NSF DARE-transforming modeling in neurorehabilitation: perspectives and opportunities from US funding agencies

• Published in: Journal of neuroengineering and rehabilitation Vol. 21; no. 1; p. 17
• Main Authors: Hwang, Grace M, Kulwatno, Jonathan, Cruz, Theresa H, Chen, Daofen, Ajisafe, Toyin, Monaco, Joseph D, Nitkin, Ralph, George, Stephanie M, Lucas, Carol, Zehnder, Steven M, Zhang, Lucy T
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 03.02.2024; BioMed Central; BMC
• Subjects: Behavior; Brain research; Clinical medicine; Computational modeling; Computational neuroscience; Computer simulation; Computer-generated environments; Conferences, meetings and seminars; Digital twin; Digital twins; Drug dosages; Empowerment; Engineering research; Engineers; Federal government; Figure of merit; Funding; Humans; Methods; National Institutes of Health (U.S.); Nervous system; Neurological Rehabilitation; Neurology; Neurorehabilitation; Patients; Physics; Rehabilitation; United States; Workforce; United States; United States > US; Federal government; Computational modeling; Digital twin; Computational neuroscience; Workforce; Neurorehabilitation; Conference
• ISSN: 1743-0003; 1743-0003
• DOI: 10.1186/s12984-024-01308-x

In recognition of the importance and timeliness of computational models for accelerating progress in neurorehabilitation, the U.S. National Science Foundation (NSF) and the National Institutes of Health (NIH) sponsored a conference in March 2023 at the University of Southern California that drew global participation from engineers, scientists, clinicians, and trainees. This commentary highlights promising applications of computational models to understand neurorehabilitation ("Using computational models to understand complex mechanisms in neurorehabilitation" section), improve rehabilitation care in the context of digital twin frameworks ("Using computational models to improve delivery and implementation of rehabilitation care" section), and empower future interdisciplinary workforces to deliver higher-quality clinical care using computational models ("Using computational models in neurorehabilitation requires an interdisciplinary workforce" section). The authors describe near-term gaps and opportunities, all of which encourage interdisciplinary team science. Four major opportunities were identified including (1) deciphering the relationship between engineering figures of merit-a term commonly used by engineers to objectively quantify the performance of a device, system, method, or material relative to existing state of the art-and clinical outcome measures, (2) validating computational models from engineering and patient perspectives, (3) creating and curating datasets that are made publicly accessible, and (4) developing new transdisciplinary frameworks, theories, and models that incorporate the complexities of the nervous and musculoskeletal systems. This commentary summarizes U.S. funding opportunities by two Federal agencies that support computational research in neurorehabilitation. The NSF has funding programs that support high-risk/high-reward research proposals on computational methods in neurorehabilitation informed by theory- and data-driven approaches. The NIH supports the development of new interventions and therapies for a wide range of nervous system injuries and impairments informed by the field of computational modeling. The conference materials can be found at https://dare2023.usc.edu/ .