More Than Spikes: On the Added Value of Non-linear Intracranial EEG Analysis for Surgery Planning in Temporal Lobe Epilepsy.

Epilepsy surgery can be a very effective therapy in medication refractory patients. During patient evaluation intracranial EEG is analyzed by clinical experts to identify the brain tissue generating epileptiform events. Quantitative EEG analysis increasingly complements this approach in research settings, but not yet in clinical routine. We investigate the correspondence between epileptiform events and a specific quantitative EEG marker. We analyzed 99 preictal epochs of multichannel intracranial EEG of 40 patients with mixed etiologies. Time and channel of occurrence of epileptiform events (spikes, slow waves, sharp waves, fast oscillations) were annotated by a human expert and non-linear excess interrelations were calculated as a quantitative EEG marker. We assessed whether the visually identified preictal events predicted channels that belonged to the seizure onset zone, that were later resected or that showed strong non-linear interrelations. We also investigated whether the seizure onset zone or the resection were predicted by channels with strong non-linear interrelations. In patients with temporal lobe epilepsy (32 of 40), epileptic spikes and the seizure onset zone predicted the resected brain tissue much better in patients with favorable seizure control after surgery than in unfavorable outcomes. Beyond that, our analysis did not reveal any significant associations with epileptiform EEG events. Specifically, none of the epileptiform event types did predict non-linear interrelations. In contrast, channels with strong non-linear excess EEG interrelations predicted the resected channels better in patients with temporal lobe epilepsy and favorable outcome. Also in the small number of patients with seizure onset in the frontal and parietal lobes, no association between epileptiform events and channels with strong non-linear excess EEG interrelations was detectable. In contrast to patients with temporal seizure onset, EEG channels with strong non-linear excess interrelations did neither predict the seizure onset zone nor the resection of these patients or allow separation between patients with favorable and unfavorable seizure control. Our study indicates that non-linear excess EEG interrelations are not strictly associated with epileptiform events, which are one key concept of current clinical EEG assessment. Rather, they may provide information relevant for surgery planning in temporal lobe epilepsy. Our study suggests to incorporate quantitative EEG analysis in the workup of clinical cases. We make the EEG epochs and expert annotations publicly available in anonymized form to foster similar analyses for other quantitative EEG methods.

Comorbidity-dependent changes in alpha and broadband electroencephalogram power during general anaesthesia for cardiac surgery.

BACKGROUND: Age and comorbidities are reported to induce neurobiological transformations in the brain. Whilst the influence of ageing on anaesthesia-induced electroencephalogram (EEG) changes has been investigated, the effect of comorbidities has not yet been explored. We hypothesised that certain diseases significantly affect frontal EEG alpha and broadband power in cardiac surgical patients. METHODS: We analysed the frontal EEGs of 589 patients undergoing isoflurane general anaesthesia from a prospective observational study. We used multi- and uni-variable regression to analyse the relationships between comorbidities and age as independent with peak and oscillatory alpha, and broadband power as dependent variables. A score of comorbidities and minimum alveolar concentration (MAC) was built to interrogate the combined effect of age and score on alpha and broadband power. RESULTS: At the univariable level, many comorbidities were associated with lower EEG alpha or broadband power. Multivariable regression indicated the independent association of numerous comorbidities and MAC with peak alpha (R2=0.19) and broadband power (R2=0.31). The association with peak alpha power is markedly reduced when the underlying broadband effect is subtracted (R2=0.09). Broadband measures themselves are more strongly correlated with comorbidities and MAC (R2=0.31) than age (R2=0.15). CONCLUSIONS: Comorbidities and age are independently associated with decreasing frontal EEG alpha and broadband power during general anaesthesia. For alpha power, the association is highly dependent on the underlying broadband effect. These findings might have significant clinical consequences for automated computation for depth of anaesthesia in comorbid patients, because misclassification might pose the risk of under- or over-dosing of anaesthetics. CLINICAL TRIAL REGISTRATION: NCT02976584.

Linear and nonlinear interrelations show fundamentally distinct network structure in preictal intracranial EEG of epilepsy patients.

Resection of the seizure generating tissue can be highly beneficial in patients with drug-resistant epilepsy. However, only about half of all patients undergoing surgery get permanently and completely seizure free. Investigating the dependences between intracranial EEG signals adds a multivariate perspective largely unavailable to visual EEG analysis, which is the current clinical practice. We examined linear and nonlinear interrelations between intracranial EEG signals regarding their spatial distribution and network characteristics. The analyzed signals were recorded immediately before clinical seizure onset in epilepsy patients who received a standardized electrode implantation targeting the mesiotemporal structures. The linear interrelation networks were predominantly locally connected and highly reproducible between patients. In contrast, the nonlinear networks had a clearly centralized structure, which was specific for the individual pathology. The nonlinear interrelations were overrepresented in the focal hemisphere and in patients with no or only rare seizures after surgery specifically in the resected tissue. Connections to the outside were predominantly nonlinear. In all patients without worthwhile improvement after resective treatment, tissue producing strong nonlinear interrelations was left untouched by surgery. Our findings indicate that linear and nonlinear interrelations play fundamentally different roles in preictal intracranial EEG. Moreover, they suggest nonlinear signal interrelations to be a marker of epileptogenic tissue and not a characteristic of the mesiotemporal structures. Our results corroborate the network-based nature of epilepsy and suggest the application of network analysis to support the planning of resective epilepsy surgery.

Quantification and Selection of Ictogenic Zones in Epilepsy Surgery.

Network models of brain dynamics provide valuable insight into the healthy functioning of the brain and how this breaks down in disease. A pertinent example is the use of network models to understand seizure generation (ictogenesis) in epilepsy. Recently, computational models have emerged to aid our understanding of seizures and to predict the outcome of surgical perturbations to brain networks. Such approaches provide the opportunity to quantify the effect of removing regions of tissue from brain networks and thereby search for the optimal resection strategy. Here, we use computational models to elucidate how sets of nodes contribute to the ictogenicity of networks. In small networks we fully elucidate the ictogenicity of all possible sets of nodes and demonstrate that the distribution of ictogenicity across sets depends on network topology. However, the full elucidation is a combinatorial problem that becomes intractable for large networks. Therefore, we combine computational models with a genetic algorithm to search for minimal sets of nodes that contribute significantly to ictogenesis. We demonstrate the potential applicability of these methods in practice by identifying optimal sets of nodes to resect in networks derived from 20 individuals who underwent resective surgery for epilepsy. We show that they have the potential to aid epilepsy surgery by suggesting alternative resection sites as well as facilitating the avoidance of brain regions that should not be resected.

Decreasing Frontal Electroencephalogram Alpha Power and Increasing Sensitivity to Volatile Anesthetics Over 3 Surgeries Within 7 Months: A Case Report.

Depth of anesthesia (DoA) monitors are widely used during general anesthesia to guide individualized dosing of hypnotics. Other than age and specific drugs, there are few reports on which comorbidities may influence the brain and the resultant electroencephalogram (EEG) of patients undergoing general anesthesia. We present a case of a patient undergoing 3 cardiac operations within 7 months with severe illness and comorbidity, leading to pronounced physical frailty and significant changes of frontal alpha power in the EEG and increased sensitivity to volatile anesthetics. These findings may have important clinical implications and should trigger further investigations on this topic.

Evaluating resective surgery targets in epilepsy patients: A comparison of quantitative EEG methods.

BACKGROUND: Quantitative analysis of intracranial EEG is a promising tool to assist clinicians in the planning of resective brain surgery in patients suffering from pharmacoresistant epilepsies. Quantifying the accuracy of such tools, however, is nontrivial as a ground truth to verify predictions about hypothetical resections is missing. NEW METHOD: As one possibility to address this, we use customized hypotheses tests to examine the agreement of the methods on a common set of patients. One method uses machine learning techniques to enable the predictive modeling of EEG time series. The other estimates nonlinear interrelation between EEG channels. Both methods were independently shown to distinguish patients with excellent post-surgical outcome (Engel class I) from those without improvement (Engel class IV) when assessing the electrodes associated with the tissue that was actually resected during brain surgery. Using the AND and OR conjunction of both methods we evaluate the performance gain that can be expected when combining them. RESULTS: Both methods' assessments correlate strongly positively with the similarity between a hypothetical resection and the corresponding actual resection in class I patients. Moreover, the Spearman rank correlation between the methods' patient rankings is significantly positive. COMPARISON WITH EXISTING METHOD(S): To our best knowledge, this is the first study comparing surgery target assessments from fundamentally differing techniques. CONCLUSIONS: Although conceptually completely independent, there is a relation between the predictions obtained from both methods. Their broad consensus supports their application in clinical practice to provide physicians additional information in the process of presurgical evaluation.

Limitations of Current Near-Infrared Spectroscopy Configuration in Detecting Focal Cerebral Ischemia During Cardiac Surgery: An Observational Case-Series Study.

Cerebral oximetry using near-infrared spectroscopy (NIRS) allows for continuous monitoring of cerebral perfusion and immediate treatment of hemodynamic perturbations. In configurations used in current clinical practice, NIRS optodes are placed on the patient`s forehead and cerebral oxygen saturation (ScO2 ) is determined in bilateral frontal cortical samples. However, focal cerebral ischemic lesions outside of the NIRS field of view may remain undetected. The objective of this observational case-series study was to investigate ScO2 measurements in patients with acute iatrogenic stroke not located in the frontal cortical region. Adult patients undergoing cardiac surgery with cardiopulmonary bypass or interventional cardiology procedures and suffering stroke in the early postoperative period were identified from the Bernese Stroke Registry and analyzed for their intraoperative ScO2 values and brain imaging data. Main outcome measures were the ScO2 values, computed tomography and magnetic resonance imaging findings. In six patients, the infarct areas were localized in the vascular territories of the posterior and/or dorsal middle cerebral arteries. One patient had watershed stroke and another one excellent collaterals resulting in normal cerebral blood volume and only subtle decrease of cerebral blood flow in initially critically perfused watershed brain areas. Intraoperative ScO2 values were entirely unremarkable or nonindicative for brain damage. Our results indicate that uneventful intraoperative NIRS monitoring does not exclude severe cerebral ischemia due to the limited field of view of commercially available NIRS devices. False negative NIRS may occur as a consequence of stroke localized outside the frontal cortex.

Elevated Ictal Brain Network Ictogenicity Enables Prediction of Optimal Seizure Control.

Recent studies have shown that mathematical models can be used to analyze brain networks by quantifying how likely they are to generate seizures. In particular, we have introduced the quantity termed brain network ictogenicity (BNI), which was demonstrated to have the capability of differentiating between functional connectivity (FC) of healthy individuals and those with epilepsy. Furthermore, BNI has also been used to quantify and predict the outcome of epilepsy surgery based on FC extracted from pre-operative ictal intracranial electroencephalography (iEEG). This modeling framework is based on the assumption that the inferred FC provides an appropriate representation of an ictogenic network, i.e., a brain network responsible for the generation of seizures. However, FC networks have been shown to change their topology depending on the state of the brain. For example, topologies during seizure are different to those pre- and post-seizure. We therefore sought to understand how these changes affect BNI. We studied peri-ictal iEEG recordings from a cohort of 16 epilepsy patients who underwent surgery and found that, on average, ictal FC yield higher BNI relative to pre- and post-ictal FC. However, elevated ictal BNI was not observed in every individual, rather it was typically observed in those who had good post-operative seizure control. We therefore hypothesize that elevated ictal BNI is indicative of an ictogenic network being appropriately represented in the FC. We evidence this by demonstrating superior model predictions for post-operative seizure control in patients with elevated ictal BNI.

Spatially extended versus frontal cerebral near-infrared spectroscopy during cardiac surgery: a case series identifying potential advantages.

Stroke due to hypoperfusion or emboli is a devastating adverse event of cardiac surgery, but early detection and treatment could protect patients from an unfavorable postoperative course. Hypoperfusion and emboli can be detected with transcranial Doppler of the middle cerebral artery (MCA). The measured blood flow velocity correlates with cerebral oxygenation determined clinically by near-infrared spectroscopy (NIRS) of the frontal cortex. We tested the potential advantage of a spatially extended NIRS in detecting critical events in three cardiac surgery patients with a whole-head fiber holder of the FOIRE-3000 continuous-wave NIRS system. Principle components analysis was performed to differentiate between global and localized hypoperfusion or ischemic territories of the middle and anterior cerebral arteries. In one patient, we detected a critical hypoperfusion of the right MCA, which was not apparent in the frontal channels but was accompanied by intra- and postoperative neurological correlates of ischemia. We conclude that spatially extended NIRS of temporal and parietal vascular territories could improve the detection of critically low cerebral perfusion. Even in severe hemispheric stroke, NIRS of the frontal lobe may remain normal because the anterior cerebral artery can be supplied by the contralateral side directly or via the anterior communicating artery.

Personalized structural image analysis in patients with temporal lobe epilepsy.

Volumetric and morphometric studies have demonstrated structural abnormalities related to chronic epilepsies on a cohort- and population-based level. On a single-patient level, specific patterns of atrophy or cortical reorganization may be widespread and heterogeneous but represent potential targets for further personalized image analysis and surgical therapy. The goal of this study was to compare morphometric data analysis in 37 patients with temporal lobe epilepsies with expert-based image analysis, pre-informed by seizure semiology and ictal scalp EEG. Automated image analysis identified abnormalities exceeding expert-determined structural epileptogenic lesions in 86% of datasets. If EEG lateralization and expert MRI readings were congruent, automated analysis detected abnormalities consistent on a lobar and hemispheric level in 82% of datasets. However, in 25% of patients EEG lateralization and expert readings were inconsistent. Automated analysis localized to the site of resection in 60% of datasets in patients who underwent successful epilepsy surgery. Morphometric abnormalities beyond the mesiotemporal structures contributed to subtype characterisation. We conclude that subject-specific morphometric information is in agreement with expert image analysis and scalp EEG in the majority of cases. However, automated image analysis may provide non-invasive additional information in cases with equivocal radiological and neurophysiological findings.

An optimal strategy for epilepsy surgery: Disruption of the rich-club?

Surgery is a therapeutic option for people with epilepsy whose seizures are not controlled by anti-epilepsy drugs. In pre-surgical planning, an array of data modalities, often including intra-cranial EEG, is used in an attempt to map regions of the brain thought to be crucial for the generation of seizures. These regions are then resected with the hope that the individual is rendered seizure free as a consequence. However, post-operative seizure freedom is currently sub-optimal, suggesting that the pre-surgical assessment may be improved by taking advantage of a mechanistic understanding of seizure generation in large brain networks. Herein we use mathematical models to uncover the relative contribution of regions of the brain to seizure generation and consequently which brain regions should be considered for resection. A critical advantage of this modeling approach is that the effect of different surgical strategies can be predicted and quantitatively compared in advance of surgery. Herein we seek to understand seizure generation in networks with different topologies and study how the removal of different nodes in these networks reduces the occurrence of seizures. Since this a computationally demanding problem, a first step for this aim is to facilitate tractability of this approach for large networks. To do this, we demonstrate that predictions arising from a neural mass model are preserved in a lower dimensional, canonical model that is quicker to simulate. We then use this simpler model to study the emergence of seizures in artificial networks with different topologies, and calculate which nodes should be removed to render the network seizure free. We find that for scale-free and rich-club networks there exist specific nodes that are critical for seizure generation and should therefore be removed, whereas for small-world networks the strategy should instead focus on removing sufficient brain tissue. We demonstrate the validity of our approach by analysing intra-cranial EEG recordings from a database comprising 16 patients who have undergone epilepsy surgery, revealing rich-club structures within the obtained functional networks. We show that the postsurgical outcome for these patients was better when a greater proportion of the rich club was removed, in agreement with our theoretical predictions.

Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone.

OBJECTIVE: To show that time-irreversible EEG signals recorded with intracranial electrodes during seizures can serve as markers of the epileptogenic zone. METHODS: We use the recently developed method of mapping time series into directed horizontal graphs (dHVG). Each node of the dHVG represents a time point in the original intracranial EEG (iEEG) signal. Statistically significant differences between the distributions of the nodes' number of input and output connections are used to detect time-irreversible iEEG signals. RESULTS: In 31 of 32 seizure recordings we found time-irreversible iEEG signals. The maximally time-irreversible signals always occurred during seizures, with highest probability in the middle of the first seizure half. These signals spanned a large range of frequencies and amplitudes but were all characterized by saw-tooth like shaped components. Brain regions removed from patients who became post-surgically seizure-free generated significantly larger time-irreversibilities than regions removed from patients who still had seizures after surgery. CONCLUSIONS: Our results corroborate that ictal time-irreversible iEEG signals can indeed serve as markers of the epileptogenic zone and can be efficiently detected and quantified in a time-resolved manner by dHVG based methods. SIGNIFICANCE: Ictal time-irreversible EEG signals can help to improve pre-surgical evaluation in patients suffering from pharmaco-resistant epilepsies.

Resected Brain Tissue, Seizure Onset Zone and Quantitative EEG Measures: Towards Prediction of Post-Surgical Seizure Control.

BACKGROUND: Epilepsy 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. METHODS: Despite 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. RESULTS: In 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. CONCLUSIONS: We 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.

Detecting functional hubs of ictogenic networks.

Quantitative EEG (qEEG) has modified our understanding of epileptic seizures, shifting our view from the traditionally accepted hyper-synchrony paradigm toward more complex models based on re-organization of functional networks. However, qEEG measurements are so far rarely considered during the clinical decision-making process. To better understand the dynamics of intracranial EEG signals, we examine a functional network derived from the quantification of information flow between intracranial EEG signals. Using transfer entropy, we analyzed 198 seizures from 27 patients undergoing pre-surgical evaluation for pharmaco-resistant epilepsy. During each seizure we considered for each network the in-, out- and total "hubs", defined respectively as the time and the EEG channels with the maximal incoming, outgoing or total (bidirectional) information flow. In the majority of cases we found that the hubs occur around the middle of seizures, and interestingly not at the beginning or end, where the most dramatic EEG signal changes are found by visual inspection. For the patients who then underwent surgery, good postoperative clinical outcome was on average associated with a higher percentage of out- or total-hubs located in the resected area (for out-hubs p = 0.01, for total-hubs p = 0.04). The location of in-hubs showed no clear predictive value. We conclude that the study of functional networks based on qEEG measurements may help to identify brain areas that are critical for seizure generation and are thus potential targets for focused therapeutic interventions.

Forbidden ordinal patterns of periictal intracranial EEG indicate deterministic dynamics in human epileptic seizures.

PURPOSE: Epileptic seizures typically reveal a high degree of stereotypy, that is, for an individual patient they are characterized by an ordered and predictable sequence of symptoms and signs with typically little variability. Stereotypy implies that ictal neuronal dynamics might have deterministic characteristics, presumably most pronounced in the ictogenic parts of the brain, which may provide diagnostically and therapeutically important information. Therefore the goal of our study was to search for indications of determinism in periictal intracranial electroencephalography (EEG) studies recorded from patients with pharmacoresistent epilepsy. METHODS: We assessed the number of forbidden ordinal patterns of 110 periictal multichannel intracranial EEG studies of 16 patients. Ordinal patterns are derived from the rank order of short sequences of consecutive EEG values. Ordinal patterns are well suited for analyzing real-world time series, for they have low sensitivity for many forms of noise and are applicable to nonstationary data. Although Gaussian random dynamics generate all possible ordinal patterns for a given sequence length, deterministic dynamics typically manifest with less random and more regular signals that miss a certain number of all the possible ordinal patterns. These missing ordinal patterns are referred to as "forbidden ordinal patterns." In this study, the number of forbidden ordinal patterns n(fp) of an EEG signal was interpreted as an indication of determinism, when it was larger than the number of forbidden patterns occurring in amplitude adjusted Fourier transform surrogates. We computed n(fp) for each EEG signal in a time-resolved way by using a moving-window approach. Then we specifically investigated n(mean)(fp) denoting the average number of forbidden patterns across all EEG signals, and n(max)(fp), which represents the number of forbidden patterns occurring in the EEG signal with the largest n(fp) during the seizure-onset period. KEY FINDINGS: The average number of forbidden patterns of all EEG signals, n(mean)(fp), typically first increased and then decreased during the seizures. However, these changes were not statistically significant relative to the preseizure time period. In contrast, n(max)(fp)typically increased significantly during the first third of the seizure period and then gradually decreased toward and beyond seizure termination. In those patients who became seizure free following surgery, a larger percentage of the EEG signals containing the maximal number of forbidden patterns during the seizure-onset period tended to be recorded from within the visually identified seizure-onset zones. SIGNIFICANCE: Our findings demonstrate a spatiotemporally limited shift of neuronal dynamics toward a more deterministic dynamic regimen, specifically pronounced during the seizure-onset period. Assessing the number of forbidden ordinal patterns of intracranial EEG provides quantitative and observer-independent information. We propose that it is at least partially complementary to classical visual EEG reading and may be diagnostically helpful to better delineate ictogenic parts of the brain.

Peri-ictal correlation dynamics of high-frequency (80-200 Hz) intracranial EEG.

PURPOSE: To assess (1) how large-scale correlation of intracranial EEG signals in the high-frequency range (80-200Hz) evolves from the pre-ictal, through the ictal into the postictal state and (2) whether the contribution of local neuronal activity to large-scale EEG correlation differentiates epileptogenic from non-epileptogenic brain tissue. METHODS: Large-scale correlation of intracranial EEG was assessed by the total correlation strength (TCS), a measure derived from the eigenvalue spectra of zero-lag correlation matrices computed in a time-resolved manner by using a moving window approach. The relative change of total correlation strength (Delta(j)) resulting from leaving out EEG channel j ("leave-one-out approach") was used to quantify the contribution of local neuronal activity to large-scale EEG correlation. RESULTS: 19 seizures of 3 patients were analyzed. On average, TCS increased significantly from the pre-ictal to the ictal, and from the ictal to the postictal state. In the pre-ictal state, Delta(j) was significantly more negative when EEG channels that recorded the electrical activity of brain tissue considered to be epileptogenic were left out; the identification of the epileptogenic area, that was subsequently surgically removed in two patients, was based on visual analysis. The spatio-temporal pattern of Delta(j) dramatically changed at seizure onsets and endings, revealing qualitative similarities between the seizures of different patients. DISCUSSION: The evolution of large-scale EEG correlation in the high-frequency range is qualitatively similar to the one previously described for the low-frequency range. Because the two patients who underwent surgery became seizure free, our findings are consistent with the hypothesis that epileptogenic brain tissue may be characterized by its relatively increased contribution to pre-ictal large-scale correlation.