Combining interictal intracranial EEG and fMRI to compute a dynamic resting-state index for surgical outcome validation
IntroductionAccurate localization of the seizure onset zone (SOZ) is critical for successful epilepsy surgery but remains challenging with current techniques. We developed a novel seizure onset network characterization tool that combines dynamic biomarkers of resting-state intracranial stereoelectro...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Network Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnetp.2024.1491967/full |
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| author | Varina L. Boerwinkle Kristin M. Gunnarsdottir Bethany L. Sussman Sarah N. Wyckoff Emilio G. Cediel Belfin Robinson William R. Reuther Aryan Kodali Sridevi V. Sarma |
| author_facet | Varina L. Boerwinkle Kristin M. Gunnarsdottir Bethany L. Sussman Sarah N. Wyckoff Emilio G. Cediel Belfin Robinson William R. Reuther Aryan Kodali Sridevi V. Sarma |
| author_sort | Varina L. Boerwinkle |
| collection | DOAJ |
| description | IntroductionAccurate localization of the seizure onset zone (SOZ) is critical for successful epilepsy surgery but remains challenging with current techniques. We developed a novel seizure onset network characterization tool that combines dynamic biomarkers of resting-state intracranial stereoelectroencephalography (rs-iEEG) and resting-state functional magnetic resonance imaging (rs-fMRI), vetted against surgical outcomes. This approach aims to reduce reliance on capturing seizures during invasive monitoring to pinpoint the SOZ.MethodsWe computed the source-sink index (SSI) from rs-iEEG for all implanted regions and from rs-fMRI for regions identified as potential SOZs by noninvasive modalities. The SSI scores were evaluated in 17 pediatric drug-resistant epilepsy (DRE) patients (ages 3–15 years) by comparing outcomes classified as successful (Engel I or II) versus unsuccessful (Engel III or IV) at 1 year post-surgery.ResultsOf 30 reviewed patients, 17 met the inclusion criteria. The combined dynamic index (im-DNM) integrating rs-iEEG and rs-fMRI significantly differentiated good (Engel I–II) from poor (Engel III–IV) surgical outcomes, outperforming the predictive accuracy of individual biomarkers from either modality alone.ConclusionThe combined dynamic network model demonstrated superior predictive performance than standalone rs-fMRI or rs-iEEG indices.SignificanceBy leveraging interictal data from two complementary modalities, this combined approach has the potential to improve epilepsy surgical outcomes, increase surgical candidacy, and reduce the duration of invasive monitoring. |
| format | Article |
| id | doaj-art-8ab35d27c382403c87cef3f98589d2d1 |
| institution | Kabale University |
| issn | 2674-0109 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Network Physiology |
| spelling | doaj-art-8ab35d27c382403c87cef3f98589d2d12025-01-28T06:41:25ZengFrontiers Media S.A.Frontiers in Network Physiology2674-01092025-01-01410.3389/fnetp.2024.14919671491967Combining interictal intracranial EEG and fMRI to compute a dynamic resting-state index for surgical outcome validationVarina L. Boerwinkle0Kristin M. Gunnarsdottir1Bethany L. Sussman2Sarah N. Wyckoff3Emilio G. Cediel4Belfin Robinson5William R. Reuther6Aryan Kodali7Sridevi V. Sarma8Division of Child Neurology, University of North Carolina in Chapel Hill, Chapel Hill, NC, United StatesDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United StatesNeuroscience Research, Barrow Neurological Institute at Phoenix Children’s Hospital, Phoenix, AZ, United StatesBrainbox Inc., Baltimore, MD, United StatesDivision of Child Neurology, University of North Carolina in Chapel Hill, Chapel Hill, NC, United StatesDivision of Child Neurology, University of North Carolina in Chapel Hill, Chapel Hill, NC, United StatesDivision of Child Neurology, University of North Carolina in Chapel Hill, Chapel Hill, NC, United StatesDivision of Child Neurology, University of North Carolina in Chapel Hill, Chapel Hill, NC, United StatesDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United StatesIntroductionAccurate localization of the seizure onset zone (SOZ) is critical for successful epilepsy surgery but remains challenging with current techniques. We developed a novel seizure onset network characterization tool that combines dynamic biomarkers of resting-state intracranial stereoelectroencephalography (rs-iEEG) and resting-state functional magnetic resonance imaging (rs-fMRI), vetted against surgical outcomes. This approach aims to reduce reliance on capturing seizures during invasive monitoring to pinpoint the SOZ.MethodsWe computed the source-sink index (SSI) from rs-iEEG for all implanted regions and from rs-fMRI for regions identified as potential SOZs by noninvasive modalities. The SSI scores were evaluated in 17 pediatric drug-resistant epilepsy (DRE) patients (ages 3–15 years) by comparing outcomes classified as successful (Engel I or II) versus unsuccessful (Engel III or IV) at 1 year post-surgery.ResultsOf 30 reviewed patients, 17 met the inclusion criteria. The combined dynamic index (im-DNM) integrating rs-iEEG and rs-fMRI significantly differentiated good (Engel I–II) from poor (Engel III–IV) surgical outcomes, outperforming the predictive accuracy of individual biomarkers from either modality alone.ConclusionThe combined dynamic network model demonstrated superior predictive performance than standalone rs-fMRI or rs-iEEG indices.SignificanceBy leveraging interictal data from two complementary modalities, this combined approach has the potential to improve epilepsy surgical outcomes, increase surgical candidacy, and reduce the duration of invasive monitoring.https://www.frontiersin.org/articles/10.3389/fnetp.2024.1491967/fulldrug-resistant epilepsyseizure onset zoneinterictal intracranial EEGresting-state fMRIdynamic network modeling |
| spellingShingle | Varina L. Boerwinkle Kristin M. Gunnarsdottir Bethany L. Sussman Sarah N. Wyckoff Emilio G. Cediel Belfin Robinson William R. Reuther Aryan Kodali Sridevi V. Sarma Combining interictal intracranial EEG and fMRI to compute a dynamic resting-state index for surgical outcome validation Frontiers in Network Physiology drug-resistant epilepsy seizure onset zone interictal intracranial EEG resting-state fMRI dynamic network modeling |
| title | Combining interictal intracranial EEG and fMRI to compute a dynamic resting-state index for surgical outcome validation |
| title_full | Combining interictal intracranial EEG and fMRI to compute a dynamic resting-state index for surgical outcome validation |
| title_fullStr | Combining interictal intracranial EEG and fMRI to compute a dynamic resting-state index for surgical outcome validation |
| title_full_unstemmed | Combining interictal intracranial EEG and fMRI to compute a dynamic resting-state index for surgical outcome validation |
| title_short | Combining interictal intracranial EEG and fMRI to compute a dynamic resting-state index for surgical outcome validation |
| title_sort | combining interictal intracranial eeg and fmri to compute a dynamic resting state index for surgical outcome validation |
| topic | drug-resistant epilepsy seizure onset zone interictal intracranial EEG resting-state fMRI dynamic network modeling |
| url | https://www.frontiersin.org/articles/10.3389/fnetp.2024.1491967/full |
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