Evidence map›Paper›PMID 30717827›Full record

ArticleeLife2019

Atypical intrinsic neural timescale in autism.

Takamitsu Watanabe, Geraint Rees, Naoki Masuda

Abstract read
In one paragraph

Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
82citing papers in PubMed, 1 pooled it
–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

82 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  9. Age Differences in Hippocampal Neural Timescales During Movie-Viewing.bioRxiv : the preprint server for biology · 2026
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  19. Criticality and increased intrinsic neural timescales in stroke.NPJ systems biology and applications · 2025
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22 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Takamitsu WatanabeInstitute of Cognitive Neuroscience, University College London, London, United Kingdom.ORCID 0000-0002-8104-6873
Geraint ReesInstitute of Cognitive Neuroscience, University College London, London, United Kingdom.
Naoki MasudaDepartment of Engineering Mathematics, University of Bristol, Bristol, United Kingdom.

Funding

European Commission 656161Japan Science and Technology Agency JPMJCR1304Japan Society for the Promotion of Science 18H06094Wellcome Trust 100227Yamaha Yamaha Sports Challenge Fellowship
6 · The paper itself

Abstract

How long neural information is stored in a local brain area reflects functions of that region and is often estimated by the magnitude of the autocorrelation of intrinsic neural signals in the area. Here, we investigated such intrinsic neural timescales in high-functioning adults with autism and examined whether local brain dynamics reflected their atypical behaviours. By analysing resting-state fMRI data, we identified shorter neural timescales in the sensory/visual cortices and a longer timescale in the right caudate in autism. The shorter intrinsic timescales in the sensory/visual areas were correlated with the severity of autism, whereas the longer timescale in the caudate was associated with cognitive rigidity. These observations were confirmed from neurodevelopmental perspectives and replicated in two independent cross-sectional datasets. Moreover, the intrinsic timescale was correlated with local grey matter volume. This study shows that functional and structural atypicality in local brain areas is linked to higher-order cognitive symptoms in autism.

Indexed as

AdultAutistic DisorderCaudate NucleusCross-Sectional StudiesElectroencephalographyFemaleHumansMagnetic Resonance ImagingMaleSomatosensory CortexTime FactorsVisual CortexYoung Adultautism spectrum disordergrey matter volumehumanintrinsic timescaleneuroscienceresting-state fMRItemporal receptive window

Identifiers

PMID30717827
PMCPMC6363380

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Registered trials

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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.