TY - JOUR
T1 - CHIMERA
T2 - Clustering of heterogeneous disease effects via distribution matching of imaging patterns
AU - Dong, Aoyan
AU - Honnorat, Nicolas
AU - Gaonkar, Bilwaj
AU - Davatzikos, Christos
N1 - Funding Information:
This work was supported by the National Institutes of Health Grant R01-AG014971. The authors would like to thank Alzheimer''s Disease Neuroimaging Initiative (ADNI) (NIH Grant U01 AG024904) and DOD ADNI (Department of Defense Award W81XWH-12-2-0012) for the data collection and sharing. ADNI is funded by the National Institute on Aging , the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the organizations as listed at http://www.adni-info.org . Asterisk indicates corresponding author. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
Publisher Copyright:
© 2015 IEEE.
PY - 2016/2/1
Y1 - 2016/2/1
N2 - Many brain disorders and diseases exhibit heterogeneous symptoms and imaging characteristics. This heterogeneity is typically not captured by commonly adopted neuroimaging analyses that seek only a main imaging pattern when two groups need to be differentiated (e.g., patients and controls, or clinical progressors and non-progressors). We propose a novel probabilistic clustering approach, CHIMERA, modeling the pathological process by a combination of multiple regularized transformations from normal/control population to the patient population, thereby seeking to identify multiple imaging patterns that relate to disease effects and to better characterize disease heterogeneity. In our framework, normal and patient populations are considered as point distributions that are matched by a variant of the coherent point drift algorithm. We explain how the posterior probabilities produced during the MAP optimization of CHIMERA can be used for clustering the patients into groups and identifying disease subtypes. CHIMERA was first validated on a synthetic dataset and then on a clinical dataset mixing 317 control subjects and patients suffering from Alzheimer's Disease (AD) and Parkison's Disease (PD). CHIMERA produced better clustering results compared to two standard clustering approaches. We further analyzed 390 T1 MRI scans from Alzheimer's patients. We discovered two main and reproducible AD subtypes displaying significant differences in cognitive performance.
AB - Many brain disorders and diseases exhibit heterogeneous symptoms and imaging characteristics. This heterogeneity is typically not captured by commonly adopted neuroimaging analyses that seek only a main imaging pattern when two groups need to be differentiated (e.g., patients and controls, or clinical progressors and non-progressors). We propose a novel probabilistic clustering approach, CHIMERA, modeling the pathological process by a combination of multiple regularized transformations from normal/control population to the patient population, thereby seeking to identify multiple imaging patterns that relate to disease effects and to better characterize disease heterogeneity. In our framework, normal and patient populations are considered as point distributions that are matched by a variant of the coherent point drift algorithm. We explain how the posterior probabilities produced during the MAP optimization of CHIMERA can be used for clustering the patients into groups and identifying disease subtypes. CHIMERA was first validated on a synthetic dataset and then on a clinical dataset mixing 317 control subjects and patients suffering from Alzheimer's Disease (AD) and Parkison's Disease (PD). CHIMERA produced better clustering results compared to two standard clustering approaches. We further analyzed 390 T1 MRI scans from Alzheimer's patients. We discovered two main and reproducible AD subtypes displaying significant differences in cognitive performance.
KW - Clustering
KW - Coherent point drift
KW - Distribution matching
KW - EM-optimization
KW - Gaussian mixture model
KW - Heterogeneity
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U2 - 10.1109/TMI.2015.2487423
DO - 10.1109/TMI.2015.2487423
M3 - Article
C2 - 26452275
AN - SCOPUS:84959386649
SN - 0278-0062
VL - 35
SP - 612
EP - 621
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 2
M1 - 2487423
ER -