Title Information
Title
Computational Brain Connectivity Using Diffusion MRI
Name: Personal
Name Part
Demiralp, Cagatay
Role
Role Term: Text
creator
Origin Information
Copyright Date
2012
Physical Description
Extent
xx, 113 p.
digitalOrigin
born digital
Note
Thesis (Ph.D. -- Brown University (2012)
Name: Personal
Name Part
Laidlaw, David
Role
Role Term: Text
Director
Name: Personal
Name Part
Hughes, John
Role
Role Term: Text
Reader
Name: Personal
Name Part
Mumford, David
Role
Role Term: Text
Reader
Name: Corporate
Name Part
Brown University. Computer Science
Role
Role Term: Text
sponsor
Genre (aat)
theses
Abstract
This dissertation shows that qualitative and quantitative characterization of patterned structures in brain connectivity data obtained using diffusion MRI not only improves the exploration of the intricate space of brain connectivity but also provides clinically meaningful measures, quantifying normal and pathological variation in the brain. To this end, we introduce a set of computational and mathematical models, algorithms, and interactive tools to explore, understand, and characterize diffusion-derived structural brain connectivity. We contribute to all stages of modeling, visualization, and analysis of brain connectivity. In modeling, our contributions are twofold. First, we model the joint distribution of local neural fiber configurations with Markov random fields and infer the most likely configuration with maximum a posteriori estimation. We demonstrate this framework's use in resolving fiber crossings. Second, we introduce new planar map representations of three-dimensional neural tract datasets. These planar representations improve the exploration of brain connectivity by reducing visual and interaction complexity. In visualization, we contribute to structure-preserving color mappings. First, we introduce Boy's surface as a model for coloring 3D line fields and show results from its application in visualizing orientation in diffusion MRI brain datasets. This coloring method is smooth and one-to-one except on a set of measure zero. Second, we propose a general coloring method based on manifold embedding that conveys spatial relations among neural fiber tracts perceptually. We also introduce a new bivariate coloring model, the flat torus, that allows finer adjustments of coloring arbitrarily. We contribute to both local and global analysis of brain connectivity. In local analysis, we introduce a geometric slicing-based coherence measure for clusters of neural tracts. Clustering refinement based on this measure leads to a significant improvement in clustering quality that is not possible directly with standard methods. We also introduce tract-based probability density functions and demonstrate their effective use in nonparametric hypothesis testing and classification. In global analysis, we propose computing the ranks of persistent homology groups in the neural tract space. This captures the effects of diffuse axonal dropout and provides a global descriptor of structural brain connectivity.
Subject
Topic
Computational brain connectivity
Subject
Topic
diffusion MRI
Subject
Topic
maximum a posteriori
Subject
Topic
diffusion tensor
Subject
Topic
crossing brain fibers
Subject
Topic
mixture of fiber orientations
Subject
Topic
tractography
Subject
Topic
visual analysis
Subject
Topic
interaction
Subject
Topic
multiscale
Subject
Topic
hierarchical clustering
Subject
Topic
brain map
Subject
Topic
structure preserving
Subject
Topic
coloring
Subject
Topic
line field
Subject
Topic
real projective plane
Subject
Topic
Boy's surface
Subject
Topic
immersion
Subject
Topic
embedding
Subject
Topic
perceptual
Subject
Topic
neural tract
Subject
Topic
cluster
Subject
Topic
coherence
Subject
Topic
clustering
Subject
Topic
refinement
Subject
Topic
slicing
Subject
Topic
Gaussian mixture model
Subject
Topic
expectation maximization
Subject
Topic
cycle
Subject
Topic
homology
Subject
Topic
persistent homology
Subject
Topic
homology groups
Subject
Topic
rank
Subject
Topic
biomarker
Subject
Topic
tract based
Subject
Topic
probability density function
Subject
Topic
density estimation
Subject
Topic
histogram
Subject
Topic
kernel
Subject
Topic
nonparametric hypothesis testing
Subject
Topic
classification.
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/1010351")
Topic
Markov random fields
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/1738538")
Topic
Diffusion tensor imaging
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/794769")
Topic
Abstraction
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/1763130")
Topic
Multiscale modeling
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/1909654")
Topic
Hierarchical clustering (Cluster analysis)
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/837764")
Topic
Brain mapping
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/1131203")
Topic
Stability
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/1122346")
Topic
Social classes
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/831950")
Topic
Biochemical markers
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/1058273")
Topic
Permutations
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/1697073")
Topic
Classification
Record Information
Record Content Source (marcorg)
RPB
Record Creation Date (encoding="iso8601")
20131218
Language
Language Term: Code (ISO639-2B)
eng
Language Term: Text
English
Identifier: DOI
10.7301/Z0N014V6
Access Condition: rights statement (href="http://rightsstatements.org/vocab/InC/1.0/")
In Copyright
Access Condition: restriction on access
Collection is open for research.
Type of Resource (primo)
dissertations