A unified framework for spatially resolved cortical activation analysis
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.
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BibTeX
@misc{knieper2026unified,
title = {{A unified framework for spatially resolved cortical activation analysis}},
author = {Lars Knieper and Nadia Müller-Voggel and Tobias Hepp and Anna von Plessen and Elisabeth Bergherr},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.11278},
eprint = {2609.11278},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.11278},
}
RIS
TY - UNPB
TI - A unified framework for spatially resolved cortical activation analysis
AU - Lars Knieper
AU - Nadia Müller-Voggel
AU - Tobias Hepp
AU - Anna von Plessen
AU - Elisabeth Bergherr
PY - 2026
DA - 2026/09/10
PB - arXiv
DO - 10.48550/arxiv.2609.11278
UR - https://arxiv.org/abs/2609.11278
AB - 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.
ER -