LREN Student Projects
Research opportunities in Neuroscience are available across a range of research groups within LREN and candidates have the opportunity to undertake research in this area. For further information and details please contact the responsible Principal Investigator.
PI: Dr. Ferath Kherif
Tutor: Valérie Zufferey
Master project. The proposed research project is based on in vivo magnetic resonance imaging (MRI) and behavioural assessment of learning/memory functions in healthy controls and patients. The overall aim is further expand on network analysis combining anatomical and functional connectivity information allowing for inferences regarding the interaction between segregated nodes within the memory network. The project has direct impact in basic understanding of the processes underlying memory and could help developing novel therapeutic approaches for patients with memory impairment. The following techniques will be used: MRI based functional and anatomical imaging, EEG, Eye-tracking and computer based behavioural tests.
PI: Dr., PD Maria G. Knyazeva
Post-doc: Dr. Cristian Carmeli
Master project. Alzheimer's disease (AD) has been shown to disrupt communication between distributed brain regions due to degeneration of structural connectivity. Remarkably, AD has a protracted preclinical period, when some degenerative changes can be detected, although they do not yet affect behavior and cognition of the person. To make a timely prognosis, it is important to understand the relationship between changes in structural and functional connectivity. This project is focused on a combined analysis of multimodal imaging data. The student will analyze EEG and MRI data and finally apply a mathematical model to unveal structure-function relation. Data from a cohort of healthy and AD subjects will be considered. The entire analysis will be performed with the Matlab software.The student will have opportunity to practice with state-of-the-art tools in neuroimaging data analysis, such as statistical parametric mapping and EEG synchronization. The potential results have high novelty and broad significance.
PI: Pr. Bogdan Draganski
Post-doc: Dr. Jürgen Dukart
Master project. Computational anatomy in depression. In this project the student will learn basics of data pre-processing and statistical analyses of longitudinal structural imaging data. The student will use a new pre-processing approach based on calculations of brain tissue grow and shrinkage rates on already acquired data of depression and bipolar patients. In a second step, the student will apply univariate (voxel-based statistics) and multivariate (support vector machine classification) statistical methods to evaluate the pre-processed data and summarize his/her results in the form of a master thesis.
PI: Pr. Bogdan Draganski
Master project. Functional anatomy of DYT1 dystonia. In this project the student will learn basics of data pre-processing and statistical analyses of functional magnetic resonance imaging (fMRI) data. The project is based on already acquired data involving an associative learning paradigm in DYT1 gene carriers and healthy controls. In a second step, the student will apply univariate statistical methods (General Linear Model - GLM) to evaluate the pre-processed data and summarize his/her results in the form of a master thesis.
PI: Pr. Bogdan Draganski
Master project. Theta-burst stimulation and its effects on cortico-cortical connectivity. In this project the student will learn basics of data pre-processing and statistical analyses of functional magnetic resonance imaging (fMRI) data. The project is based on already acquired data testing the effects of theta-burst repetitive transcranial magnetic stimulation (rTMS) on associative learning in healthy controls. In a second step, the student will apply univariate statistical methods (General Linear Model - GLM) to evaluate the pre-processed data and summarize his/her results in the form of a master thesis.
