The goal of this Clinical Neuroengineering Training Program (CNTP) is to train a cohort of engineers and scientists with an interdisciplinary approach linking the traditional areas of biomedical engineering and physical science, neuroscience, and clinical practice. We have designed the CNTP around the premise that interdisciplinary research is a mode of research by teams or individuals that integrates information, data, techniques, tools, perspectives, concepts, and/or theories from two or more disciplines to advance fundamental understanding or to solve problems whose solutions are beyond the scope of a single discipline. Students conducting interdisciplinary research also benefit greatly from the guidance of mentors in the three disciplines represented on their dissertation committees. Co-mentoring allows students to have direct relationships with researchers in the different fields while synthesizing the training and advice to form their own skills and experiences for their future interdisciplinary research goals. One of our objectives in making the CNTP a successful training program is that the research conducted by CNTP trainees will have an impact on multiple fields or disciplines, and produce results that feed back into and enhance disciplinary research. It will also create students with an expanded research vocabulary and abilities in more than one discipline, and with an enhanced understanding of the interconnectedness inherent in complex problems. As the name, Clinical Neuroengineering implies, the foundation and driving force of our program is the Clinic. This emphasis on the Clinic is the common thread that ties together all aspects of our program: The research of our trainers is designed to answer clinical questions;the formal course plan for our trainees includes clinical rotations in the hospital, a course in medical ethics and another in public policy. This focus on clinical medicine is almost universally absent in graduate education. We believe this makes our Program distinctly different from any other department or training program in the country, and makes the educational experience for our trainees truly interdisciplinary.

Public Health Relevance

The next decade will see unprecedented opportunities as well as challenges for medical science. Biological knowledge and understanding is increasing at an exponential rate particularly as to human disease. At the same time, there is growing dissatisfaction on the part of U.S. citizens for what is perceived as lack of translation to direct human benefit. Further, delivering new treatments is becoming increasingly problematic because of the remarkable changes in health-care delivery. Insurers and government regulators are insisting on new and very different means of demonstrating efficacy, particularly as it relates to quality of life and cost-effectiveness. Indeed, Evidence Based Medicine has become a mantra of health care administrators and one that biomedical engineers must not only understand but also master. Consequently, the context in which the scientist and engineer must operate has expanded greatly and into areas not typically addressed by traditional curricula. Rather, interdisciplinary teams of engineers, physicians, clinicians, and scientists will be critical to future medical successes. This means that the scientist or engineer of the future must have more than a passing acquaintance with many disciplines. The engineer or scientist must be able to problem solve in a multi-dimensional context. They must be able to orchestrate different disciplines;recognizing where and when each individual discipline is appropriate and needed. The goal of this Clinical Neuroengineering Training Program (CNTP) is to train a cohort of engineers and scientists with an interdisciplinary approach linking the traditional areas of biomedical engineering and physical science, neuroscience, and clinical practice.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Institutional National Research Service Award (T32)
Project #
5T32EB011434-04
Application #
8642177
Study Section
Special Emphasis Panel (ZEB1-OSR-D (J1))
Program Officer
Baird, Richard A
Project Start
2011-04-01
Project End
2016-03-31
Budget Start
2014-04-01
Budget End
2015-03-31
Support Year
4
Fiscal Year
2014
Total Cost
$178,043
Indirect Cost
$8,976
Name
University of Wisconsin Madison
Department
Physics
Type
Schools of Medicine
DUNS #
161202122
City
Madison
State
WI
Country
United States
Zip Code
53715
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Mohanty, Rosaleena; Sinha, Anita M; Remsik, Alexander B et al. (2018) Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity. Front Neurosci 12:353
Mohanty, Rosaleena; Sinha, Anita M; Remsik, Alexander B et al. (2018) Early Findings on Functional Connectivity Correlates of Behavioral Outcomes of Brain-Computer Interface Stroke Rehabilitation Using Machine Learning. Front Neurosci 12:624
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