{
  "abstract": "Disruptive behaviors problems, such as irritability/anger and maladaptive aggression, are associated with altered connectivity across networks supporting emotion regulation. However, multimodal connectome markers of concurrent severity and prospective symptom change, including sex-specific patterns, remain unclear. Using a large, sex-balanced sample from the Adolescent Brain Cognitive Development (ABCD) Study, we applied multimodal connectome-based predictive modelling (CPM) to test whether resting-state functional (n = 7,556; 49.3% female) and structural (N = 8,978; 48.4%) connectivity predicted baseline disruptive behavior severity (at ages 9-10) and 2-year symptom change (from ages 9-10 to 11-12). Models were adjusted for covariates, permutation-tested, and FDR corrected. Secondary analyses tested sex-stratified models separately in males and females. In models emerging as significant, follow-up analyses tested for distinct vs shared predictive edges. Functional and structural connectomes predicted baseline severity ({rho} = 0.087 and {rho} = 0.071, respectively). Functional connectivity also predicted prospective symptom change ({rho} = 0.038), whereas prospective structural prediction was predominately with negative edges ({rho} = 0.026). Functional connectivity models were significant in both sexes, while prospective structural connectivity models were significant in males, but not females. Frontoparietal, medial frontal, sensorimotor, salience, cerebellar, and visual networks contributed across models. Cross-sectional models showed a more distributed pattern while prospective models showed a more focal pattern. These findings identify complementary, largely distinct functional and structural connectome patterns associated with current disruptive behavior risk and subsequent symptom change, supporting the importance of developmental timing, sex, and imaging modality in developing brain-based biomarkers of disruptive behavior risk in youth.",
  "abstract_shown": true,
  "affiliations": [
    "Child Study Center, Yale School of Medicine, New Haven, Connecticut, USA",
    "Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA",
    "Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut, USA",
    "Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA",
    "Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, USA",
    "Wu Tsai Institute, Yale University, New Haven, Connecticut, USA",
    "Yale Biomedical Imaging Institute, Yale School of Medicine, New Haven, Connecticut, USA",
    "Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA",
    "Department of Statistics and Data Science, Yale University, New Haven, Connecticut, USA",
    "Department of Psychology, Yale University, New Haven, Connecticut, USA",
    "Department of Social and Behavioral Sciences, Yale School of Public Health, New Haven, Connecticut, USA"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
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        0
      ],
      "name": "Eleni Christofilea"
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        0
      ],
      "name": "Kaiting Mai"
    },
    {
      "affs": [
        1
      ],
      "name": "Qinghao Liang"
    },
    {
      "affs": [
        2
      ],
      "name": "Raimundo X. Rodriguez"
    },
    {
      "affs": [
        0
      ],
      "name": "Caroline Lloyd"
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    {
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        4,
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        6
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      "name": "Yize Zhao"
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    {
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        7,
        8
      ],
      "name": "Dustin Scheinost"
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    {
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      ],
      "name": "Denis G. Sukhodolsky"
    },
    {
      "affs": [
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        5,
        6,
        9,
        10
      ],
      "name": "Karim Ibrahim",
      "orcid": "0000-0002-8205-6723"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9d58ezkgyr649.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.26.761676",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.26.761676v1.full",
  "id": "9d58ezkgyr649",
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    "2692-8205"
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  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.26.761676v1",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
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  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
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      "neuroscience"
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      "heading",
      "Article"
    ]
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  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.26.761676v1.full.pdf",
  "pmcid": "PMC13733863",
  "pmid": "42990334",
  "published": "2026-10-26",
  "publisher": "Cold Spring Harbor Laboratory Preprints",
  "ris": "https://research.draconitic.com/paper/9d58ezkgyr649.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:21:10Z",
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    {
      "first_seen": "2026-11-08T12:23:33Z",
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  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
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  ],
  "title": "Multimodal Connectome Markers of Disruptive Behavior Problems in Youth: Distinct Patterns of Concurrent Severity and Prospective Symptom Change",
  "title_text": "Multimodal Connectome Markers of Disruptive Behavior Problems in Youth: Distinct Patterns of Concurrent Severity and Prospective Symptom Change",
  "type": "preprint",
  "updated_at": "2026-11-08T12:23:33Z",
  "url": "https://research.draconitic.com/paper/9d58ezkgyr649",
  "venue": "bioRxiv",
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}