Neural fragility as an EEG marker of the seizure onset zone

A, Li; Huynh, Chester; Fitzgerald, Zachary; Cajigas, Iahn; Brusko, Damian; Jagid, Jonathan; Claudio, Angel; Kanner, Andrés M.; Hopp, Jennifer L.; Chen, Stephanie; Haagensen, Jennifer; Johnson, Emily L.; Anderson, William S.; Crone, Nathan E.; Inati, Sara K.; Zaghloul, Kareem A.; Bulacio, Juan; González-Martínez, Jorge; Sarma, Sridevi V.

doi:10.1038/s41593-021-00901-w

Cited by 78 publications

(93 citation statements)

References 70 publications

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“…In order to control against overfitting of the models, we used a nested CV scheme, where we split the dataset into a training, validation and a held-out test dataset. 24 In each such split, the hyperparameters were tuned using the training and validation data (70% of the dataset), and performance was then evaluated on the test dataset by applying a varying threshold to the model's output and computing a receiver operating characteristic (ROC) curve, which plots true positive rates against false positive rates for various threshold values. We then selected the threshold that maximized prediction accuracy in each split and evaluated performance by comparing each patient's predicted outcome to the actual outcome.…”

Section: Discussionmentioning

confidence: 99%

“…To evaluate the source-sink indices as interictal iEEG markers of the EZ, we tested their efficacy in predicting surgical outcomes following the same procedure as Li et al 24 (see Supplementary Fig. 2 for a schematic of the experimental design) and compared performance against that of clinicians as well as HFOs, the most common interictal iEEG marker of the EZ.…”

Section: Methodsmentioning

confidence: 99%

“…To evaluate the SSMs as interictal iEEG markers of the EZ, we tested their efficacy in predicting surgical outcomes following the same procedure as Li et al 14 (Supplementary Fig.…”

Section: Predicting Surgical Outcomes Using Source-sink Metricsmentioning

confidence: 99%

“…No iEEG markers are currently used in the clinical workflow to specifically assist in the identification of the EZ although many computational approaches have been proposed. In line with the standard of care visual analysis, most of the proposed methods depend on seizure data (e.g., 20,[24][25][26][27][28][29][30][31][32][33] ). Nevertheless, using seizure independent (interictal) data has been of high interest as well, as this could significantly speed up the pre-surgical evaluation process.…”

Section: Introductionmentioning

confidence: 99%

“…In line with the standard of care visual analysis, most of the proposed methods depend on seizure data. [13][14][15][16][17][18][19][20][21][22][23][24] Nevertheless, using interictal data has been of high interest as this could significantly improve the intracranial monitoring when it is interpreted in conjunction with the ictal data. The most frequently proposed interictal marker of the EZ are high frequency oscillations (HFOs).…”

Section: Introductionmentioning

confidence: 99%

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Source-sink connectivity: A novel interictal EEG marker for seizure localization

Gunnarsdottir

Smith

et al. 2021

Preprint

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Over 15 million epilepsy patients worldwide have medically refractory epilepsy (MRE), i.e., they do not respond to anti-epileptic drugs. Successful surgery is a hopeful alternative for seizure freedom but can only be achieved through complete resection or disconnection of the epileptogenic zone (EZ), the brain region(s) where seizures originate. Unfortunately, surgical success rates vary between 30%-70% because no clinically validated biological markers of the EZ exist. Localizing the EZ has thus become a costly and time-consuming process during which a team of clinicians obtain non-invasive neuroimaging data, which is often followed by invasive monitoring involving days-to-weeks of EEG recordings captured intracranially (iEEG). Clinicians visually inspect iEEG data, looking for abnormal activity (e.g., low-voltage high frequency activity) on individual channels occurring immediately before seizures. They also look for abnormal spikes that occur on iEEG between seizures ("resting-state"). In the end, clinicians use <1% of the iEEG data captured to assist in EZ localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored data to better diagnose and treat patients. Intracranial EEG offers a unique opportunity to observe rich epileptic cortical network dynamics but waiting for seizures to occur increases patient risks associated invasive monitoring. In this study, we aim to leverage iEEG data in between seizures by developing a new networked-based resting-state iEEG marker of the EZ. We hypothesize that when a patient is not seizing, it is because the EZ is inhibited by neighboring nodes. We develop an algorithm that identifies two groups of nodes from the resting-state iEEG network: those that are continuously inhibiting a set of neighboring nodes ("sources") and the inhibited nodes themselves ("sinks"). Specifically, patient-specific dynamical network models (DNMs) are estimated from minutes of iEEG and their connectivity properties reveal top sources and sinks in the network, with each node being quantified by a source-sink index (SSI). We validate the SSI index in a retrospective analysis of 65 patients by using the SSI of the annotated EZ as a metric to predict surgical outcomes. SSI predicts with an accuracy of 79%, compared to the accuracy of clinicians being 43% (successful outcomes). In failed outcomes, we identify regions of the brain with high SSIs that were untreated. When compared to high frequency oscillations, the most common resting-state iEEG feature proposed for EZ localization, SSI outperformed in predictive power (by a factor of 1.2) suggesting SSI as a resting-state EEG fingerprint of the EZ.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…To evaluate the SSMs as interictal iEEG markers of the EZ, we tested their efficacy in predicting surgical outcomes following the same procedure as Li et al 14 (Supplementary Fig.…”

Section: Predicting Surgical Outcomes Using Source-sink Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Source-sink connectivity: A novel interictal EEG marker for seizure localization

Gunnarsdottir

Smith

et al. 2021

Preprint

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Soft Bioelectronics for Neuroengineering: New Horizons in the Treatment of Brain Tumor and Epilepsy

Yoo,

Kim,

Choi

et al. 2023

Adv Healthcare Materials

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Soft bioelectronic technologies for neuroengineering have shown remarkable progress, which include novel soft material technologies and device design strategies. Such technological advances that are initiated from fundamental brain science are applied to clinical neuroscience and provided meaningful promises for significant improvement in the diagnosis efficiency and therapeutic efficacy of various brain diseases recently. System‐level integration strategies in consideration of specific disease circumstances can enhance treatment effects further. Here, recent advances in soft implantable bioelectronics for neuroengineering, focusing on materials and device designs optimized for the treatment of intracranial disease environments, are reviewed. Various types of soft bioelectronics for neuroengineering are categorized and exemplified first, and then details for the sensing and stimulating device components are explained. Next, application examples of soft implantable bioelectronics to clinical neuroscience, particularly focusing on the treatment of brain tumor and epilepsy are reviewed. Finally, an ideal system of soft intracranial bioelectronics such as closed‐loop‐type fully‐integrated systems is presented, and the remaining challenges for their clinical translation are discussed.

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The Role of High-Frequency Oscillation Networks in Managing Pharmacoresistant Epilepsy

Weiss,

Staba

2023

Pharmacoresistance in Epilepsy

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Neural fragility as an EEG marker of the seizure onset zone

Cited by 78 publications

References 70 publications

Source-sink connectivity: A novel interictal EEG marker for seizure localization

Source-sink connectivity: A novel interictal EEG marker for seizure localization

Soft Bioelectronics for Neuroengineering: New Horizons in the Treatment of Brain Tumor and Epilepsy

The Role of High-Frequency Oscillation Networks in Managing Pharmacoresistant Epilepsy

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