Temporally delayed linear modelling (TDLM) measures replay in both animals and humans

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 met...

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Published ineLife Vol. 10
Main Authors Liu, Yunzhe, Dolan, Raymond J, Higgins, Cameron, Penagos, Hector, Woolrich, Mark W, Ólafsdóttir, H Freyja, Barry, Caswell, Kurth-Nelson, Zeb, Behrens, Timothy E
Format Journal Article
LanguageEnglish
Published England eLife Sciences Publications Ltd 07.06.2021
eLife Sciences Publications, Ltd
Subjects
Animals
Behavior, Animal
Brain - physiology
Cognitive ability
decoding
Electrophysiology
Evoked Potentials
Humans
Linear Models
Magnetoencephalography
Maze Learning
Medical imaging
MEG/EEG
Mental Recall
Methods
Models, Neurological
Nervous system
Neuroimaging
Neuroscience
Neurosciences
Photic Stimulation
Rats
reactivation
replay
Time Factors
Time series
Visual Perception
mouse
electrophysiology
neuroscience
reactivation
MEG/EEG
replay
human
decoding
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Summary: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.
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ISSN:2050-084X
2050-084X
DOI:10.7554/eLife.66917