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Jul 30, 2022

Advancing dynamic brain imaging with AI

Posted by in categories: biotech/medical, robotics/AI

MRI, electroencephalography (EEG) and magnetoencephalography have long served as the tools to study brain activity, but new research from Carnegie Mellon University introduces a novel, AI-based dynamic brain imaging technology which could map out rapidly changing electrical activity in the brain with high speed, high resolution, and low cost. The advancement comes on the heels of more than thirty years of research that Bin He has undertaken, focused on ways to improve non-invasive dynamic brain imaging technology.

Brain is distributed over the three-dimensional brain and rapidly changes over time. Many efforts have been made to image and dysfunction, and each method bears pros and cons. For example, MRI has commonly been used to study , but is not fast enough to capture brain dynamics. EEG is a favorable alternative to MRI technology however, its less-than-optimal spatial resolution has been a major hindrance in its wide utility for imaging.

Electrophysiological source imaging has also been pursued, in which scalp EEG recordings are translated back to the brain using and machine learning to reconstruct dynamic pictures of brain activity over time. While EEG source imaging is generally cheaper and faster, specific training and expertise is needed for users to select and tune parameters for every recording. In new published work, He and his group introduce a first of its kind AI-based dynamic brain imaging methodology, that has the potential of imaging dynamics of neural circuits with precision and speed.

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