Brain Connectivity Analysis Using Machine Learning

Project description:

The selected interns will work with graduate students on developing novel methods to analyze functional and structural brain connectivity. Two applications are considered for this project: brain fiber clustering and dynamic functional connectivity analysis.

For the first application, recent machine learning techniques, based on dictionary learning, will be used to group the white matter fiber tracts into prominent bundles. Extracting these bundles is essential to perform a high-level analysis of structural brain connectivity. The methods developed for this application will be tested on real diffusion MRI data. The role of the intern for this application will be to assist the graduate students in programming and testing core functions of the proposed method. The intern will also work on developing programs to visualize the clustering results.

The second application targeted by the project is related to multi-subject dynamic functional connectivity (FC). So far, functional connectivity analyses in resting-state fMRI have been dominated by static FC, which compute the correlation between whole time-series from different voxels. Recently, dynamic FC for resting-state fMRI has been proposed, by considering small spatio-temporal windows from individual fMRI data and computing how the correlation changes between the signals captured in these windows. The project will use co-clustering techniques to jointly analyze the data from multiple subjects. Once again, the intern will be involved in the implementation and testing of the proposed method.

Faculty Supervisor:

Christian Desrosiers


Astha Sharma



Computer science





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