1. Computational Modeling of Anatomical and Functional Variability in Populations Polina Golland Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology

2. 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.

3. Polina Golland, MIT CSAIL Aging Study 400 subjects, ages 18-96 –Some older subjects diagnosed with MCI 3 Templates: Young Old Middle

4. Polina Golland, MIT CSAIL Age Distributions 2 Templates3 Templates

5. 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.

6. Polina Golland, MIT CSAIL Function Migration in Tumor Patients

7. 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 Connectivity Control Template Schizophrenia Template

8. Polina Golland, MIT CSAIL Connectivity Changes in Schizophrenia