Temporally Delayed Linear Modelling (TDLM) Measures Replay in Both Animals and Humans

Overview

Journal Elife

Specialty Biology

Date 2021 Jun 7

PMID 34096501

Citations 16

Authors

Yunzhe Liu

Raymond J Dolan

Cameron Higgins

Hector Penagos

Mark W Woolrich

H Freyja Olafsdottir

Caswell Barry

Zeb Kurth-Nelson

Timothy E Behrens

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

Replay-triggered brain-wide activation in humans.

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Shared structure facilitates working memory of multiple sequences.

Huang Q, Luo H Elife. 2024; 12.

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Reactivation strength during cued recall is modulated by graph distance within cognitive maps.

Kern S, Nagel J, Gerchen M, Gursoy C, Meyer-Lindenberg A, Kirsch P Elife. 2024; 12.

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Heuristics in risky decision-making relate to preferential representation of information.

Russek E, Moran R, Liu Y, Dolan R, Huys Q Nat Commun. 2024; 15(1):4269.

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Trajectories through semantic spaces in schizophrenia and the relationship to ripple bursts.

Nour M, McNamee D, Liu Y, Dolan R Proc Natl Acad Sci U S A. 2023; 120(42):e2305290120.

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