Cataloged from PDF version of article.Thesis (Ph.D.): Bilkent University, Department of Materials Science and Nanotechnology, İhsan Doğramacı Bilkent University, 2018.Includes bibliographical references (leaves 191-216).The brain is a large-scale, intricate web of neurons, known as the connectome.
By representing the brain as a network i.e. a set of nodes connected by edges,
one can study its organization by using concepts from graph theory to evaluate
various measures. We have developed BRAPH - BRain Analysis using graPH
theory, a MatLab, object-oriented freeware that facilitates the connectivity analysis
of brain networks. BRAPH provides user-friendly interfaces that guide the
user through the various steps of the connectivity analysis, such as, calculating
adjacency matrices, evaluating global and local measures, performing group
comparisons by non-parametric permutations and assessing the communities in a
network. To demonstrate its capabilities, we performed connectivity analyses of
structural and functional data in two separate studies. Furthermore, using graph
theory, we showed that structural magnetic resonance imaging (MRI) undirected
networks of stable mild cognitive impairment (sMCI) subjects, late MCI converters
(lMCIc), early MCI converters (eMCIc), and Alzheimer’s Disease (AD)
patients show abnormal organization. This is indicated, at global level, by decreases
in clustering and transitivity accompanied by increases in path length
and modularity and, at nodal level, by changes in nodal clustering and closeness
centrality in patient groups when compared to controls. In samples that do not
exhibit differences in the undirected analysis, we propose the usage of directed
networks to assess any topological changes due to a neurodegenerative disease.
We demonstrate that such changes can be identified in Alzheimer’s and Parkinson’s
patients by using directed networks built by delayed correlation coefficients.
Finally, we put forward a method that improves the reconstruction of the brain
connectome by utilizing the delays in the dynamic behavior of the neurons. We
show that this delayed correlation method correctly identifies 70% to 80% of the
real connections in simulated networks and performs well in the identification of
their global and nodal properties.by Mite Mijalkov.Ph.D