Evidence map›Paper›PMID 42809561›Full record

ArticlePLoS biology2026

Intrinsic timescale develops hierarchically from sensorimotor to association cortex in youth and stabilizes in adulthood.

Golia Shafiei, Joëlle Bagautdinova, Valerie J Sydnor, Dani S Bassett, Deanna M Barch, Matthew Cieslak, Yong Fan, Elizabeth Flook, Alexandre R Franco, Gregory Kiar and 8 more

Abstract read
In one paragraph

Article in PLoS biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors.

Golia ShafieiPenn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.ORCID https://orcid.org/0000-0002-2036-5571
Joëlle BagautdinovaPenn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.ORCID https://orcid.org/0000-0001-7201-467X
Valerie J SydnorDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
Dani S BassettDepartment of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Deanna M BarchDepartment of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America.
Matthew CieslakPenn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Yong FanDepartment of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Elizabeth FlookPenn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Alexandre R FrancoChild Mind Institute, New York, New York, United States of America.
Gregory KiarChild Mind Institute, New York, New York, United States of America.
Audrey C LuoPenn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Michael P MilhamChild Mind Institute, New York, New York, United States of America.
Linden ParkesDepartment of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, New Jersey, United States of America.
Taylor SaloPenn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Leah H SomervilleDepartment of Psychology and Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States of America.
Tien T TongPenn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Russell T ShinoharaDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Theodore D SatterthwaitePenn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.

Funding

Longitudinal Mapping of Network Development Underlying Executive Dysfunction in AdolescenceR01MH113550 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI Danielle Smith Bassett, Theodore Satterthwaite · 2018 to 2026
$6.9M
Personalized Functional Network Modeling to Characterize and Predict Psychopathology in YouthR01EB022573 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI Yong Fan, Theodore Satterthwaite · 2016 to 2026
$6.0M
Inter-modal Coupling Image AnalyticsR01MH112847 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI Theodore Satterthwaite, Russell Takeshi Shinohara · 2017 to 2026
$5.9M
Precision mapping of individualized executive networks in youthR37MH125829 · NIMH · UNIVERSITY OF MINNESOTA · PI Damien A Fair, Theodore Satterthwaite · 2021 to 2026
$4.7M
The Neuroimaging Brain Chart Software SuiteU24NS130411 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI Christos Davatzikos, Yong Fan · 2023 to 2026
$3.7M
NIBIB NIH HHS R01 EB022573NIMH NIH HHS R01 MH112847NIMH NIH HHS R01 MH113550NIMH NIH HHS R37 MH125829NINDS NIH HHS U24 NS130411
6 · The paper itself

Abstract

Intrinsic timescale is a commonly used measure of spontaneous neural dynamics that quantifies the temporal window of processing of neuronal populations. Intrinsic timescale displays a hierarchical cortical organization across multiple species and imaging modalities, with shorter timescales in sensorimotor cortex compared to association cortex. However, less is known about how intrinsic timescale evolves during human brain development and whether its cortical maturation patterns generalize to independent developmental samples. Here we estimate the intrinsic timescale in two independent cross-sectional datasets of youth (HCPD: n = 565; HBN: n = 729; age range 8-22 years) and investigate its neurodevelopmental patterns. We find that developmental patterns in the intrinsic timescale follow a hierarchical pattern that recapitulates an axis spanning sensorimotor to association cortices (S-A axis). Our analysis of an independent healthy young adult dataset (HCPYA: n = 973, age range 22-37 years) underscores the specificity of these developmental findings, suggesting that the intrinsic timescale develops along the S-A axis in youth and stabilizes in adulthood. Together, these results reveal convergence between major axes of cortical organization and development, highlighting intrinsic timescale as a principled marker of hierarchical brain maturation in youth.

Indexed as

Cerebral CortexSensorimotor CortexAdolescentAdultChildCross-Sectional StudiesFemaleHumansMagnetic Resonance ImagingMaleNeurodevelopmentTime FactorsYoung Adult

Identifiers

PMID42809561
PMCPMC13638041

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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.