Summary: Novel AI technology allows researchers to understand which brain regions directly interact with each other, which helps guide the placement of electrodes for DBS to treat neurological diseases.

Source: Mayo Clinic

For millions of people with epilepsy and movement disorders such as Parkinson’s disease, electrical stimulation of the brain already is widening treatment possibilities. In the future, electrical stimulation may help people with psychiatric illness and direct brain injuries, such as stroke.

However, studying how brain networks interact with each other is complicated. Brain networks can be explored by delivering brief pulses of electrical current in one area of a patient’s brain while measuring voltage responses in other areas. In principle, one should be able to infer the structure of brain networks from these data. However, with real-world data, the problem is difficult because the recorded signals are complex, and a limited amount of measurements can be made.

To make the problem manageable, Mayo Clinic researchers developed a set of paradigms, or viewpoints, that simplify comparisons between effects of electrical stimulation on the brain. Because a mathematical technique to characterize how assemblies of inputs converge in human brain regions did not exist in the scientific literature, the Mayo team collaborated with an international expert in artificial intelligence (AI) algorithms to develop a new type of algorithm called “basis profile curve identification.”

Every electrode interaction resulted in hundreds to thousands of time points to be studied using the new algorithm. Image is in the public domain

In a study published in PLOS Computational Biology, a patient with a brain tumor underwent placement of an electrocorticographic electrode array to locate seizures and map brain function before a tumor was removed. Every electrode interaction resulted in hundreds to thousands of time points to be studied using the new algorithm.

“Our findings show that this new type of algorithm may help us understand which brain regions directly interact with one another, which in turn may help guide placement of electrodes for stimulating devices to treat network brain diseases,” says Kai Miller, M.D., Ph.D., a Mayo Clinic neurosurgeon and first author of the study.

“As new technology emerges, this type of algorithm may help us to better treat patients with epilepsy, movement disorders like Parkinson’s disease, and psychiatric illnesses like obsessive compulsive disorder and depression.”

“Neurologic data to date is perhaps the most challenging and exciting data to model for AI researchers,” says Klaus-Robert Mueller, Ph.D., study co-author and member of the Google Research Brain Team. Dr. Mueller is co-director of the Berlin Institute for the Foundations of Learning and Data and director of the Machine Learning Group ― both at Technical University of Berlin.

In the study, the authors provide a downloadable code package so others may explore the technique. “Sharing the developed code is a core part of our efforts to help reproducibility of research,” says Dora Hermes, Ph.D., a Mayo Clinic biomedical engineer and senior author.

About this AI research news

Author: Press Office
Source: Mayo Clinic
Contact: Press Office – Mayo Clinic
Image: The image is in the public domain

Original Research: Open access.
“Basis profile curve identification to understand electrical stimulation effects in human brain networks” by Kai J. Miller et al. PLOS Computational Biology


Abstract

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Basis profile curve identification to understand electrical stimulation effects in human brain networks

Brain networks can be explored by delivering brief pulses of electrical current in one area while measuring voltage responses in other areas.

We propose a convergent paradigm to study brain dynamics, focusing on a single brain site to observe the average effect of stimulating each of many other brain sites. Viewed in this manner, visually-apparent motifs in the temporal response shape emerge from adjacent stimulation sites.

This work constructs and illustrates a data-driven approach to determine characteristic spatiotemporal structure in these response shapes, summarized by a set of unique “basis profile curves” (BPCs). Each BPC may be mapped back to underlying anatomy in a natural way, quantifying projection strength from each stimulation site using simple metrics. Our technique is demonstrated for an array of implanted brain surface electrodes in a human patient.

This framework enables straightforward interpretation of single-pulse brain stimulation data, and can be applied generically to explore the diverse milieu of interactions that comprise the connectome.



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