Blind Separation of sources
in function MRI Sequences
Presented By:
Eldad Klaiman
Limor Goldenberg
Supervised By:
Michael & Alex Bronstein
Dr. Michael Zibulevsky
The Kasher Contest - In memory of Yehoraz Kasher
The Problem
functional MRI:
Blind Source Separation
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Important tool for studying the
human brain activity.
High spatial resolution,
flexibility, harmlessness – made
it popular.
The BOLD technique: produce
an image of the blood
oxygenation level throughout
the brain.
A sequence of scans is in a
short period of time, when the
subject is asked to perform
some task. High oxygenation
levels represent high activity of
the brain regions responsible for
the task.
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Linear mixture of independent
sources
No a priori information is known
about their properties.
“Blind Source Separation" =
the problem of separating such
sources.
There exist powerful tools to
solve it.
Focus on the approach of
sparse representations, which
has proved its advantages in
different works in the field.
fMRI-BSS Model
Sources
Separated
Sources
Mixtures
S1
M1’
M1
S2
Pre
Processing
fMRI
Mn
Sn
m  As
S1’
Separation
Mn’
-Noise removal
-Identify
Background
-Sparse
Representation
Sn’
ŝ  A 1  m
fMRI Simulation
• Background – Brain Image.
(x  x )  ( y  y )
S
(
x
,
y
)

exp{
}
• Spatial Function:

• Hemodynamics: H (t )  t  exp{  (t  t ) }
2
2
0
0
i
s
2
0.3
i
t
0
• Gaussian Noise
M (k )   Si ( x, y )  H i (t ) Background  Noise
i
fMRI simulator GUI
fMRI Simulator - Results
“fMRI” frames
Hemodynamics
Preprocessing –
Sparse Representations
• Wavelet Packets is used
to create sparse images.
• “Best” Node is selected
by sparseness Criteria
• Scatter plot of resulting images:
chasing the illusive “X”
Geometric Separation
• Clustering - FCM.
• Angle Histogram.
Separation Example
Source #1:
3 spatial
components
Source #2:
2 spatial
components
Issues Encountered
• Preprocessing : Zero-mean, LPF, etc.
• Sparseness Criteria :
Shannon entropy selected.
• Stability / Parametric Sensitivity :
thresholds.
Principal Component Analysis
• Problems of high order:
more mixtures than sources
• Problem dimension reduced using PCA
PRINCOMP( )
PCA Revelations
(1) Background Separated
from activity sources
(2) No need to know
the exact number of
sources.
ICA - Infomax
• Artificial Neural Network Viewpoint,
maximize output Entropy.
• InfoMax ICA Matlab Toolbox:
(courtesy of Scott Makeig & Co.)
• Preliminary Results can be obtained without
mixture preprocessing.
ICA Separation Example
ICA Notes
• Sign and Order limitations.
• Improved robustness and quality, compared
to geometric separation.
• In most cases, the sparse representation
improved the quality of separation.
Application on
the Real Thing
Real fMRI Issues
False Artifact Sources –
created due to head
movement, Noise.
Background separated
from activity sources.
Conclusions
• Achieved good results by geometric and
ICA separation.
• ICA – robustness, quality.
• PCA – model selection, added values.
• Potential as fMRI analysis tool.
– Quick, low cost.
– Exact knowledge of simulation flow - not needed.
– Not relying on high time resolution.
Further Progress
• A new horizon for fMRI-ICA academic
research and projects.
• A “friendly” and enhanced fMRI-ICA
application was developed for simple, useroriented application of algorithm.
• Experimental application of the separation
algorithm on LORETA (EEG-CAT).
Thanks to…
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Nethaniel’s Brain
Dr. Michael Zibulevsky
Johanan Erez and the Lab team
Michael & Alex Bronstein
Anat Grinfeld
Sparseness Criteria
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x 1   xi
L1:
% x : xi  0
L0:
Shannon Entropy: H ( x)   xi log xi
Clusters: q  max( d )
min(  )
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