The fields of biology, psychology, and biomedical engineering have generated exciting new advances in the study of neural systems underlying behavior. Individually, these disciplines have individually provided novel insights into brain function and provide opportunities for improved understanding of disorders of the nervous system, healthy and disordered development, and communication. However, the rapid advancement of scientific progress has been limited by the boundaries surrounding the disciplines. Moreover, neuroscientists that are firmly grounded in an array of approaches used by biologists, psychologists, and engineers will best advance new research technologies such as non-invasive functional imaging and neural prosthetics. A training model that is thoroughly interdisciplinary is needed. At Washington University, we have developed such a model: The Cognitive, Computational, and Systems Neuroscience (CCSN) Pathway produces rigorously trained independent investigators that will lead a new generation of scientists who study the brain in truly integrated interdisciplinary investigations. CCSN serves students from the PhD programs in Biomedical Engineering, Psychology, and Neuroscience. The core of CCSN is a two-year curriculum that emphasizes interdisciplinarity, collaboration, and project-based instruction. In the first year, students take courses that bring them up to speed on the core concepts and methods in Cognitive Psychology, Biological Neural Computation, and Neural Systems. In the second year, students participate in two unique courses that have been specially designed as the capstone to the CCSN pathway Advanced CCSN and Project Building in CCSN. Advanced CCSN consists of a series of interdisciplinary case studies in cutting-edge brain science topics. Each topic is presented as a module by a faculty team drawn from the three home programs. Modules include team-based projects and peer review as well as primary source readings and classroom lectures and discussions. Project Building in CCSN is a fully student-driven course. In collaboration with the faculty leader, each student designs an independent interdisciplinary research project. The faculty leader helps them to assemble an interdisciplinary faculty advising team, to whom they present their project multiple times throughout the semester. Faculty advising is complemented by peer advising including written peer review, culminating in a research grant-style project proposal. Surrounding the core CCSN curriculum is a rich penumbra of activities. These are designed to provide intellectual training and to build a cohort of scientists with the identification and social skills necessary to conduct research in interdisciplinary teams. Formal coursework is provided in Mathematics and Statistics of Experimental Neuroscience, and by an intensive mini- course preceding Advanced CCSN. Immersive Encounters with distinguished visiting scientists provide high-intensity exposure to cutting-edge research. In collaboration with the Saint Louis Science Center, CCSN trains students to communicate with the public and helps them build programs and presentations to teach children and adults about the brain and mind. In its initial phases, CCSN has produced cohorts of young brain scientists on the fast track to new discoveries. Evaluations from students, faculty, and an outside advisory team indicate the pathway is on track for continued growth.

Public Health Relevance

Project)Narrative) Cutting edge research in brain science is increasingly interdisciplinary, and traditional discipline-based graduate training programs strain to accommodate this development. The Cognitive, Computational & Systems Neuroscience pathway at Washington University represents a unique new model for training 21st century brain scientists. Such training will produce a generation of scientists effectively equipped to produce breakthroughs in neurological disease, mental illness, and neural engineering.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Institutional National Research Service Award (T32)
Project #
5T32NS073547-05
Application #
8877643
Study Section
Special Emphasis Panel (ZNS1-SRB-P (56))
Program Officer
Korn, Stephen J
Project Start
2011-07-01
Project End
2016-06-30
Budget Start
2015-07-01
Budget End
2016-06-30
Support Year
5
Fiscal Year
2015
Total Cost
$2
Indirect Cost
$11,048
Name
Washington University
Department
Neurology
Type
Schools of Medicine
DUNS #
068552207
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
Holmes, Charles D; Papadimitriou, Charalampos; Snyder, Lawrence H (2018) Dissociation of LFP Power and Tuning in the Frontal Cortex during Memory. J Neurosci 38:8177-8186
Moran, Erin K; Culbreth, Adam J; Barch, Deanna M (2018) Emotion Regulation Predicts Everyday Emotion Experience and Social Function in Schizophrenia. Clin Psychol Sci 6:271-279
Barbour, Dennis L; Howard, Rebecca T; Song, Xinyu D et al. (2018) Online Machine Learning Audiometry. Ear Hear :
Lerman-Sinkoff, Dov B; Sui, Jing; Rachakonda, Srinivas et al. (2017) Multimodal neural correlates of cognitive control in the Human Connectome Project. Neuroimage 163:41-54
Yu, Alfred B; Zacks, Jeffrey M (2017) Transformations and representations supporting spatial perspective taking. Spat Cogn Comput 17:304-337
Culbreth, Adam J; Westbrook, Andrew; Daw, Nathaniel D et al. (2016) Reduced model-based decision-making in schizophrenia. J Abnorm Psychol 125:777-787
Lerman-Sinkoff, Dov B; Barch, Deanna M (2016) Network community structure alterations in adult schizophrenia: identification and localization of alterations. Neuroimage Clin 10:96-106
Culbreth, Adam; Westbrook, Andrew; Barch, Deanna (2016) Negative symptoms are associated with an increased subjective cost of cognitive effort. J Abnorm Psychol 125:528-536
Culbreth, Adam J; Westbrook, Andrew; Xu, Ziye et al. (2016) Intact Ventral Striatal Prediction Error Signaling in Medicated Schizophrenia Patients. Biol Psychiatry Cogn Neurosci Neuroimaging 1:474-483
Song, Xinyu D; Wallace, Brittany M; Gardner, Jacob R et al. (2015) Fast, Continuous Audiogram Estimation Using Machine Learning. Ear Hear 36:e326-35

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