Evidence map›Paper›PMID 38570842›Full record

ReviewJournal of neuroengineering and rehabilitation2024

NSF DARE-Transforming modeling in neurorehabilitation: Four threads for catalyzing progress.

Francisco J Valero-Cuevas, James Finley, Amy Orsborn, Natalie Fung, Jennifer L Hicks, He Helen Huang, David Reinkensmeyer, Nicolas Schweighofer, Douglas Weber, Katherine M Steele

Abstract readReview
In one paragraph

Review 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 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Review
  3. Self-Organizing Recruitment of Compensatory Cortical Areas Post-Stroke Can Maximize Residual Motor Performance.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026
    Article
  4. Article
  5. Review
  6. Ethnokinesiology: towards a neuromechanical understanding of cultural differences in movement.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
    Article
  7. An Efficient Framework for Personalizing EMG-Driven Musculoskeletal Models Based on Reinforcement Learning.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2024
    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

10 authors.

Francisco J Valero-CuevasAlfred E. Mann Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, 90089, CA, USA. valero@usc.edu.
James FinleyDivision of Biokinesiology and Physical Therapy, University of Southern California, 1540 Alcazar St 155, Los Angeles, 90033, CA, USA.
Amy OrsbornDepartment of Electrical and Computer Engineering, University of Washington, 185 W Stevens Way NE, Box 352500, Seattle, 98195, WA, USA.
Natalie FungThomas Lord Department of Computer Science, University of Southern California, 941 Bloom Walk, Los Angeles, 90089, CA, USA.
Jennifer L HicksDepartment of Bioengineering, Stanford University, 443 Via Ortega, Stanford, 94305, CA, USA.
He Helen HuangJoint Department of Biomedical Engineering, North Carolina State University, 1840 Entrepreneur Dr Suite 4130, Raleigh, 27606, NC, USA.
David ReinkensmeyerDepartment of Mechanical and Aerospace Engineering, UCI Samueli School of Engineering, 3225 Engineering Gateway, Irvine, 92697, CA, USA.
Nicolas SchweighoferAlfred E. Mann Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, 90089, CA, USA.
Douglas WeberDepartment of Mechanical Engineering and the Neuroscience Institute, Carnegie Mellon University, 5000 Forbes Avenue, B12 Scaife Hall, Pittsburgh, 15213, PA, USA.
Katherine M SteeleDepartment of Mechanical Engineering, University of Washington, 3900 E Stevens Way NE, Box 352600, Seattle, 98195, WA, USA.

Funding

TR&D Project 3: OpenSim for PredictionP41EB027060 · NIBIB · STANFORD UNIVERSITY · PI Joy P Ku · 2020 to 2026
$9.7M
Technology DevelopmentP2CHD101913 · NICHD · STANFORD UNIVERSITY · PI DELP, SCOTT L · 2020 to 2024
$4.1M
A Data Science Approach To Personalizing Sensorimotor Training Post-StrokeR56NS126748 · NINDS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI REMY-NERIS, OLIVIER, SCHWEIGHOFER, NICOLAS · 2022 to 2022
$717k
Functional reorganization of reticulospinal drive in hemiparetic strokeR21NS113613 · NINDS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI VALERO-CUEVAS, FRANCISCO J · 2019 to 2020
$454k
NIBIB NIH HHS P41 EB027060NICHD NIH HHS P2C HD101913NINDS NIH HHS R21 NS113613NINDS NIH HHS R56 NS126748
6 · The paper itself

Abstract

We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation's Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling 'In-the-Wild'. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

Indexed as

Neurological RehabilitationPersons with DisabilitiesAlgorithmsComputer SimulationHumansSoftwareAdaptationBig-dataComputational neuroscienceConferenceHuman-device interactionsPersonalizationPlasticityRehabilitationWearables

Identifiers

PMID38570842
PMCPMC10988973

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.