The neuroscience research at UGA structures itself after the inherent multi-disciplinarity of the field it represents. Bridging many units and campuses, our research faculty and students seek novel non-traditional approaches toward the many issues related to brain health. Our research community includes individuals from Franklin College, the College of Veterinary Medicine, College of Agriculture and Environmental Sciences, the College of Pharmacy, and the Medical Partnership with Augusta University. Such diversity allows for a unique breadth of research in multiple species facilitating in the easy adaptation from the basic studies that are conducted in rodents, stem cells, drosophila, zebrafish, planaria, mosquito and yeast to larger species such as dogs, pigs and primates to clinical studies that potentially translate to new opportunities in human therapies. To achieve this aim, researchers utilize the University’s competitive network of facilities dedicated to imaging technology, bioinformatics, bioengineering, and complex behavioral paradigms, allowing comprehensive investigation of these diverse animal models.

Themes and Strengths

Although participating faculty members represent diverse fields of neuroscience, the following complementary areas of strength and collaboration define our program’s research:

• Developmental Neurobiology
• Neuroimmunology and Glial Biology
• Neurodegeneration
• Regenerative Neuroscience
• Reward and Addiction Mechanisms

To facilitate discovery in these key areas, neuroscience faculty developed specific technical expertise, resources, and instrumentation. These unique technical strengths include both traditional tried-and-true neuroscience tools and cutting-edge emerging technologies:

• Comprehensive imaging resources and expertise
• Tissue and cell bioengineering
• Diverse model organisms: pig, rodent, drosophila, zebrafish, planarian, mosquito, yeast
• Multiple behavioral paradigms
• Chemical biology and high throughput screening capability
• Electrophysiology
• Bioinformatics, biostatistics, and computational modeling