MAR 13, 2019 1:40 PM PDT

Clinical Updates: Towards Artificial Intelligence Based Adaptive Deep Brain Stimulation in Movement Disorders

Presented at: Neuroscience 2019
C.E. Credits: P.A.C.E. CE Florida CE
Speaker
  • Clinician Scientist, Movement Disorders and Neuromodulation Unit at the Department of Neurology, Charité - Universitätsmedizin Berlin, Germany
    Biography
      Wolf-Julian Neumann, MD, is a clinician scientist in the Movement Disorders and Neuromodulation Unit at the Department of Neurology at the largest European University Hospital, Charité - Universitätsmedizin Berlin, Germany. His doctoral thesis describes invasive oscillatory biomarkers for major depressive disorder from the human limbic system. He has built his expertise in Movement Disorders and Neurophysiology during his postdoctoral research in Berlin under supervision from Andrea A. Kühn. Research visits to the Wellcome Centre for Integrative Neuroimaging in Queen Square, London (Prof. Dr. Vladimir Litvak) and the Department of Functional Neurosurgery at University of Pittsburgh (Prof. Dr. R. Mark Richardson) have complemented his methodological expertise.

      He since develops methods for multimodal and multidimensional data analysis for clinical neuroscience applications. His current work combines computational modelling, deep learning, structural and functional connectivity mapping (fMRI), invasive (LFP/ECoG) and non-invasive (EEG/MEG) recordings, to elucidate the role of the basal ganglia in health and disease.

    Abstract

    Deep brain stimulation (DBS) represents one of the major clinical breakthroughs in the age of translational neuroscience. In 1987, Benabid and colleagues demonstrated that high-frequency stimulation can mimic the effects of ablative neurosurgery in Parkinson's disease (PD), while offering two key advantages to previous procedures: adjustability and reversibility. Deep brain stimulation is now an established therapeutic approach that robustly alleviates symptoms in patients with movement disorders, such as Parkinson's disease, essential tremor, and dystonia, who present with inadequate or adverse responses to medication. Currently, stimulation electrodes are implanted in specific target regions of the basal ganglia-thalamic circuit and stimulation pulses are delivered chronically. Next-generation stimulation strategies will capitalize on recent advances in recurrent artificial neural networks to inspire intelligent neural prosthetics that learn when patients sleep, walk, speak or exhibit symptoms such as tremor or bradykinesia. This talk will give detailed insight into potential biomarkers and discuss neurosurgical and computational strategies, implementing advances in artificial intelligence, to further elevate the therapeutic potential of DBS by capitalizing on its modifiable nature. Development of intelligent aDBS, with an ability to deliver highly personalized treatment regimens and to create symptom-specific therapeutic strategies in real-time, could allow for significant further improvements in the quality of life for movement disorders patients with DBS that ultimately could outperform traditional drug treatment.


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