{
  "abstract": "Lesion-symptom mapping is widely used to identify causal relationships between brain structures and behaviour, and has played a central role in neuropsychologically informed network models of cognition. However, even recent approaches remain constrained by a one-to-one mapping framework, which oversimplifies the complex relationships between network-level damage and cognitive deficits. In addition, the non-orthogonality of cortical and white matter damage makes it difficult to disentangle their distinct contributions. Here, we used graph-based multilayer network analysis to address these limitations and evaluate clinical relevance. Using neuroanatomical and longitudinal neuropsychological data from 252 patients who underwent awake neurosurgery for low-grade glioma, we constructed interactive, three-layer networks for each hemisphere. Layer 1 comprised neuropsychological tasks (NT), layer 2 structural disconnections (SD), and layer 3 cortical damage (CD). Nodes represented tasks, white matter tracts, and cortical parcels, respectively, whereas within-layer edges captured correlations in performance or co-occurring damage patterns. Multilayer community detection identified domain- and hemisphere-specific brain-behaviour motifs linking executive, language, and spatial functions to distinct combinations of cortical and white matter disruption, a pattern confirmed by two spatial embedding approaches. Centrality analyses revealed a continuum of mapping relationships, ranging from one-to-one to one-to-many associations, indicating that tasks such as verbal fluency are better explained by multiple disconnection mechanisms. Additional analyses uncovered many-to-one and many-to-many relationships and highlighted tracts and cortical regions with domain-general relevance. Together, these findings support a neurobiologically grounded, network-oriented account of how structural brain damage gives rise to cognitive deficits, with implications for clinical care.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Roxane Nave"
    },
    {
      "name": "Sam Ng"
    },
    {
      "name": "Sylvie Moritz-Gasser"
    },
    {
      "name": "Lorelei Berger"
    },
    {
      "name": "Emmanuel Mandonnet"
    },
    {
      "name": "Hugues Duffau"
    },
    {
      "name": "Guillaume Herbet"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/et3xr3p366ymc.bib",
  "cited_by": 0,
  "doi": "10.1016/j.nicl.2026.104052",
  "fulltext_url": null,
  "id": "et3xr3p366ymc",
  "issns": [
    "2213-1582"
  ],
  "landing_url": "https://hal.science/hal-05736070v1",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.scco"
    ],
    [
      "hal",
      "0.sdv"
    ],
    [
      "hal",
      "1.scco.neur"
    ],
    [
      "hal",
      "1.sdv.mhep"
    ],
    [
      "hal",
      "1.sdv.neu"
    ],
    [
      "hal",
      "2.sdv.neu.sc"
    ],
    [
      "openalex",
      "2805|Functional Brain Connectivity Studies"
    ]
  ],
  "openalex": "W7204252405",
  "pdf_url": "https://hal.science/hal-05736070/document",
  "pmcid": null,
  "pmid": "42669223",
  "published": "2026-09-02",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/et3xr3p366ymc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:27Z",
      "native_id": "hal-05736070",
      "source": "hal",
      "url": "https://hal.science/hal-05736070v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:1.scco.neur",
      "subfield": "neuroscience"
    },
    {
      "field": "medicine",
      "origin": "hal:1.sdv.mhep",
      "subfield": "general"
    }
  ],
  "title": "Beyond one-to-one mappings: Modelling distributed lesion-symptom relationships with multilayer networks",
  "title_text": "Beyond one-to-one mappings: Modelling distributed lesion-symptom relationships with multilayer networks",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:27Z",
  "url": "https://research.draconitic.com/paper/et3xr3p366ymc",
  "venue": "Neuroimage-Clinical",
  "versions": []
}