Hosted by the Center for Neuroregeneration and Department of Neurosurgery 

Data Risk and Rewards: From Preliminary to Publication

Throughout its history, neural regeneration has been a challenging field due to the complexity of modeling and assessing plasticity. Modeling the injured nervous system is difficult, given its innate complexity. Repair and regeneration require a multidisciplinary approach, integrating tools from biology, engineering, immunology and more. A common problem is empirical testing of neural regeneration at anatomical and functional levels. Ultimately, the complex nature of the nervous system and the neural regeneration field requires consideration from multiple disciplines to generate investigative principles that lead to
scientific rigor and reproducibility. This mini course aims to deepen participants’ understanding of the mechanics and synthesis of project development in systems neuroscience and the implementation and interpretation of experimental design, scientific rigor and statistics.

About Zusman Neural Network

The Zusman Neural Network aims to stimulate new ideas that will fill the gap between physiology and functional-based brain stimulation technologies and the molecular and cellular understanding of innate neuronal plasticity. This is achieved through a hyper-focused environment in workshops with an incubator set up, allowing participants to attend technical presentations on targeted subjects and providing a facilitatory environment to promote communication and collaboration between attendees. We further this by providing educational opportunities to the next generation of scientists aimed at deepening their understanding of the mechanics and synthesis of project development in systems neuroscience as well as the implementation and interpretation of experimental design, scientific rigor, and statistics in neuroscience.

Purpose

Throughout its history, neural regeneration has been a challenging discipline in terms of modeling and rigorous assessment of plasticity. Rigorously modeling the injured nervous system is a challenge given its innate complexity. Further, repair/regeneration of the nervous system requires a multidisciplinary approach, along with the integration of multiple tools and many areas of expertise (e.g., biology, engineering, immunology), making it an undoubtedly complex discipline. One of the most common problems seen in the field since its inception has been the empirical testing of neural regeneration, particularly both at the anatomical and the functional level. Ultimately, the complex nature of the nervous system and the neural regeneration field requires a level of consideration from multiple disciplines in order to generate investigative principles that lead to scientific rigor and reproducibility. This mini-course aims to deepen trainees’ understanding of the mechanics and synthesis of project development in systems neuroscience as well as the implementation and interpretation of experimental design, scientific rigor, and statistics in neuroscience.

Topics

Our mission is to be an educational hub for trainees and early-stage investigators interested in learning historical principles as well as modern tools for the rigorous analysis of neural regeneration.

• Databases and Data Management

• Chain of Command in Data Security

• Rigor and Reproducibility