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These changes likely result from and give insight into the reorganization within spared and injured spinal circuitry. Previously, we developed computational models of the mouse spinal locomotor circuitry controlling speed-dependent gait expression (Danner et al. 2017; Zhang et al. 2022). Here, we adapted these models to the rat and used the adapted model to explore potential circuit-level changes underlying altered gait expression observed after recovery from two different thoracic spinal cord injuries (lateral hemisection and contusion) that have roughly comparable levels of locomotor recovery (Danner et al., 2023). The model reproduced experimentally observed gait expression before injury and after recovery from lateral hemisection and contusion, and suggests two distinct, injury-specific routes to restored function. First, recovery after lateral hemisection required substantial functional restoration of damaged descending drive and long propriospinal connections, suggesting compensatory plasticity through formation of detour pathways. Second, recovery after a moderate midline contusion predominantly relied on reorganization of spared sublesional networks and altered control of supralesional cervical circuits, compensating for weakened propriospinal and descending pathways. Despite these differences, sensitivity analysis revealed that restored activation of sublesional lumbar rhythm-generating circuits and appropriately balanced lumbar commissural connectivity are the key determinants of post-injury gait expression, suggesting that injury symmetry shapes how the cord reorganizes, but effective recovery in both cases depends on re-engaging these lumbar networks, which makes them prime targets for therapeutic intervention.<\/jats:p>","DOI":"10.1101\/2025.05.17.654682","type":"posted-content","created":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T20:15:19Z","timestamp":1747858519000},"source":"Crossref","is-referenced-by-count":0,"title":["Reorganization of spinal neural connectivity following recovery after thoracic spinal cord injury: insights from computational modelling"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1971-9707","authenticated-orcid":false,"given":"Natalia A.","family":"Shevtsova","sequence":"first","affiliation":[{"name":"Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew B.","family":"Lockhart","sequence":"additional","affiliation":[{"name":"Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3461-349X","authenticated-orcid":false,"given":"Ilya A.","family":"Rybak","sequence":"additional","affiliation":[{"name":"Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David S. 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