Mapping the Human Chronnectome

Strategic Research Initiatives

Sanmi Koyejo: Computer Science

Brad Sutton: Bioengineering

Sepideh F. Sadaghiani: Psychology

Research Goals

The human brain remains one of the most fascinating and important, yet least understood complex biological systems. This project seeks to leverage recent computational and engineering advances to revolutionize our understanding of time-varying brain functional connectivity, also known as the chronnectome. Decades of research have been devoted to uncovering the principles of human brain function. The desire to uncover the mechanisms of brain function has led to major initiatives from funding agencies all over the world. This proposal lays the groundwork for developing multimodal data analysis algorithms and getting preliminary results with both simulated and experimental data. The results of this proposal are helping to attract external funding, and enabling collaborations among ECE, CS, BIOE, Neuroscience, Psychology, Carle, CI-MED, and the Beckman Institute. Our long term goal is to ensure Illinois leadership in chronnectome engineering research, particularly in imaging and modeling, leading to scientific and clinical impact.

We are developing novel machine learning tools that leverage recent engineering advances in magnetic resonance imaging pioneered at Illinois. First, is a recent advance in high spatial resolution imaging. Illinois has recently acquired an ultra-high field human 7T MRI scanner, one of the first clinically-approved 7T systems in the US and the first in a community hospital setting. This will enable the ability to measure brain and at a much higher resolution than is currently possible at most imaging centers in the world. Second is a recent advance in multi-modal imaging. Illinois has pioneered the technology for simultaneously measuring fMRI and Electroencephalography (EEG). These engineering advances enable novel multimodal machine learning tools that can leverage the complementary strengths of EEG and fMRI. Taken together, these engineering advances provide the ability to measure human brain function at unprecedented temporal and spatial resolution. Our research goals are to develop novel machine learning tools for estimating the chronnectome from multimodal imaging data and investigate its associations to cognition, behavior, and disease.