biau brain image analysis unit biostatistics & computing institute of psychiatry london, u.k....
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BBrain IImage AAnalysis UUnitBiostatistics & ComputingInstitute of PsychiatryLondon, U.K.
Vincent P. GiampietroVincent P. [email protected]
Introduction toIntroduction to
fMRI AnalysisfMRI Analysis
LondonLondon
LondonLondon
LondonLondonThe Institute of Psychiatry is in Camberwell…
LondonLondonThe Institute of Psychiatry is in Camberwell…
My goalsMy goals
To give you a good idea of what fMRI analysis really does
To fight against the black box way of analysing fMRI datasets
Without showing you any of these:
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The fMRI challengeThe fMRI challenge
In fMRI, the signal change due to activation (BOLD effect) is very subtle: it amounts to about less than 4% of the baseline signal at 1.5T (double that at 3T)The challenge is to detect a small signal embedded in background noisefMRI analysis is a digital signal processing problemSome of the analysis methods are directly adapted from other signal processing domains, such as voice recognition (wavelet transforms)
How large is a 1.5T magnetic field?How large is a 1.5T magnetic field?Roughly the same as
More or less 50000 times
A simple fMRI experimentA simple fMRI experiment
39s
30s
AU
DIO
VIS
UA
L
5mn
3s (TR)
3D brain volume
16 7.7mm thick slicesmatrix size = 64x64
1 image voxel
Voxel=pixel in 3D
7.7mm
3.75mm
3.75mm
64 voxels
64 v
oxel
s
10 volumes
Multiplex audio-visual (e.g. internal control check for global changes in drug trials)
The dataThe data
One slice100 images
One image4096 voxels
Voxels
64 voxels
64 v
oxel
s
3.75mm
3.75
mm
1 TR = 3st=1xTR=3s
t=100xTR=300s
fMRI analysisfMRI analysis
Getting the images from the MR scannerPre-processing the raw dataAnalysing a single subject experimentAnalysing a group of subjectsComparing different groupsUsing more advanced analysis methods
I have scanned a subjectWhat should I do next?
How do I get the red blobs?
Getting the data from MRGetting the data from MRThe grand image format debate
Public server
your Sun/PC
Soon - DICOM formatDDigital IImaging and COCOmmunication in MMedicine
Now - Native MR scanner format
Soon - NIfTI formatNNeuroimaging IInfformatics TTechnology IInitiative
MR scanner
Now - Analyze format
The files are anonymised
http://nifti.nimh.nih.gov/
http://medical.nema.org/http://www.psychology.nottingham.ac.uk/staff/cr1/dicom.html
Pre-processing the raw dataPre-processing the raw dataDo we need it ?
Without With
Really bad dataset with huge head motion
Simon Surguladze
Pre-processing the raw dataPre-processing the raw dataraw data
movement correction
detrending
smoothing
preprocessed data
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Pre-processing the raw dataPre-processing the raw dataMovement correction
The two main movement artefacts in fMRI are:
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Time (TR)
Mot
ion
corr
ecte
d (v
oxel
)
X Rotation
Experimental model
stimulus correlatedbetween scans
Rozmin Halari
Pre-processing the raw dataPre-processing the raw dataMovement correction (co-registration)
Rigid body realignment (3 translations + 3 rotations)Registered images written by tricubic spline/linear interpolation
3D average registration template
Pre-processing the raw dataPre-processing the raw data
Stimulus correlated motion is fitted as “activated” by the modelWithout motion and spin correction, the results are useless
Stimulus correlated motionAnalysis only
Motion correction + analysis
Motion correction + spin excitation history correction + analysis
Pre-processing the raw dataPre-processing the raw dataStimulus uncorrelated motion
Stimulus uncorrelated motion doesn’t “mess up” the results
Motion and spin correction increase the power the fMRI analysis
Motion correction + spin excitation history correction + analysis
Analysis only
Motion correction + analysis
Movement-related autocorrelationIn the magnet, the positions of the nuclei at time t are spatially and temporally related to the positions of the same nuclei at time t-1 (and actually up to t-3)Can be corrected by using autoregressive pre-whitening
Pre-processing the raw dataPre-processing the raw dataDetrending – Spin excitation history correction
Without With
Problem in space
Magneticfield
Problem in time
Pre-processing the raw dataPre-processing the raw dataDetrending – Spin excitation history correction
Pre-processing the raw dataPre-processing the raw dataDetrending – Scanner drift
Bef
ore
Aft
er
Linear trend
Non-uniformity in the magnetic field
Electronic interferences due to temperature fluctuations in the imaging hardware
time
Pre-processing the raw dataPre-processing the raw data
Ray Norbury
Non-linear but periodic trends
Easily “filtered out” using high/low/band-pass filters
Detrending – Other trends
Pre-processing the raw dataPre-processing the raw dataSmoothing – Spatial filtering
Digital filtering
Filter kernel Original Image
X Y f(X)f(Y)
Filtered Image
= convolution (~the image is multiplied by the filter kernel)
f( )
Pre-processing the raw dataPre-processing the raw dataSmoothing – Spatial filtering
Mean filter
ΣNew value = 1/9 x Pixeli,j x Filteri,j
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3x3 mean filter
1125 1014 850
1310 1243 1138
1315 1338 1282
1138
(convolution)1180
126
Gaussian filter
2D Gaussian distribution (mean (0,0) and σ=1)
Discrete approximation to Gaussian function with σ =1.0
Full Width at Half MaximumFWHM = 2.355 x σFWHM = 2.355 voxelsVoxel size = 3.75x3.75mmFWHM = 8.83 mm
standard deviation
Pre-processing the raw dataPre-processing the raw dataSmoothing – Spatial filtering
The problem…– To maximise the effect, the size of the filter should
match the size of the activated regions in the image– But brain structures come in many different sizes and
shapes so smoothing the images may do more harm than good…
To smooth or not to smooth?– Adaptive (steerable) filtering (CCA - CCanonical CCorrelation
AAnalysis)– No smoothing at all… – But it is worth remembering that some analysis packages
require smoothing for their statistical analysis to work…
Pre-processing the raw dataPre-processing the raw dataSmoothing – Spatial filtering
Time
Pre-processing the raw dataPre-processing the raw dataSmoothing – Temporal filtering
Moving average filter
8-point low pass
Analysing a single subject experimentAnalysing a single subject experimentThe model file – Experiment description
39s
30s
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DIO
VIS
UA
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5mn
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…0 00 00 00 00 00 00 00 00 00 01 01 01 01 11 1…
…0.501 14.56052.517 25.50998.517 39.509110.517 62.517120.517 72.517133.509 84.517172.501 146.501247.525 158.501259.525 186.501269.525 197.509280.517 211.509…
Model file for block design
Model file for event related design
t=0.501s t=14.560s
1 TR (3s)
Analysing a single subject experimentAnalysing a single subject experiment
4s 8s
Gamma Variate Kernels
The model file – Experiment description
Experiment
Real BOLD response
or- Physiological models (Balloon
model)- Adaptive models (GLM extensions)
- Model free analysis (ICA)
- 1 Gamma function & its 1st derivative
Analysing a single subject experimentAnalysing a single subject experimentThe model file – Experiment description
(convolution)4
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Analysing a single subject experimentAnalysing a single subject experimentModel fitting
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Model for the visual stimulation
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Real time series from the visual cortex
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Real time series from the auditory cortex
The model is usually fitted using least square fitting
Analysing a single subject experimentAnalysing a single subject experimentModel fitting – Good fit (1 gamma function)
Real time series Fitted model
Analysing a single subject experimentAnalysing a single subject experimentModel fitting – Bad fit (1 gamma function)
Calculate a goodness of fit statistic– For each pixel– For each condition
This generates statistical maps of the brain (one per condition and per interaction)Null hypothesis– There is no experimental effect– There is no relationship between the voxel time series and
the experimental model
How do you decide if your statistics are significant or not ?– Parametric statistics (lots of assumptions…among other
things the data need to have a Normal distribution)– Non parametric statistics (distribution-free)
Analysing a single subject experimentAnalysing a single subject experimentModel fitting
Non parametric statistics (the way we do it)– Statistic used: Sum of Square Quotient (SSQ)
– SSQ = ratio of model to residual sum of squares
Analysing a single subject experimentAnalysing a single subject experimentModel fitting
iii
ii
fittedTSrealTS
fittedTSSSQ 2
2
– Use randomisation testing to determinate the p value of the statistic ( this is the non-parametric bit)
– If p< α then the null hypothesis is rejected, there is a statistically significant relationship between the experimental model and the studied voxel time series
– The voxel is activated and gets coloured
Analysing a single subject experimentAnalysing a single subject experiment
Randomisation tests ???
Statistical tests in which the data are repeatedly mixed
A test statistic is computed for each data shuffle
The proportion of data divisions with as large a test statistic value as the value for the original results determinates the significance of the results
Computer intensive and memory hungry…
(E.S. Edgington 1995)
A simple example– 2 treatments A and B– Hypothesis: A measurements > B measurements
– 4 patients a, b, c and d
Analysing a single subject experimentAnalysing a single subject experiment
A B Patient Measurement Patient Measurement a 6 b 3 c 7 d 4 A=6.5 B =3.5
t=4.24
Randomisation tests ???
A Simple example– 6 permutations possible of the patients to form 2 groups
– We calculate the t statistic for every permutation
Analysing a single subject experimentAnalysing a single subject experimentRandomisation tests ???
– None of the permutations have a statistical value higher or equal to 4.24 (the statistical value for the real situation).
– The one-tailed significance (p value) associated with the obtained results is therefore 1/6=0.167
A B A B A B A B A B A B 3 6 3 4 3 4 4 3 4 3 6 3 4 7 6 7 7 6 6 7 7 6 7 4 X 3.5 6.5 4.5 5.5 5 5 5 5 5.5 4.5 6.5 3.5 t -4.24 -0.47 0 0 0.47 4.24
real (observed)situation
Analysing a single subject experimentAnalysing a single subject experimentCluster analysis
Clustering– Connects activated voxels from the same brain structure– Can reinforce sub-threshold activations by “pushing them
to the surface” and eliminates single activated voxels
Levels of clustering– Per slice (2D)
– Per volume (3D)
– In time (4D)
You get the idea !
Analysing a single subject experimentAnalysing a single subject experimentThe resultsA
UD
IOV
ISU
AL
Interlude…
Interlude…
Spatial NormalisationSpatial Normalisation
What is it?– Process of transforming an image for an individual
subject to match a standard brain or brain template
What do we want to do with it ?– Check the activations on the standard atlas (functional
localisation)– Compare groups of subjects
How do we do it ?– Mostly by using automatic warping methods
Talairach atlas (Talairach and Tournoux)– “Co-Planar Stereotaxic Atlas of the Human Brain” (1988)– Detailed atlas of brain sections with a coordinate system and Brodmann
regions– “Proportional grid” of brain imaging– But…made from the post-mortem brain of a 60-year old alcoholic french
woman
MNI/ICBM templates– MMontreal NNeurological IInstitute / IInternational CConsortium of BBrain
MMapping– Average of hundreds of brains– 241 brains were manually scaled to the Talairach brain to produce an
temporary template– MNI305 is made of 305 brains mapped to this template– ICBM152 is made of 152 brains registered to MNI305 (current template)– No Brodmann regions…
Spatial NormalisationSpatial NormalisationBrain templates
Spatial NormalisationSpatial NormalisationTalairach mapping
fMRI
1st registration
Structural space
2nd registration
Talairach space
High-resolutionstructural image
Talairach template
More smoothing if needed…
More statistical analysis…
More cluster analysis…
More pretty pictures…
Spatial NormalisationSpatial NormalisationTalairach mapping
but…
The resultsSpatial NormalisationSpatial Normalisation
Strongest activated cluster– 64 voxels.– Talairach coordinates: x=-55, y=-14, z=9 (activation focus).– Left side, slice 10.– Brodmann Area 42, Auditory Association Cortex
Spatial NormalisationSpatial NormalisationThe results
Spatial NormalisationSpatial Normalisation
2D
The results
The results in virtual reality
Spatial NormalisationSpatial Normalisation
Analysing groups of subjectsAnalysing groups of subjects
Individual analysis
Group mapping
ANCOVA
differences
similarities
Individual analysis
Group mapping
Using more advanced analysis methodsUsing more advanced analysis methods
Improved registration/warping (e.g. non-linear)Constrained BOLD fit / Wavelet denoisingNew randomisation methods (e.g. cyclic wavelet permutation of the residuals)Trend analysisPath analysisConnectivity analysisTime series extractionCorrelation/Partial Correlation analysisReal time fMRI analysisCombined EEG/fMRI…
ConclusionConclusion
Most of the analysis packages give you robust semi-automated methods of fMRI analysis “from MR to ANCOVA and much more…”The analysis process can look like a black box but we make our best to try to explain what we are doingIn all the labs, there is constant work on improving and validatingvalidating the existing methods and on writing the next ones…Please, be patient and understanding !!! (especially if you are a beta tester…)
BBrain IImage AAnalysis UUnitBiostatistics & ComputingInstitute of PsychiatryLondon, U.K.
Vincent P. GiampietroVincent P. [email protected]
Introduction toIntroduction to
fMRI AnalysisfMRI Analysis