Evidence map›Paper›PMID 31690494›Full record

ArticleBiological psychiatry2020

Neurostructural Heterogeneity in Youths With Internalizing Symptoms.

Antonia N Kaczkurkin, Aristeidis Sotiras, Erica B Baller, Ran Barzilay, Monica E Calkins, Ganesh B Chand, Zaixu Cui, Guray Erus, Yong Fan, Raquel E Gur and 10 more

Open access · greenAbstract read
In one paragraph

Article in Biological psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 1 pooled it
5.4field-weighted citation impact, top 4% 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

32 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Structural Brain Correlates of Anxiety During Development.Current topics in behavioral neurosciences · 2025
    Review
  11. Article
  12. Review
  13. Intracortical Myelin in Youths at Risk for Depression.Biological psychiatry global open science · 2024
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
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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

20 authors at 5 institutions in 1 country.

Antonia N KaczkurkinDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychology, Vanderbilt University, Nashville, Tennessee. Electronic address: antoniak@pennmedicine.upenn.edu.
Aristeidis SotirasDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiology, Washington University, St. Louis, Missouri.
Erica B BallerDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Ran BarzilayDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Monica E CalkinsDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Ganesh B ChandDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania.
Zaixu CuiDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Guray ErusDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania.
Yong FanDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania.
Raquel E GurDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Ruben C GurDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Philadelphia Veterans Administration Medical Center, Philadelphia, Pennsylvania.
Tyler M MooreDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
David R RoalfDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Adon F G RosenDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Kosha RuparelDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Russell T ShinoharaCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania.
Erdem VarolDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Grossman Center for the Statistics of Mind, Center for Theoretical Neuroscience, Department of Statistics, Columbia University, New York, New York.
Daniel H WolfDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania.
Christos DavatzikosDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania.
Theodore D SatterthwaiteDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania.
University of Pennsylvania · USColumbia University · USPhiladelphia VA Medical Center · USVanderbilt University · USWashington University in St. Louis · US

Funding

Neurodevelopmental Genomics: Trajectories of Complex PhenotypesRC2MH089983 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI GUR, RAQUEL E · 2009 to 2011
$14.3M
2/2 Neurodevelopmental Genomics: Trajectories of Complex PhenotypesRC2MH089924 · NIMH · CHILDREN'S HOSP OF PHILADELPHIA · PI HAKONARSON, HAKON · 2009 to 2011
$10.7M
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
Reproducible imaging-based brain growth charts for psychiatryR01MH120482 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI MILHAM, MICHAEL PETER, SATTERTHWAITE, THEODORE · 2019 to 2023
$3.5M
Adolescent Neurodevelopment and Impaired Intrinsic Motivation in Psychosis RiskR01MH113565 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI WOLF, DANIEL H · 2018 to 2022
$2.9M
Mapping Heterogeneity of Neuroanatomical Imaging Signatures of Psychosis via Pattern AnalysisR01MH112070 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2017 to 2020
$2.7M
Longitudinal multi-modal neuroimaging of irritability in youthR01MH107703 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI SATTERTHWAITE, THEODORE · 2015 to 2018
$2.6M
Virtual Resection to Treat EpilepsyR01NS099348 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI BASSETT, DANIELLE SMITH, LITT, BRIAN · 2016 to 2019
$2.3M
Biomedical Image Computing and Informatics ClusterS10OD023495 · OD · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2017 to 2017
$1.9M
Statistical methods for large and complex databases of ultra-high-dimensionalR01NS085211 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI SHINOHARA, RUSSELL TAKESHI · 2013 to 2017
$1.8M
NIBIB NIH HHS R01 EB022573NIH HHS S10 OD023495NIMH NIH HHS F31 MH011365NIMH NIH HHS K99 MH117274NIMH NIH HHS R00 MH117274NIMH NIH HHS R01 MH107235NIMH NIH HHS R01 MH107703NIMH NIH HHS R01 MH112070NIMH NIH HHS R01 MH112847NIMH NIH HHS R01 MH113550NIMH NIH HHS R01 MH113565NIMH NIH HHS R01 MH120482NIMH NIH HHS RC2 MH089924NIMH NIH HHS RC2 MH089983NINDS NIH HHS R01 NS085211NINDS NIH HHS R01 NS099348
6 · The paper itself

Abstract

backgroundInternalizing disorders such as anxiety and depression are common psychiatric disorders that frequently begin in youth and exhibit marked heterogeneity in treatment response and clinical course. Given that symptom-based classification approaches do not align with underlying neurobiology, an alternative approach is to identify neurobiologically informed subtypes based on brain imaging data.

methodsWe used a recently developed semisupervised machine learning method (HYDRA [heterogeneity through discriminative analysis]) to delineate patterns of neurobiological heterogeneity within youths with internalizing symptoms using structural data collected at 3T from a sample of 1141 youths.

resultsUsing volume and cortical thickness, cross-validation methods indicated 2 highly stable subtypes of internalizing youths (adjusted Rand index = 0.66; permutation-based false discovery rate p < .001). Subtype 1, defined by smaller brain volumes and reduced cortical thickness, was marked by impaired cognitive performance and higher levels of psychopathology than both subtype 2 and typically developing youths. Using resting-state functional magnetic resonance imaging and diffusion images not considered during clustering, we found that subtype 1 also showed reduced amplitudes of low-frequency fluctuations in frontolimbic regions at rest and reduced fractional anisotropy in several white matter tracts. In contrast, subtype 2 showed intact cognitive performance and greater volume, cortical thickness, and amplitudes during rest compared with subtype 1 and typically developing youths, despite still showing clinically significant levels of psychopathology.

conclusionsWe identified 2 subtypes of internalizing youths differentiated by abnormalities in brain structure, function, and white matter integrity, with one of the subtypes showing poorer functioning across multiple domains. Identification of biologically grounded internalizing subtypes may assist in targeting early interventions and assessing longitudinal prognosis.

Indexed as

White MatterAdolescentAnisotropyAnxiety DisordersBrainHumansMagnetic Resonance ImagingCortical thicknessHeterogeneityInternalizingStructureVolumeYouth

Identifiers

PMID31690494
PMCPMC7007843
OpenAlexW2974327728

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.