Targeting neuroplasticity to improve motor recovery after stroke: an artificial neural network model

Transforming modeling in neurorehabilitation: clinical insights for personalized rehabilitation

Practicing clinicians in neurorehabilitation continue to lack a systematic evidence base to personalize rehabilitation therapies to individual patients and thereby maximize outcomes. Computational modeling- collecting, analyzing, and modeling neurorehabilitation data- holds great promise. A key question is how can computational modeling contribute to the evidence base for personalized rehabilitation? As representatives of the clinicians and clinician-scientists who attended the 2023 NSF DARE conference at USC, here we offer our perspectives and discussion on this topic. Our overarching thesis is that clinical insight should inform all steps of modeling, from construction to output, in neurorehabilitation and that this process requires close collaboration between researchers and the clinical community. We start with two clinical case examples focused on motor rehabilitation after stroke which provide context to the heterogeneity of neurologic injury, the complexity of post-acute neurologic care, the neuroscience of recovery, and the current state of outcome assessment in rehabilitation clinical care. Do we provide different therapies to these two different patients to maximize outcomes? Asking this question leads to a corollary: how do we build the evidence base to support the use of different therapies for individual patients? We discuss seven points critical to clinical translation of computational modeling research in neurorehabilitation- (i) clinical endpoints, (ii) hypothesis- versus data-driven models, (iii) biological processes, (iv) contextualizing outcome measures, (v) clinical collaboration for device translation, (vi) modeling in the real world and (vii) clinical touchpoints across all stages of research. We conclude with our views on key avenues for future investment (clinical-research collaboration, new educational pathways, interdisciplinary engagement) to enable maximal translational value of computational modeling research in neurorehabilitation.

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

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/ .

Neurophysiological and Clinical Effects of Upper Limb Robot-Assisted Rehabilitation on Motor Recovery in Patients with Subacute Stroke: A Multicenter Randomized Controlled Trial Study Protocol

BACKGROUND

The efficacy of upper limb (UL) robot-assisted therapy (RAT) on functional improvement after stroke remains unclear. However, recently published randomized controlled trials have supported its potential benefits in enhancing the activities of daily living, arm and hand function, and muscle strength. Task-specific and high-intensity exercises are key points in facilitating motor re-learning in neurorehabilitation since RAT can provide an assisted-as-needed approach. This study aims to investigate the clinical effects of an exoskeleton robotic system for UL rehabilitation compared with conventional therapy (CT) in people with subacute stroke. As a secondary aim, we seek to identify patients' characteristics, which can predict better recovery after UL-RAT and detects whether it could elicit greater brain stimulation.

METHODS

A total of 84 subacute stroke patients will be recruited from 7 Italian rehabilitation centers over 3 years. The patients will be randomly allocated to either CT (control group, CG) or CT plus UL-RT through an Armeo^®Power (Hocoma AG, CH, Volketswil, Switzerland) exoskeleton (experimental group, EG). A sample stratification based on distance since onset, DSO (DSO ≤ 30; DSO > 30), and Fugl-Meyer Assessment (FM)-UL (FM-UL ≤ 22; 22 < FM-UL ≤ 44) will be considered for the randomization. The outcomes will be recorded at baseline (T0), after 25 + 3 sessions of intervention (T1), and at 6 months post-stroke (T2). The motor functioning assessed by the FM-UL (0-66) will be considered the primary outcome. The clinical assessments will be set based on the International Classification of Function, Disability and Health (ICF). A patient satisfaction questionnaire will be evaluated in the EG at T1. A subgroup of patients will be evaluated at T0 and T1 via electroencephalography. Their brain electrical activity will be recorded during rest conditions with their eyes closed and open (5 min each).

CONCLUSION

The results of this trial will provide an in-depth understanding of the efficacy of early UL-RAT through a whole arm exoskeleton and how it may relate to the neural plasticity process. The trial was registered at ClinicalTrial.gov with the registration identifier NCT04697368.