Background: The brain is known as a complex ‘network’ of interconnected and communicating neurons with an underlying organizational structure. While there are many computational tools available to conduct network connectivity analyses between the distinct regions of our brain (e.g. graph theory analysis), the current approaches are mostly atlas-based and rely on a-priori knowledge of boundaries between brain regions. For example, while a traditional brain atlas consists of ~ 100 regions, high-resolution brain networks may contain up to 140,000 brain areas or so-called nodes. Determining the optimal ‘nodal parcellation scheme’ that balances the trade-off between within-node functional homogeneity and computational power is an important challenge to be investigated in the field of brain network connectivity (or connectomics).
Project description: The student will be involved in a multi-center study that investigates the relationship between brain connectivity and abnormal protein spreading (amyloid and tau) in the brains of patients with Alzheimer’s disease. There are currently over 700 brain scans acquired from different imaging modalities (amyloid- and tau-PET, structural MRI, diffusion MRI and functional MRI) in both healthy volunteers and patients in different stages of Alzheimer’s disease. The student will work on the integration of a computational pipeline to investigate node-to-node brain connectivity using structural and functional MRI techniques.
The primary objectives will be to:
(i) Develop node-wise brain networks (graphs) based on different nodal parcellation schemes,
(ii) Study the influence of nodal parcellation schemes on brain connectivity metrics between healthy and Alzheimer’s disease.
Relevance: Alzheimer’s disease is the most common neurodegenerative disease, affecting over 50 million people worldwide. In the current absence of effective disease-modifying treatment strategies, there is an immense need to better understand the mechanisms of the disease. The use of unbiased high-resolution parcellation schemes has been suggested to allow for the identification of key network nodes within the brains of patients affected by Alzheimer’s disease. This may ultimately contribute to novel insights into how the abnormal proteins spread within and between networks in the brain as well as facilitate the development of novel therapies that are directed towards halting this protein propagation.
The student will be supervised by Dr. Maged Goubran (https://medbio.utoronto.ca/faculty/goubran). Our lab, located at the Sunnybrook Research Institute, develops novel computational, machine learning & imaging tools to probe, predict and understand neuronal and vascular circuit alterations in neurological disorders, including Alzheimer’s disease, stroke and traumatic brain injury. It consists of a multidisciplinary team of engineers, computer- and neuroscientists and software developers, who are responsible for the development and implementation of tools to quantify various imaging markers of brain disease. As such, students will have the opportunity to gain not only computational research experience but also a diverse exposure to clinical and translational research, and will be part of an inclusive and stimulative lab environment.
Opportunity/experience that the student will gain:
- hands-on programming experience with human brain imaging data
- opportunity to write a conference abstract and involvement in resulting papers
- learning cutting-edge data-driven techniques in network analysis
- exposure to Alzheimer’s disease and translational research
- journal club discussions and scientific paper reading
- engagement with other students and projects in the lab
- access to computational resources needed to accomplish the aims
Eligibility:
- Programming experience (e.g. Bash, Python, Matlab, C++)
- Able to work 8-10 hours/week during the January-April term with potential for extension.
- CSC494H1/95 project or volunteer/research experience
Contact information: If you are interested in being part of this exciting research project or if you have additional questions, please contact our postdoctoral fellow Dr. Julie Ottoy: Julie.ottoy@sri.utoronto.ca.