CHIMERA: Clustering of heterogeneous disease effects via distribution matching of imaging patterns

Aoyan Dong, Nicolas Honnorat, Bilwaj Gaonkar, Christos Davatzikos

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


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.

Original languageEnglish (US)
Article number2487423
Pages (from-to)612-621
Number of pages10
JournalIEEE Transactions on Medical Imaging
Issue number2
StatePublished - Feb 1 2016
Externally publishedYes


  • Clustering
  • Coherent point drift
  • Distribution matching
  • EM-optimization
  • Gaussian mixture model
  • Heterogeneity

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Electrical and Electronic Engineering
  • Computer Science Applications


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