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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 Laboratory Massachusetts Institute of Technology. Population Modeling. Traditional Approach: External information defines populations

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Computational Modeling of Anatomical and Functional Variability in Populations

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

  2. 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) • Sabuncuet al. IEE TMI 2009.

  3. Aging Study • 400 subjects, ages 18-96 • Some older subjects diagnosed with MCI 3 Templates: Old Young Middle

  4. Age Distributions 2 Templates 3 Templates

  5. 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. Function Migration in Tumor Patients

  7. Joint Model of Connectivity Control Template • 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 Schizophrenia Template

  8. Connectivity Changes in Schizophrenia

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