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Carolina Aguilar, INBRAIN Neuroelectronics - Graphene Core Technology for the Brain | LSI USA '24

Graphene core technology designed to decode brain signals into medical solutions in the field of neuroelectronic therapies with the objective to restore patients' quality of life in those affected with brain-origin disorders.
Speakers
Carolina Aguilar
Carolina Aguilar
INBRAIN Neuroelectronics

Carolina Aguilar  0:03  
We are INBRAIN, a company I think their section of medtech deep tech and data health using graphene to decode neural signals into breakthrough medical solutions. We are a 50 people team divided into two companies in brain. That's applications for the central nervous system. An area is the collaboration with Merck. For applications in the peripheral nervous system like this, we can capture the combined opportunity of neuro and bioelectronics, which is together $25 billion dollars. I have a secret on Reiki news. We just got approval for the first inhuman, which is the first time graphene will be placed in the brain of a human being, which is a historic event. Why do we need graphene because after years of clinical practice with metals like platinum iridium, we've been only able to decode the size of a zebrafish brain, which is around 100,000 neurons. However, the brain is nearly 100 billion neurons, and one out of three people have neurological related disorders. 30% are refractory to medical treatment. And this caused a huge burden for the healthcare system about 800 billion per year. And every leap in humanity has been linked to a key material from a stone age to silicon age where we are today. We believe that by dimensional materials like graphene represent that breakthrough opportunity in Euro technology. Graphene is the thinnest material known to man is an atom thick yet is 200 times stronger than steel, biocompatible flexible, which makes it an ideal candidate for a neural interface. What is the problem that current neuron bioelectronic therapies are lagging behind. They are based on big metal platforms that are bulky and need a lot of power to operate. We have also low density and poor resolution interfaces. This is as you see, can fit maybe at max one or two contacts on the nuclei and not ideally in the right side of the nuclei. Plus, most of them, they can only decode or stimulate but don't respond in open or closed loop. This is all we are solving. Actually we are developing a head mounted platform that operates with a cortical brain computer interface module and a deeper interface that collaborate together in a bi directional way, actually decoding live in real time pathological biomarkers and creating therapeutic applications. The brain communicates like a radio with frequency brain waves. And naturally each of these frequency waves are in different areas of the brain and actually decode and operate different functions. Actually, graphene outperforms metal in the identification of all these frequency waves, and actually is able to decode these biomarkers in very high resolution. That's what we are doing decoding networks in high resolution. And what you have in the screen is just an example of a motor symptom, a Parkinson opportunity, where actually we place a cortical interface on the motor cortex of the brain and a deeper interface in the basal ganglia and together identify these pathological biomarkers that need to be actually corrected to keep those patients in the highest percentage of on time on therapy with that travels on symptoms. We are building in brain as a biotech platform. The first application is Parkinson's disease because it's very easy to disrupt the low count and metal business. The second is epilepsy. And the third one is more on the brain computer interface in translation of third to speech. This is the comparison versus metal. You have on the metal side Milly metric sizes about six millimeters square contact size, very low count on the right, you have graphene with micrometric micrometric precision about 25 micrometers to 300 micrometers sizes up to 1024 contacts. And again you can also put it in cortical and deep interfaces. With that we drive a higher decoding a better modulation. And also we can miniaturize. This is data an example of our GLP safety study that led to the first thing human clearance which compares the decoding accuracy of metals which is about 50% to the decoding accuracy of graphene, which is about 90 200%. And the more contacts you have, the better the accuracy becomes. And then what you can see on the right is actually the precision that we can drive in identifying this local field potentials, from the stimulation in the case of this ship of neck muscles, jaw muscles, or upper lip muscles to be very precise, the applications here are two more and epilepsy resections. But also the programming of prosthesis, for instance, with very high resolution. Now also a lower resolution when it becomes about the brain, it's more complex than what we think. On the right you see a 36 contact interface with one centimeter electrode interspace. What do you know from there? What can you do from there? No much, because what you see is that the brain is dynamic, and is actually evolving as is operating a particular task. So here we tested our 256 channel interface. And what you can see is that also in the lower graph, you see that same in various low motion, which corresponds to various low waves that are also on the brain to frequency waves of 0.01 hertz, or 0.5 hertz, which actually matters cannot see. So this complexity is what we can actually undercover with graphene. And by the way, on those as low waves is where actually we can see the preliminary build up of a epileptic seizure, so we can stop it, we have published that in nature, as you see. Now, when we are the Guardian model networks, many players are trying to close the loop and treat Parkinson's disease with a bit of Bang. And here we demonstrate in a preclinical experiment with pigs, that actually moving the lack of the pig up and down. intraoperatively show us that the decoding of movement is much more complex. It's just one beta band. Here you have alpha, beta, gamma, low gamma, high gamma, and you can see how they lead goes up and down different biomarkers are involved on that. And therefore the decoding again, is more complex than just one biomarker. So we've handed on 40 contests we have demonstrated that actually, we can identify these biomarkers in micrometers, my micrometric is space. So you will see on the upper graph, that in about 200 micrometers, we can identify physiological tremor, this is a non human primate that we've been having in a chronic mode for three months. And then on the pathological tremor site on the bottom graph, you can see bright marks that show up the correlation to tremor, and the understanding of what biomarker actually best correlate to tremor, then we have used that in a very advanced machine learning algorithm to actually create a closed loop. And we have achieved therapeutic response actually using 50 to 70%, less energy and time requirements. So we have we are advancing with our two brothers. The first one is a cortical interface that will be soon commercialized. This is the one that is going to first inhuman is just a 510 K. And the second is the chronic platform that as you see, attain FDA breakthrough designation and is going now so doors first inhuman with our series B round. But that's not it, we are in a mission to decode the whole neural system. And that's why we partnered with Merck to actually develop also applications on the peripheral nerve. Why because the Bible's nerve is the perfect gave gate from the central nervous system to the peripheral nervous system, and it commands most of our most important organs. So there's a lot of applications that we can develop there. And this is what we're doing. We want to disrupt the low density market and start from where it exists today, the one 7 billion opportunity with currently reimbursement codes, and then keep on going towards the more brain computer interface market that is now being developed by players like neuralink and precision, and then end up on that bigger opportunity. That combined makes the 25 billion.

Our team has done it before. It's a very knowledgeable team from materials but also from industry. They were in Philips, then they created sapiens, sapiens was acquired by Medtronic, they stay in Medtronic. Then they created onward another Embree from the clinical board. We have frontier centers like a Stanford also Oxford, UCL, and by the way, we also have visionaries. They like the graphene, Nobel Prize Laureate Cyril Kostya Novoselov that is also keeping us at the forefront of other by dimensional materials and also on the evolutions of graphene. This is said we have around open we are closing actually we have a term sheet that we are signing, but we have a steel a space for five to 10 million for an additional player and I hope you join us in lining up the path to a better new future for improving patient's lives thank you

 

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