Graph theoretical measures of fast ripple networks improve the accuracy of post-operative seizure outcome prediction

A novel way to use cross-validation to measure connectivity by machine learning allows epilepsy surgery outcome prediction

The rate of success of epilepsy surgery, ensuring seizure-freedom, is limited by the lack of epileptogenicity biomarkers. Previous evidence supports the critical role of functional connectivity during seizure generation to characterize the epileptogenic network (EN). However, EN dynamics is highly variable across patients, hindering the development of diagnostic biomarkers. Without relying on specific connectivity variables, we focused on a general hypothesis that the EN undergoes the greatest magnitude of connectivity change during seizure generation, compared to other brain networks. To test this hypothesis, we developed a novel method for quantifying connectivity change between network states and applied it to identify surgical resection areas. A network state was represented by random snapshots of connectivity within a defined time interval of an intracranial EEG recording. A binary classifier was applied to classify two network states. The classifier generalization performance estimated by cross-validation was employed as a continuous measure of connectivity change. The algorithm generated a network by iteratively adding nodes until the connectivity change magnitude decreased. The resulting network was compared to the surgical resection, and the overlap score was used to predict post-surgical outcomes. The framework was evaluated in a consecutive cohort of 21 patients with a post-surgical follow-up of minimum 3 years. The best overlap between connectivity change networks and resections was obtained at the transition from pre-seizure to seizure (surgical outcome prediction ROC-AUC=90.3%). However, all patients except one were correctly classified when considering the most informative time intervals. Time intervals proportional to seizure length were more informative than the almost universally used fixed intervals. This study demonstrates that connectivity can be successfully classified with a machine learning analysis and provide information for distinguishing a separate epileptogenic functional network. In summary, the connectivity change analysis could accurately identify epileptogenic networks validated by surgery outcome classification. Connectivity change magnitude at seizure transition could potentially serve as an EN biomarker. The tool provided by this study may aid surgical decision-making.

Networks through the lens of high-frequency oscillations

To date, there is no neurophysiologic or neuroimaging biomarker that can accurately delineate the epileptogenic network. High-frequency oscillations (HFO) have been proposed as biomarkers for epileptogenesis and the epileptogenic network. The pathological HFO have been associated with areas of seizure onset and epileptogenic tissue. Several studies have demonstrated that the resection of areas with high rates of pathological HFO is associated with favorable postoperative outcomes. Recent studies have demonstrated the spatiotemporal organization of HFO into networks and their potential role in defining epileptogenic networks. Our review will present the existing literature on HFO-associated networks, specifically focusing on their role in defining epileptogenic networks and their potential significance in surgical planning.

Simulated resections and responsive neurostimulator placement can optimize postoperative seizure outcomes when guided by fast ripple networks

In medication-resistant epilepsy, the goal of epilepsy surgery is to make a patient seizure free with a resection/ablation that is as small as possible to minimize morbidity. The standard of care in planning the margins of epilepsy surgery involves electroclinical delineation of the seizure-onset zone and incorporation of neuroimaging findings from MRI, PET, single-photon emission CT and magnetoencephalography modalities. Resecting cortical tissue generating high-frequency oscillations has been investigated as a more efficacious alternative to targeting the seizure-onset zone. In this study, we used a support vector machine (SVM), with four distinct fast ripple (FR: 350-600 Hz on oscillations, 200-600 Hz on spikes) metrics as factors. These metrics included the FR resection ratio, a spatial FR network measure and two temporal FR network measures. The SVM was trained by the value of these four factors with respect to the actual resection boundaries and actual seizure-free labels of 18 patients with medically refractory focal epilepsy. Leave-one-out cross-validation of the trained SVM in this training set had an accuracy of 0.78. We next used a simulated iterative virtual resection targeting the FR sites that were of highest rate and showed most temporal autonomy. The trained SVM utilized the four virtual FR metrics to predict virtual seizure freedom. In all but one of the nine patients who were seizure free after surgery, we found that the virtual resections sufficient for virtual seizure freedom were larger in volume (P < 0.05). In nine patients who were not seizure free, a larger virtual resection made five virtually seizure free. We also examined 10 medically refractory focal epilepsy patients implanted with the responsive neurostimulator system and virtually targeted the responsive neurostimulator system stimulation contacts proximal to sites generating FR at highest rates to determine if the simulated value of the stimulated seizure-onset zone and stimulated FR metrics would trend towards those patients with a better seizure outcome. Our results suggest the following: (i) FR measures can accurately predict whether a resection, defined by the standard of care, will result in seizure freedom; (ii) utilizing FR alone for planning an efficacious surgery can be associated with larger resections; (iii) when FR metrics predict the standard-of-care resection will fail, amending the boundaries of the planned resection with certain FR-generating sites may improve outcome and (iv) more work is required to determine whether targeting responsive neurostimulator system stimulation contact proximal to FR generating sites will improve seizure outcome.

Connectivity of high-frequency bursts as SOZ localization biomarker

For patients with refractory epilepsy, the seizure onset zone (SOZ) plays an essential role in determining the specific regions of the brain that will be surgically resected. High-frequency oscillations (HFOs) and connectivity-based approaches have been identified among the potential biomarkers to localize the SOZ. However, there is no consensus on how connectivity between HFO events should be estimated, nor on its subject-specific short-term reliability. Therefore, we propose the channel-level connectivity dispersion (CLCD) as a metric to quantify the variability in synchronization between individual electrodes and to identify clusters of electrodes with abnormal synchronization, which we hypothesize to be associated with the SOZ. In addition, we developed a specialized filtering method that reduces oscillatory components caused by filtering broadband artifacts, such as sharp transients, spikes, or direct current shifts. Our connectivity estimates are therefore robust to the presence of these waveforms. To calculate our metric, we start by creating binary signals indicating the presence of high-frequency bursts in each channel, from which we calculate the pairwise connectivity between channels. Then, the CLCD is calculated by combining the connectivity matrices and measuring the variability in each electrode's combined connectivity values. We test our method using two independent open-access datasets comprising intracranial electroencephalography signals from 89 to 15 patients with refractory epilepsy, respectively. Recordings in these datasets were sampled at approximately 1000 Hz, and our proposed CLCDs were estimated in the ripple band (80-200 Hz). Across all patients in the first dataset, the average ROC-AUC was 0.73, and the average Cohen's d was 1.05, while in the second dataset, the average ROC-AUC was 0.78 and Cohen's d was 1.07. On average, SOZ channels had lower CLCD values than non-SOZ channels. Furthermore, based on the second dataset, which includes surgical outcomes (Engel I-IV), our analysis suggested that higher CLCD interquartile (as a measure of CLCD distribution spread) is associated with favorable outcomes (Engel I). This suggests that CLCD could significantly assist in identifying SOZ clusters and, therefore, provide an additional tool in surgical planning for epilepsy patients.

Simulated resections and RNS placement can optimize post-operative seizure outcomes when guided by fast ripple networks

In medication-resistant epilepsy, the goal of epilepsy surgery is to make a patient seizure free with a resection/ablation that is as small as possible to minimize morbidity. The standard of care in planning the margins of epilepsy surgery involves electroclinical delineation of the seizure onset zone (SOZ) and incorporation of neuroimaging findings from MRI, PET, SPECT, and MEG modalities. Resecting cortical tissue generating high-frequency oscillations (HFOs) has been investigated as a more efficacious alternative to targeting the SOZ. In this study, we used a support vector machine (SVM), with four distinct fast ripple (FR: 350-600 Hz on oscillations, 200-600 Hz on spikes) metrics as factors. These metrics included the FR resection ratio (RR), a spatial FR network measure, and two temporal FR network measures. The SVM was trained by the value of these four factors with respect to the actual resection boundaries and actual seizure free labels of 18 patients with medically refractory focal epilepsy. Leave one out cross-validation of the trained SVM in this training set had an accuracy of 0.78. We next used a simulated iterative virtual resection targeting the FR sites that were highest rate and showed most temporal autonomy. The trained SVM utilized the four virtual FR metrics to predict virtual seizure freedom. In all but one of the nine patients seizure free after surgery, we found that the virtual resections sufficient for virtual seizure freedom were larger in volume (p<0.05). In nine patients who were not seizure free, a larger virtual resection made five virtually seizure free. We also examined 10 medically refractory focal epilepsy patients implanted with the responsive neurostimulator system (RNS) and virtually targeted the RNS stimulation contacts proximal to sites generating FR at highest rates to determine if the simulated value of the stimulated SOZ and stimulated FR metrics would trend toward those patients with a better seizure outcome. Our results suggest: 1) FR measures can accurately predict whether a resection, defined by the standard of care, will result in seizure freedom; 2) utilizing FR alone for planning an efficacious surgery can be associated with larger resections; 3) when FR metrics predict the standard of care resection will fail, amending the boundaries of the planned resection with certain FR generating sites may improve outcome; and 4) more work is required to determine if targeting RNS stimulation contact proximal to FR generating sites will improve seizure outcome.

High frequency oscillation network dynamics predict outcome in non-palliative epilepsy surgery

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.

Pathological neurons generate ripples at the UP-DOWN transition disrupting information transfer

OBJECTIVE

To confirm and investigate why pathological high-frequency oscillations (pHFOs), including ripples (80-200 Hz) and fast ripples (200-600 Hz), are generated during the UP-DOWN transition of the slow wave and if information transmission mediated by ripple temporal coupling is disrupted in the seizure-onset zone (SOZ).

METHODS

We isolated 217 total units from 175.95 intracranial electroencephalography (iEEG) contact-hours of synchronized macro- and microelectrode recordings from 6 patients. Sleep slow oscillation (.1-2 Hz) epochs were identified in the iEEG recording. iEEG HFOs that occurred superimposed on the slow wave were transformed to phasors and adjusted by the phase of maximum firing in nearby units (i.e., maximum UP). We tested whether, in the SOZ, HFOs and associated action potentials (APs) occur more often at the UP-DOWN transition. We also examined ripple temporal correlations using cross-correlograms.

RESULTS

At the group level in the SOZ, HFO and HFO-associated AP probability was highest during the UP-DOWN transition of slow wave excitability (p < < .001). In the non-SOZ, HFO and HFO-associated AP was highest during the DOWN-UP transition (p < < .001). At the unit level in the SOZ, 15.6% and 20% of units exhibited more robust firing during ripples (Cohen's d = .11-.83) and fast ripples (d = .36-.90) at the UP-DOWN transition (p < .05 f.d.r. corrected), respectively. By comparison, also in the SOZ, 6.6% (d = .14-.30) and 8.5% (d = .33-.41) of units had significantly less firing during ripples and fast ripples at the UP-DOWN transition, respectively. Additional data shows that ripple and fast ripple temporal correlations, involving global slow waves, between the hippocampus, entorhinal cortex, and parahippocampal gyrus were reduced by >50% in the SOZ compared to the non-SOZ (N = 3).

SIGNIFICANCE

The UP-DOWN transition of slow wave excitability facilitates the activation of pathological neurons to generate pHFOs. Ripple temporal correlations across brain regions may be important in memory consolidation and are disrupted in the SOZ, perhaps by pHFO generation.

Evolution of Stereo-Electroencephalography at Massachusetts General Hospital

The practice of invasive monitoring for presurgical epilepsy workup has evolved at Massachusetts General Hospital (MGH) in parallel to the evolution in the field's understanding of epilepsy as a network disorder. Implantations have shifted from an emphasis on singularly finding single foci for the purpose of resection to a network-hypothesis-driven approach aiming to delineate patients' seizure networks with the goal of developing surgical interventions that disrupt critical nodes of these networks. Here, the authors review all invasive monitoring cases at MGH from April 2016 through June 2023 to describe how this paradigm shift has taken form.

The role of quantitative markers in surgical prognostication after stereoelectroencephalography

OBJECTIVE

Stereoelectroencephalography (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.

METHODS

Fifty-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.

RESULTS

EI 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.

INTERPRETATION

Ictal 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.

Fast ripples reflect increased excitability that primes epileptiform spikes

The neuronal circuit disturbances that drive inter-ictal and ictal epileptiform discharges remain elusive. Using a combination of extra-operative macro-electrode and micro-electrode inter-ictal recordings in six pre-surgical patients during non-rapid eye movement sleep, we found that, exclusively in the seizure onset zone, fast ripples (200-600 Hz), but not ripples (80-200 Hz), frequently occur <300 ms before an inter-ictal intra-cranial EEG spike with a probability exceeding chance (bootstrapping, P < 1e-5). Such fast ripple events are associated with higher spectral power (P < 1e-10) and correlated with more vigorous neuronal firing than solitary fast ripple (generalized linear mixed-effects model, P < 1e-9). During the intra-cranial EEG spike that follows a fast ripple, action potential firing is lower than during an intra-cranial EEG spike alone (generalized linear mixed-effects model, P < 0.05), reflecting an inhibitory restraint of intra-cranial EEG spike initiation. In contrast, ripples do not appear to prime epileptiform spikes. We next investigated the clinical significance of pre-spike fast ripple in a separate cohort of 23 patients implanted with stereo EEG electrodes, who underwent resections. In non-rapid eye movement sleep recordings, sites containing a high proportion of fast ripple preceding intra-cranial EEG spikes correlate with brain areas where seizures begin more than solitary fast ripple (P < 1e-5). Despite this correlation, removal of these sites does not guarantee seizure freedom. These results are consistent with the hypothesis that fast ripple preceding EEG spikes reflect an increase in local excitability that primes EEG spike discharges preferentially in the seizure onset zone and that epileptogenic brain regions are necessary, but not sufficient, for initiating inter-ictal epileptiform discharges.

Fast ripples reflect increased excitability that primes epileptiform spikes

The neuronal circuit disturbances that drive interictal and ictal epileptiform discharges remains elusive. Using a combination of extraoperative macro- and micro-electrode interictal recordings in six presurgical patients during non-rapid eye movement (REM) sleep we found that, exclusively in the seizure onset zone, fast ripples (FR; 200-600Hz), but not ripples (80-200 Hz), frequently occur <300 msec before an interictal intracranial EEG (iEEG) spike with a probability exceeding chance (bootstrapping, p<1e-5). Such FR events are associated with higher spectral power (p<1e-10) and correlated with more vigorous neuronal firing than solitary FR (generalized linear mixed-effects model, GLMM, p<1e-3) irrespective of FR power. During the iEEG spike that follows a FR, action potential firing is lower than during a iEEG spike alone (GLMM, p<1e-10), reflecting an inhibitory restraint of iEEG spike initiation. In contrast, ripples do not appear to prime epileptiform spikes. We next investigated the clinical significance of pre-spike FR in a separate cohort of 23 patients implanted with stereo EEG electrodes who underwent resections. In non-REM sleep recordings, sites containing a high proportion of FR preceding iEEG spikes correlate with brain areas where seizures begin more than solitary FR (p<1e-5). Despite this correlation, removal of these sites does not guarantee seizure freedom. These results are consistent with the hypothesis that FR preceding EEG spikes reflect an increase in local excitability that primes EEG spike discharges preferentially in the seizure onset zone and that epileptogenic brain regions are necessary, but not sufficient, for initiating interictal epileptiform discharges.

Stimulation better targets fast-ripple generating networks in super responders to the responsive neurostimulator system

How responsive neurostimulation (RNS) decreases seizure frequency is unclear. Stimulation may alter epileptic networks during inter-ictal epochs. Definitions of the epileptic network vary but fast ripples (FRs) may be an important substrate. We, therefore, examined whether stimulation of FR-generating networks differed in RNS super responders and intermediate responders. In 10 patients, with subsequent RNS placement, we detected FRs from stereo-electroencephalography (SEEG) contacts during pre-surgical evaluation. The normalized coordinates of the SEEG contacts were compared with those of the eight RNS contacts, and RNS-stimulated SEEG contacts were defined as those within 1.5 cm³ of the RNS contacts. We compared the post-RNS placement seizure outcome to (1) the ratio of stimulated SEEG contacts in the seizure-onset zone (SOZ stimulation ratio [SR]); (2) the ratio of FR events on stimulated contacts (FR SR); and (3) the global efficiency of the FR temporal correlational network on stimulated contacts (FR SGe). We found that the SOZ SR (p = .18) and FR SR (p = .06) did not differ in the RNS super responders and intermediate responders, but the FR SGe did (p = .02). In super responders, highly active desynchronous sites of the FR network were stimulated. RNS that better targets FR networks, as compared to the SOZ, may reduce epileptogenicity more.