The DOI matches its Crossref record; 224 of the 224 references it cites were found in a registry or an index.
Neuromechanical Principles Underlying Movement Modularity and Their Implications for Rehabilitation
Open accessAbstract
Summary Neuromechanical principles define the properties and problems that shape neural solutions for movement. Although the theoretical and experimental evidence is debated, we present arguments for consistent structures in motor patterns, i.e. motor modules, that are neuromechanical solutions for movement particular to an individual and shaped by evolutionary, developmental, and learning processes. As a consequence, motor modules may be useful in assessing sensorimotor deficits specific to an individual, and define targets for the rational development of novel rehabilitation therapies that enhance neural plasticity and sculpt motor recovery. We propose that motor module organization is disrupted and may be improved by therapy in spinal cord injury, stroke, and Parkinson’s disease. Recent studies provide insights into the yet unknown underlying neural mechanisms of motor modules, motor impairment and motor learning, and may lead to better understanding of the causal nature of modularity and its underlying neural substrates.
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Cites
Takahashi, K., & Yamanaka, S. (2006) · Cell · doi:10.1016/j.cell.2006.07.024 · Cited by 26,673
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Learning the parts of objects by non-negative matrix factorization
Lee, D. D., & Seung, H. S. (1999) · Nature · doi:10.1038/44565 · Cited by 13,868
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388
The co-ordination and regulation of movements
(1969) · Brain Research · doi:10.1016/0006-8993(69)90278-9 · Cited by 5,043
The co-ordination and regulation of movements
Foley, J. (1968) · Journal of the Neurological Sciences · doi:10.1016/0022-510x(68)90166-4 · Cited by 3,420
Optimal feedback control as a theory of motor coordination
Todorov, E., & Jordan, M. I. (2002) · Nature Neuroscience · doi:10.1038/nn963 · Cited by 3,081 · cited 3×
“In simulations, motor modules have been shown to emerge based on optimal control of multi-jointed and multilegged systems and produce near-optimal motor performance (Berniker et al., 2009; Chhabra and Jacobs, 2006; Kurtzer et al., 2006; McKay and Ting, 2012; Todorov and Jordan, 2002).”
Attention and the control of posture and gait: a review of an emerging area of research
Woollacott, M., & Shumway-Cook, A. (2002) · Gait & Posture · doi:10.1016/s0966-6362(01)00156-4 · Cited by 2,690
THE BASAL GANGLIA: FOCUSED SELECTION AND INHIBITION OF COMPETING MOTOR PROGRAMS
MINK, J. W. (1996) · Progress in Neurobiology · doi:10.1016/s0301-0082(96)00042-1 · Cited by 2,486
The role of the basal ganglia in habit formation
Yin, H. H., & Knowlton, B. J. (2006) · Nature Reviews Neuroscience · doi:10.1038/nrn1919 · Cited by 2,402
Cited by
Current principles of motor control, with special reference to vertebrate locomotion
Grillner, S., & El Manira, A. (2020) · Physiological Reviews · doi:10.1152/physrev.00015.2019 · Cited by 560
Magrinelli, F., Picelli, A., Tocco, P., Federico, A., Roncari, L., Smania, N., Zanette, G., & Tamburin, S. (2016) · Parkinson's Disease · doi:10.1155/2016/9832839 · Cited by 369
Hug, F., Avrillon, S., Ibáñez, J., & Farina, D. (2023) · The Journal of Physiology · doi:10.1113/jp283698 · Cited by 154
Metadata
- Authors
- Ting, L. H., Chiel, H. J., Trumbower, R. D., Allen, J. L., McKay, J. L., Hackney, M. E., & Kesar, T. M.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2015-04
- Volume
- 86
- Issue
- 1
- Pages
- 38-54
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W2083831782
- Cited by
- 411
- References
- 224
- FWCI
- 19.91
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Ting, L. H., Chiel, H. J., Trumbower, R. D., Allen, J. L., McKay, J. L., Hackney, M. E., & Kesar, T. M. (2015). Neuromechanical Principles Underlying Movement Modularity and Their Implications for Rehabilitation. Neuron. https://doi.org/10.1016/j.neuron.2015.02.042
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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