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CONNECTIVITY-BASED PARCELLATION OF PUTAMEN
USING RESTING STATE FMRI DATA
Yiming Zhang1
, Aiping Liu1
, Sun Nee Tan2
, Martin J. McKeown2
and Z. Jane Wang1
1
Dept. of Electrical and Computer Engineering, University of British Columbia
2
Dept. of Neurology and Pacific Parkinson’s Research Centre, University of British Columbia
Brain Connectivity Studies
What is connectivity studies?
Brain connectivity studies can be conducted at either voxel (the minimum unit in fMRI data) level or the
Region-of-Interest(ROI) level. The latter one is more promising since voxel based method will bring heavy
computational burden. ROI based approaches require an accurate definition of ROI.
Connectivity-Based Brain Parcellation
What is functional magnetic resonance imaging(fMRI) ?
FMRI is a functional neuroimaging technique that measures brain activity by detecting associated changes
in blood flow. fMRI is a noninvasive imaging technique for studying the brain functions.
Connectivity studies, based on either resting state or task related data, focus on the interactions between
different brain regions instead of a single activate area. Connectivity studies often lead to a connectivity
network which will help us to have a better understanding about how the brain works.
Why connectivity-based method ?
Why do brain parcellation?
There are two types of methods to define ROIs. One is based on anatomical landmarks which is difficult
and often inaccurate. In addition, recent studies revealed that one anatomically integrated region could
have separated functional subunits1
. The other one, connectivity-based method, is designed based on
functional properties of brain regions, either intra-region organization or inter-region interaction.
Connectivity-based method is more suitable and accurate as a preprocess procedure in connectivity
analysis.
Functional Parcellation of Putamen Region
What is the function of putamen ?
Putamen, locating in forebrain, forms the dorsal striatum along with caudate. Dorsal striatum is considered to be responsible for motion control.
Why is it important to do functional parcellation on putamen?
In motor control loops, recent studies revealed that dorsal striatum could be divided into Dorsolateral Striatum(DLS) and Dorsomedial Striatum(DMS) where
DLS is related with habitual control while DMS is related with goal-directed control2
. Parkinson’s disease patients suffer from dysfunctional habitual control
which is believed to be related with DLS, functional parcellation of putamen will provide us a better understanding about Parkinson’s disease.
Important regions in Motor control loops
In motor control loops, putamen mainly interacts with three areas, Orbitofrontal(OF), Cingulate Gyrus(CG) and Sensorimotor(SMA). SMA mainly interacts
with DLS while OF and CG are mainly correlated with DMS, but their relationship are not rigorous. Right is the description of connections between Putamen
and three regions in motor control loops.
Framework Overview
Step 1 : Data prepare
Task region signal Reference Region (average)
Reference Region 1
Reference Region 2
Reference Region 3
Apply linear regression model with spatially regularized
fused Lasso penalty to get connectivity features of each
voxels and then iteratively merge voxel into groups, get
three parcellation results.
Step 2 : Get parcels according to different
reference regions
Step 3 : Combine these results into one final
result using graph cut algorithm
Different colors indicate different groups.
Spatially Regularized Fused Lasso Model Graph-Cut Optimization
Why spatially regularized fused Lasso?
Normal Lasso(Least Absolute Shrinkage and Selection Operator) regression is a shrinkage and selection
method that encourages sparsity among variables. Fused Lasso is another type of regularization method
that encourage features to be similar between variables.
Why linear model ?
We use linear model to learn the connectivity features between reference region and voxels in task
region.
What is fused Lasso?
It is well known that similar connectivity patterns tend to exist in spatially adjacent voxels, with spatially
regularized fused Lasso, we could force the adjacent voxels to share similar connectivity patterns despite
of noise.
Formulated optimization problem
Test on Real FMRI Data Set
What is Graph-Cut optimization?
Graph-Cut algorithm is a combinatorial optimization technique which is widely applied in area of
computer vision, like image smoothing and segmentation3
.
What does it do?
In our framework, Graph-Cut algorithm is used for assign voxels which have different group assignments
in results according to different reference regions.
Test on Synthetic Data Set
In this work, we tested our algorithm on a synthetic data
set with two different settings: one data set(syn-data1)
was generated with normal spatial noise, another data
set(syn-data1) was generated with few voxels’ signal
corrupted by large noise. We compared results with
other two popular connectivity based parcellation
methods: K-means clustering and Modularity detection.
Fig. 1 and Table 1 show the results on synthetic data
set.
Conclusion
In real data application, four healthy subjects were recruited,
resting-state data were gathered and preprocessed. Putamen
was parcellated into two parts respectively according to CG,
OF and SMA. Graph-Cut optimization was applied to combine
the three results, t-test was used to determine which region is
DLS or DMS. Final parcellation result was obtained and shown
in fig.2.
Results showed the parcellation were consistent across
subjects, connectivity patterns were in accordance with
our prior knowledge.
We proposed a novel framework for parcellating one brain region into several functional subunits based on
their functional connectivity patterns with other reference brain regions. Simulation results showed that we
have bested other two popular methods in dealing data with few outliers that were corrupted by large noise.
In a real fMRI study, we succeed in parcellating Putamen region into two functional subunits, DLS and DMS.
The extracted functional subunits themselves are of great interest to study the influence of aging and
disease. In the future, we plan to use this method to investigate the changes of DLS in subjects with
Parkinson’s disease since it may serve as a potential biomarker of Parkinson’s disease.
Reference:
[1] Johansen-Berg, Heidi, et al. "Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex." Proceedings of the National Academy of
Sciences of the United States of America 101.36 (2004): 13335-13340.
[2] Gruber, Aaron J., and Robert J. McDonald. "Context, emotion, and the strategic pursuit of goals: interactions among multiple brain systems controlling motivated
behavior." Frontiers in behavioral neuroscience 6 (2012).
[3] Papadimitriou, Christos H., and Kenneth Steiglitz. Combinatorial optimization: algorithms and complexity. Courier Corporation, 1998.
Linear regression model