Estimation of brain network ictogenicity predicts outcome from epilepsy surgery.
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ABSTRACT:
SUBMITTER: Goodfellow M
PROVIDER: S-EPMC4935897 | biostudies-literature | 2016 Jul
REPOSITORIES: biostudies-literature
Publications
Estimation of brain network ictogenicity predicts outcome from epilepsy surgery.
Goodfellow M M Rummel C C Abela E E Richardson M P MP Schindler K K Terry J R JR
Scientific reports 20160707
Surgery is a valuable option for pharmacologically intractable epilepsy. However, significant post-operative improvements are not always attained. This is due in part to our incomplete understanding of the seizure generating (ictogenic) capabilities of brain networks. Here we introduce an in silico, model-based framework to study the effects of surgery within ictogenic brain networks. We find that factors conventionally determining the region of tissue to resect, such as the location of focal br ...[more]
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Project description:High frequency oscillations are a promising biomarker of outcome in intractable epilepsy. Prior high frequency oscillation work focused on counting high frequency oscillations on individual channels, and it is still unclear how to translate those results into clinical care. We show that high frequency oscillations arise as network discharges that have valuable properties as predictive biomarkers. Here, we develop a tool to predict patient outcome before surgical resection is performed, based on only prospective information. In addition to determining high frequency oscillation rate on every channel, we performed a correlational analysis to evaluate the functional connectivity of high frequency oscillations in 28 patients with intracranial electrodes. We found that high frequency oscillations were often not solitary events on a single channel, but part of a local network discharge. Eigenvector and outcloseness centrality were used to rank channel importance within the connectivity network, then used to compare patient outcome by comparison with the seizure onset zone or a proportion within the proposed resected channels (critical resection percentage). Combining the knowledge of each patient's seizure onset zone resection plan along with our computed high frequency oscillation network centralities and high frequency oscillation rate, we develop a Naïve Bayes model that predicts outcome (positive predictive value: 100%) better than predicting based upon fully resecting the seizure onset zone (positive predictive value: 71%). Surgical margins had a large effect on outcomes: non-palliative patients in whom most of the seizure onset zone was resected ('definitive surgery', ≥ 80% resected) had predictable outcomes, whereas palliative surgeries (<80% resected) were not predictable. These results suggest that the addition of network properties of high frequency oscillations is more accurate in predicting patient outcome than seizure onset zone alone in patients with most of the seizure onset zone removed and offer great promise for informing clinical decisions in surgery for refractory epilepsy.
| S-EPMC10881100 | biostudies-literature
Project description:ObjectiveWe assessed preoperative structural brain networks and clinical characteristics of patients with drug-resistant temporal lobe epilepsy (TLE) to identify correlates of postsurgical seizure recurrences.MethodsWe examined data from 51 patients with TLE who underwent anterior temporal lobe resection (ATLR) and 29 healthy controls. For each patient, using the preoperative structural, diffusion, and postoperative structural MRI, we generated 2 networks: presurgery network and surgically spared network. Standardizing these networks with respect to controls, we determined the number of abnormal nodes before surgery and expected to be spared by surgery. We incorporated these 2 abnormality measures and 13 commonly acquired clinical data from each patient into a robust machine learning framework to estimate patient-specific chances of seizures persisting after surgery.ResultsPatients with more abnormal nodes had a lower chance of complete seizure freedom at 1 year and, even if seizure-free at 1 year, were more likely to relapse within 5 years. The number of abnormal nodes was greater and their locations more widespread in the surgically spared networks of patients with poor outcome than in patients with good outcome. We achieved an area under the curve of 0.84 ± 0.06 and specificity of 0.89 ± 0.09 in predicting unsuccessful seizure outcomes (International League Against Epilepsy [ILAE] 3-5) as opposed to complete seizure freedom (ILAE 1) at 1 year. Moreover, the model-predicted likelihood of seizure relapse was significantly correlated with the grade of surgical outcome at year 1 and associated with relapses up to 5 years after surgery.ConclusionNode abnormality offers a personalized, noninvasive marker that can be combined with clinical data to better estimate the chances of seizure freedom at 1 year and subsequent relapse up to 5 years after ATLR.Classification of evidenceThis study provides Class II evidence that node abnormality predicts postsurgical seizure recurrence.
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Project description:ObjectiveTo localize the seizure onset zone (SOZ) and irritative zone (IZ) using electric source imaging (ESI) on intracranial EEG (iEEG) and assess their clinical value in predicting epilepsy surgery outcome in children with focal cortical dysplasia (FCD).MethodsWe analyzed iEEG data from 25 children with FCD-associated medically refractory epilepsy (MRE) who underwent surgery. We performed ESI on ictal onset to localize SOZ (ESI-SOZ) and on interictal discharges to localize IZ (ESI-IZ). We tested whether resection of ESI-SOZ and ESI-IZ predicted good surgical outcome (Engel 1). We further compared the prediction performance of ESI-SOZ and ESI-IZ to those of SOZ and IZ defined using conventional methods, i.e. by identifying iEEG-contacts showing ictal onsets (conventional-SOZ) or being the most interictally active (conventional-IZ).ResultsThe proximity of ESI-SOZ (p = 0.043, odds-ratio = 3.9) and ESI-IZ (p = 0.011, odds-ratio = 7.04) to resection has higher effect on patients' outcome than proximity of conventional-SOZ (p = 0.17, odds-ratio = 1.7) and conventional-IZ (p = 0.038, odds-ratio = 2.6). Resection of ESI-SOZ and ESI-IZ presented higher discriminative power in predicting outcome (68% and 60%) than conventional-SOZ and conventional-IZ (48% and 53%).ConclusionsLocalizing SOZ and IZ via ESI on iEEG offers higher predictive value compared to conventional-iEEG interpretation.SignificanceiEEG-ESI may help surgical planning and facilitate prognostic assessment of children with FCD-associated MRE.
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Project description:Patients with drug-resistant epilepsy often require surgery to become seizure-free. While laser ablation and implantable stimulation devices have lowered the morbidity of these procedures, seizure-free rates have not dramatically improved, particularly for patients without focal lesions. This is in part because it is often unclear where to intervene in these cases. To address this clinical need, several research groups have published methods to map epileptic networks but applying them to improve patient care remains a challenge. In this study we advance clinical translation of these methods by: (i) presenting and sharing a robust pipeline to rigorously quantify the boundaries of the resection zone and determining which intracranial EEG electrodes lie within it; (ii) validating a brain network model on a retrospective cohort of 28 patients with drug-resistant epilepsy implanted with intracranial electrodes prior to surgical resection; and (iii) sharing all neuroimaging, annotated electrophysiology, and clinical metadata to facilitate future collaboration. Our network methods accurately forecast whether patients are likely to benefit from surgical intervention based on synchronizability of intracranial EEG (area under the receiver operating characteristic curve of 0.89) and provide novel information that traditional electrographic features do not. We further report that removing synchronizing brain regions is associated with improved clinical outcome, and postulate that sparing desynchronizing regions may further be beneficial. Our findings suggest that data-driven network-based methods can identify patients likely to benefit from resective or ablative therapy, and perhaps prevent invasive interventions in those unlikely to do so.
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Project description:The Val66Met polymorphism of brain-derived neurotrophic factor is associated with decreased brain-derived neurotrophic factor secretion and poor outcome after acute neurological injury. We hypothesized that the Met allele is associated with worsening of functional outcome after brain arteriovenous malformation resection.Three hundred forty-one surgically treated patients with brain arteriovenous malformation with outcome data were genotyped for Val66Met. Outcome was change in modified Rankin Scale preoperatively versus postoperatively, dichotomized into poor (change >0) or good outcome (change ?0). Likelihood ratio tests for interactions and logistic regression analysis were performed.A significant interaction (P=0.03) of Val66Met genotype and hemorrhagic presentation existed; thus, ruptured and unruptured patients were considered separately. The Met allele was associated with increased risk of poor outcome among patients presenting unruptured (OR, 2.15; 95% CI, 1.02-4.55; P=0.045) but not ruptured (OR, 0.54; 95% CI, 0.19-1.53; P=0.25), adjusting for covariates.The Val66Met polymorphism is associated with worsened surgical outcome in patients with unruptured brain arteriovenous malformation, a group that currently has no good risk predictors. Further studies replicating these findings are needed.
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Project description:ObjectiveHigh-frequency oscillations (HFOs) in the intracerebral electroencephalogram (EEG) have been linked to the seizure onset zone (SOZ). We investigated whether HFOs can delineate epileptogenic areas even outside the SOZ by correlating the resection of HFO-generating areas with surgical outcome.MethodsTwenty patients who underwent a surgical resection for medically intractable epilepsy were studied. All had presurgical intracerebral EEG (500Hz filter and 2,000Hz sampling rate), at least 12-month postsurgical follow-up, and a postsurgical magnetic resonance imaging (MRI). HFOs (ripples, 80-250Hz; fast ripples, >250Hz) were identified visually during 5 to 10 minutes of slow-wave sleep. Rates and extent of HFOs and interictal spikes in resected versus nonresected areas, assessed on postsurgical MRIs, were compared with surgical outcome (Engel's classification). We also evaluated the predictive value of removing the SOZ in terms of surgical outcome.ResultsThe mean duration of follow-up was 22.7 months. Eight patients had good (Engel classes 1 and 2) and 12 poor (classes 3 and 4) surgical outcomes. Patients with a good outcome had a significantly larger proportion of HFO-generating areas removed than patients with a poor outcome. No such difference was seen for spike-generating regions or the SOZ.InterpretationThe correlation between removal of HFO-generating areas and good surgical outcome indicates that HFOs could be used as a marker of epileptogenicity and may be more accurate than spike-generating areas or the SOZ. In patients in whom the majority of HFO-generating tissue remained, a poor surgical outcome occurred.
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Project description:This prospective study determined the use of intracranially recorded spectral responses during naming tasks in predicting neuropsychological performance following epilepsy surgery. We recruited 65 patients with drug-resistant focal epilepsy who underwent preoperative neuropsychological assessment and intracranial EEG recording. The Clinical Evaluation of Language Fundamentals evaluated the baseline and postoperative language function. During extra-operative intracranial EEG recording, we assigned patients to undergo auditory and picture naming tasks. Time-frequency analysis determined the spatiotemporal characteristics of naming-related amplitude modulations, including high gamma augmentation at 70-110 Hz. We surgically removed the presumed epileptogenic zone based on the intracranial EEG and MRI abnormalities while maximally preserving the eloquent areas defined by electrical stimulation mapping. The multivariate regression model incorporating auditory naming-related high gamma augmentation predicted the postoperative changes in Core Language Score with r2 of 0.37 and in Expressive Language Index with r2 of 0.32. Independently of the effects of epilepsy and neuroimaging profiles, higher high gamma augmentation at the resected language-dominant hemispheric area predicted a more severe postoperative decline in Core Language Score and Expressive Language Index. Conversely, the model incorporating picture naming-related high gamma augmentation predicted the change in Receptive Language Index with an r2 of 0.50. Higher high gamma augmentation independently predicted a more severe postoperative decline in Receptive Language Index. Ancillary regression analysis indicated that naming-related low gamma augmentation and alpha/beta attenuation likewise independently predicted a more severe Core Language Score decline. The machine learning-based prediction model suggested that naming-related high gamma augmentation, among all spectral responses used as predictors, most strongly contributed to the improved prediction of patients showing a >5-point Core Language Score decline (reflecting the lower 25th percentile among patients). We generated the model-based atlas visualizing sites, which, if resected, would lead to such a language decline. With a 5-fold cross-validation procedure, the auditory naming-based model predicted patients who had such a postoperative language decline with an accuracy of 0.80. The model indicated that virtual resection of an electrical stimulation mapping-defined language site would have increased the relative risk of the Core Language Score decline by 5.28 (95% confidence interval: 3.47-8.02). Especially, that of an electrical stimulation mapping-defined receptive language site would have maximized it to 15.90 (95% confidence interval: 9.59-26.33). In summary, naming-related spectral responses predict neuropsychological outcomes after epilepsy surgery. We have provided our prediction model as an open-source material, which will indicate the postoperative language function of future patients and facilitate external validation at tertiary epilepsy centres.
| S-EPMC9014727 | biostudies-literature