{
  "abstract": "Lesion network mapping (LNM) is a powerful tool for linking distributed brain lesions to symptom-relevant networks, but similar LNM maps across unrelated symptoms have raised concerns about its statistical validity and specificity. Here we introduce robust LNM (rLNM), a statistically principled framework that combines two complementary controls: an anatomy-preserving spatial null model to account for connectome-derived hub bias shared across symptoms, and hypothesis-driven contrasts to resolve selective specificity among related symptoms. rLNM effectively controls both false positives and negatives. Across 10 lesion datasets (384 lesions) and two task-fMRI meta-analyses (339 experiments), rLNM recovers distinct symptom-specific networks (mean r = 0.044), which show significantly reduced similarity to the connectome hub pattern (r = 0.12 vs. 0.33 in conventional LNM). In 56 patients with neurosurgical lesions, rLNM predicts postoperative language decline at the single-patient level. rLNM thus resolves these concerns and lays a unified statistical foundation for network-symptom inferenc.",
  "abstract_shown": true,
  "affiliations": [
    "Changping Laboratory"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 20,
  "authors": [
    {
      "name": "W. Cui"
    },
    {
      "name": "J. Zhu"
    },
    {
      "name": "Y. Long"
    },
    {
      "name": "F. Wang"
    },
    {
      "name": "Y. Yin"
    },
    {
      "name": "X. Wang"
    },
    {
      "name": "X. Fu"
    },
    {
      "name": "B. Zhang"
    },
    {
      "name": "Y. Lu"
    },
    {
      "name": "W. Zhang"
    },
    {
      "name": "J. Huang"
    },
    {
      "name": "L. Li"
    },
    {
      "name": "G. Luan"
    },
    {
      "name": "Y. Lin"
    },
    {
      "name": "E. M. Gordon"
    },
    {
      "name": "D. Wang"
    },
    {
      "name": "A. Zalesky"
    },
    {
      "name": "N. U. F. Dosenbach"
    },
    {
      "name": "J. Ren"
    },
    {
      "affs": [
        0
      ],
      "name": "H. Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cn5ys6k1y45fy.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.05.18.725812",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.05.18.725812v3.full",
  "id": "cn5ys6k1y45fy",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.05.18.725812v3",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ],
    [
      "openalex",
      "2805|Functional Brain Connectivity Studies"
    ]
  ],
  "openalex": "W7161627084",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.05.18.725812v3.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-05-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cn5ys6k1y45fy.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T12:21:00Z",
      "native_id": "10.64898/2026.05.18.725812",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.05.18.725812v3"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Robust lesion network mapping reveals symptom-specific circuits",
  "title_text": "Robust lesion network mapping reveals symptom-specific circuits",
  "type": "preprint",
  "updated_at": "2026-11-08T00:20:56Z",
  "url": "https://research.draconitic.com/paper/cn5ys6k1y45fy",
  "venue": "bioRxiv",
  "versions": []
}