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BORDEAUX, France--( BUSINESS WIRE )--RebrAIn, a pioneering precision neuro-surgery platform company, has recently received its second FDA 510(k) clearance for its OptimMRI software, which now includes a new machine learning model to assist in targeting the infero-lateral part of the VIM for lesioning techniques such as MRgFUS and Radiosurgery. This new software version already features FDA-cleared machine learning models for the STN and VIM regions of interest to assist in targeting Deep Brain Stimulation (DBS). RebrAIn has secured its second FDA 510(k) clearance in the past year, expanding the software's capabilities to include the infero-lateral part of the VIM. RebrAIn’s software as a medical device (SaMD) is designed to revolutionize neurosurgical target planning by combining advanced AI algorithms, trained on clinical data, with MRI sequences to provide neurosurgeons with the most optimal targets for lesioning and electrode placement. David Caumartin, CEO of RebrAIn, stated, “Our extension to US customers to better plan targets for lesioning is a significant step in RebrAIn’s growth and strategic transformation. The US market represents the largest opportunity to enable personalized targeting for neurological disorders such as Essential Tremor. The ability to offer AI clinical targeting to neurosurgical suites will open many collaborations nationwide, which today are treated with DBS and MR-guided focused ultrasound. Since our recent discussions at US congresses, it was necessary for us to offer neurosurgeons a complete service.” RebrAIn’s co-founders, Prof. Emmanuel Cuny MD and Prof. Nejib Zemzemi PhD, will be presenting on “VIM Lesions in Radiosurgery and Focused Ultrasound: Assessment of the Accuracy of Two Targeting Models Based on Machine Learning” at the World Society for Stereotactic and Functional Neurosurgery (WSSFN) biennial meeting in Chicago, IL, on September 5, 2024. “Our extension to US customers to better plan targets for lesioning is a significant step in RebrAIn’s growth and strategic transformation,” said David Caumartin, CEO of RebrAIn. “The US market is the largest, and the ability to offer AI clinical targeting will open many national collaborations.” To learn more, visit us at Booth #9 during the WSSFN meeting, check out our website www.rebrain.eu , or contact us directly. About RebrAIn RebrAIn is a France-based company that enables precise targeting for DBS and lesioning treatments for patients suffering from severe Parkinson's or essential tremor disease. Its SaaS solution helps neurosurgeons to precisely identify the target area for surgical intervention. Its novel approach uses machine learning of clinical patient data to predict the optimal treatment zone in each patient’s brain. For more information, visit www.rebrain.eu . About the World Society for Stereotactic and Functional Neurosurgery Since 1961, the WSSFN has been regularly held and continues to be a leading neurosurgery conference. At the biennial meetings, the top leaders in the field present the most recent innovations in clinical and basic research as well as new and effective ways to provide material support. Meta Description: "RebrAIn receives its second FDA 510(k) clearance for OptimMRI software, enhancing targeting in stereotactic and functional neurosurgery for Deep Brain Stimulation and lesioning techniques. The company will present VIM targeting advancements at the WSSFN congress." Tags: FDA 510(k) Clearance, Deep Brain Stimulation (DBS), Stereotactic Neurosurgery, Functional Neurosurgery, Medical Device Software, Machine Learning in Healthcare, MR-guided Focused Ultrasound (MRgFUS), Radiosurgery, Neurotechnology, WSSFN Congress, RebrAIn, Essential Tremor Treatment, AI in Healthcare, Precision Neurosurgery, Healthcare Innovations.

EDINA, Minn., Nov. 2, 2023 /PRNewswire/ -- Surgical Information Sciences, a medical device company focused on improved visualization of brain structures for deep brain stimulation (DBS) surgery, announced the commencement of its post-market study, the Visualization of the STN and GPi for DBS Surgery in Patients with Parkinson's Disease (VISION Study). This study aims to evaluate the potential of SIS technology in enhancing the accuracy of DBS implant placement for patients battling Parkinson's Disease. The VISION Study will involve the participation of 90 patients across multiple sites, and it represents a significant step in the pursuit of improved treatment for those living with this challenging condition. Millions of people worldwide are impacted by Parkinson's Disease and it affects their daily lives. DBS has shown promise in managing parkinsonian symptoms, but its success is closely tied to the precision of implant placement within the subthalamic nucleus (STN) and globus pallidus internus (GPi). SIS believes that its cutting-edge visualization technology has the potential to significantly improve the accuracy of this procedure, ultimately leading to improved patient outcomes. Dr. Patrick Senatus, a neurosurgeon at Hartford Hospital in Hartford, CT, commented, "The VISION Study presents an opportunity to assist surgeons and programming physicians in treating patients with Parkinson's Disease. By enhancing visualization, the accuracy of DBS implant placement may be improved through further customizing targeting of therapeutic brain regions. I am enthusiastic about the possibilities that this research holds." "Today marks a momentous occasion for SIS. We are proud to take the first step toward demonstrating improved outcomes of DBS surgery through patient specific visualization of target structures and lead placement," said Brad Swatfager, President and Chief Executive Officer. "Our team has worked hard to develop this cutting-edge technology, and we look forward to collaborating with healthcare professionals across the country to explore its full potential and improve the experience for physicians and patients." About Surgical Information Sciences: Surgical Information Sciences has developed software designed to enhance clinical images for the visualization of anatomical structures of the brain. The software applies deep learning models to predict and visualize the shape and location of the structures and is currently cleared by the FDA and has CE Mark. For more information about Surgical Information Sciences, please visit our website at . Media Contact: Susan Vnoucek, [email protected] SOURCE Surgical Information Sciences

To ensure a quick halt, brain circuit architecture avoids a slow process of integration in favor of quicker differentiation, a new neuroscience study finds. Your new apartment is just a couple of blocks down the street from the bus stop but today you are late and you see the bus roll past you. You break into a full sprint. Your goal is to get to the bus as fast as possible and then to stop exactly in front of the doors (which are never in exactly the same place along the curb) to enter before they close. To stop quickly and precisely enough, a new MIT study in mice finds, the mammalian brain is niftily wired to implement principles of calculus. One might think that coming to a screeching halt at a target after a flat out run would be as simple as a reflex, but catching a bus or running right up to a visually indicated landmark to earn a water reward (as the mice did), is a learned, visually guided, goal-directed feat. In such tasks, which are a major interest in the lab of senior author Mriganka Sur, Newton Professor of Neuroscience in The Picower Institute for Learning and Memory at MIT, the crucial decision to switch from one behavior (running) to another (stopping) comes from the brain's cortex, where the brain integrates the learned rules of life with sensory information to guide plans and actions. "The goal is where the cortex comes in," said Sur, a faculty member of MIT's Department of Brain and Cognitive Sciences. "Where am I supposed to stop to achieve this goal of getting on the bus." And that's also where it gets complicated. The mathematical models of the behavior that postdoc and study lead author Elie Adam developed predicted that a "stop" signal going directly from the M2 region of the cortex to regions in the brainstem, which actually control the legs, would be processed too slowly. "You have M2 that is sending a stop signal, but when you model it and go through the mathematics, you find that this signal, by itself, would not be fast enough to make the animal stop in time," said Adam, whose work appears in the journal Cell Reports. So how does the brain speed up the process? What Adam, Sur and co-author Taylor Johns found was that M2 sends the signal to an intermediary region called the subthalamic nucleus (STN), which then sends out two signals down two separate paths that re-converge in the brainstem. Why? Because the difference made by those two signals, one inhibitory and one excitatory, arriving one right after the other turns the problem from one of integration, which is a relatively slow adding up of inputs, to differentiation, which is a direct recognition of change. The shift in calculus implements the stop signal much more quickly. Adam's model employing systems and control theory from engineering -- accurately -- predicted the speed needed for a proper stop and that differentiation would be necessary to achieve it, but it took a series of anatomical investigations and experimental manipulations to confirm the model's predictions. First, Adam confirmed that indeed M2 was producing a surge in neural activity only when the mice needed to achieve their trained goal of stopping at the landmark. He also showed it was sending the resulting signals to the STN. Other stops for other reasons did not employ that pathway. Moreover, artificially activating the M2-STN pathway compelled the mice to stop and artificially inhibiting it caused mice overrun the landmark somewhat more often. The STN certainly then needed to signal the brainstem -- specifically the pedunculopontine nucleus (PPN) in the mesenecephalic locomotor region. But when the scientists looked at neural activity starting in the M2 and then quickly resulting in the PPN, they saw that different types of cells in the PPN responded with different timing. Particularly, before the stop, excitatory cells were active and their activity reflected the speed of the animal during stops. Then, looking at the STN, they saw two kinds of surges of activity around stops -- one slightly slower than the other -- that were conveyed either directly to PPN through excitation or indirectly via the substantia nigra pars reticulata (SNr) through inhibition. The net result of the interplay of these signals in the PPN was an inhibition sharpened by excitation. That sudden change could be quickly found by differentiation to implement stopping. "An inhibitory surge followed by excitation can create a sharp [change of] signal," Sur said. The study dovetails with other recent papers. Working with Picower Institute investigator Emery N. Brown, Adam recently produced a new model of how deep brain stimulation in the STN quickly corrects motor problems that result from Parkinson's disease. And last year members of Sur's lab, including Adam, published a study showing how the cortex overrides the brain's more deeply ingrained reflexes in visually guided motor tasks. Together such studies contribute to understanding how the cortex can consciously control instinctually wired motor behaviors but also how important deeper regions, such as the STN, are to quickly implementing goal-directed behavior. A recent review from the lab expounds on this. Adam speculated that the "hyperdirect pathway" of cortex-to-STN communications may have a role broader than quickly stopping action, potentially expanding beyond motor control to other brain functions such as interruptions and switches in thinking or mood. The JPB Foundation, the National Institutes of Health and the Simons Foundation Autism Research Initiative funded the study.