|Dynamical Clustering Connectivity Model
Inspired by the N-body problem from physics, we studied brain connectivity using a simulation of dynamical nodes in a network. The nodes correspond to anatomical regions in the cortex and instead of gravity the forces between them correspond to their connectivity in the brain. From this simulation we record features such as particle speed, locations, and we can find different clusters of connectivity. In the simulations's final equilibrium state, we used particle locations to generate an N-body connectivity matrix that was able to effectivity discriminate disease.
1. G. Prasad, J. Burkart, S.H. Joshi, T.M. Nir, A.W. Toga, and P.M. Thompson, A Dynamical Clustering Model of Brain Connectivity Inspired by the N-Body Problem, Medical Image Computing and Computer-Assisted Intervention Workshop on Multimodal Brain Image Analysis, Nagoya, Japan, 2013.
|Refining Brain Connectivity Networks
The standard approach to understand brain connectivity is to parcellate the cortex into anatomical regions and find the relationship between these nodes as represented in a connectivity matrix and connectivity network. However, the choice of nodes plays a significant role in the resulting connectivity information and we developed a method to select the cortical parcellation and set of nodes that optimally represents connectivity in the context of disease or any type of effect we are trying to pick up in our dataset.
1. G. Prasad, S.H. Joshi, T.M. Nir, A.W. Toga, and P.M. Thompson, Refining Brain Connectivity Networks to Optimally Identify Brain Disease: The EPIC Algorithm, Organization for Human Brain Mapping Meeting, 2013.
|Flow-Based Connectivity in Alzhiemer's Disease
We developed a new maximum flow based measure of connectivity in the brain. Our method is based on the traditional maximum flow problem incoportationg prior information from tractography. We test the new measure on a cohort of healthy subjects and Alzheimer's Disease patients by comparing network measures derived from a flow based connectivity matrix.
1. G. Prasad, S.H. Joshi, T.M. Nir, A.W. Toga, and P.M. Thompson, Brain Connectivity based on Maximum Flow in Alzheimer's Disease: The EMFATIC Method, Organization for Human Brain Mapping Meeting, 2013.
2. G. Prasad, S.H. Joshi, T.M. Nir, A.W. Toga, and P.M. Thompson, Flow-Based Network Measures of Brain Connectivity in Alzheimer's Disease, IEEE International Symposium on Biomedical Imaging, 2013.
|Tractography Density Analysis in Alzheimer's Disease
We show how to optimize a tractography method, which computes fibers that represent the white matter pathways in our brain. The method uses high resolution diffusion MRI data combined with a lookup table to make computations more efficient. With this increased ability to compute fibers in the brain we study how the quantity or density of these fibers effects our measures of connectivity in the brain using a variety of network measures to understand differents in healthy subjects versus those with Alzheimer's disease.
1. G. Prasad, T.M. Nir, A.W. Toga, and P.M. Thompson, Fiber Density and Connectivity in Alzheimer's Disease, Organization for Human Brain Mapping Meeting, 2013.
2. G. Prasad, T.M. Nir, A.W. Toga, and P.M. Thompson, Tractography Density and Network Measures in Alzheimer's Disease, IEEE International Symposium on Biomedical Imaging, 2013.
|White Matter Analysis
We developed a novel representation of the structure of white matter tissure in the brain called a maximum density path (MDP). This representation allows for comparisons across a population of subjects and a simple visualization to understand the relationship that white matter pathways have with disease, aging, and genetics.
1. G. Prasad, S. Joshi, N. Jahanshad, J. Villalon, G.I. de Zubicaray, K.L. McMahon, N.G. Martin, M.J. Wright, I. Aganj, G. Sapiro, A.W. Toga, and P.M. Thompson, Genetic Analysis of Fibers in White Matter Pathways from HARDI Images, Organization for Human Brain Mapping Meeting, 2012. PDF
2. G. Prasad, S. Joshi, N. Jahanshad, A.W. Toga, and P.M. Thompson, White Matter Tract Analysis in 367 Adults using Fiber Clustering, Maximum Density Paths, and Curve Registration, Medical Image Computing and Computer-Assisted Intervention Workshop on Computational Diffusion MRI, 2011. PDF
3. G. Prasad, N. Jahanshad, I. Aganj, C. Lenglet, G. Sapiro, A.W. Toga, and P.M. Thompson, Atlas-based Fiber Clustering for Multi-subject Analysis of High Angular Resolution Diffusion Imaging Tractography, IEEE International Symposium on Biomedical Imaging, 2011. PDF
|Deformable Organisms Segmentation
We combined ideas from artificial life and deformable models to create a set of deformable organisms that are represented as meshes and are able to cooperatively follow a segmentation plan to locate the brain. We developed organisms representing the skin, eyes, and brain.
1. G. Prasad, A.A. Joshi, A.W. Toga, D. Terzopoulos, and P.M. Thompson, Brain Segmentation using Deformable Organisms and Error Learning, Medical Image Computing and Computer-Assisted Intervention Workshop on Mathematical Foundations of Computational Anatomy, 2011. PDF
2. G. Prasad, A.J. Joshi, P.M. Thompson, A.W. Toga, D.W. Shattuck, and D. Terzopoulos, Skull-stripping With Deformable Organisms, IEEE International Symposium on Biomedical Imaging (ISBI), 2011. PDF
|Uncertainty of Brain Segmentations
We developed a method using markov chain monte carlo (MCMC) to characterize the uncertainty in a segmentation of the brain. The framework is general enough to work with many types of image models and can identify instances when segmentations are hampered by poor image quality or abnormalities in the brain tissue. A detailed log of some of the research that went into this project can be found in my brain segmentation blog.
1. K.R. Beutner III, G. Prasad, E. Fletcher, C. DeCarli, and O.T. Carmichael, Estimating Uncertainty in Brain Region Delineations, Proc. of the Information Processing in Medical Imaging (IPMI), 2009. PDF