Elevated Ictal Brain Network Ictogenicity Enables Prediction of Optimal Seizure Control.
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ABSTRACT:
SUBMITTER: Lopes MA
PROVIDER: S-EPMC5837986 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveThe aim of this study was to identify relevant risk factors for epileptic seizures upon initial diagnosis of a brain tumor and to develop and validate a machine learning based prediction to allow for a tailored risk-based antiepileptic therapy.MethodsClinical, electrophysiological and high-resolution imaging data was obtained from a consecutive cohort of 1051 patients with newly diagnosed brain tumors. Factor-associated seizure risk difference allowed to determine the relevance of specific topographic, demographic and histopathologic variables available at the time of diagnosis for seizure risk. The data was divided in a 70/30 ratio into a training and test set. Different machine learning based predictive models were evaluated before a generalized additive model (GAM) was selected considering its traceability while maintaining high performance. Based on a clinical stratification of the risk factors, three different GAM were trained and internally validated.ResultsA total of 923 patients had full data and were included. Specific topographic anatomical patterns that drive seizure risk could be identified. The involvement of allopallial, mesopallial or primary motor/somatosensory neopallial structures by brain tumors results in a significant and clinically relevant increase in seizure risk. While topographic input was most relevant for the GAM, the best prediction was achieved by a combination of topographic, demographic and histopathologic information (Validation: AUC: 0.79, Accuracy: 0.72, Sensitivity: 0.81, Specificity: 0.66).ConclusionsThis study identifies specific phylogenetic anatomical patterns as epileptic drivers. A GAM allowed the prediction of seizure risk using topographic, demographic and histopathologic data achieving fair performance while maintaining transparency.
| S-EPMC7711280 | biostudies-literature
Project description:BackgroundEpilepsy surgery is a potentially curative treatment option for pharmacoresistent patients. If non-invasive methods alone do not allow to delineate the epileptogenic brain areas the surgical candidates undergo long-term monitoring with intracranial EEG. Visual EEG analysis is then used to identify the seizure onset zone for targeted resection as a standard procedure.MethodsDespite of its great potential to assess the epileptogenicty of brain tissue, quantitative EEG analysis has not yet found its way into routine clinical practice. To demonstrate that quantitative EEG may yield clinically highly relevant information we retrospectively investigated how post-operative seizure control is associated with four selected EEG measures evaluated in the resected brain tissue and the seizure onset zone. Importantly, the exact spatial location of the intracranial electrodes was determined by coregistration of pre-operative MRI and post-implantation CT and coregistration with post-resection MRI was used to delineate the extent of tissue resection. Using data-driven thresholding, quantitative EEG results were separated into normally contributing and salient channels.ResultsIn patients with favorable post-surgical seizure control a significantly larger fraction of salient channels in three of the four quantitative EEG measures was resected than in patients with unfavorable outcome in terms of seizure control (median over the whole peri-ictal recordings). The same statistics revealed no association with post-operative seizure control when EEG channels contributing to the seizure onset zone were studied.ConclusionsWe conclude that quantitative EEG measures provide clinically relevant and objective markers of target tissue, which may be used to optimize epilepsy surgery. The finding that differentiation between favorable and unfavorable outcome was better for the fraction of salient values in the resected brain tissue than in the seizure onset zone is consistent with growing evidence that spatially extended networks might be more relevant for seizure generation, evolution and termination than a single highly localized brain region (i.e. a "focus") where seizures start.
| S-EPMC4626164 | biostudies-literature
Project description:Epilepsy is a neurological disorder in which patients have recurrent seizures. Seizures occur in conjunction with abnormal electrical brain activity which can be recorded by the electroencephalogram (EEG). Often, this abnormal brain activity consists of high amplitude regular spike-wave oscillations as opposed to low amplitude irregular oscillations in the non-seizure state. Active brain stimulation has been proposed as a method to terminate seizures prematurely, however, a general and widely-applicable approach to optimal stimulation protocols is still lacking. In this study we use a computational model of epileptic spike-wave dynamics to evaluate the effectiveness of a pseudospectral method to simulated seizure abatement. We incorporate brain connectivity derived from magnetic resonance imaging of a subject with idiopathic generalized epilepsy. We find that the pseudospectral method can successfully generate time-varying stimuli that abate simulated seizures, even when including heterogeneous patient specific brain connectivity. The strength of the stimulus required varies in different brain areas. Our results suggest that seizure abatement, modeled as an optimal control problem and solved with the pseudospectral method, offers an attractive approach to treatment for in vivo stimulation techniques. Further, if optimal brain stimulation protocols are to be experimentally successful, then the heterogeneity of cortical connectivity should be accounted for in the development of those protocols and thus more spatially localized solutions may be preferable.
| S-EPMC4453481 | biostudies-literature
Project description:Absence seizures are generalized, cortico-thalamo-cortical (CTC) high power electroencephalographic (EEG) or electrocorticographic (ECoG) events that initiate and terminate suddenly. ECoG recordings of absence seizures in animal models of genetic absence epilepsy show a sudden spike-wave-discharge (SWD) onset that rapidly emerges from normal ECoG activity. However, given that absence seizures occur most often during periods of drowsiness or quiet wakefulness, we wondered whether SWD onset correlates with pre-ictal changes in network activity. To address this, we analyzed ECoG recordings of both spontaneous and induced SWDs in rats with genetic absence epilepsy. We discovered that the duration and intensity of spontaneous SWDs positively correlate with pre-ictal 20-40Hz (?) spectral power and negatively correlate with 4-7Hz (Ø) power. In addition, the output of thalamocortical neurons decreases within the same pre-ictal window of time. In separate experiments we found that the propensity for SWD induction was correlated with pre-ictal ? power. These results argue that CTC networks undergo a pre-seizure state transition, possibly due to a functional reorganization of cortical microcircuits, which leads to the generation of absence seizures.
| S-EPMC5607110 | biostudies-literature
Project description:Functional magnetic resonance imaging (fMRI) has shown elevations in the blood-oxygen-level-dependent (BOLD) signal associated with, but insensitive for, seizure. Rather than evaluating absolute BOLD signal elevations, assessing rhythmic oscillations in the BOLD signal with fMRI may improve the accuracy of seizure mapping. We report a case of a patient with non-convulsive, right hemispheric seizures who underwent fMRI. Unbiased processing methods revealed a map of rhythmically oscillating BOLD signal over the cortical region affected by seizure, and synchronous BOLD signal in the contralateral cerebellum. High-resolution fMRI may help identify the spatial topography of seizure and provide insights into seizure physiology.
| S-EPMC9775283 | biostudies-literature
Project description:ObjectiveThe treatment of focal epilepsies is largely predicated on the concept that there is a "focus" from which the seizure emanates. Yet, the physiological context that determines if and how ictal activity starts and propagates remains poorly understood. To delineate these phenomena more completely, we studied activity outside the seizure-onset zone prior to and during seizure initiation.MethodsStereotactic depth electrodes were implanted in 17 patients with longstanding pharmacoresistant epilepsy for lateralization and localization of the seizure-onset zone. Only seizures with focal onset in mesial temporal structures were used for analysis. Spectral analyses were used to quantify changes in delta, theta, alpha, beta, gamma, and high gamma frequency power, in regions inside and outside the area of seizure onset during both preictal and seizure initiation periods.ResultsIn the 78 seizures examined, an average of 9.26% of the electrode contacts outside of the seizure focus demonstrated changes in power at seizure onset. Of interest, seizures that were secondarily generalized, on average, showed power changes in a greater number of extrafocus electrode contacts at seizure onset (16.7%) compared to seizures that remained focal (3.8%). The majority of these extrafocus changes occupied the delta and theta bands in electrodes placed in the ipsilateral, lateral temporal lobe. Preictally, we observed extrafocal high-frequency power decrements, which also correlated with seizure spread.SignificanceThis widespread activity at and prior to the seizure-onset time further extends the notion of the ictogenic focus and its relationship to seizure spread. Further understanding of these extrafocus, periictal changes might help identify the neuronal dynamics underlying the initiation of seizures and how therapies can be devised to control seizure activity.
| S-EPMC6031475 | biostudies-literature
Project description:The ultimate goal of epileptology is the complete abolishment of epileptic seizures. This might be achieved by a system that predicts seizure onset combined with a system that interferes with the process that leads to the onset of a seizure. Seizure prediction remains, as of yet, unresolved in absence-epilepsy, due to the sudden onset of seizures. We have developed a real-time absence seizure prediction algorithm, evaluated it and implemented it in an on-line, closed-loop brain stimulation system designed to prevent the spike-wave-discharges (SWDs), typical for absence epilepsy, in a genetic rat model. The algorithm corretly predicted 88% of the SWDs while the remaining were quickly detected. A high number of false-positive detections occurred mainly during light slow-wave-sleep. Inclusion of criteria to prevent false-positives greatly reduced the false alarm rate but decreased the sensitivity of the algoritm. Implementation of the latter version into a closed-loop brain-stimulation-system resulted in a 72% decrease in seizure activity. In contrast to long standing beliefs that SWDs are unpredictable, these results demonstrate that they can be predicted and that the development of closed-loop seizure prediction and prevention systems is a feasable step towards interventions to attain control and freedom from epileptic seizures.
| S-EPMC5447660 | biostudies-literature
Project description:High frequency oscillations have been proposed as a clinically useful biomarker of seizure generating sites. We used a unique set of human microelectrode array recordings (four patients, 10 seizures), in which propagating seizure wavefronts could be readily identified, to investigate the basis of ictal high frequency activity at the cortical (subdural) surface. Sustained, repetitive transient increases in high gamma (80-150 Hz) amplitude, phase-locked to the low-frequency (1-25 Hz) ictal rhythm, correlated with strong multi-unit firing bursts synchronized across the core territory of the seizure. These repetitive high frequency oscillations were seen in recordings from subdural electrodes adjacent to the microelectrode array several seconds after seizure onset, following ictal wavefront passage. Conversely, microelectrode recordings demonstrating only low-level, heterogeneous neural firing correlated with a lack of high frequency oscillations in adjacent subdural recording sites, despite the presence of a strong low-frequency signature. Previously, we reported that this pattern indicates a failure of the seizure to invade the area, because of a feedforward inhibitory veto mechanism. Because multi-unit firing rate and high gamma amplitude are closely related, high frequency oscillations can be used as a surrogate marker to distinguish the core seizure territory from the surrounding penumbra. We developed an efficient measure to detect delayed-onset, sustained ictal high frequency oscillations based on cross-frequency coupling between high gamma amplitude and the low-frequency (1-25 Hz) ictal rhythm. When applied to the broader subdural recording, this measure consistently predicted the timing or failure of ictal invasion, and revealed a surprisingly small and slowly spreading seizure core surrounded by a far larger penumbral territory. Our findings thus establish an underlying neural mechanism for delayed-onset, sustained ictal high frequency oscillations, and provide a practical, efficient method for using them to identify the small ictal core regions. Our observations suggest that it may be possible to reduce substantially the extent of cortical resections in epilepsy surgery procedures without compromising seizure control.
| S-EPMC3859220 | biostudies-literature
Project description:IntroductionSurgical treatment of drug-resistant epilepsy relies on the identification of the seizure onset zone (SOZ) and often requires intracranial EEG (iEEG). We have developed a new approach for non-invasive magnetic and electric source imaging of the SOZ (MSI-SOZ and ESI-SOZ) from ictal magnetoencephalography (MEG) and EEG recordings, using wavelet-based Maximum Entropy on the Mean (wMEM) method. We compared the performance of MSI-SOZ and ESI-SOZ with interictal spike source localization (MSI-spikes and ESI-spikes) and clinical localization of the SOZ (i.e., based on iEEG or lesion topography, denoted as clinical-SOZ).MethodsA total of 46 MEG or EEG seizures from 13 patients were analyzed. wMEM was applied around seizure onset, centered on the frequency band showing the strongest power change. Principal component analysis applied to spatiotemporal reconstructed wMEM sources (0.4-1 s around seizure onset) identified the main spatial pattern of ictal oscillations. Qualitative sublobar concordance and quantitative measures of distance and spatial overlaps were estimated to compare MSI/ESI-SOZ with MSI/ESI-Spikes and clinical-SOZ.ResultsMSI/ESI-SOZ were concordant with clinical-SOZ in 81% of seizures (MSI 90%, ESI 64%). MSI-SOZ was more accurate and identified sources closer to the clinical-SOZ (P = 0.012) and to MSI-Spikes (P = 0.040) as compared with ESI-SOZ. MSI/ESI-SOZ and MSI/ESI-Spikes did not differ in terms of concordance and distance from the clinical-SOZ.ConclusionswMEM allows non-invasive localization of the SOZ from ictal MEG and EEG. MSI-SOZ performs better than ESI-SOZ. MSI/ESI-SOZ can provide important additional information to MSI/ESI-Spikes during presurgical evaluation. Hum Brain Mapp 37:2528-2546, 2016. © 2016 Wiley Periodicals, Inc.
| S-EPMC6867380 | biostudies-literature
Project description:PurposeAdequate epileptic seizure detection may have the potential to minimize seizure-related complications and improve treatment evaluation. Autonomic changes often precede ictal electroencephalographic discharges and therefore provide a promising tool for timely seizure detection. We reviewed the literature for seizure detection algorithms using autonomic nervous system parameters.MethodsThe PubMed and Embase databases were systematically searched for original human studies that validate an algorithm for automatic seizure detection based on autonomic function alterations. Studies on neonates only and pilot studies without performance data were excluded. Algorithm performance was compared for studies with a similar design (retrospective vs. prospective) reporting both sensitivity and false alarm rate (FAR). Quality assessment was performed using QUADAS-2 and recently reported quality standards on reporting seizure detection algorithms.ResultsTwenty-one out of 638 studies were included in the analysis. Fifteen studies presented a single-modality algorithm based on heart rate variability (n = 10), heart rate (n = 4), or QRS morphology (n = 1), while six studies assessed multimodal algorithms using various combinations of HR, corrected QT interval, oxygen saturation, electrodermal activity, and accelerometry. Most studies had small sample sizes and a short follow-up period. Only two studies performed a prospective validation. A tendency for a lower FAR was found for retrospectively validated algorithms using multimodal autonomic parameters compared to those using single modalities (mean sensitivity per participant 71-100% vs. 64-96%, and mean FAR per participant 0.0-2.4/h vs. 0.7-5.4/h).ConclusionsThe overall quality of studies on seizure detection using autonomic parameters is low. Unimodal autonomic algorithms cannot reach acceptable performance as false alarm rates are still too high. Larger prospective studies are needed to validate multimodal automatic seizure detection.
| S-EPMC6459795 | biostudies-literature