TY - JOUR TI - Temporally delayed linear modelling (TDLM) measures replay in both animals and humans AU - Liu, Yunzhe AU - Dolan, Raymond J AU - Higgins, Cameron AU - Penagos, Hector AU - Woolrich, Mark W AU - Ólafsdóttir, H Freyja AU - Barry, Caswell AU - Kurth-Nelson, Zeb AU - Behrens, Timothy E A2 - Colgin, Laura L A2 - Kemere, Caleb A2 - van der Meer, Matthijs VL - 10 PY - 2021 DA - 2021/06/07 SP - e66917 C1 - eLife 2021;10:e66917 DO - 10.7554/eLife.66917 UR - https://doi.org/10.7554/eLife.66917 AB - There are rich structures in off-task neural activity which are hypothesized to reflect fundamental computations across a broad spectrum of cognitive functions. Here, we develop an analysis toolkit – temporal delayed linear modelling (TDLM) – 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. KW - replay KW - reactivation KW - decoding KW - MEG/EEG KW - electrophysiology JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -