CHIMERA: Clustering of Heterogeneous Disease Effects via Distribution Matching of Imaging Patterns

• Published in: IEEE transactions on medical imaging Vol. 35; no. 2; pp. 612 - 621
• Main Authors: Aoyan Dong, Honnorat, Nicolas, Gaonkar, Bilwaj, Davatzikos, Christos
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aged; Aged, 80 and over; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Atrophy; Brain - diagnostic imaging; Cluster Analysis; Clustering; Clustering methods; coherent point drift; Dementia - diagnostic imaging; Disease control; Diseases; distribution matching; EM-optimization; Gaussian mixture model; Heterogeneity; Humans; Image Processing, Computer-Assisted - methods; Imaging; Magnetic Resonance Imaging; Male; Medical imaging; Middle Aged; Neuroimaging - methods; Parkinson Disease - diagnostic imaging; Pathology; Patients; Reproducibility of Results; Sociology; Statistics; Transformations
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2015.2487423

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.