Allineamento di Superfici CelebraliMethods in Biomedical Image
Processing and Analysis – Quantitative Analysis of statistical
parametric map in fMRI
Outline
• Statistical Parametric Map comparison
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
fMRI Qualitative Analysis Schema
GOAL
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
What are the areas actually active for the received stimulus
?
Answer to this question can be an hard task…
1. The data is volumetric 2. The data is noisy 3. It is hard to
find a direct
correlation of the color and Voxel Probability of activation
The analysis is likely to be presumptive … the doctor only verifies
a priori hypothesis …
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
Quantitative Analysis of SPM statistical Parametric Map
Data Aquisition
GOAL
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
Quantitative Analysis of SPM schema
Preprocessing: Segmentation and Co Registration with an anatomical
Atlas
GOAL : brief rappresentation of the information that you can read
in the activation map
Each active voxel is assigned to one anatomic area
For Each area is compute an activation activation
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
Activation Weighted Index : AWI
• Anatomical and Functional brain atlases are available in
particular, Juelich atlas divides brain into 121 regions.
• the SPM was normalized respect to its own maximum value and then
registered with Juelich atlas. For each functional area considered
by Juelich atlas, the Activation Weighted Index (AWI) is defined
as:
1 wi = is normalized voxel value in the jth area
NJ = is voxel total number in the jth area
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
AWI Vector: a navigator in the SPM
13 Broca’s Area L 16 Broca’s Area R 55 Primary somatosensory cortex
BA3a L' 65 Secondary
somatosensory cortex L‘ 91 Premotor cortex BA6 L
85 Visual cortex L 86 Visual cortex R 87 Visual cortex L
27 Inferior parietal lobulePF L 31 Inferior parietal lobulePFmL
(Wernicke)
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
AWI Vector: features Vector
MOST
ACTIVE
AREA
A N
O M
A L
O U
52 Primary somatosensory Cortex BA2 Right 0.04 21% 0.13 72%
54 Primary somatosensory Cortex BA1 Right 0.1 52% 0.18 99%
27 Inferior parietal lobule PF Left 0.06 31% 0.12 66% 33 Inferior
parietal lobule PFop Left 0.07 36% 0.14 77%
Mechanic Effect
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
fMRI Evaluation: Sensitivity and Specificity
• The AWI description is brief, but comprehensive and directly
comparable with an Appropriate Ground Truth.
• fMRI results are difficult to assess
• The definition of aGround Truth is A BIG PROBLEM !!!
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
•
• In this context, sensitivity index represents the algorithm
capability of correctly recognize functional areas actually related
to the corresponding functional task, whilst specificity index
represents the capability of correctly show as not activated areas
not related to the functional task under inspection.
fMRI Evaluation: Sensitivity and Specificity
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), TRUE (AWI TRUE AWI T
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Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
SPM Statistical Agreement
“Clinicians often wish to have data on, for example, cardiac stroke
volume or blood pressure where direct measurement without adverse
effects is difficult or impossible. The true values remain unknown.
Instead indirect methods are used, and a new method has to be
evaluated by comparison with an established technique rather than
with the true quantity. If the new method agrees sufficiently well
with the old, the old may be replaced. This is very different from
calibration, where known quantities are measured by a new method
and the result compared with the true value or with measurements
made by a highly accurate method. When two methods are compared
neither provides an unequivocally correct measurement, so we try to
assess the degree of agreement. But how?”
J. Martin Bland, Douglas G. Altman (1987) STATISTICAL METHODS FOR
ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
Lancet, i, 307-310.
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
Confusion matrix
• A confusion matrix (Kohavi and Provost, 1998) contains
information about actual and predicted classifications done by a
classification system. Performance of such systems is commonly
evaluated using the data in the matrix. The following table shows
the confusion matrix for a two class classifier:
Actual
Predicted
A
A
B
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
Cohen Kappa Index
• Cohen's Kappa measures the agreement between two raters who each
classify N items into C mutually exclusive categories
• where Pr(a) is the relative observed agreement among
raters,
and Pr(e) is the hypothetical probability of chance agreement,
using the observed data to calculate the probabilities of each
observer randomly saying each category.
• If the raters are in complete agreement then κ = 1. • If there is
no agreement among the raters other than what
would be expected by chance (as defined by Pr(e)), κ =-1
)Pr(1
)Pr()Pr(
e
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
Cohen Kappa Index : Example
The relative observed agreement among raters
the hypothetical probability of chance agreement, using the
observed data to calculate the probabilities of each observer
randomly saying each category
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
Actual
Predicted
A
A
8
B
B
3
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
Actual
Predicted
A
A
8
B
B
3
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
SPM Confusion matrix
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
Example
Actual
Predicted
A
A
8
B
B
3
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
Actual
Predicted
A
A
8
B
B
3
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
SPM Confusion matrix
• Consider two activation maps SPM2 SPM1 normalized in a unit
interval [0-1].
• The range is divided into a number of quantization levels
• SPM Confusion Matrix:
The cell(i, j) of the matrix contains the number of active voxels
that assumes value i-th in the SPM1 and j_th in the SPM2
i,j 1 2 3 4 5 6 7 8 9 10 11
Val 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
SPM Confusion matrix: Example
37.0 51.01
0
0
0-0.2
0.2-0.4
0.4-0.6
0.6-0.8
0.8-1
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
SPM Confusion Matrix: Real Data(1)
• Task: Left Finger Tapping
SW1 SW2 Intersection
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
SPM Confusion Matrix: Real Data(1)
-0.010 )Pr(1
)Pr()Pr( 1.0
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
SPM Confusion Matrix: Real Data(2)
• Task: Right Finger Tapping
SW1 SW2 Intersection
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
SPM Confusion Matrix: Real Data(2)
S P
M 1
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
• The Kappa index can be calculated if the following conditions are
true :
Instances are independent
Judges assess independently
The categories of the scale are independent mutually exclusive and
exhaustive
Not true
The SPM are processed indioendently
Statistical Agreement for Dependent Class
In this application the istance are the activation value of each
voxel the GLM is an univariate method than the activation of each
voxel is indipendent from the other
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
Weighted Kappa
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
Weighted Kappa
0.5 0.2
0.2 0.4
74.0 51.01
0
0
0-0.2
0.2-0.4
0.4-0.6
0.6-0.8
0.8-1
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
Weighted Kappa: Real Data(1)
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
Weighted Kappa: Real Data(2)
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
Another point of view : Information Overlap
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
Another point of view : information Overlap
0.5 0.2
0.2 0.4
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
0.5 0.2
0.2 0.4
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
information Overlap: Threshold 0.4
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
information Overlap: Threshold 0.6
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
information Overlap: Threshold 0.8
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
information Overlap Threshold 1
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
Comet
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
SPM Examples : good Agreement
• Task: Right Finger Tapping • SW1 FEAT FSL • SW2 Philips
iViewBODL
SW1 SW2 Intersection
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
SPM Examples : good Agreement
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
Conclusion
• The definition of the functional areas acctually releted to the
recived stimolus is an hard task
• A quantitative comparison could be a valuable aid for the
neuroradiologist for the identification of significantly active
areas.
• Same preprocessing steps are necessary for perform the SPM
evaluation, the goodness of the qualitative analysis is directly
related to the performance of the preprocessing phase :
Brain Segmentation (Lession3 30 May)
Registration ….
Methods in Biomedical Image Processing and Analysis – Quantitative
Analysis of statistical parametric map in fMRI
References
• Zang, J., Liang, L., Anderson, J. R., Gatewood, L., Rottengerg,
D. A., Strother, S. C., A Java-based processing pipeline evaluation
system for assessment of univariate general linear model and
multivariate canonical variate analysis-based pipelines,
Neuroinformatics, 6(2), 123- 134 (2008).
• Oakes, T. R., Johnstone, T., Ores Walsh, K. S., Greischar, L. L.,
Alexander, A. L., Fox, A. S., Davidson, R. J., Comparison of fMRI
motion correction software tools, Neuroimage, 28(3), 529-543
(2005).
• Morgan, V. L., Dawant, B. M., Li, Y. and Pickens, D.R.,
Comparison of fMRI statistical software packages and strategies for
analysis of images containing random and stimulus- correlated
motion, Comp. Med. Imaging Graph, 31(6),436-446 (2007).
• Pickens, D.R., Li, Y., Morgan, V. L. and Dawant, B. M.,
Development of computer-generated phantoms for FMRI software
evaluation”, Magnetic Resonance Imaging, 23(5), 653-663
(2001).
• http://www.fmrib.ox.ac.uk/fsl/ •
http://www.fmrib.ox.ac.uk/analysis/research/bet/ • Jaccard, P. The
distribution of the flora in the alpine zone, New Phytologist,
11(2), 37-50,
(1912). • http://en.wikipedia.org/wiki/Cohen%27s_kappa • Valentina
Pedoia, Vittoria Colli, Sabina Strocchi, Cristina Vite, Elisabetta
Binaghi, Leopoldo
Conte. fMRI analysis software tools: an evaluation framework, in
Proceedings SPIE Medical Imaging 2011, Feb 2011