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Workshop 6 Titles and Abstracts
Author: Jim Fallon, Department of Anatomy and Neurobiology, University of California, Irvine
Title: Neuroanatomy and Imaging
Behavior is assumed to emerge from specific circuits in the brain. These circuits are routinely inferred from functional brain imaging patterns. Differences in patterns of functional images between, for example, task conditions, drug conditions, and between control and pathological conditions are routinely used to inform researchers of basic biological mechanisms and pathophysiological processes in normal and abnormal brain function. There are, however, multiple levels and principles of organization of brain circuitry, often beyond the resolution and/or functional capabilities of imaging techniques such as PET, fMRI, and DTI. Furthermore, each neurological/psychiatric disorder differentially affects neuroanatomical modules and types of circuitry, and these must be borne in mind in the analyses and discussion of implied circuitry in imaging experiments.
Author: Polina Golland, Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology
Title: Modeling Anatomical Heterogeneity in Populations
We present iCluster, a fast and efficient algorithm that clusters a
set of images while co-registering them using a parameterized,
nonlinear transformation model. The output of the algorithm is a small
number of template images that represent different modes in a
population. This is in contrast with traditional, hypothesis-driven
computational anatomy approaches that assume a single template to
construct an atlas. We derive the algorithm based on a generative
model of an image population as a mixture of deformable template
images. The experimental results demonstrate that the algorithm can
discover interesting sub-populations, suggesting applications in
atlas-based segmentation and statistical analysis of anatomical
differences in clinical studies.
This is joint work with Mert Sabuncu and Serdar Balci.
Author: Xiaoping P. Hu, Ph.D., Professor and Georgia Research Alliance, Endowed Eminent Scholar in Imaging, Coulter Department of Biomedical Engineering, Georgia Tech and Emory University
Title: Brain Connectivity Derived from fMRI Data
Functional MRI (fMRI) not only allows for localizing specific brain activity but also provides temporal information that can be used to derive connectivity within the brain. This paper will describe some techniques for assessing brain connectivity and demonstrate them with exemplar applications. Specifically, we will discuss assessing local coherence in fMRI data based on the integration of correlation function and its neurobiological relevance and applications. In addition, analysis of effective connectivity using Granger causality analysis will be described, with an emphasis on a multivariate approach and its application to examine group differences in tactile perception and to examine dynamic changes in the motor network during fatigue.
Author: John Melonakos, School of ECE, Georgia Institute of Technology
Title: Geodesic Tractography Segmentation for DW-MRI Analysis
Many frameworks have been proposed for the analysis of brain DW-MRI imagery. The objective of these frameworks is to yield a greater understanding of structure and connectivity within the brain and the relation of these to function. In this talk, we present a framework for the analysis of DW-MRI datasets that consists of two components: 1) an optimal path connecting two regions of interest and 2) a volumetric fiber bundle segmentation, initialized on the optimal path. This framework has the advantage of providing both connectivity and structural information about fiber bundles. Also, in this talk, we discuss the pros/cons of this framework and challenges in the state-of-the-art of fiber bundle segmentation.
Author: William (Sandy) Wells, Department of Radiology, Brigham and Women's Hospital
Title: A Marginalized MAP Approach and EM Optimization for Pair-Wise Registration
We formalize the pair-wise registration problem in
a maximum a posteriori (MAP) framework that employs a
multinomial model of joint intensities with parameters for
which we only have a prior distribution. To obtain an MAP
estimate of the aligning transformation alone, we treat the
multinomial parameters as nuisance parameters, and
marginalize them out. If the prior on those is
uninformative, the marginalization leads to registration by
minimization of joint entropy. With an informative prior,
the marginalization leads to minimization of the entropy of
the data pooled with pseudo observations from the prior. In
addition, we show that the marginalized objective function
can be optimized by the Expectation-Maximization (EM)
algorithm, which yields a simple and effective iteration for
solving entropy-based registration problems.
Experimentally, we demonstrate the effectiveness of the
resulting EM iteration for rapidly solving a challenging
intra-operative registration problem.
This is joint work with Lilla Zollei and Mark Jenkinson. |
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