Evidence map›Paper›PMID 34118787›Full record

ArticleNeurobiology of aging2021

Transient neural network dynamics in cognitive ageing.

Roni Tibon, Kamen A Tsvetanov, Darren Price, David Nesbitt, Cam Can, Richard Henson

Abstract read
In one paragraph

Article in Neurobiology of aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. Article
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  5. Temporal autocorrelation is predictive of age-An extensive MEG time-series analysis.Proceedings of the National Academy of Sciences of the United States of America · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Roni TibonMRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK. Electronic address: roni.tibon@mrc-cbu.cam.ac.uk.
Kamen A TsvetanovDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Department of Psychology, University of Cambridge, Cambridge, UK.
Darren PriceMRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK.
David NesbittMRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK.
Cam CanCambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, UK.
Richard HensonMRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK; Department of Psychiatry, University of Cambridge, Cambridge, UK.

Funding

Biotechnology and Biological Sciences Research Council BB/H008217/1Medical Research Council MC_UU_00005/8Medical Research Council SUAG/010 RG91365
6 · The paper itself

Abstract

It is important to maintain cognitive function in old age, yet the neural substrates that support successful cognitive ageing remain unclear. One factor that might be crucial, but has been overlooked due to limitations of previous data and methods, is the ability of brain networks to flexibly reorganize and coordinate over a millisecond time-scale. Magnetoencephalography (MEG) provides such temporal resolution, and can be combined with Hidden Markov Models (HMMs) to characterise transient neural states. We applied HMMs to resting-state MEG data from a large cohort (N=595) of population-based adults (aged 18-88), who also completed a range of cognitive tasks. Using multivariate analysis of neural and cognitive profiles, we found that decreased occurrence of "lower-order" brain networks, coupled with increased occurrence of "higher-order" networks, was associated with both increasing age and decreased fluid intelligence. These results favour theories of age-related reductions in neural efficiency over current theories of age-related functional compensation, and suggest that this shift might reflect a stable property of the ageing brain.

Indexed as

CognitionAdolescentAdultAgedAged, 80 and overCognitive AgingCohort StudiesFemaleHumansMagnetoencephalographyMaleMarkov ChainsMiddle AgedNerve NetRestYoung AdultAgeingCanonical correlation analysisCognitionFluid intelligenceHidden Markov modelMagnetoencephalography

Identifiers

PMID34118787
PMCPMC8345312

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.