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Abstract
Using graph theory to analyse the architecture of the human brain, the connectome, has gained increasing interest in the last decade. In this work we extend graph measures, which have previously been developed for other applications, to include prior medical information. These extended measures are then evaluated on a collective of Autism Spectrum Disorder patients. Examining their performance in comparison to other traditionally used measures we show, that they are a valuable new tool in the analysis of the human connectome. We then further evaluate their performance over a range of network densities in order to determine the range at which they supply the most valuable information. By doing an in depth evaluation of these measures we aim to reduce the amount of guesswork in choosing variables in the analysis of the connectome and help to improve the comparability of different studies.
Document type: | Dissertation |
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Supervisor: | Oelfke, Prof. Dr. Uwe |
Date of thesis defense: | 2 July 2014 |
Date Deposited: | 10 Jul 2014 07:54 |
Date: | 2014 |
Faculties / Institutes: | The Faculty of Physics and Astronomy > Dekanat der Fakultät für Physik und Astronomie Service facilities > German Cancer Research Center (DKFZ) |
DDC-classification: | 004 Data processing Computer science 530 Physics 610 Medical sciences Medicine |
Controlled Keywords: | Medical Imaging |
Uncontrolled Keywords: | Network analysis |