Integration of Spatiotemporal Dynamics and Structural Connectivity for Automated Epileptogenic Zone Localization in Temporal Lobe Epilepsy Article Swipe
Linxia Xiao
,
Qingqing Zheng
,
Sixian Li
,
Yanjie Wei
,
Weixin Si
,
Yi Pan
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/tnsre.2025.3595906
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/tnsre.2025.3595906
Accurate localization of the epileptogenic zone (EZ) is essential for surgical success in temporal lobe epilepsy. While stereoelectroencephalography (SEEG) and structural magnetic resonance imaging (MRI) provide complementary insights, existing unimodal methods fail to fully capture epileptogenic brain activity, and multimodal fusion remains challenging due to data complexity and surgeon-dependent interpretations. To address these issues, we proposed a novel multimodal framework to improve EZ localization with SEEG-drived electrophysiology with structural connectivity in temporal lobe epilepsy. By retrospectively analyzing SEEG, post-implant Computed Tomography (CT) and MRI (T1 & Diffusion Tensor Imaging (DTI)) data from 15 patients, we reconstructed SEEG electrode positions and obtained the SEEG and structural connectivity fusion features. We then proposed a spatiotemporal co-attention deep neural network (ST-CANet) to identify the fusion features, categorizing electrodes into seizure onset zone (SOZ), propagation zone (PZ), and non-involved zone (NIZ). Anatomical EZ boundaries were delineated by fusing the electrode position and classification information on brain atlas. The proposed method was evaluated based on the identification and localization performance of three epilepsy-related zones. The experiment results demonstrate that our method achieves 98.08% average accuracy and outperforms other identification methods, and improves the localization with Dice similarity coefficients (DSC) of 95.65% (SOZ), 92.13% (PZ), and 99.61% (NIZ), aligning with clinically validated surgical resection areas. This multimodal fusion strategy based on electrophysiological and structural connectivity information promises to assist neurosurgeons in accurately localizing EZ and may find broader applications in preoperative planning for epilepsy surgeries.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tnsre.2025.3595906
- OA Status
- diamond
- Cited By
- 1
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412984893
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412984893Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tnsre.2025.3595906Digital Object Identifier
- Title
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Integration of Spatiotemporal Dynamics and Structural Connectivity for Automated Epileptogenic Zone Localization in Temporal Lobe EpilepsyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
-
Linxia Xiao, Qingqing Zheng, Sixian Li, Yanjie Wei, Weixin Si, Yi PanList of authors in order
- Landing page
-
https://doi.org/10.1109/tnsre.2025.3595906Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/tnsre.2025.3595906Direct OA link when available
- Concepts
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Temporal lobe, Epilepsy, Dynamics (music), Neuroscience, Computer science, Artificial intelligence, Psychology, PedagogyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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37Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.electrophysiological | 215 |
| abstract_inverted_index.stereoelectroencephalography | 17 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.8891153 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |