Collaborative Research Team Project #07

Joint Analysis of Neuroimaging Data: High-Dimensional Problems, Spatiotemporal Models and Computation

This project brings together researchers from multiple fields to solve problems in analyzing high-dimensional neuroimaging data. Spatiotemporal models and computations for analyzing data from multiple neuroimaging modalities, or for combining brain imaging with genomics, will be investigated. 

Research Category: Health & Biology

Why Study Neuroimaging Data?

Uncovering the mysteries of the brain, including its function and structure, is a key challenge of modern science. With recent advances in the speed and accuracy of data acquisition from neuroimaging tools*, we can gain a better understanding of brain health and disease.

In early neuroimaging, most studies were focused on detecting activated regions of the brain using a single neuroimaging modality. Recently, researchers have turned their attention to more complicated problems that involve integrating complementary sources of information.

The development of statistical methods for these problems has fallen behind the technological advances that allow us to collect the data. This project will bring together researchers in various disciplines to develop, test, apply, and propagate new methods. 

*tools such as functional Magnetic Resonance Imaging (fMRI), Diffusion Tensor Imaging (DTI), Electroencephalography (EEG), and Magnetoencephalography (MEG).

Areas of Exploration

Regression Modeling

Includes developing a sparse projection regression modeling framework for the high-dimensional analysis of combined neuroimaging and genomic (SNP) data.

Solutions for NIP

Includes developing computationally-feasible solutions for the neuroelectromagnetic inverse problem (NIP). This is based on combined magnetoencephalography (MEG) and electroencephalography (​​EEG) data.

The Gut-Brain Connection

Includes investigating the physiological connection between the gastrointestinal tract and the brain. This is achieved using metagenomics data to model the relationship between neural outcomes and the human intestinal microbiome.

Solving Global Challenges

Research Team’s Goal

To develop solutions for analyzing and modeling brain imaging and genomic data from multiple sources. This data will be used to investigate physiological connections between the brain and intestinal microbiome.

People Behind the Project

Project Team

Farouk Nathoo  | University of Victoria

Linglong Kong  | University of Alberta


Peter Kim  | University of Guelph

Christine Lee  | McMaster University

Timothy Johnson  |  University of Michigan

Hongtu Zhu  | University of North Carolina Chapel Hill

Researchers at the University of Victoria, the University of Alberta, the University of Toronto, and Oregon State University.


Joint Analysis of Neuroimaging Data: High-Dimensional Problems, Spatiotemporal Models and Computation is a Collaborative Research Team project. This program tackles complex problems through a three-year research and training agenda.

CANSSI offers approximately $200,000 for this type of project, which requires a team of faculty, postdocs, and students.