Magnetic particle imaging (MPI) is an emerging technology that could allow researchers and clinicians to glimpse organs, in real time, with a resolution and sensitivity superior to commonly used tools like magnetic resonance imaging (MRI).
MPI is particularly attractive for examining the brain, because it delivers images at a much faster rate than MRI by tracing harmless, injectable magnetic nanoparticles that can traverse the blood-brain barrier. Those nanoparticles—and their interactions with electric and magnetic fields—can generate imaging data from larger swaths of the brain than invasive electrodes.
Aviad Hai
“Now the challenge in brain imaging is to find the right sensors that will allow you to get these functional imaging readouts,” says Aviad Hai, an assistant professor of biomedical engineering at the University of Wisconsin-Madison who develops minimally invasive tools to monitor neural activity.
In a May 2022 paper in the Nature open-access journal Scientific Reports, Hai and three of his students used computer modeling to test the feasibility of one such class of particles for recording whole-brain electrophysiology via magnetic particle imaging. Their work provides both a theoretical backing for using magnetoelectric nanoparticles in brain imaging and a way to assess particles for a range of sensing applications.
Hai, PhD student Ilhan Bok (BSCmpE ’20), and undergraduate researchers Ido Haber (BSNeuro ’21) and Xiaofei Qu (BSBME ’22) created a highly realistic model of magnetoelectric nanoparticles in the brain. The particles consist of cobalt ferrite cores that are magnetically responsive and outer shells of barium titanate that are piezoelectric, meaning they generate mechanical strain when under an electric field—in this case, from the brain’s electromagnetic activity.
While several research groups, including at the Max Planck Institute for Intelligent Systems in Germany and Rice University, have demonstrated promising results using these nanoparticles for brain stimulation, researchers have also proposed applying them as viable options for brain imaging via MPI.
Hai’s group decided to put that idea to the test, using real structural data of neurons from the Allen Institute for Brain Science to simulate imaging readouts at both a single brain-cell level and in larger neural networks. They’ve made their models open source to allow others to build upon them and probe alternative approaches.
“It makes the first real estimate or prediction of the signal that we would pick up if we injected these types of particles into the brain and tried to read them using magnetic modalities such as magnetic particle imaging,” says Hai.
To do that, Bok learned to use the simulation software COMSOL Multiphysics to model the nanoparticles and modeled neurons and brain networks in Python, drawing upon programming skills he first taught himself as a teenager on Madison’s west side. Along the way, he collaborated with Haber and Qu in a process he called “mutual mentoring.”
Bok, who received support for the project from a UW-Madison Global Health Institute award and the Wisconsin Alumni Research Foundation’s Fall Research Competition, is now fabricating some of the nanostructures outlined in the paper in the Nanoscale Fabrication Center in the Engineering Centers Building on campus.
He has also worked with Thor Larson, another undergraduate researcher in the Hai Lab, to construct an MPI system from scratch, which will allow the group to test different nanoparticles. Hai is also exploring collaborations with UW-Madison colleague Jiamian Hu, an assistant professor of materials science and engineering and an expert in piezoelectric and magnetoelectric materials who has provided important advice for the project.
“I think this model is really significant and it’s going to have a lot of impact on how people fabricate magnetoelectric structures in the future,” says Bok, a PhD student in electrical and computer engineering. “And I think it’s a great stepping stone for developing and ultimately deploying magnetic imaging techniques in general, including magnetic particle imaging, for the treatment of a broad set of neurological disorders, but also for the enhancement of brain-computer interfaces.”
Assistant Professor Aviad Hai is also part of the Grainger Institute for Engineering’s neuroengineering focus and the Wisconsin Institute for Translational Neuroengineering, both at UW-Madison.
Funding for this research also came from the National Institute of Neurological Disorders and Stroke and the Office of the Director’s Common Fund at the National Institutes of Health via Grant DP2NS122605 and the National Institute of Biomedical Imaging and Bioengineering via Grant K01EB027184.