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Evaluation of mixed clustering for fMRI analysis
NemoImage
11, Number
5, 2000,
Part 2 of 2
Parts
1 D E k[”
METHODS
- ACQUISITION
Evaluation of Mixed clustering for fMRI analysis Frederique Diebold*, Jack Foucber*i,
Marc Etienne Meyer*
“Institut de physique biologique - CNRS UPRES-A 7004 - ULP 67000 Strasbourg France TClinique neurologique - Hospices civiles de Strasbourg - France Introduction The classical approach of fMR1 analysis consists in the modelling of the temporal modulation of the cerebral activity. However, it can be assumed that more than intensity differences, one could find unexpected temporal differences in activated regions. Several approaches have been proposed for this blind source separation problem applied to fMR1 (PCA, Fuzzy clustering’, ICA’). We propose to explore the possibility of a mixed clustering approach using simulated data. Method Mixed
clustering
index-a.
is based on the successive
use of two algorithms
implemented
under Medimax
(htrp://alsace/iph/girim/medimax/
html):
K-means: it starts from a limited number of primary centers (100 voxel time course) preselected to be as orthogonal as possible (correlation coefficient - cc - as close as possible to 0). The other voxels are aggregated with them as a function of the distance from each, using cc. A new temporal evolution is computed for each aggregate and serves as a secondary center, and so on, until a stabilisation value has been reached (stability 100%). Hierarchical aggregation : the resulting groups are further aggregated using the same distance criteria. The resulting dendogram (see fig 2) represents the distance between groups of voxels. A mixed approach permits to maintain the computational speed (K-means) and to intuitively visualize the distance between clusters (hierarchical aggregation). Application We injected two known functions (boxcar and boxcar plus decrement cc 0.45) within the subject was at rest (200 scans 642x32, S/N-SO). Signal modification ranges from 0.5 to 5 %. Data was further spatially smoothed. A limited number of voxels were first preselected using a cc of 0.2 with the boxcar function.
a true registered
time series acquired
while
Results For the lowest (0.5%) signal modification, 40-70% of the voxels were grouped in the same cluster, with limited unmodified voxels (O-40%), depending on the node explored. Quasi perfect sorting can be achieved for 2-3% signal change. Furthermore, despite the undeterministic behavior of the k-mean algorithm, dense primary cluster preselection permits to obtain reproductible results when algorithm is run four times on the same data.
Fig.
2
Conclusion Thus the mixed clustering exploration of the different
approach activation
leads to interesting components.
results
regarding
its discrimination
power.
It also
permits
an easy
Reference 1. Baumgartner R, et al. Quantification in functional magnetic resonance imaging: Reson Imaging 1998;16(2): 1 IS-25 2. McKeown MJ, et al. Spatially independent activity patterns in functional MRI Nat1 Acad Sci U S A. 1998 Feb 3:9X3):803-10.
s503
fuzzy
clustering
data during
vs. correlation
the snoop
analysis
color-naming
Magn
task. Proc
11, Number
5, 2000,
Part 2 of 2
Parts
1 D E k[”
METHODS
- ACQUISITION
Evaluation of Mixed clustering for fMRI analysis Frederique Diebold*, Jack Foucber*i,
Marc Etienne Meyer*
“Institut de physique biologique - CNRS UPRES-A 7004 - ULP 67000 Strasbourg France TClinique neurologique - Hospices civiles de Strasbourg - France Introduction The classical approach of fMR1 analysis consists in the modelling of the temporal modulation of the cerebral activity. However, it can be assumed that more than intensity differences, one could find unexpected temporal differences in activated regions. Several approaches have been proposed for this blind source separation problem applied to fMR1 (PCA, Fuzzy clustering’, ICA’). We propose to explore the possibility of a mixed clustering approach using simulated data. Method Mixed
clustering
index-a.
is based on the successive
use of two algorithms
implemented
under Medimax
(htrp://alsace/iph/girim/medimax/
html):
K-means: it starts from a limited number of primary centers (100 voxel time course) preselected to be as orthogonal as possible (correlation coefficient - cc - as close as possible to 0). The other voxels are aggregated with them as a function of the distance from each, using cc. A new temporal evolution is computed for each aggregate and serves as a secondary center, and so on, until a stabilisation value has been reached (stability 100%). Hierarchical aggregation : the resulting groups are further aggregated using the same distance criteria. The resulting dendogram (see fig 2) represents the distance between groups of voxels. A mixed approach permits to maintain the computational speed (K-means) and to intuitively visualize the distance between clusters (hierarchical aggregation). Application We injected two known functions (boxcar and boxcar plus decrement cc 0.45) within the subject was at rest (200 scans 642x32, S/N-SO). Signal modification ranges from 0.5 to 5 %. Data was further spatially smoothed. A limited number of voxels were first preselected using a cc of 0.2 with the boxcar function.
a true registered
time series acquired
while
Results For the lowest (0.5%) signal modification, 40-70% of the voxels were grouped in the same cluster, with limited unmodified voxels (O-40%), depending on the node explored. Quasi perfect sorting can be achieved for 2-3% signal change. Furthermore, despite the undeterministic behavior of the k-mean algorithm, dense primary cluster preselection permits to obtain reproductible results when algorithm is run four times on the same data.
Fig.
2
Conclusion Thus the mixed clustering exploration of the different
approach activation
leads to interesting components.
results
regarding
its discrimination
power.
It also
permits
an easy
Reference 1. Baumgartner R, et al. Quantification in functional magnetic resonance imaging: Reson Imaging 1998;16(2): 1 IS-25 2. McKeown MJ, et al. Spatially independent activity patterns in functional MRI Nat1 Acad Sci U S A. 1998 Feb 3:9X3):803-10.
s503
fuzzy
clustering
data during
vs. correlation
the snoop
analysis
color-naming
Magn
task. Proc