Computational Modeling of Anatomical and Functional Variability in Populations

Polina Golland

Computer Science and Artificial Intelligence LaboratoryMassachusetts Institute of Technology

Polina Golland, MIT CSAIL

Population Modeling

• Traditional Approach:– External information defines populations

• Images explain variability– Unimodal assumption: “average brain”

• Computational anatomy

• Our solution:– Images define populations

• External information correlates with image structure– Key idea: multiple templates

• Collaborators and Pubs:– R. Buckner (Harvard, HMS), M. Shenton (BWH, HMS)– Sabuncu et al. IEE TMI 2009.

Polina Golland, MIT CSAIL

Aging Study

• 400 subjects, ages 18-96– Some older subjects diagnosed with MCI

3 Templates:

Young OldMiddle

Polina Golland, MIT CSAIL

Age Distributions

2 Templates 3 Templates

Polina Golland, MIT CSAIL

Functional Geometry• Anatomy-free model of connectivity

– Use co-activation to embed in a functional space– Align embedded patterns across subjects

• Collaborators & Pubs:– A. Golby (BWH, HMS)– Langs et al. NIPS 2010, IPMI 2011.

Polina Golland, MIT CSAIL

Function Migration in Tumor Patients

Polina Golland, MIT CSAIL

• Unified model– Functional co-activations (fMRI)– Anatomical connectivity (DWI)– Population differences

• Collaborators & Pubs:– C.F. Westin, M. Kubicki (BWH, HMS)– Venkataraman et al. MICCAI 2010

Joint Model of ConnectivityControl Template

CA

CF

Schizophrenia Template

SA

SF

Polina Golland, MIT CSAIL

Connectivity Changes in Schizophrenia