Functional brain networks constructed in subjects’ native spaces
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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu |
Master's thesis
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Author
Date
2018-08-20
Department
Major/Subject
Computer Science
Mcode
SCI3042
Degree programme
Master’s Programme in Computer, Communication and Information Sciences
Language
en
Pages
65
Series
Abstract
Due to the structure of the human brain and its functioning, it can be modelized as a network, i.e. a collection of nodes connected by links. One of the approaches consists in constructing functional brain networks, where nodes are identified by regions of interest (ROIs) composed of neurons with similar functional activation. The main issue of this kind of approaches is how to define nodes and links. In a typical functional brain network construction pipeline, one critical step is the registration of brain areas into templates which makes possible statistical analysis and comparison across different subjects and studies. Different registration methods can lead to different results. Here, we compare standard space registration method with a new method, called inverse-registration, starting from raw data provided by functional Magnetic Resonance Imaging (fMRI). In standard registration, data is projected into a standard common space, which is the same across different subjects and studies. In inverse registration approach, a template from Standard space is (inverse) registered to structural space and the obtained images are finally registered to the functional subject space. We found out that inverse registration leads to nodes with increased functional homogeneity, while basic network properties are not significantly affected. These results suggest that inverse registration is a better registration method, although it is very recent and further investigation is needed before generalizing this statement.Description
Supervisor
Saramäki, JariThesis advisor
Korhonen, OnervaKeywords
brain networks, computational neuroscience, inverse registration, ROIs, functional homogeneity