ATTENTION/WARNING - NE PAS DÉPOSER ICI/DO NOT SUBMIT HERE

Ceci est la version de TEST de DIAL.mem. Veuillez ne pas soumettre votre mémoire sur ce site mais bien à l'URL suivante: 'https://thesis.dial.uclouvain.be'.
This is the TEST version of DIAL.mem. Please use the following URL to submit your master thesis: 'https://thesis.dial.uclouvain.be'.
 

Bayesian Inference for Dynamic Graphical Models with Applications in Neurosciences

Violetta, Giorgio
(2024)

Files

VIOLETTA_04342200_2024.pdf
  • Closed access
  • Adobe PDF
  • 529.62 KB

Details

Supervisors
San Martin Gutiérrez, Ernesto
Faculty
Faculté des sciences économiques, sociales, politiques et de communication
Degree label
Master [120] en sciences économiques, orientation générale, à finalité approfondie
Abstract
This paper explores Bayesian applications in the study of dynamic interconnections between brain regions, measured through blood oxygenation levels in functional Magnetic Resonance Imaging (fMRI) data, an active area of research in Neuroscience. The analysis follows recent modeling approaches for the dynamic functional connectivity of the brain, leveraging dynamic graphical representation within a Hidden Markov Model framework. Beginning with an extensive literature review and essential concepts for modeling brain connectivity, the paper then introduces a Bayesian graphical model for functional connectivity. Finally, it presents an extension of the model to address effective connectivity.