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One theory posits that the brain recognizes past errors, enabling more efficient learning from them. If this is the case, there should be a modification in the neural response to errors during re-exposure to the perturbation. To investigate this hypothesis, we used fMRI to measure brain activity as participants adapted to a visuomotor perturbation across two sessions spaced one day apart, focusing on neural responses to movement errors. The magnitude of the movement error was incorporated into different types of GLMs to study error-related activation and co-activation (or functional connectivity). We identified a cerebello-thalamo-cortical network involved in processing movement errors during adaptation. We observed strengthened connectivity within this network during re-adaptation, particularly between the cerebellar lobule VI and the ventrolateral thalamus, as well as between the primary somatosensory cortex and the rostral cingulate motor zone. Importantly, participants with the greatest increases in connectivity strength also exhibited the largest amounts of savings. These results establish a link between the brain\u2019s ability to represent errors and the phenomenon of savings.<\/jats:p>","DOI":"10.1007\/s00429-025-03013-4","type":"journal-article","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T04:11:38Z","timestamp":1760328698000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Savings in visuomotor learning are associated with connectivity changes within a cerebello-thalamo-cortical network encoding movement errors"],"prefix":"10.1007","volume":"230","author":[{"given":"Lucas","family":"Struber","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laurent","family":"Lamalle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pierre-Alain","family":"Barraud","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aur\u00e9lien","family":"Courvoisier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rafael","family":"Laboissi\u00e8re","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takayuki","family":"Ito","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincent","family":"Nougier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David J","family":"Ostry","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabien","family":"Cignetti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,13]]},"reference":[{"key":"3013_CR1","doi-asserted-by":"publisher","unstructured":"Abraham A, Pedregosa F, Eickenberg M, Gervais P, Mueller A, Kossaifi J, Gramfort A, Thirion B, Varoquaux G (2014) Machine learning for neuroimaging with scikit-learn. 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