Evidence map›Paper›PMID 41578121›Full record

ArticleNature communications2026

Two axes of white matter development.

Audrey C Luo, Steven L Meisler, Valerie J Sydnor, Aaron Alexander-Bloch, Joëlle Bagautdinova, Deanna M Barch, Dani S Bassett, Christos Davatzikos, Alexandre R Franco, Jeff Goldsmith and 18 more

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Spatiotemporal white-matter development across early childhood.Cerebral cortex (New York, N.Y. : 1991) · 2026
    Article
  2. Article
  3. Review
  4. Article
  5. White matter reflects the childhood exposome.bioRxiv : the preprint server for biology · 2026
    Article
  6. Article
  7. Article
  8. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

28 authors.

Audrey C LuoPenn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Steven L MeislerPenn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-8888-1572
Valerie J SydnorDepartment of Psychiatry, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA.ORCID 0000-0002-8640-668X
Aaron Alexander-BlochDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0001-6554-1893
Joëlle BagautdinovaPenn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0001-7201-467X
Deanna M BarchDepartment of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.ORCID 0000-0003-1693-8506
Dani S BassettDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-6183-4493
Christos DavatzikosCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-1025-8561
Alexandre R FrancoCenter for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
Jeff GoldsmithDepartment of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, USA.
Raquel E GurDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Ruben C GurDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-4082-8502
Fengling HuPenn Statistics in Imaging and Visualization Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0003-1081-5038
Marc JaskirDepartment of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Gregory KiarCenter for Data Analytics, Innovation, and Rigor, Child Mind Institute, New York, NY, USA.ORCID 0000-0001-8915-496X
Arielle S KellerDepartment of Psychological Sciences, University of Connecticut, Storrs, CT, USA.ORCID 0000-0003-4708-1672
Bart LarsenDepartment of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.ORCID 0000-0001-7896-4342
Allyson P MackeyDepartment of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0003-2969-0028
Michael P MilhamCenter for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA.ORCID 0000-0003-3532-1210
David R RoalfDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-1728-9782
Golia ShafieiPenn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Russell T ShinoharaCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
Leah H SomervilleDepartment of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA.ORCID 0000-0002-3747-6884
Sarah M WeinsteinDepartment of Epidemiology and Biostatistics, Temple University College of Public Health, Philadelphia, PA, USA.
Jason D YeatmanGraduate School of Education, Stanford University, Stanford, CA, USA.ORCID 0000-0002-2686-1293
Matthew CieslakPenn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-1931-4734
Ariel RokemDepartment of Psychology and eScience Institute, University of Washington, Seattle, WA, USA.ORCID 0000-0003-0679-1985
Theodore D SatterthwaitePenn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. sattertt@pennmedicine.upenn.edu.ORCID 0000-0001-7072-9399

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM007170 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI BRASS, LAWRENCE F · 1985 to 2022
$54.0M
Mapping the Human Connectome During Typical DevelopmentU01MH109589 · NIMH · WASHINGTON UNIVERSITY · PI BARCH, DEANNA, BOOKHEIMER, SUSAN Y · 2016 to 2019
$17.1M
CLINICAL RESEARCH TRAINING IN CHILD PSYCHIATRYT32MH018951 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI TINA R GOLDSTEIN · 1990 to 2026
$9.7M
Statistical Methods for Multilevel Multivariate Functional StudiesR01NS060910 · NINDS · JOHNS HOPKINS UNIVERSITY · PI Ciprian M Crainiceanu · 2009 to 2026
$8.3M
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
Training for Transformative Discovery in PsychiatryT32MH016804 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI SWEET, ROBERT A · 1985 to 2025
$6.2M
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 Genetics of Personalized Functional MRI NetworksR01MH132934 · NIMH · CHILDREN'S HOSP OF PHILADELPHIA · PI Aaron Felix Alexander-Bloch · 2023 to 2026
$4.2M
Harmonization of Multi-Site Neuroimaging Data from Complex Study DesignsR01MH123550 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI SHINOHARA, RUSSELL TAKESHI · 2020 to 2025
$4.0M
Ultra-high field GluCEST MRI and MRS in youth at risk for psychosisR01MH120174 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI ROALF, DAVID R · 2020 to 2024
$3.8M
National Science Foundation (NSF) 1934292NIMH NIH HHS F31 MH136685NIMH NIH HHS R01 MH132934NIMH NIH HHS R01 MH133843NIMH NIH HHS T32 MH016804NIMH NIH HHS T32 MH018951U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) R01EB022573U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) R01EB027585U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) T32GM007170U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) 1L30MH131061-01U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) 5T32MH019112U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) F31MH136685U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R00MH127293U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R01MH112847U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R01MH113550U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R01MH119219U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R01MH120174U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R01MH120482U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R01MH123550U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R01MH123563U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R01MH132934U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R01MH133843U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R37MH125829U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) RF1MH116920U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) RF1MH121867U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) RF1MH121868U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) RF1MH126699U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) T32MH016804U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) T32MH018951U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) U01MH109589U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) R01NS060910U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) U24NS130411
6 · The paper itself

Abstract

Despite decades of neuroimaging research, how white matter develops along the length of major tracts in humans remains unknown. Here, we identify fundamental patterns of white matter maturation by examining developmental variation along major, long-range cortico-cortical tracts in youth ages 5-23 years using diffusion MRI from three large-scale, cross-sectional datasets (total N = 2716). Across datasets, we delineate two replicable axes of human white matter development. First, we find a deep-to-superficial axis, in which superficial tract regions near the cortical surface exhibit greater age-related change than deep tract regions. Second, we demonstrate that the development of superficial tract regions aligns with the cortical hierarchy defined by the sensorimotor-association axis, with tract ends adjacent to sensorimotor cortices maturing earlier than those adjacent to association cortices. These results reveal developmental variation along tracts that conventional tract-average analyses have previously obscured, challenging the implicit assumption that white matter tracts mature uniformly along their length. Such developmental variation along tracts may have functional implications, including mitigating ephaptic coupling in densely packed deep tract regions and tuning neural synchrony through hierarchical development in superficial tract regions - ultimately refining neural transmission in youth.

Indexed as

White MatterAdolescentChildChild, PreschoolCross-Sectional StudiesDiffusion Magnetic Resonance ImagingDiffusion Tensor ImagingFemaleHumansMaleNeural PathwaysNeurodevelopmentYoung Adult

Identifiers

PMID41578121
PMCPMC12929777

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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.