computational modeling of anatomical and functional variability in populations

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