Msc positions 2017

Predicting cognitive decline using brain imaging

Identifying biomarkers of brain disorders from neuroimaging data is an exciting and

rapidly growing research area at the intersection of machine learning, biomedical

engineering and neuroscience. Detecting brain disorders before clinical symptoms

appear is projected to be possible by combining brain imaging

and advanced computational techniques as minute changes in

the brain structure and function precede visible symptoms.

My team develops advanced machine learning approaches for finding patterns that are predictive of disease

from brain images. Our major focus is in Alzheimer's disease and there are several possible projects suitable

for MSc thesis work. The exact topic and the scope can be decided based on the interests and skills of the student.

Basic computational skills are required (Matlab/Python/R).

Inter-subject correlation based analysis of fMRI

In the inter-subject correlation (ISC) based analysis of the functional magnetic resonance

imaging (fMRI) data, the extent of shared processing across subjects during the experiment

is determined by calculating correlation coefficients between the fMRI time series of the

subjects in the corresponding brain locations. This implies that ISC can be used to analyze

fMRI data without explicitly modeling the stimulus and thus ISC is a potential method to analyze

fMRI data acquired under complex naturalistic stimuli. We have developed a graphical user interface (GUI)

based software package, ISC Toolbox https://www.nitrc.org/projects/isc-toolbox/, implemented in Matlab for computing various ISC based analyses. Currently,

we are preparing the release of the version 3.0 of the toolbox and would welcome a MSc thesis worker to validate

new functionalities of the toolbox. Familiarity with Matlab is required.