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).

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.