Predicting seizure onset zones from interictal intracranial EEG using functional connectivity and machine learning
Abstract Functional connectivity (FC) analyses of intracranial EEG (iEEG) signals can potentially improve the mapping of epileptic networks in drug-resistant focal epilepsy. However, it remains unclear whether FC-based metrics provide additional value beyond established epilepsy biomarkers such as e...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-02679-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849325995108073472 |
|---|---|
| author | Jared Pilet Scott A. Beardsley Chad Carlson Christopher T. Anderson Candida Ustine Sean Lew Wade Mueller Manoj Raghavan |
| author_facet | Jared Pilet Scott A. Beardsley Chad Carlson Christopher T. Anderson Candida Ustine Sean Lew Wade Mueller Manoj Raghavan |
| author_sort | Jared Pilet |
| collection | DOAJ |
| description | Abstract Functional connectivity (FC) analyses of intracranial EEG (iEEG) signals can potentially improve the mapping of epileptic networks in drug-resistant focal epilepsy. However, it remains unclear whether FC-based metrics provide additional value beyond established epilepsy biomarkers such as epileptic spikes and high-frequency oscillations (HFOs). Using interictal iEEG data from 26 patients, we estimated FC across eight frequency bands (4–290 Hz) using amplitude envelope correlation (AEC) and phase locking value (PLV). From the resulting FC-matrices, we estimated two graph metrics each to derive 32 FC-based features. We also extracted features related to spikes, HFOs, and power spectral densities (PSD). A trained support vector machine (SVM) classifier predicted seizure onset zones (SOZs) with an area under the ROC curve (AUC) of 0.91 for node-level 4-fold cross-validation (CV), 0.69 for patient-level 4-fold CV, and 0.73 for patient-level leave-one-out CV. Notably, gamma-band graph features from AECs outperformed spikes and HFOs in SOZ prediction when using an equivalent number of features. Our results strongly suggest that AEC-based features may provide more information about epileptogenicity compared to PLV-based features. Furthermore, machine learning provides a robust approach for identifying useful FC-based features and integrating information from putative biomarkers of epilepsy to better localize epileptogenic networks. |
| format | Article |
| id | doaj-art-c2caa8c6f3b945e2877c6ab08a19aa98 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c2caa8c6f3b945e2877c6ab08a19aa982025-08-20T03:48:15ZengNature PortfolioScientific Reports2045-23222025-05-0115111510.1038/s41598-025-02679-4Predicting seizure onset zones from interictal intracranial EEG using functional connectivity and machine learningJared Pilet0Scott A. Beardsley1Chad Carlson2Christopher T. Anderson3Candida Ustine4Sean Lew5Wade Mueller6Manoj Raghavan7Joint Department of Biomedical Engineering, Marquette University and Medical College of WisconsinJoint Department of Biomedical Engineering, Marquette University and Medical College of WisconsinDepartment of Neurology, Medical College of WisconsinDepartment of Neurology, Medical College of WisconsinDepartment of Neurology, Medical College of WisconsinDepartment of Neurosurgery, Medical College of WisconsinDepartment of Neurosurgery, Medical College of WisconsinDepartment of Neurology, Medical College of WisconsinAbstract Functional connectivity (FC) analyses of intracranial EEG (iEEG) signals can potentially improve the mapping of epileptic networks in drug-resistant focal epilepsy. However, it remains unclear whether FC-based metrics provide additional value beyond established epilepsy biomarkers such as epileptic spikes and high-frequency oscillations (HFOs). Using interictal iEEG data from 26 patients, we estimated FC across eight frequency bands (4–290 Hz) using amplitude envelope correlation (AEC) and phase locking value (PLV). From the resulting FC-matrices, we estimated two graph metrics each to derive 32 FC-based features. We also extracted features related to spikes, HFOs, and power spectral densities (PSD). A trained support vector machine (SVM) classifier predicted seizure onset zones (SOZs) with an area under the ROC curve (AUC) of 0.91 for node-level 4-fold cross-validation (CV), 0.69 for patient-level 4-fold CV, and 0.73 for patient-level leave-one-out CV. Notably, gamma-band graph features from AECs outperformed spikes and HFOs in SOZ prediction when using an equivalent number of features. Our results strongly suggest that AEC-based features may provide more information about epileptogenicity compared to PLV-based features. Furthermore, machine learning provides a robust approach for identifying useful FC-based features and integrating information from putative biomarkers of epilepsy to better localize epileptogenic networks.https://doi.org/10.1038/s41598-025-02679-4Machine learningIntracranial EEGFunctional connectivitySeizure onset zoneEpileptogenicityBiomarkers |
| spellingShingle | Jared Pilet Scott A. Beardsley Chad Carlson Christopher T. Anderson Candida Ustine Sean Lew Wade Mueller Manoj Raghavan Predicting seizure onset zones from interictal intracranial EEG using functional connectivity and machine learning Scientific Reports Machine learning Intracranial EEG Functional connectivity Seizure onset zone Epileptogenicity Biomarkers |
| title | Predicting seizure onset zones from interictal intracranial EEG using functional connectivity and machine learning |
| title_full | Predicting seizure onset zones from interictal intracranial EEG using functional connectivity and machine learning |
| title_fullStr | Predicting seizure onset zones from interictal intracranial EEG using functional connectivity and machine learning |
| title_full_unstemmed | Predicting seizure onset zones from interictal intracranial EEG using functional connectivity and machine learning |
| title_short | Predicting seizure onset zones from interictal intracranial EEG using functional connectivity and machine learning |
| title_sort | predicting seizure onset zones from interictal intracranial eeg using functional connectivity and machine learning |
| topic | Machine learning Intracranial EEG Functional connectivity Seizure onset zone Epileptogenicity Biomarkers |
| url | https://doi.org/10.1038/s41598-025-02679-4 |
| work_keys_str_mv | AT jaredpilet predictingseizureonsetzonesfrominterictalintracranialeegusingfunctionalconnectivityandmachinelearning AT scottabeardsley predictingseizureonsetzonesfrominterictalintracranialeegusingfunctionalconnectivityandmachinelearning AT chadcarlson predictingseizureonsetzonesfrominterictalintracranialeegusingfunctionalconnectivityandmachinelearning AT christophertanderson predictingseizureonsetzonesfrominterictalintracranialeegusingfunctionalconnectivityandmachinelearning AT candidaustine predictingseizureonsetzonesfrominterictalintracranialeegusingfunctionalconnectivityandmachinelearning AT seanlew predictingseizureonsetzonesfrominterictalintracranialeegusingfunctionalconnectivityandmachinelearning AT wademueller predictingseizureonsetzonesfrominterictalintracranialeegusingfunctionalconnectivityandmachinelearning AT manojraghavan predictingseizureonsetzonesfrominterictalintracranialeegusingfunctionalconnectivityandmachinelearning |