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<p>Non-invasive electrophysiology lacks methods that accurately reconstruct whole-brain spatiotemporal dynamics while incorporating individual cortical geometry, leaving current electroencephalography and magnetoencephalography source imaging limited by simplistic or biologically implausible priors. Here we show that embedding patient-specific geometric basis function (GBF), eigenmodes derived from each individual’s cortical surface, provides a powerful anatomic constraint that resolves the inverse problem and improves reconstruction fidelity. The method allows reconstruction of the sources as linear combinations of geometric organization of neural dynamics. We validate GBF across a meta-source benchmark, task-evoked data, resting-state networks, intracranial stimulation and epilepsy data. Results demonstrate that GBF yields high localization accuracy and captures fast spatiotemporal dynamics consistent with anatomical pathways. These findings suggest that both spontaneous and evoked whole-brain activity can be described by hundreds of geometric modes, providing a compact yet accurate representation of neural sources. By linking cortical geometry to electrophysiological dynamics, GBF offers a versatile source imaging tool for both scientific and clinical applications.</p>

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A geometry aware framework enhances noninvasive mapping of whole human brain dynamics

  • 期刊论文

  • 发表日期：

    2026年4月27日

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  • Song Wang,
  • Kexin Lou,
  • Chen Wei,
  • Zhiyuan Sheng,
  • Jiahao Tang,
  • Kaining Peng,
  • Xinke Shen,
  • Shuhao Mei,
  • Liang Chen,
  • Dongfeng Gu,
  • Quanying Liu

摘要

Non-invasive electrophysiology lacks methods that accurately reconstruct whole-brain spatiotemporal dynamics while incorporating individual cortical geometry, leaving current electroencephalography and magnetoencephalography source imaging limited by simplistic or biologically implausible priors. Here we show that embedding patient-specific geometric basis function (GBF), eigenmodes derived from each individual’s cortical surface, provides a powerful anatomic constraint that resolves the inverse problem and improves reconstruction fidelity. The method allows reconstruction of the sources as linear combinations of geometric organization of neural dynamics. We validate GBF across a meta-source benchmark, task-evoked data, resting-state networks, intracranial stimulation and epilepsy data. Results demonstrate that GBF yields high localization accuracy and captures fast spatiotemporal dynamics consistent with anatomical pathways. These findings suggest that both spontaneous and evoked whole-brain activity can be described by hundreds of geometric modes, providing a compact yet accurate representation of neural sources. By linking cortical geometry to electrophysiological dynamics, GBF offers a versatile source imaging tool for both scientific and clinical applications.