Evidence map›Paper›PMID 33226336›Full record

ArticleeLife2020

Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture.

Richard Gao, Ruud L van den Brink, Thomas Pfeffer, Bradley Voytek

Open access · goldAbstract read
In one paragraph

Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 188 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
188citing papers in PubMed, 2 pooled it
18.3field-weighted citation impact, top 1% of its field
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

188 citing papers in PubMed, 2 syntheses or guidelines pooled it, 328 citations in OpenAlex.

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  17. Hierarchical Flows of Human Cortical Activity.bioRxiv : the preprint server for biology · 2026
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128 more citing papers are in PubMed but not listed here.

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

4 authors at 3 institutions in 3 countries.

Richard GaoDepartment of Cognitive Science, University of California, San Diego, La Jolla, United States.ORCID 0000-0001-5916-6433
Ruud L van den BrinkSection Computational Cognitive Neuroscience, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.ORCID 0000-0002-3142-7248
Thomas PfefferCenter for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain.ORCID 0000-0001-9561-3085
Bradley VoytekDepartment of Cognitive Science, University of California, San Diego, La Jolla, United States.ORCID 0000-0003-1640-2525
University of California San Diego · USUniversität Hamburg · DEUniversitat Pompeu Fabra · ES

Funding

Tools for parameterizing and visualizing electrophysiological rhythmic and arrhythmic featuresR01GM134363 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI VOYTEK, BRADLEY T. · 2019 to 2022
$1.4M
Alfred P. Sloan Foundation FG-2015-66057National Science Foundation BCS-1736028Natural Sciences and Engineering Research Council of Canada CGSD3-488052-2016NIGMS NIH HHS R01 GM134363Whitehall Foundation 2017-12-73
6 · The paper itself

Abstract

Complex cognitive functions such as working memory and decision-making require information maintenance over seconds to years, from transient sensory stimuli to long-term contextual cues. While theoretical accounts predict the emergence of a corresponding hierarchy of neuronal timescales, direct electrophysiological evidence across the human cortex is lacking. Here, we infer neuronal timescales from invasive intracranial recordings. Timescales increase along the principal sensorimotor-to-association axis across the entire human cortex, and scale with single-unit timescales within macaques. Cortex-wide transcriptomic analysis shows direct alignment between timescales and expression of excitation- and inhibition-related genes, as well as genes specific to voltage-gated transmembrane ion transporters. Finally, neuronal timescales are functionally dynamic: prefrontal cortex timescales expand during working memory maintenance and predict individual performance, while cortex-wide timescales compress with aging. Thus, neuronal timescales follow cytoarchitectonic gradients across the human cortex and are relevant for cognition in both short and long terms, bridging microcircuit physiology with macroscale dynamics and behavior.

Indexed as

Models, NeurologicalAdolescentAdultAgingAnimalsCerebral CortexElectrocorticographyFemaleHumansMacacaMaleMemory, Short-TermMiddle AgedNeuronsTranscriptomeYoung Adultcomputational biologycortical gradientsfunctional specializationhumanneuronal timescalesneurosciencerhesus macaquespectral analysissystems biologytranscriptomics

Identifiers

PMID33226336
PMCPMC7755395
OpenAlexW3107189325

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.