PT  - JOURNAL ARTICLE
AU  - Yunzhe Liu
AU  - Raymond J Dolan
AU  - Hector Luis Penagos-Vargas
AU  - Zeb Kurth-Nelson
AU  - Timothy Behrens
TI  - Measuring Sequences of Representations with Temporally Delayed Linear Modelling
AID  - 10.1101/2020.04.30.066407
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.04.30.066407
4099  - http://biorxiv.org/content/early/2020/05/12/2020.04.30.066407.short
4100  - http://biorxiv.org/content/early/2020/05/12/2020.04.30.066407.full
AB  - There are rich structures in off-task neural activity. For example, task related neural codes are thought to be reactivated in a systematic way during rest. This reactivation is hypothesised to reflect a fundamental computation that supports a variety of cognitive functions. Here, we introduce an analysis toolkit (TDLM) for analysing this activity. TDLM combines nonlinear classification and linear temporal modelling to testing for statistical regularities in sequences of neural representations. It is developed using non-invasive neuroimaging data and is designed to take care of confounds and maximize sequence detection ability. The method can be extended to rodent electrophysiological recordings. We outline how TDLM can successfully reveal human replay during rest, based upon non-invasive magnetoencephalography (MEG) measurements, with strong parallels to rodent hippocampal replay. TDLM can therefore advance our understanding of sequential computation and promote a richer convergence between animal and human neuroscience research.Competing Interest StatementThe authors have declared no competing interest.