Since 1999, the Center for Cognitive Neuroscience (CCN) has served as the central focus at Duke University for research, education, and training in the psychological, computational, and biological mechanisms of higher mental function; variability in these mechanisms among individuals, across the lifespan, and between species; application of these mechanisms to real-world problems; and their dissolution in disease and mental disorders.
Cognitive neuroscience is by its nature interdisciplinary, and addresses longstanding questions about brain and mind from new perspectives that cut across traditional intellectual and departmental boundaries. CCN research focuses on perception, attention, memory, language, emotion, decision making, social interaction, morality, motor control, executive function, and the evolution and development of mental processes. To advance this agenda, the CCN and its activities bring together faculty from multiple schools in the University, including Arts & Sciences, Medical School, Pratt School of Engineering, and Fuqua Business School, representing the Departments of Psychology & Neuroscience, Neurobiology, Psychiatry, Biomedical Engineering, Philosophy, Evolutionary Anthropology, Computer Science, Linguistics, Neurology, Radiology, Finance, and Marketing.
CCN works with students at all levels within Duke. Students enrolled in the cognitive neuroscience program will gain a thorough understanding of the intellectual issues that drive this rapidly growing field, as well as expertise in the major methods for cognitive brain research. The overarching aim of the program is to train students in innovative approaches to research on higher human brain functions, including, but not limited to, perception, attention, memory, language, emotion, motor control, executive functions, consciousness and the evolution of mental processes.
Duke University undergraduate students may obtain training in cognitive neuroscience in the laboratory of a participating faculty member. Those interested in experience in cognitive neuroscience should contact individual faculty members. Students with a strong interest in cognitive neuroscience are encouraged to explore Duke's Undergraduate Studies in Neuroscience.
Graduate students interested in cognitive neuroscience often apply through the Cognitive Neuroscience Admitting Program, designed for students interested in an integrated approach to the study of cognitive neuroscience. Students apply directly to this admitting program; however, the Ph.D. is granted from one of the participating departments.
CCN coordinates a postdoctoral training program for scientists holding the Ph.D. or M.D. degrees or their equivalent. Postdoctoral trainees may conduct research in humans or animals using a variety of techniques, or may identify two faculty sponsors and develop interdisciplinary training plans. Trainees are supported by individual faculty research grants from the National Institutes of Health (NIH) and National Science Foundation, and by individual fellowships from NIH, the James S. McDonnell Foundation and Pew Charitable Trust, and other public and private sources. Postdoctoral trainees should contact individual faculty members to inquire about training opportunities.
Neuroimaging is a vital component of a variety of research studies at DIBS. With the help of Duke's Brain Imaging and Analysis Center, researchers collect multi-modal brain imaging data to understand the structure and function of the human brain. They mainly employ magnetic resonance imaging (MRI) to collect structural MRI, functional MRI, diffusion tensor imaging and other forms of MRI. Andrew Michael, PhD, Director of Imaging analytics and Informatics, works closely with neuroscientists and clinicians at Duke and the Duke Brain Imaging and Analysis Center to ensure cutting edge MR technology and advanced analytic methodologies are utilized in brain imaging studies. He supports Duke research groups in brain imaging study design, implementing quality control protocols, application of pre and post processing methods and in analyzing brain data using both conventional statistical and advanced machine learning methods.