{
  "abstract": "Cluster-based permutation tests are widely used for analyzing MEG data, even though they are limited to cluster-level inference and do not provide spatially resolved effect estimates. We propose a regression-based framework for modeling brain activity directly on the cortical surface. Spatial effects are represented using Wendland radial basis functions, and model-based gradient boosting is employed for data-driven selection and estimation of localized activation patterns. This yields interpretable, spatially resolved effect estimates while mitigating the need for extensive multiple testing correction. In a simulation study with heterogeneous signal structures, the proposed approach recovers localized effects that are difficult to detect using cluster-based methods. An application to experimental MEG data further illustrates its ability to reveal spatially specific activation patterns. Overall, this unified framework provides a flexible and interpretable approach for modeling cortical surface data within a unified statistical setting.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.11278",
  "author_total": 5,
  "authors": [
    {
      "name": "Lars Knieper"
    },
    {
      "name": "Nadia Müller-Voggel"
    },
    {
      "name": "Tobias Hepp"
    },
    {
      "name": "Anna von Plessen"
    },
    {
      "name": "Elisabeth Bergherr"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6w8v143gw9by1.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.11278",
  "fulltext_url": null,
  "id": "6w8v143gw9by1",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.11278",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.CO"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2002|Spatial and Panel Data Analysis"
    ]
  ],
  "openalex": "W7212235750",
  "pdf_url": "https://arxiv.org/pdf/2609.11278",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6w8v143gw9by1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:36Z",
      "native_id": "2609.11278",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.11278"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.CO",
      "subfield": "statistics"
    }
  ],
  "title": "A unified framework for spatially resolved cortical activation analysis",
  "title_text": "A unified framework for spatially resolved cortical activation analysis",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:36Z",
  "url": "https://research.draconitic.com/paper/6w8v143gw9by1",
  "venue": "arXiv",
  "versions": []
}