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Bayesian Estimation of Continuous Brain Connectivity with Log-Gaussian Cox Processes — arXiv:2610.11250

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.11250
License (as archived): CC0-1.0
Archived: 2026-10-09T06:27:44.092Z
SHA-256 of the archived text: dbcce930b316c49d4eaba1224021df48e2ba0acff9d9fdeb1aee58aa7fd8b6ef
Stable URL of this page: https://metraipolis.com/documento/dc_8cb577aa0bd7

How to cite

Bayesian Estimation of Continuous Brain Connectivity with Log-Gaussian Cox Processes — arXiv:2610.11250 — arXiv (en), https://arxiv.org/abs/2610.11250 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_8cb577aa0bd7 (Metraipolis archive dc_8cb577aa0bd7, sha256 dbcce930b316c49d4eaba1224021df48e2ba0acff9d9fdeb1aee58aa7fd8b6ef)

BibTeX

@misc{dc8cb577aa0bd7,
  title = {Bayesian Estimation of Continuous Brain Connectivity with Log-Gaussian Cox Processes — arXiv:2610.11250},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.11250},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_8cb577aa0bd7; sha256 dbcce930b316c49d4eaba1224021df48e2ba0acff9d9fdeb1aee58aa7fd8b6ef},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.11250. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Continuous structural connectivity describes the connection between any two points of the cortical surface by an intensity function. Based on the endpoints of tractography streamlines, existing methods estimate this function mostly by kernel smoothing, which only gives a point estimate. We propose a Bayesian framework that estimates continuous connectivity with uncertainty quantification. The endpoints of tractography streamlines, mapped to two spheres, are modeled by a log-Gaussian Cox process on the product of the spheres with a Matérn Gaussian process prior. With the finite element representation of the prior, the Laplace approximation of the posterior enables the computation on a fine mesh with over 400,000 latent variables per hemisphere pair. Numerical experiments show the accuracy of the point estimate of this approach and the coverage of its credible intervals. In the Adolescent Brain Cognitive Development Study, the proposed method predicts held-out streamlines better than kernel smoothing.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.ME, nuovi lavori del giorno).
Categorie arXiv: Methodology (stat.ME) ; Computation (stat.CO).
Pagina del lavoro: https://arxiv.org/abs/2610.11250 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T05:51:46.695Z dalla pagina https://arxiv.org/list/stat.ME/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).