Quantitative approaches to guide epilepsy surgery from intracranial EEG

Bernabei, John M.; A, Li; Silva, Alexander B; Smith, Robert F.; Gunnarsdottir, Kristin M.; Ong, Ian; Davis, Kathryn A.; Sinha, Nishant; Sarma, Sridevi V.; Litt, Brian

doi:10.1093/brain/awad007

Cited by 32 publications

(29 citation statements)

References 140 publications

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“…Our findings here did not depend on the presence of iEEG data, and even iEEG data were available we only included a low-resolution description of them. IEEG data does provide the most detailed information of epileptogenic activity, and is it often the most valuable tool to identify the epileptogenic zone or predict surginal outcome for patients with complicated ethiology [22,25,27,[33][34][35][36]. In fact, for the modeling cohort we found the best classification results using the goodness-of-fit of the ESSES-predicted seizure propagation patterns to the iEEG seizures, in agreement with previous studies [27].…”

Section: Discussionsupporting

confidence: 90%

“…Importantly, the global parameters of ESSES were defined at the population level, and the model was individualized for each patient via patient-specific MEG networks, which characterized the local spreading probabilities. As a consequence, ESSES can be extended to patients without iEEG recordings, in contrast to previous modeling studies, which typically required the existence of patient-specific iEEG data to individualize the model for each patient [22,25,27,[33][34][35][36]. IEEG allows for a highly resolved description of seizure dynamics, but its spatial sampling is sparse and it is highly invasive.…”

Section: Introductionmentioning

confidence: 99%

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Individualized epidemic spreading models predict epilepsy surgery outcomes: a pseudo-prospective study

Millán

Straaten

Stam

et al. 2023

Preprint

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Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but up to 50% of patients continue to have seizures one year after the resection. In order to aid presurgical planning and predict postsurgical outcome in a patient-by-patient basis, we developed a framework of individualized computational models that combine epidemic spreading with patient-specific connectivity and epileptogeneity maps: the Epidemic Spreading Seizure and Epilepsy Surgery framework (ESSES). The ESSES parameters were fitted in a retrospective study (N=15) to reproduce invasive electroencephalography (iEEG)-recorded seizures. ESSES could not only reproduce the iEEG-recorded seizures, but significantly better so for patients with good (seizure-free, SF) than bad (non-seizure-free, NSF) outcome (area under the curve AUC=0.73). Once the model parameters were set in the retrospective study, ESSES can be applied also to patients without iEEG data. We illustrate here the clinical applicability of ESSES with a pseudo-prospective study (N=34) with a blind setting (to the resection strategy and surgical outcome) that emulated the presurgical conditions. ESSES could predict the chances of good outcome after any resection by finding patient-specific optimal resection strategies, which we found to be smaller for SF than NSF patients, suggesting an intrinsic difference in the network organization or presurgical evaluation results of NSF patients. The actual surgical plan also overlapped more with the optimal resection, and had a larger effect in decreasing modeled seizure propagation, for SF patients than for NSF patients. Overall, ESSES could correctly predict 75% of NSF and 80.8% of SF cases pseudo-prospectively. Our results show that individualised computational models may inform surgical planning by suggesting optimal resections and providing information on the likelihood of a good outcome after a proposed resection. This is the first time that such a model is validated on a fully independent cohort without the need for iEEG recordings.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Individualized epidemic spreading models predict epilepsy surgery outcomes: a pseudo-prospective study

Millán

Straaten

Stam

et al. 2023

Preprint

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“…Although not the focus of this study, further characterization of CCEPs as a multiregional response may provide novel, clinically relevant insights for the mapping of epileptogenic networks. 56 Robust CCEPs were most often observed near the stimulation site, adjacent to the stimulating electrode, or along the same lead. These closely spaced electrodes may be best characterized by Euclidean distance, which could explain why this metric led to a higher frequency of traveling waves relative to path length or geodesic distance, metrics which are more biologically plausible paths for subcortical and cortical traveling wave propagation.…”

Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Macroscale traveling waves evoked by single-pulse stimulation of the human brain

Campbell

Davis

Anderson

et al. 2023

Preprint

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Emerging evidence suggests that the temporal dynamics of cortico-cortical evoked potentials (CCEPs) may be used to characterize the patterns of information flow between and within brain networks. At present, however, the spatiotemporal dynamics of CCEP propagation cortically and subcortically are incompletely understood. We hypothesized that CCEPs propagate as an evoked traveling wave emanating from the site of stimulation. To elicit CCEPs, we applied single-pulse stimulation to stereoelectroencephalography (SEEG) electrodes implanted in 21 adult patients with intractable epilepsy. For each robust CCEP, we measured the timing of the maximal descent in evoked local field potentials and broadband high-gamma power (70-150 Hz) envelopes relative to the distance between the recording and stimulation contacts using three different metrics (i.e., Euclidean distance, path length, geodesic distance), representing direct, subcortical, and transcortical propagation, respectively. Many evoked responses to single-pulse electrical stimulation appear to propagate as traveling waves (~17-30%), even in the sparsely sampled, three-dimensional SEEG space. These results provide new insights into the spatiotemporal dynamics of CCEP propagation.

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“…7,9 The EZN quantification could facilitate SEEG interpretation and help to establish surgical decision based on objective criteria. [10][11][12] Seizures, interictal spikes, and high-frequency oscillations (HFOs, 80-500 Hz) are recognized as electrophysiological biomarkers of the EZN. 13,14 Ictal epileptogenicity measures quantify the spectral content of SEEG signals and/or changes in functional connectivity at seizure onset.…”

Section: Introductionmentioning

confidence: 99%

The role of quantitative markers in surgical prognostication after stereoelectroencephalography

Makhalova,

Madec,

Medina Villalon

et al. 2023

Ann Clin Transl Neurol

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ObjectiveStereoelectroencephalography (SEEG) is the reference method in the presurgical exploration of drug‐resistant focal epilepsy. However, prognosticating surgery on an individual level is difficult. A quantified estimation of the most epileptogenic regions by searching for relevant biomarkers can be proposed for this purpose. We investigated the performances of ictal (Epileptogenicity Index, EI; Connectivity EI, cEI), interictal (spikes, high‐frequency oscillations, HFO [80–300 Hz]; Spikes × HFO), and combined (Spikes × EI; Spikes × cEI) biomarkers in predicting surgical outcome and searched for prognostic factors based on SEEG‐signal quantification.MethodsFifty‐three patients operated on following SEEG were included. We compared, using precision‐recall, the epileptogenic zone quantified using different biomarkers (EZq) against the visual analysis (EZC). Correlations between the EZ resection rates or the EZ extent and surgical prognosis were analyzed.ResultsEI and Spikes × EI showed the best precision against EZc (0.74; 0.70), followed by Spikes × cEI and cEI, whereas interictal markers showed lower precision. The EZ resection rates were greater in seizure‐free than in non‐seizure‐free patients for the EZ defined by ictal biomarkers and were correlated with the outcome for EI and Spikes × EI. No such correlation was found for interictal markers. The extent of the quantified EZ did not correlate with the prognosis.InterpretationIctal or combined ictal–interictal markers overperformed the interictal markers both for detecting the EZ and predicting seizure freedom. Combining ictal and interictal epileptogenicity markers improves detection accuracy. Resection rates of the quantified EZ using ictal markers were the only statistically significant determinants for surgical prognosis.

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Quantitative approaches to guide epilepsy surgery from intracranial EEG

Cited by 32 publications

References 140 publications

Individualized epidemic spreading models predict epilepsy surgery outcomes: a pseudo-prospective study

Individualized epidemic spreading models predict epilepsy surgery outcomes: a pseudo-prospective study

Macroscale traveling waves evoked by single-pulse stimulation of the human brain

The role of quantitative markers in surgical prognostication after stereoelectroencephalography

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