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Neurostructural Heterogeneity in Youths With Internalizing Symptoms

  • Antonia N. Kaczkurkin
    Correspondence
    Address correspondence to Antonia N. Kaczkurkin, Ph.D., Richards Building, 5th Floor, Suite 5A, 3700 Hamilton Walk, Philadelphia, PA 19104-6085.
    Affiliations
    Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania

    Department of Psychology, Vanderbilt University, Nashville, Tennessee
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  • Aristeidis Sotiras
    Affiliations
    Department 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
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  • Erica B. Baller
    Affiliations
    Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Ran Barzilay
    Affiliations
    Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Monica E. Calkins
    Affiliations
    Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Ganesh B. Chand
    Affiliations
    Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania

    Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Zaixu Cui
    Affiliations
    Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Guray Erus
    Affiliations
    Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania

    Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Yong Fan
    Affiliations
    Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania

    Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Raquel E. Gur
    Affiliations
    Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania

    Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Ruben C. Gur
    Affiliations
    Department 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
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  • Tyler M. Moore
    Affiliations
    Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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  • David R. Roalf
    Affiliations
    Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Adon F.G. Rosen
    Affiliations
    Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Kosha Ruparel
    Affiliations
    Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Russell T. Shinohara
    Affiliations
    Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania

    Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Erdem Varol
    Affiliations
    Department 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
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  • Daniel H. Wolf
    Affiliations
    Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania

    Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Christos Davatzikos
    Affiliations
    Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania

    Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Theodore D. Satterthwaite
    Affiliations
    Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania

    Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania
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Published:September 18, 2019DOI:https://doi.org/10.1016/j.biopsych.2019.09.005

      Abstract

      Background

      Internalizing 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.

      Methods

      We 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.

      Results

      Using 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.

      Conclusions

      We 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.

      Keywords

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