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Physiol."],"abstract":"<jats:p>\n                    Computational models of the neuromusculoskeletal system provide a deterministic approach to investigate input-output relationships in the human motor system. Neuromusculoskeletal models are typically used to estimate muscle activations and forces that are consistent with observed motion under healthy and pathological conditions. However, many movement pathologies originate in the brain, including stroke, cerebral palsy, and Parkinson\u2019s disease, while most neuromusculoskeletal models deal exclusively with the peripheral nervous system and do not incorporate models of the motor cortex, cerebellum, or spinal cord. An integrated understanding of motor control is necessary to reveal underlying neural-input and motor-output relationships. To facilitate the development of integrated corticomuscular motor pathway models, we provide an overview of the neuromusculoskeletal modelling landscape with a focus on integrating computational models of the motor cortex, spinal cord circuitry,\n                    <jats:italic>\u03b1<\/jats:italic>\n                    -motoneurons\u2009 and skeletal muscle in regard to their role in generating voluntary muscle contraction. Further, we highlight the challenges and opportunities associated with an integrated corticomuscular pathway model, such as challenges in defining neuron connectivities, modelling standardisation, and opportunities in applying models to study emergent behaviour. Integrated corticomuscular pathway models have applications in brain-machine-interaction, education, and our understanding of neurological disease.\n                  <\/jats:p>","DOI":"10.3389\/fphys.2023.1095260","type":"journal-article","created":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T11:49:47Z","timestamp":1683719387000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Linking cortex and contraction\u2014Integrating models along the corticomuscular pathway"],"prefix":"10.3389","volume":"14","author":[{"given":"Lysea","family":"Haggie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Schmid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver","family":"R\u00f6hrle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thor","family":"Besier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angus","family":"McMorland","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harnoor","family":"Saini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,5,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1038\/nn.4241","article-title":"Building functional networks of spiking model neurons","volume":"19","author":"Abbott","year":"2016","journal-title":"Nat. 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