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To test this hypothesis, we acquired and analyzed a magnetoencephalography (MEG) dataset containing recordings from one female adult participant, with several scanning days spanning over 6 months. The participant passively attended to visual stimuli with the same stimulus presented within each session. We demonstrate that the specific scanning day can be predicted from the brain responses in a simple passive viewing paradigm, suggesting continuous temporal changes in neural activity over long time scales. However, information from one scanning day could be used to robustly decode the animacy of objects on a different scanning day, and importantly, decoding accuracy did not suffer with increasing time intervals between scans. That is, cross-decoding accuracy remained stable over months, despite between-scanning-day variability. These findings suggest that while processing of visual stimuli shows variability over long time scales, the core neural structure underlying object recognition remains stable and non-stimulus-specific. The results were validated in the open-access THINGS-MEG dataset, which employs a similar paradigm but covers a shorter longitudinal timespan. We find similar results across the four additional participants.<\/jats:p>","DOI":"10.1523\/eneuro.0344-25.2026","type":"journal-article","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T17:50:11Z","timestamp":1781113811000},"page":"ENEURO.0344-25.2026","update-policy":"https:\/\/doi.org\/10.1523\/eneuro.crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Long-Term Variability in Visual Processing versus Perceptual Stability"],"prefix":"10.1523","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6277-0460","authenticated-orcid":false,"given":"Laura 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