New algorithms could help activate next-generation deep brain stimulation
Deep Brain Stimulation (DBS) can ease the tremors associated with Parkinson’s disease or relieve chronic pain by sending small electrical impulses directly to the brain. While this technique works for many patients, the researchers want to make DBS devices a bit smarter by adding the ability to sense brain activity and tailor stimuli accordingly.
now, New algorithm This could be an important step towards such an adaptive DBS developed by bioengineers at Brown University. This algorithm removes an important obstacle that makes it difficult to perceive DBS systems. Brain signal While delivering at the same time Stimulation..
“We know that there is electronic signals In brain We want to record these signals and make them available for automatic regulation of neuromodulatory therapies, ”said Brown University, assistant professor of biomedical engineering, responsible for the research explaining the algorithms. The author, David Balton, said. The problem is, the stimulus creates an electrical artifact that damages the signal you’re trying to record. We have therefore developed a way to identify and eliminate these artifacts, leaving only the signals of interest from the brain. “
The study is published in the journal Cell report methodThis work was co-directed by Ph.D., Nicole Provenza. Applicants working in Brown’s Boughton lab and Evan Dustin van Rain with a doctorate. The University of Minnesota students who worked on this project as an undergraduate at Brown University were advised by Boaton and Matthew Harrison, associate professor of applied mathematics. Borton’s lab is affiliated with Brown’s Carney Institute for Brain Science.
The DBS system typically consists of electrodes built into the brain that are connected to a device such as a pacemaker implanted in the chest. The electrical pulse is delivered at a constant frequency defined by your doctor. The frequency of stimulation can be adjusted according to the evolution of the state of health, but this must be done manually by the doctor. If the device can detect the disease biomarker and respond automatically, it can lead to more effective DBS treatment with potentially fewer side effects.
Several factors make it difficult to detect and stimulate at the same time, the researchers say. On the one hand, the frequency sign of the stimulus Artifact It can overlap the signal with the brain signal that the researcher wishes to detect. Therefore, simply cutting the frequency band to remove artifacts can also remove important signals. To remove an artifact and leave other data in place, you need to determine the exact waveform of the artifact, but that’s another problem. Implantable brain sensors are generally designed to operate with minimum power, so at the rate at which the sensor samples electrical signals, the resolution of the data is considerably lower. It is difficult to accurately identify the waveform of the artifact with data at such low resolution.
To avoid this problem, the researchers found a way to convert the low-resolution data into high-resolution waveform images. The sensor does not collect high resolution data, but collects large amounts of data over time. Brown’s team used smart math to find a way to combine bits of data into a high-resolution image of an artifact waveform.
“Basically we take the average of the samples recorded at similar points along the waveform of the artifact,” says Dastin-van Rijn. “This allows us to predict and eliminate the contribution of artifacts in these types of samples.”
With a series of laboratory experiments Computer simulationThe team showed that their algorithms are superior to other methods in their ability to separate signals from artifacts. The team also showed that the algorithms can be used on data previously collected from human and animal models to accurately identify and remove artifacts.
“One of the great advantages of our method is that it can distinguish between the two, even though the signal of interest is very similar to a simulation artifact,” says Provenza. “This way you can get rid of the artifacts while leaving the signal intact.”
Another advantage, according to the researchers, is that the algorithm is inexpensive in terms of computation. It can work in real time on current DBS devices. This opens the door to real-time artifact filtering, allowing recording and stimulation at the same time.
“This is the key to adaptive systems,” says Borton. “If we can get rid of stimulus artifacts while recording important biomarkers, eventually we will have a closed-loop processing system. ”
Evan M. Dastin-van Rijn et al, Discovery of biomarkers during therapeutic neural regulation by PARRM: methods of reconstruction and elimination of period-based artifacts, Cell report method (2021). DOI: 10.1016 / j.crmeth.2021.100010
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