Change search
ReferencesLink to record
Permanent link

Direct link
Computing network centrality measures on fMRI data using fully weighted adjacency matrices
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

A lot of interesting research is currently being done in the field of neuroscience, a recent subject being the effort to analyse the the human brain connectome and its functional connectivity. One way this is done is by applying graph-theory based network analysis, such as centrality, on data from fMRI measurements. This involves creating a graph representation from a correlation matrix containing the correlations over time between all measured voxels. Since the input data can be very big, this results in computations that are too memory and time consuming for an ordinary computer. Researchers have used different techniques to work around this problem, examples include thresholding correlations when creating the adjacency matrix and using a smaller input data with lower resolution.This thesis proposes three ways to compute two different centrality measures, degree centrality and eigenvector centrality, on fully weighted adjacency matrices that are built from complete correlation matrices computed from high resolution input data. The first is reducing the problem by doing the calculations in optimal order and avoiding the construction of the large correlation matrix. The second solution is to distribute and do the computations in parallel on a large computer cluster using MPI. The third solution is to calculate as large sets as possible on an ordinary laptop using shared-memory parallelism with OpenMP. Algorithms are presented for the different solutions, and the effectiveness of the implementations of them is tested.

Place, publisher, year, edition, pages
UMNAD, 1053
National Category
Engineering and Technology
URN: urn:nbn:se:umu:diva-128177OAI: diva2:1050088
External cooperation
Västerbottens Läns Landsting
Educational program
Bachelor of Science Programme in Computing Science
Available from: 2016-11-28 Created: 2016-11-28 Last updated: 2016-11-28Bibliographically approved

Open Access in DiVA

fulltext(327 kB)18 downloads
File information
File name FULLTEXT01.pdfFile size 327 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
Department of Computing Science
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 18 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 55 hits
ReferencesLink to record
Permanent link

Direct link