one transformation from one image to the other can be image to the other can be represented as a vector field, represented as a vector field, describing the movements to describing the movements to

apply at each apply at each voxelvoxel..

�� The second part of the normalization uses nonThe second part of the normalization uses non--linear deformations.linear deformations.

SurfaceSurface--based Normalizationbased Normalization

�� Often more accurate to register cortical features Often more accurate to register cortical features than than ‘‘oldold’’ volume based methods. Often limited volume based methods. Often limited to cortical surface.to cortical surface.

�� A combined method (surface based for cortical A combined method (surface based for cortical and volume based for deep brain structures) is and volume based for deep brain structures) is available in available in FreesurferFreesurfer..

orig white pial

inflated sphere patch

original white pial

Image credit: Sarah W

hittle &

Dominic Dwye

r free-surfer talk

Normalization: quality checkNormalization: quality check

�� Visual inspection using e.g. Visual inspection using e.g. CheckRegCheckReg in SPMin SPM

�� Mean T2*, T1, TemplateMean T2*, T1, Template

Allows checking the coregistration and normalization worked


�� Segmented (normalized) gray matter and white matter Segmented (normalized) gray matter and white matter

vs. priors (i.e. the gray and white matter images used vs. priors (i.e. the gray and white matter images used

with the template) with the template)

Sometimes the overall shape looks ok but the segmentation was not

too good – best to check this as well


�� Outlines of template vs. normalized T1Outlines of template vs. normalized T1

�� Average normalized imagesAverage normalized images

�� Distance of normalized data to the templateDistance of normalized data to the template

�� MovieMovie

Lots of tools available on websitesFor SPM users, I wrote a script to do this automaticallysee https://sourceforge.net/projects/spm-qa-tools


Average of Normalized T2* Image Outlines Average/T1

Interpolation methodsInterpolation methods

P. P. ThevenazThevenaz, T. , T. BluBlu & M Unser& M Unser

Interpolation revisedInterpolation revised

IEEE Trans Med Imaging, 19 (2000), p 739IEEE Trans Med Imaging, 19 (2000), p 739--758 758

Write down the new imagesWrite down the new images

�� Each transformation (realignment, segment, Each transformation (realignment, segment,

normalize) comes with different methods of normalize) comes with different methods of

interpolationinterpolation

�� Each time an image is transformed, Each time an image is transformed, voxelsvoxels dondon’’t align t align

with the original ones and new with the original ones and new voxelsvoxels have to be have to be

created created –– their value is inferred from neighbours.their value is inferred from neighbours.

There is no need to apply each transformation. Parameters are stored in the header and for a new transform, previous parameters are applied and the new transform computed. Only when all is computed, one has to write images. At this stage all parameters are applied. This allows to interpolate voxel values only once.


Interpolation methods include i) nearest neighbour ii) linear

interpolation iii) higher-order interpolation like sinc

interpolation or spline.

http://imp4-2008.blogspot.co.uk/

http://www.hyperzoid.com/csc492/rendering.html

Loss of resolution

Tends to blur images


Interpolation methods include i) nearest neighbour ii) linear

interpolation iii) higher-order interpolation like sinc

interpolation or spline.

SmoothingSmoothing

Smoothing: Why?Smoothing: Why?

�� Increase signal to noise by removing highIncrease signal to noise by removing high--frequency frequency

information (smallinformation (small--scale changes in the image)scale changes in the image)

�� InterInter--subject averaging as spatial normalization subject averaging as spatial normalization

cannot perfectly align all structurescannot perfectly align all structures

�� Increase validity of statistics when using random field Increase validity of statistics when using random field

theory.theory.

Smoothing: How?Smoothing: How?

Before convolution Convolved with a circle Convolved with a Gaussian

Each voxel, after smoothing, effectively becomes the

result of applying a weighted region of interest. In SPM, smoothing is a convolution with a 3D Gaussian kernel, and

the kernel is defined in terms of FWHM (full width at half

maximum)

2D illustration using the conv2 function in Matlab

Smoothing: How much?Smoothing: How much?

�� Depends on effects to be detected Depends on effects to be detected �� Matched filter Matched filter theorem: smoothing kernel = expected signal.theorem: smoothing kernel = expected signal.

�� Practically FWHM 2 times the Practically FWHM 2 times the voxelvoxel size is appropriate size is appropriate for random fields theory, whilst improving SNR.for random fields theory, whilst improving SNR.

�� May consider varying kernel size if interested in different May consider varying kernel size if interested in different brain regions (e.g. hippocampus brain regions (e.g. hippocampus --vsvs-- parietal cortex)parietal cortex)))

Picture credit: http://fcp-indi.github.com/docs/user/smoothing.html

Functional MRI data preprocessing

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