computational modeling of anatomical and functional variability in populations
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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
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