The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Motor learning: its relevance to stroke recovery and neurorehabilitation
Abstract
PURPOSE OF REVIEW: Much of neurorehabilitation rests on the assumption that patients can improve with practice. This review will focus on arm movements and address the following questions: (i) What is motor learning? (ii) Do patients with hemiparesis have a learning deficit? (iii) Is recovery after injury a form of motor learning? (iv) Are approaches based on motor learning principles useful for rehabilitation? RECENT FINDINGS: Motor learning can be broken into kinematic and dynamic components. Studies in healthy subjects suggest that retention of motor learning is best accomplished with variable training schedules. Animal models and functional imaging in humans show that the mature brain can undergo plastic changes during both learning and recovery. Quantitative motor control approaches allow differentiation between compensation and true recovery, although both improve with practice. Several promising new rehabilitation approaches are based on theories of motor learning. These include impairment oriented-training (IOT), constraint-induced movement therapy (CIMT), electromyogram (EMG)-triggered neuromuscular stimulation, robotic interactive therapy and virtual reality (VR). SUMMARY: Motor learning mechanisms are operative during spontaneous stroke recovery and interact with rehabilitative training. For optimal results, rehabilitation techniques should be geared towards patients' specific motor deficits and possibly combined, for example, CIMT with VR. Two critical questions that should always be asked of a rehabilitation technique are whether gains persist for a significant period after training and whether they generalize to untrained tasks.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Nudo, R. J., Wise, B. M., SiFuentes, F., & Milliken, G. W. (1996) · Science · doi:10.1126/science.272.5269.1791 · Cited by 1,911
Contextual interference effects on the acquisition, retention, and transfer of a motor skill.
Shea, J. B., & Morgan, R. L. (1979) · Journal of Experimental Psychology: Human Learning and Memory · doi:10.1037/0278-7393.5.2.179 · Cited by 1,227
Lum, P. S., Burgar, C. G., Shor, P. C., Majmundar, M., & Van der Loos, M. (2002) · Archives of Physical Medicine and Rehabilitation · doi:10.1053/apmr.2001.33101 · Cited by 1,159
Virtual Environments for Motor Rehabilitation: Review
Holden, M. K. (2005) · CyberPsychology & Behavior · doi:10.1089/cpb.2005.8.187 · Cited by 1,154
Understanding the pattern of functional recovery after stroke: Facts and theories
Kwakkel, G., Kollen, B., & Lindeman, E. (2004) · Restorative Neurology and Neuroscience · doi:10.3233/rnn-2004-00282 · Cited by 727
Cited by
Dynamic reconfiguration of human brain networks during learning
Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1018985108 · Cited by 1,705
Harnessing neuroplasticity for clinical applications
Cramer, S. C., Sur, M., Dobkin, B. H., O'Brien, C., Sanger, T. D., Trojanowski, J. Q., Rumsey, J. M., Hicks, R., Cameron, J., Chen, D., et al. (2011) · Brain · doi:10.1093/brain/awr039 · Cited by 1,259
Hatem, S. M., Saussez, G., della Faille, M., Prist, V., Zhang, X., Dispa, D., & Bleyenheuft, Y. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00442 · Cited by 918
Neuroplasticity in the context of motor rehabilitation after stroke
Dimyan, M. A., & Cohen, L. G. (2011) · Nature Reviews Neurology · doi:10.1038/nrneurol.2010.200 · Cited by 732
Robot-based hand motor therapy after stroke
Takahashi, C. D., Der-Yeghiaian, L., Le, V., Motiwala, R. R., & Cramer, S. C. (2008) · Brain · doi:10.1093/brain/awm311 · Cited by 624
Metadata
- Authors
- Krakauer, J. W.
- Venue
- Current Opinion in Neurology
- Published
- 2006-02
- Volume
- 19
- Issue
- 1
- Pages
- 84-90
- ISSN
- 1350-7540 (ISSN-L)
- OpenAlex
- W2073246877
- Cited by
- 1,275
- References
- 5
- FWCI
- 30.29
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Motor learning: its relevance to stroke recovery and neurorehabilitation. https://app.cochat.ai/papers/10.1097/01.wco.0000200544.29915.cc
- The paper
- Krakauer, J. W. (2006). Motor learning: its relevance to stroke recovery and neurorehabilitation. Current Opinion in Neurology. https://doi.org/10.1097/01.wco.0000200544.29915.cc
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.