quantitative analysis of statistical parametric activation map

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
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 …
Quantitative Analysis of SPM statistical Parametric Map
Data Aquisition
GOAL
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
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
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)
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
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 !!!
•
• 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
12110max1
11min11
0max1
min
) - TRUE, - AWI ( - TRUE) ( - AWI) (T-
) - TRUE, AWI ( - TRUE) ( AWI F
), TRUE (AWI TRUE AWI T
J
J
J
J
J
J
J
J
J
J
J
J
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.
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
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
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
Actual
Predicted
A
A
8
B
B
3
Actual
Predicted
A
A
8
B
B
3
SPM Confusion matrix
Example
Actual
Predicted
A
A
8
B
B
3
Actual
Predicted
A
A
8
B
B
3
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
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
SPM Confusion Matrix: Real Data(1)
• Task: Left Finger Tapping
SW1 SW2 Intersection
-0.010 )Pr(1
)Pr()Pr( 1.0
• Task: Right Finger Tapping
S P
M 1
• 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
Weighted Kappa

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
Weighted Kappa: Real Data(1)
Weighted Kappa: Real Data(2)
Another point of view : Information Overlap

Another point of view : information Overlap
0.5 0.2
0.2 0.4
0.5 0.2
0.2 0.4
information Overlap: Threshold 0.4
information Overlap Threshold 1
Comet
SPM Examples : good Agreement
• Task: Right Finger Tapping • SW1 FEAT FSL • SW2 Philips iViewBODL
SPM Examples : good Agreement
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 ….
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

quantitative analysis of statistical parametric activation map

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