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Master theses

Current and past ideas and concepts for Master Theses.

Coupled two-way clustering as a tool to study time-resolved brain connectivity

Subject

Neuroimaging studies that investigate the involvement of brain regions in various cognitive and perceptual tasks have become increasingly prevalent and has provided insight about the brain’s activity in health and disease.

Connectivity that varies over time has been called dynamic functional connectivity (https://en.wikipedia.org/wiki/Dynamic_functional_connectivity). This thesis considers a novel approach, originally developed for genetics applications, to derive a dynamic functional connectivity representation of the brain.

Kind of work

The coupled two-way clustering (CTWC) approach to gene microarray data analysis [1] identifies subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge. By identifying relevant subsets of the data and focusing on them this algorithm is able to discover partitions and correlations that were masked and hidden when the full dataset was used in the analysis.
We propose the use of CTWC to the analysis of time-varying functional connectivity in the brain, where gene and samples are replaced by brain regions and time points of the recorded activity respectively: the goal is to find iteratively clusters of brain regions and the time intervals in which these clusters appear stable, and thus to provide an alternative description of dynamic functional connectivity.

Framework of the Thesis

Gad Getz, Erel Levine, and Eytan Domany, “ Coupled two-way clustering analysis of gene microarray data”, Proc Natl Acad Sci U S A. 2000 Oct 24 97(22): 12079–12084. http://www.pnas.org/content/97/22/12079.full

In collaboration with Prof. Daniele Marinazzo (UGhent-Department of data Analysis) daniele.marinazzo@ugent.be

Expected Student Profile

- Following an MSc in a field related to one or more of the following: electrical engineering, computer science, applied mathematics, Biomedical Engineering
- Experience with signal processing, machine learning and statistics.
- Strong programming skills (one of the following Matlab, R, Python, …)
- Ability to write scientific reports and communicate research results at conferences in English.

Promotor

Prof. Hichem Sahli

+32 (0)2 629 2916

hsahli@etrovub.be

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