Monday, 6 May 2013

Colloquium Presentation: Prof. Polina Golland, MIT, on better ways to investigate fMRI data.

Prof. Polina Golland
Computer Science and Artificial Intelligence Laboratory (CSAIL)
Massachusetts Institute of Technology

Title: Alignment-Free Exploratory Analysis of fMRI Data

Date: Thursday 9th May 2013, 12-1pm in Keats Reading Room (AVLG17) (video streaming to AV3 in the Ourimbah library)
If you would like to meet with Prof. Golland, please contact A/Prof Scott Brown (

Abstract: We present an exploratory method for simultaneous parcellation of multisubject fMRI data into functionally coherent areas. Our motivation comes from visual fMRI studies with increasingly large number of image categories. The method is based on a solely functional representation of the fMRI data and a hierarchical probabilistic model that accounts for both inter-subject and intra-subject forms of variability in fMRI responses. The resulting algorithm finds a functional parcellation of the individual brains along with a set of population-level clusters. The model eliminates the need for spatial normalization while still enabling us to fuse data from multiple subjects.
If time permits, I will also discuss our current research in
characterizing the spatial variability of activation patterns across
(Joint work with Danial Lashkari, Ramesh Sridharan, George Chen,
Ed Vul and Nancy Kanwisher.)

Bio: I am an associate professor in the EECS Department and the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. My primary research interest is in developing novel techniques for image analysis and understanding. I particularly enjoy working on algorithms that either explore the geometry of the world and the imaging process in a new way or improve image-based inference through statistical modeling of the image data. I am interested in shape modeling and representation, predictive modeling and visualization of statistical models. My current research focuses on developing statistical analysis methods for characterization of biological processes using images (from MRI to microscopy) as a source of information. In this domain, I am interested in modeling biological shape and function, how they relate to each other and vary across individuals.