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Mapping epileptogenic brain using a unified spatial–temporal– spectral source imaging framework.

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Title: Mapping epileptogenic brain using a unified spatial–temporal– spectral source imaging framework.
Authors: Jiang, Xiyuan1, Cai, Zhengxiang1, Gonsisko, Colton1, Worrell, Gregory A.2, He, Bin1 bhe1@andrew.cmu.edu
Source: Proceedings of the National Academy of Sciences of the United States of America. 12/16/2025, Vol. 122 Issue 50, p1-11. 11p.
Subjects: Spectral imaging, Brain function localization, Electrophysiology, Neurophysiology, Oscillations, Partial epilepsy, Computer-assisted image analysis (Medicine)
Abstract: Noninvasive electrophysiological source imaging (ESI) is a valuable tool for localizing and imaging brain activity, with significant potential to aid presurgical planning in focal drug-resistant epilepsy (fDRE) patients. Scalp electroencephalography (EEG) biomarkers, including interictal spikes, high-frequency oscillations (HFOs), and seizures, each offer unique capabilities in estimating the epileptogenic zone (EZ). However, there is a limited quantitative understanding of how these biomarkers differ in source-imaging precision, requiring distinct processing pipelines. Here, we developed a spatial–temporal–spectral imaging (STSI) framework for precision source imaging, and quantitatively evaluated various epilepsy biomarkers for source imaging in 2,081 individual events (spikes, HFOs, and seizures) from a cohort of 42 fDRE patients, comparing results to clinical ground truth such as surgical resection outcomes and intracranial EEG-defined seizure onset zones. The STSI enabled quantitative comparisons across key EEG epilepsy-related biomarkers, with averaged localization errors of 6.67 mm for seizures, 8.73 mm for HFOs overlapping with spikes (pHFO), 10.28 mm for HFO-riding spikes (pSpike), 19.59 mm for general spikes (aSpike), and 36.53 mm for general HFOs (aHFO), respectively, for seizure-free patients. These findings indicate that HFOs overlapping with spikes is the most spatially accurate interictal biomarker for mapping the EZ. The proposed STSI framework not only establishes a unified analysis approach for epileptic biomarkers to enhance presurgical planning in focal drug-resistant epilepsy, but could also generalize as a versatile tool for mapping event-related potentials, neural oscillations, and dynamic brain states, within a single framework to advance cognitive neuroscience research and clinical management of neurological and psychiatric disorders. [ABSTRACT FROM AUTHOR]
Copyright of Proceedings of the National Academy of Sciences of the United States of America is the property of National Academy of Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Mapping epileptogenic brain using a unified spatial–temporal– spectral source imaging framework.
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  Data: <searchLink fieldCode="AR" term="%22Jiang%2C+Xiyuan%22">Jiang, Xiyuan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Cai%2C+Zhengxiang%22">Cai, Zhengxiang</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Gonsisko%2C+Colton%22">Gonsisko, Colton</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Worrell%2C+Gregory+A%2E%22">Worrell, Gregory A.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22He%2C+Bin%22">He, Bin</searchLink><relatesTo>1</relatesTo><i> bhe1@andrew.cmu.edu</i>
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  Data: Noninvasive electrophysiological source imaging (ESI) is a valuable tool for localizing and imaging brain activity, with significant potential to aid presurgical planning in focal drug-resistant epilepsy (fDRE) patients. Scalp electroencephalography (EEG) biomarkers, including interictal spikes, high-frequency oscillations (HFOs), and seizures, each offer unique capabilities in estimating the epileptogenic zone (EZ). However, there is a limited quantitative understanding of how these biomarkers differ in source-imaging precision, requiring distinct processing pipelines. Here, we developed a spatial–temporal–spectral imaging (STSI) framework for precision source imaging, and quantitatively evaluated various epilepsy biomarkers for source imaging in 2,081 individual events (spikes, HFOs, and seizures) from a cohort of 42 fDRE patients, comparing results to clinical ground truth such as surgical resection outcomes and intracranial EEG-defined seizure onset zones. The STSI enabled quantitative comparisons across key EEG epilepsy-related biomarkers, with averaged localization errors of 6.67 mm for seizures, 8.73 mm for HFOs overlapping with spikes (pHFO), 10.28 mm for HFO-riding spikes (pSpike), 19.59 mm for general spikes (aSpike), and 36.53 mm for general HFOs (aHFO), respectively, for seizure-free patients. These findings indicate that HFOs overlapping with spikes is the most spatially accurate interictal biomarker for mapping the EZ. The proposed STSI framework not only establishes a unified analysis approach for epileptic biomarkers to enhance presurgical planning in focal drug-resistant epilepsy, but could also generalize as a versatile tool for mapping event-related potentials, neural oscillations, and dynamic brain states, within a single framework to advance cognitive neuroscience research and clinical management of neurological and psychiatric disorders. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Proceedings of the National Academy of Sciences of the United States of America is the property of National Academy of Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1073/pnas.2510015122
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        Text: English
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      – SubjectFull: Electrophysiology
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      – SubjectFull: Oscillations
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      – SubjectFull: Partial epilepsy
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      – SubjectFull: Computer-assisted image analysis (Medicine)
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      – TitleFull: Mapping epileptogenic brain using a unified spatial–temporal– spectral source imaging framework.
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            NameFull: Jiang, Xiyuan
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              Text: 12/16/2025
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              Y: 2025
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