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Here, we develop an analysis toolkit \u2013 temporal delayed linear modelling (TDLM) \u2013 for analysing such activity. TDLM is a domain-general method for finding neural sequences that respect a pre-specified transition graph. It combines nonlinear classification and linear temporal modelling to test for statistical regularities in sequences of task-related reactivations. TDLM is developed on the non-invasive neuroimaging data and is designed to take care of confounds and maximize sequence detection ability. Notably, as a linear framework, TDLM can be easily extended, without loss of generality, to capture rodent replay in electrophysiology, including in continuous spaces, as well as addressing second-order inference questions, for example, its temporal and spatial varying pattern. We hope TDLM will advance a deeper understanding of neural computation and promote a richer convergence between animal and human neuroscience.<\/jats:p>","DOI":"10.7554\/elife.66917","type":"journal-article","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T08:01:50Z","timestamp":1623052910000},"update-policy":"https:\/\/doi.org\/10.7554\/elife.66917","source":"Crossref","is-referenced-by-count":53,"title":["Temporally delayed linear modelling (TDLM) measures replay in both animals and humans"],"prefix":"10.7554","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0836-9403","authenticated-orcid":true,"given":"Yunzhe","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Cognitive Neuroscience and Learning, IDG\/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China"},{"name":"Chinese Institute for Brain Research, Beijing, China"},{"name":"Max Planck University College London Centre for Computational 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