[gov3009-l] Applied Statistics Workshop - 2/11 - Kayhan Batmanghelich

Anton Strezhnev astrezhnev at fas.harvard.edu
Mon Feb 9 12:09:20 EST 2015


Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming *Kayhan
Batmanghelich*, a post-doc at the MIT Computer Science and Artificial
Intelligence Lab. He will be presenting work entitled *Joint Modeling
Imaging and Genetics: a Probabilistic Approach*.  Please find the abstract
below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations/presenter-kayhan-batmanghelich>
.

As usual, we will meet in CGIS Knafel Room 354 and lunch will be provided.
See you all there!

-- Anton

Title:  Joint Modeling Imaging and Genetics: a Probabilistic Approach

Abstract:
An increasing number of clinical and imaging research studies is collecting
various additional information including genetic data. The goals of the
emerging field of imaging genetics can be summarized into two aims: 1)
using imaging biomarkers as an intermediate phenotype to uncover underlying
biological mechanisms of diseases; 2) phenotype discovery.

In this talk, we will focus on the first goal, namely using imaging as an
intermediate phenotype, and briefly discuss the second goal of discovering
image-based phenotypes associated with a disease. We propose a unified
Bayesian framework for detecting genetic variants associated with a disease
while exploiting image-based features as an intermediate phenotype. Imaging
genetics methods typically comprise two separate steps. First, image
features are selected based on their relevance to the disease phenotype.
Second, a set of genetic variants is identified to explain the selected
features. In contrast, our method performs these tasks simultaneously to
ultimately assign probabilistic measures of relevance to both genetic and
imaging markers. We derive an efficient approximate inference algorithm
that handles high dimensionality of imaging genetic data. We evaluate the
algorithm on synthetic data and show that it outperforms traditional
models. We also illustrate the application of the method in the context of
the Alzhemer's disease (ADNI dataset).
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