Evidence map›Paper›PMID 38310271›Full record

ArticleJournal of neuroengineering and rehabilitation2024

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

Grace M Hwang, Jonathan Kulwatno, Theresa H Cruz, Daofen Chen, Toyin Ajisafe, Joseph D Monaco, Ralph Nitkin, Stephanie M George, Carol Lucas, Steven M Zehnder and 1 more

Abstract read
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Grace M HwangNational Institute of Neurological Disorders and Stroke, National Institutes of Health, Rockville, MD, 20852, USA. Grace.Hwang@nih.gov.
Jonathan KulwatnoDirectorate for Engineering, National Science Foundation, 2415 Eisenhower Avenue, Alexandria, VA, 22314, USA.
Theresa H CruzEunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20817, USA.
Daofen ChenNational Institute of Neurological Disorders and Stroke, National Institutes of Health, Rockville, MD, 20852, USA.
Toyin AjisafeEunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20817, USA.
Joseph D MonacoNational Institute of Neurological Disorders and Stroke, National Institutes of Health, Rockville, MD, 20852, USA.
Ralph NitkinEunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20817, USA.
Stephanie M GeorgeDirectorate for Engineering, National Science Foundation, 2415 Eisenhower Avenue, Alexandria, VA, 22314, USA.
Carol LucasDirectorate for Engineering, National Science Foundation, 2415 Eisenhower Avenue, Alexandria, VA, 22314, USA.
Steven M ZehnderDirectorate for Engineering, National Science Foundation, 2415 Eisenhower Avenue, Alexandria, VA, 22314, USA.
Lucy T ZhangDirectorate for Engineering, National Science Foundation, 2415 Eisenhower Avenue, Alexandria, VA, 22314, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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/ .

Indexed as

National Institutes of Health (U.S.)Neurological RehabilitationHumansUnited StatesComputational modelingComputational neuroscienceConferenceDigital twinFederal governmentNeurorehabilitationWorkforce

Identifiers

PMID38310271
PMCPMC10837948

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.