MAR 13, 2019 12:20 PM PDT

Innovative Neurotechnologies: RAVE: R Analysis and Visualization of Intracranial Encephalography

Presented at: Neuroscience 2019
C.E. Credits: P.A.C.E. CE Florida CE
Speakers
  • Professor, Vice Chair of Basic Research, Departments of Neurosurgery and Neuroscience, Director of the Core for Advanced MRI, Baylor College of Medicine
    Biography
      Dr. Beauchamp completed his undergraduate degree in Biology at Harvard University, studying directionally-selective ganglion cells in the rabbit retina. He received his PhD from University of California, San Diego, using fMRI to examine the effects of attention on the motion processing system. He then completed a postdoc at the intramural research program of the NIMH. His first faculty position was at the University of Texas Medical School in Houston, and in 2015 he moved to Baylor College of Medicine. He is the Vice-Chair for Basic Research in the Department of Neurosurgery and the Academic Director of the Core for Advanced MRI. His laboratory investigates the neural mechanisms of multisensory speech perception and visual perception in human participants.
    • Assistant Professor, Neurosurgery, Baylor College of Medicine
      Biography
        Dr. John Magnotti is currently an assistant professor in the Department of Neurosurgery at Baylor College of Medicine in Houston, TX. He started his journey to neuroscience as a computer science and psychology major at James Madison University in the Shenandoah Valley of Virginia. He received a doctorate in cognitive and behavioral sciences and a masters in statistics from Auburn University. After graduate school, John moved to the Texas Medical Center to join Michael Beauchamp's laboratory as a postdoctoral fellow, studying the neural basis of multisensory integration. After several fun postdoc years, John was promoted to faculty in 2017.

      Abstract

      Direct recording of neural activity from the human brain (intracranial encephalography, iEEG, also known as electrocorticography, ECoG) using implanted electrodes is one of the fastest-growing techniques in neuroscience. A key obstacle to progress is the vast quantity of data collected with ECOG.

      Current analysis and visualization tools are not designed to interact with the large volume of ECOG data in a simple, yet comprehensive way; this bottleneck prevents investigators from producing actionable discoveries.

      RAVE overcomes this obstacle by providing a powerful, comprehensive, free, user-friendly toolkit that makes it easy to analyze and view vast quantities of ECOG data. As a software tool designed from the ground-up for analysis and visualization of ECOG data, RAVE is the ideal tool for all current and future ECOG users, increasing productivity and the potential for transformative discoveries by the growing cadre of ECOG investigators.
          
      The development of RAVE is driven by three imperatives.  The first is to keep users "close to the data" so that users may make discoveries about the brain without being misled by artifacts. The second imperative is rigorous statistical methodology. RAVE is developed using the powerful statistical language “R” and leverages existing framework of validated R packages. The final imperative is "play well with others". RAVE makes it easy to seamlessly incorporate RAVE into existing analysis streams.


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