A geometry aware framework enhances noninvasive mapping of whole human brain dynamics
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arXiv
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| Format: | Preprint |
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2026
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| author | Wang, Song Lou, Kexin Wei, Chen Sheng, Zhiyuan Tang, Jiahao Peng, Kaining Shen, Xinke Mei, Shuhao Chen, Liang Gu, Dongfeng Liu, Quanying |
| author_facet | Wang, Song Lou, Kexin Wei, Chen Sheng, Zhiyuan Tang, Jiahao Peng, Kaining Shen, Xinke Mei, Shuhao Chen, Liang Gu, Dongfeng Liu, Quanying |
| contents | 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 participant-specific Geometric Basis Functions (GBFs), eigenmodes derived from each individual's cortical surface, provides a powerful anatomic constraint that resolves the inverse problem and improves reconstruction fidelity. The method reconstructs neural sources as linear combinations of geometric basis functions, thereby aligning source estimates with the geometric organization of neural dynamics. We validate GBF across the Meta-Source Benchmark, task-evoked data, resting-state networks, intracranial stimulation, and epilepsy data. The 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25592 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A geometry aware framework enhances noninvasive mapping of whole human brain dynamics Wang, Song Lou, Kexin Wei, Chen Sheng, Zhiyuan Tang, Jiahao Peng, Kaining Shen, Xinke Mei, Shuhao Chen, Liang Gu, Dongfeng Liu, Quanying Neurons and Cognition Signal Processing 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 participant-specific Geometric Basis Functions (GBFs), eigenmodes derived from each individual's cortical surface, provides a powerful anatomic constraint that resolves the inverse problem and improves reconstruction fidelity. The method reconstructs neural sources as linear combinations of geometric basis functions, thereby aligning source estimates with the geometric organization of neural dynamics. We validate GBF across the Meta-Source Benchmark, task-evoked data, resting-state networks, intracranial stimulation, and epilepsy data. The 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. |
| title | A geometry aware framework enhances noninvasive mapping of whole human brain dynamics |
| topic | Neurons and Cognition Signal Processing |
| url | https://arxiv.org/abs/2604.25592 |