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

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 on a patient-by-patient basis, we developed a framework of individualized computational models that combines epidemic spreading with patient-specific connectivity and epileptogeneity maps: the Epidemic Spreading Seizure and Epilepsy Surgery framework (ESSES). ESSES parameters were fitted in a retrospective study (N = 15) to reproduce invasive electroencephalography (iEEG)-recorded seizures. ESSES reproduced the iEEG-recorded seizures, and significantly better so for patients with good (seizure-free, SF) than bad (nonseizure-free, NSF) outcome. 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 presurgical conditions. By setting the model parameters in the retrospective study, ESSES could be applied also to patients without iEEG data. ESSES could predict the chances of good outcome after any resection by finding patient-specific model-based 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 overlapped more with the model-based 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 alternative 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 with a fully independent cohort and without the need for iEEG recordings.

Individualized computational models of epilepsy surgery capture some of the key aspects of seizure propagation and the resective surgery. It is to be established whether this information can be integrated during the presurgical evaluation of the patient to improve surgical planning and the chances of a good surgical outcome. Here we address this question with a pseudo-prospective study that applies a computational framework of seizure propagation and epilepsy surgery­the ESSES framework­in a pseudo-prospective study mimicking the presurgical conditions. We found that within this pseudo-prospective setting, ESSES could correctly predict 75% of NSF and 80.8% of SF cases. This finding suggests the potential of individualised computational models to inform surgical planning by suggesting alternative resections and providing information on the likelihood of a good outcome after a proposed resection.

The relationship between pathological brain activity and functional network connectivity in glioma patients.

PURPOSE: Glioma is associated with pathologically high (peri)tumoral brain activity, which relates to faster progression. Functional connectivity is disturbed locally and throughout the entire brain, associating with symptomatology. We, therefore, investigated how local activity and network measures relate to better understand how the intricate relationship between the tumor and the rest of the brain may impact disease and symptom progression. METHODS: We obtained magnetoencephalography in 84 de novo glioma patients and 61 matched healthy controls. The offset of the power spectrum, a proxy of neuronal activity, was calculated for 210 cortical regions. We calculated patients' regional deviations in delta, theta and lower alpha network connectivity as compared to controls, using two network measures: clustering coefficient (local connectivity) and eigenvector centrality (integrative connectivity). We then tested group differences in activity and connectivity between (peri)tumoral, contralateral homologue regions, and the rest of the brain. We also correlated regional offset to connectivity. RESULTS: As expected, patients' (peri)tumoral activity was pathologically high, and patients showed higher clustering and lower centrality than controls. At the group-level, regionally high activity related to high clustering in controls and patients alike. However, within-patient analyses revealed negative associations between regional deviations in brain activity and clustering, such that pathologically high activity coincided with low network clustering, while regions with 'normal' activity levels showed high network clustering. CONCLUSION: Our results indicate that pathological activity and connectivity co-localize in a complex manner in glioma. This insight is relevant to our understanding of disease progression and cognitive symptomatology.

The role of epidemic spreading in seizure dynamics and epilepsy surgery.

Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but only leads to seizure freedom for roughly two in three patients. To address this problem, we designed a patient-specific epilepsy surgery model combining large-scale magnetoencephalography (MEG) brain networks with an epidemic spreading model. This simple model was enough to reproduce the stereo-tactical electroencephalography (SEEG) seizure propagation patterns of all patients (N = 15), when considering the resection areas (RA) as the epidemic seed. Moreover, the goodness of fit of the model predicted surgical outcome. Once adapted for each patient, the model can generate alternative hypothesis of the seizure onset zone and test different resection strategies in silico. Overall, our findings indicate that spreading models based on patient-specific MEG connectivity can be used to predict surgical outcomes, with better fit results and greater reduction on seizure propagation linked to higher likelihood of seizure freedom after surgery. Finally, we introduced a population model that can be individualized by considering only the patient-specific MEG network, and showed that it not only conserves but improves the group classification. Thus, it may pave the way to generalize this framework to patients without SEEG recordings, reduce the risk of overfitting and improve the stability of the analyses.

The longitudinal relation between executive functioning and multilayer network topology in glioma patients.

Many patients with glioma, primary brain tumors, suffer from poorly understood executive functioning deficits before and/or after tumor resection. We aimed to test whether frontoparietal network centrality of multilayer networks, allowing for integration across multiple frequencies, relates to and predicts executive functioning in glioma. Patients with glioma (n = 37) underwent resting-state magnetoencephalography and neuropsychological tests assessing word fluency, inhibition, and set shifting before (T1) and one year after tumor resection (T2). We constructed binary multilayer networks comprising six layers, with each layer representing frequency-specific functional connectivity between source-localized time series of 78 cortical regions. Average frontoparietal network multilayer eigenvector centrality, a measure for network integration, was calculated at both time points. Regression analyses were used to investigate associations with executive functioning. At T1, lower multilayer integration (p = 0.017) and epilepsy (p = 0.006) associated with poorer set shifting (adj. R2 = 0.269). Decreasing multilayer integration (p = 0.022) and not undergoing chemotherapy at T2 (p = 0.004) related to deteriorating set shifting over time (adj. R2 = 0.283). No significant associations were found for word fluency or inhibition, nor did T1 multilayer integration predict changes in executive functioning. As expected, our results establish multilayer integration of the frontoparietal network as a cross-sectional and longitudinal correlate of executive functioning in glioma patients. However, multilayer integration did not predict postoperative changes in executive functioning, which together with the fact that this correlate is also found in health and other diseases, limits its specific clinical relevance in glioma.

Regional healthy brain activity, glioma occurrence and symptomatology.

It is unclear why exactly gliomas show preferential occurrence in certain brain areas. Increased spiking activity around gliomas leads to faster tumour growth in animal models, while higher non-invasively measured brain activity is related to shorter survival in patients. However, it is unknown how regional intrinsic brain activity, as measured in healthy controls, relates to glioma occurrence. We first investigated whether gliomas occur more frequently in regions with intrinsically higher brain activity. Second, we explored whether intrinsic cortical activity at individual patients' tumour locations relates to tumour and patient characteristics. Across three cross-sectional cohorts, 413 patients were included. Individual tumour masks were created. Intrinsic regional brain activity was assessed through resting-state magnetoencephalography acquired in healthy controls and source-localized to 210 cortical brain regions. Brain activity was operationalized as: (i) broadband power; and (ii) offset of the aperiodic component of the power spectrum, which both reflect neuronal spiking of the underlying neuronal population. We additionally assessed (iii) the slope of the aperiodic component of the power spectrum, which is thought to reflect the neuronal excitation/inhibition ratio. First, correlation coefficients were calculated between group-level regional glioma occurrence, as obtained by concatenating tumour masks across patients, and group-averaged regional intrinsic brain activity. Second, intrinsic brain activity at specific tumour locations was calculated by overlaying patients' individual tumour masks with regional intrinsic brain activity of the controls and was associated with tumour and patient characteristics. As proposed, glioma preferentially occurred in brain regions characterized by higher intrinsic brain activity in controls as reflected by higher offset. Second, intrinsic brain activity at patients' individual tumour locations differed according to glioma subtype and performance status: the most malignant isocitrate dehydrogenase-wild-type glioblastoma patients had the lowest excitation/inhibition ratio at their individual tumour locations as compared to isocitrate dehydrogenase-mutant, 1p/19q-codeleted glioma patients, while a lower excitation/inhibition ratio related to poorer Karnofsky Performance Status, particularly in codeleted glioma patients. In conclusion, gliomas more frequently occur in cortical brain regions with intrinsically higher activity levels, suggesting that more active regions are more vulnerable to glioma development. Moreover, indices of healthy, intrinsic excitation/inhibition ratio at patients' individual tumour locations may capture both tumour biology and patients' performance status. These findings contribute to our understanding of the complex and bidirectional relationship between normal brain functioning and glioma growth, which is at the core of the relatively new field of 'cancer neuroscience'.

Epidemic models characterize seizure propagation and the effects of epilepsy surgery in individualized brain networks based on MEG and invasive EEG recordings.

Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients. However, seizure-freedom is currently achieved in only 2/3 of the patients after surgery. In this study we have developed an individualized computational model based on MEG brain networks to explore seizure propagation and the efficacy of different virtual resections. Eventually, the goal is to obtain individualized models to optimize resection strategy and outcome. We have modelled seizure propagation as an epidemic process using the susceptible-infected (SI) model on individual brain networks derived from presurgical MEG. We included 10 patients who had received epilepsy surgery and for whom the surgery outcome at least one year after surgery was known. The model parameters were tuned in in order to reproduce the patient-specific seizure propagation patterns as recorded with invasive EEG. We defined a personalized search algorithm that combined structural and dynamical information to find resections that maximally decreased seizure propagation for a given resection size. The optimal resection for each patient was defined as the smallest resection leading to at least a 90% reduction in seizure propagation. The individualized model reproduced the basic aspects of seizure propagation for 9 out of 10 patients when using the resection area as the origin of epidemic spreading, and for 10 out of 10 patients with an alternative definition of the seed region. We found that, for 7 patients, the optimal resection was smaller than the resection area, and for 4 patients we also found that a resection smaller than the resection area could lead to a 100% decrease in propagation. Moreover, for two cases these alternative resections included nodes outside the resection area. Epidemic spreading models fitted with patient specific data can capture the fundamental aspects of clinically observed seizure propagation, and can be used to test virtual resections in silico. Combined with optimization algorithms, smaller or alternative resection strategies, that are individually targeted for each patient, can be determined with the ultimate goal to improve surgery outcome. MEG-based networks can provide a good approximation of structural connectivity for computational models of seizure propagation, and facilitate their clinical use.

Optimization of epilepsy surgery through virtual resections on individual structural brain networks.

The success of epilepsy surgery in patients with refractory epilepsy depends upon correct identification of the epileptogenic zone (EZ) and an optimal choice of the resection area. In this study we developed individualized computational models based upon structural brain networks to explore the impact of different virtual resections on the propagation of seizures. The propagation of seizures was modelled as an epidemic process [susceptible-infected-recovered (SIR) model] on individual structural networks derived from presurgical diffusion tensor imaging in 19 patients. The candidate connections for the virtual resection were all connections from the clinically hypothesized EZ, from which the seizures were modelled to start, to other brain areas. As a computationally feasible surrogate for the SIR model, we also removed the connections that maximally reduced the eigenvector centrality (EC) (large values indicate network hubs) of the hypothesized EZ, with a large reduction meaning a large effect. The optimal combination of connections to be removed for a maximal effect were found using simulated annealing. For comparison, the same number of connections were removed randomly, or based on measures that quantify the importance of a node or connection within the network. We found that 90% of the effect (defined as reduction of EC of the hypothesized EZ) could already be obtained by removing substantially less than 90% of the connections. Thus, a smaller, optimized, virtual resection achieved almost the same effect as the actual surgery yet at a considerably smaller cost, sparing on average 27.49% (standard deviation: 4.65%) of the connections. Furthermore, the maximally effective connections linked the hypothesized EZ to hubs. Finally, the optimized resection was equally or more effective than removal based on structural network characteristics both regarding reducing the EC of the hypothesized EZ and seizure spreading. The approach of using reduced EC as a surrogate for simulating seizure propagation can suggest more restrictive resection strategies, whilst obtaining an almost optimal effect on reducing seizure propagation, by taking into account the unique topology of individual structural brain networks of patients.

MEG detection of high frequency oscillations and intracranial-EEG validation in pediatric epilepsy surgery.

OBJECTIVE: To assess the feasibility of automatically detecting high frequency oscillations (HFOs) in magnetoencephalography (MEG) recordings in a group of ten paediatric epilepsy surgery patients who had undergone intracranial electroencephalography (iEEG). METHODS: A beamforming source-analysis method was used to construct virtual sensors and an automatic algorithm was applied to detect HFOs (80-250 Hz). We evaluated the concordance of MEG findings with the sources of iEEG HFOs, the clinically defined seizure onset zone (SOZ), the location of resected brain structures, and with post-operative outcome. RESULTS: In 8/9 patients there was good concordance between the sources of MEG HFOs and iEEG HFOs and the SOZ. Significantly more HFOs were detected in iEEG relative to MEG t(71) = 2.85, p < .05. There was good concordance between sources of MEG HFOs and the resected area in patients with good and poor outcome, however HFOs were also detected outside of the resected area in patients with poor outcome. CONCLUSION: Our findings demonstrate the feasibility of automatically detecting HFOs non-invasively in MEG recordings in paediatric patients, and confirm compatibility of results with invasive recordings. SIGNIFICANCE: This approach provides support for the non-invasive detection of HFOs to aid surgical planning and potentially reduce the need for invasive monitoring, which is pertinent to paediatric patients.

Understanding Global Brain Network Alterations in Glioma Patients.

Introduction: Glioma patients show increased global brain network clustering related to poorer cognition and epilepsy. However, it is unclear whether this increase is spatially widespread, localized in the (peri)tumor region only, or decreases with distance from the tumor. Materials and Methods: Weighted global and local brain network clustering was determined in 71 glioma patients and 53 controls by using magnetoencephalography. Tumor clustering was determined by averaging local clustering of regions overlapping with the tumor, and vice versa for non-tumor regions. Euclidean distance was determined from the tumor centroid to the centroids of other regions. Results: Patients showed higher global clustering compared with controls. Clustering of tumor and non-tumor regions did not differ, and local clustering was not associated with distance from the tumor. Post hoc analyses revealed that in the patient group, tumors were located more often in regions with higher clustering in controls, but it seemed that tumors of patients with high global clustering were located more often in regions with lower clustering in controls. Conclusions: Glioma patients show non-local network disturbances. Tumors of patients with high global clustering may have a preferred localization, namely regions with lower clustering in controls, suggesting that tumor localization relates to the extent of network disruption. Impact statement This work uses the innovative framework of network neuroscience to investigate functional connectivity patterns associated with brain tumors. Glioma (primary brain tumor) patients experience cognitive deficits and epileptic seizures, which have been related to brain network alterations. This study shows that glioma patients have a spatially widespread increase in global network clustering, which cannot be attributed to local effects of the tumor. Moreover, tumors occur more often in brain regions with higher network clustering in controls. This study emphasizes the global character of network alterations in glioma patients and suggests that preferred tumor locations are characterized by particular network profiles.

Mapping language networks and their association with verbal abilities in paediatric epilepsy using MEG and graph analysis.

Recent theoretical models of language have emphasised the importance of integration within distributed networks during language processing. This is particularly relevant to young patients with epilepsy, as the topology of the functional network and its dynamics may be altered by the disease, resulting in reorganisation of functional language networks. Thus, understanding connectivity within the language network in patients with epilepsy could provide valuable insights into healthy and pathological brain function, particularly when combined with clinical correlates. The objective of this study was to investigate interactions within the language network in a paediatric population of epilepsy patients using measures of MEG phase synchronisation and graph-theoretical analysis, and to examine their association with language abilities. Task dependent increases in connectivity were observed in fronto-temporal networks during verb generation across a group of 22 paediatric patients (9 males and 13 females; mean age 14 years). Differences in network connectivity were observed between patients with typical and atypical language representation and between patients with good and poor language abilities. In addition, node centrality in left frontal and temporal regions was significantly associated with language abilities, where patients with good language abilities had significantly higher node centrality within inferior frontal and superior temporal regions of the left hemisphere, compared to patients with poor language abilities. Our study is one of the first to apply task-based measures of MEG network synchronisation in paediatric epilepsy, and we propose that these measures of functional connectivity and node centrality could be used as tools to identify critical regions of the language network prior to epilepsy surgery.

Postoperative oscillatory brain activity as an add-on prognostic marker in diffuse glioma.

INTRODUCTION: Progression-free survival (PFS) in glioma patients varies widely, even when stratifying for known predictors (i.e. age, molecular tumor subtype, presence of epilepsy, tumor grade and Karnofsky performance status). Neuronal activity has been shown to accelerate tumor growth in an animal model, suggesting that brain activity may be valuable as a PFS predictor. We investigated whether postoperative oscillatory brain activity, assessed by resting-state magnetoencephalography is of additional value when predicting PFS in glioma patients. METHODS: We included 27 patients with grade II-IV gliomas. Each patient's oscillatory brain activity was estimated by calculating broadband power (0.5-48 Hz) in 56 epochs of 3.27 s and averaged over 78 cortical regions of the Automated Anatomical Labeling atlas. Cox proportional hazard analysis was performed to test the predictive value of broadband power towards PFS, adjusting for known predictors by backward elimination. RESULTS: Higher broadband power predicted shorter PFS after adjusting for known prognostic factors (n = 27; HR 2.56 (95% confidence interval (CI) 1.15-5.70); p = 0.022). Post-hoc univariate analysis showed that higher broadband power also predicted shorter overall survival (OS; n = 38; HR 1.88 (95% CI 1.00-3.54); p = 0.038). CONCLUSIONS: Our findings suggest that postoperative broadband power is of additional value in predicting PFS beyond already known predictors.

Simultaneous MEG and EEG to detect ripples in people with focal epilepsy.

OBJECTIVE: We studied ripples (80-250 Hz) simultaneously recorded in electroencephalography (EEG) and magnetoencephalography (MEG) to evaluate the differences. METHODS: Simultaneous EEG and MEG were recorded in 30 patients with drug resistant focal epilepsy. Ripples were automatically detected and visually checked in virtual channels throughout the cortex. The number and location of ripples in EEG and MEG were compared to each other and to a region of interest (ROI) defined by clinically available information. RESULTS: Eleven patients showed ripples in both MEG and EEG, 11 only in EEG and one only in MEG. Twenty-four percent of the ripples occurred simultaneously in EEG and MEG, 71% only in EEG, and 5% only in MEG. Three patients without spikes in EEG showed EEG ripples. Ripple localization was concordant with the ROI in 80% of patients with MEG ripples, as opposed to 62% full or partial concordance for EEG ripples. With the optimal threshold for localizing the ROI, sensitivity and specificity were more than 80%. CONCLUSIONS: Ripples in MEG are less frequent but more specific and sensitive for the region of interest than ripples in EEG. Ripples in EEG can exist without spikes in the EEG. SIGNIFICANCE: Ripples in MEG and EEG provide complementary information.

Understanding cognitive functioning in glioma patients: The relevance of IDH-mutation status and functional connectivity.

INTRODUCTION: Cognitive deficits occur frequently in diffuse glioma patients, but are limitedly understood. An important marker for survival in these patients is isocitrate dehydrogenase (IDH) mutation (IDH-mut). Patients with IDH-mut glioma have a better prognosis but more often suffer from epilepsy than patients with IDH-wildtype (IDH-wt) glioma, who are generally older and more often have cognitive deficits. We investigated whether global brain functional connectivity differs between patients with IDH-mut and IDH-wt glioma, and whether this measure reflects variations in cognitive functioning in these subpopulations beyond the associated differences in age and presence of epilepsy. METHODS: We recorded magnetoencephalography and tested cognitive functioning in 54 diffuse glioma patients (31 IDH-mut, 23 IDH-wt). Global functional connectivity between 78 atlas regions spanning the entire cortex was calculated in two frequency bands (theta and alpha). Group differences in global functional connectivity were tested, as was their association with cognitive functioning, controlling for age, education, and presence of epilepsy. RESULTS: Patients with IDH-wt glioma had lower functional connectivity in the alpha band than patients with IDH-mut glioma (p = 0.040, corrected for age and presence of epilepsy). Lower alpha band functional connectivity was associated with poorer cognitive performance (p < 0.034), corrected for age, education, and presence of epilepsy. CONCLUSION: Global functional connectivity is lower in patients with IDH-wt diffuse glioma compared to patients with IDH-mut diffuse glioma. Moreover, having lower functional alpha connectivity relates to poorer cognitive performance in patients with diffuse glioma, regardless of age, education, and presence of epilepsy.

Cognitive functioning and functional brain networks in postoperative WHO grade I meningioma patients.

INTRODUCTION: Meningioma patients often have subtle cognitive deficits that might be attributed to the tumor itself, to surgical treatment, or to the occurrence of seizures and their treatment. Magnetoencephalography (MEG) analysis of resting-state functional networks (RSNs) could help to understand the neurophysiological basis of cognitive impairment in these patients. We explored the correlation between RSN functional connectivity and topology of functional networks on the one hand, and cognition on the other hand in WHO grade I meningioma patients. METHODS: Twenty adult WHO grade I meningioma patients who had undergone tumor resection, as well as 20 healthy matched controls, were included. Neuropsychological assessment was done through a standardized test battery. MEG data were recorded, and projected to the anatomical space of the Automated Anatomical Labeling atlas. Functional connectivity (PLI), within the default mode network (DMN) and the bilateral frontoparietal networks were correlated to cognitive performance. Minimum spanning tree (MST) characteristics were correlated with cognitive functioning. RESULTS: Compared to healthy controls, meningioma patients had lower working memory capacity (p = 0.037). Within the patient group, lower working memory performance was associated with lower DMN connectivity and a lower maximum MST degree in the theta band (resp. p = 0.044 and p = 0.003). CONCLUSIONS: This study shows that cognitive functioning is correlated with functional connectivity in the default mode network and hub-pathology in WHO grade I meningioma patients. Future longitudinal studies are needed to corroborate these findings and to further investigate the pathophysiology of cognitive deficits and possible changes in functional brain networks in meningioma patients.

Oscillatory brain activity associates with neuroligin-3 expression and predicts progression free survival in patients with diffuse glioma.

INTRODUCTION: Diffuse gliomas have local and global effects on neurophysiological brain functioning, which are often seen as 'passive' consequences of the tumor. However, seminal preclinical work has shown a prominent role for neuronal activity in glioma growth: mediated by neuroligin-3 (NLGN3), increased neuronal activity causes faster glioma growth. It is unclear whether the same holds true in patients. Here, we investigate whether lower levels of oscillatory brain activity relate to lower NLGN3 expression and predict longer progression free survival (PFS) in diffuse glioma patients. METHODS: Twenty-four newly diagnosed patients with diffuse glioma underwent magnetoencephalography and subsequent tumor resection. Oscillatory brain activity was approximated by calculating broadband power (0.5-48 Hz) of the magnetoencephalography. NLGN3 expression in glioma tissue was semi-quantitatively assessed by immunohistochemistry. Peritumor and global oscillatory brain activity was then compared between different levels of NLGN3 expression with Kruskal-Wallis tests. Cox proportional hazards analyses were performed to estimate the predictive value of oscillatory brain activity for PFS. RESULTS: Patients with low expression of NLGN3 had lower levels of global oscillatory brain activity than patients with higher NLGN3 expression (P < 0.001). Moreover, lower peritumor (hazard ratio 2.17, P = 0.008) and global oscillatory brain activity (hazard ratio 2.10, P = 0.008) predicted longer PFS. CONCLUSIONS: Lower levels of peritumor and global oscillatory brain activity are related to lower NLGN3 expression and longer PFS, corroborating preclinical research. This study highlights the important interplay between macroscopically measured brain activity and glioma progression, and may lead to new therapeutic interventions in diffuse glioma patients.

Localization of the Epileptogenic Zone Using Interictal MEG and Machine Learning in a Large Cohort of Drug-Resistant Epilepsy Patients.

Objective: Epilepsy surgery results in seizure freedom in the majority of drug-resistant patients. To improve surgery outcome we studied whether MEG metrics combined with machine learning can improve localization of the epileptogenic zone, thereby enhancing the chance of seizure freedom. Methods: Presurgical interictal MEG recordings of 94 patients (64 seizure-free >1y post-surgery) were analyzed to extract four metrics in source space: delta power, low-to-high-frequency power ratio, functional connectivity (phase lag index), and minimum spanning tree betweenness centrality. At the group level, we estimated the overlap of the resection area with the five highest values for each metric and determined whether this overlap differed between surgery outcomes. At the individual level, those metrics were used in machine learning classifiers (linear support vector machine (SVM) and random forest) to distinguish between resection and non-resection areas and between surgery outcome groups. Results: The highest values, for all metrics, overlapped with the resection area in more than half of the patients, but the overlap did not differ between surgery outcome groups. The classifiers distinguished the resection areas from non-resection areas with 59.94% accuracy (95% confidence interval: 59.67-60.22%) for SVM and 60.34% (59.98-60.71%) for random forest, but could not differentiate seizure-free from not seizure-free patients [43.77% accuracy (42.08-45.45%) for SVM and 49.03% (47.25-50.82%) for random forest]. Significance: All four metrics localized the resection area but did not distinguish between surgery outcome groups, demonstrating that metrics derived from interictal MEG correspond to expert consensus based on several presurgical evaluation modalities, but do not yet localize the epileptogenic zone. Metrics should be improved such that they correspond to the resection area in seizure-free patients but not in patients with persistent seizures. It is important to test such localization strategies at an individual level, for example by using machine learning or individualized models, since surgery is individually tailored.

Virtual localization of the seizure onset zone: Using non-invasive MEG virtual electrodes at stereo-EEG electrode locations in refractory epilepsy patients.

In some patients with medically refractory epilepsy, EEG with intracerebrally placed electrodes (stereo-electroencephalography, SEEG) is needed to locate the seizure onset zone (SOZ) for successful epilepsy surgery. SEEG has limitations and entails risk of complications because of its invasive character. Non-invasive magnetoencephalography virtual electrodes (MEG-VEs) may overcome SEEG limitations and optimize electrode placement making SOZ localization safer. Our purpose was to assess whether interictal activity measured by MEG-VEs and SEEG at identical anatomical locations were comparable, and whether MEG-VEs activity properties could determine the location of a later resected brain area (RA) as an approximation of the SOZ. We analyzed data from nine patients who underwent MEG and SEEG evaluation, and surgery for medically refractory epilepsy. MEG activity was retrospectively reconstructed using beamforming to obtain VEs at the anatomical locations corresponding to those of SEEG electrodes. Spectral, functional connectivity and functional network properties were obtained for both, MEG-VEs and SEEG time series, and their correlation and reliability were established. Based on these properties, the approximation of the SOZ was characterized by the differences between RA and non-RA (NRA). We found significant positive correlation and reliability between MEG-VEs and SEEG spectral measures (particularly in delta [0.5-4 Hz], alpha2 [10-13 Hz], and beta [13-30 Hz] bands) and broadband functional connectivity. Both modalities showed significantly slower activity and a tendency towards increased broadband functional connectivity in the RA compared to the NRA. Our findings show that spectral and functional connectivity properties of non-invasively obtained MEG-VEs match those of invasive SEEG recordings, and can characterize the SOZ. This suggests that MEG-VEs might be used for optimal SEEG planning and fewer depth electrode implantations, making the localization of the SOZ safer and more successful.

Automatic detection and visualisation of MEG ripple oscillations in epilepsy.

High frequency oscillations (HFOs, 80-500 Hz) in invasive EEG are a biomarker for the epileptic focus. Ripples (80-250 Hz) have also been identified in non-invasive MEG, yet detection is impeded by noise, their low occurrence rates, and the workload of visual analysis. We propose a method that identifies ripples in MEG through noise reduction, beamforming and automatic detection with minimal user effort. We analysed 15 min of presurgical resting-state interictal MEG data of 25 patients with epilepsy. The MEG signal-to-noise was improved by using a cross-validation signal space separation method, and by calculating ~ 2400 beamformer-based virtual sensors in the grey matter. Ripples in these sensors were automatically detected by an algorithm optimized for MEG. A small subset of the identified ripples was visually checked. Ripple locations were compared with MEG spike dipole locations and the resection area if available. Running the automatic detection algorithm resulted in on average 905 ripples per patient, of which on average 148 ripples were visually reviewed. Reviewing took approximately 5 min per patient, and identified ripples in 16 out of 25 patients. In 14 patients the ripple locations showed good or moderate concordance with the MEG spikes. For six out of eight patients who had surgery, the ripple locations showed concordance with the resection area: 4/5 with good outcome and 2/3 with poor outcome. Automatic ripple detection in beamformer-based virtual sensors is a feasible non-invasive tool for the identification of ripples in MEG. Our method requires minimal user effort and is easily applicable in a clinical setting.

Dynamic hub load predicts cognitive decline after resective neurosurgery.

Resective neurosurgery carries the risk of postoperative cognitive deterioration. The concept of 'hub (over)load', caused by (over)use of the most important brain regions, has been theoretically postulated in relation to symptomatology and neurological disease course, but lacks experimental confirmation. We investigated functional hub load and postsurgical cognitive deterioration in patients undergoing lesion resection. Patients (n = 28) underwent resting-state magnetoencephalography and neuropsychological assessments preoperatively and 1-year after lesion resection. We calculated stationary hub load score (SHub) indicating to what extent brain regions linked different subsystems; high SHub indicates larger processing pressure on hub regions. Dynamic hub load score (DHub) assessed its variability over time; low values, particularly in combination with high SHub values, indicate increased load, because of consistently high usage of hub regions. Hypothetically, increased SHub and decreased DHub relate to hub overload and thus poorer/deteriorating cognition. Between time points, deteriorating verbal memory performance correlated with decreasing upper alpha DHub. Moreover, preoperatively low DHub values accurately predicted declining verbal memory performance. In summary, dynamic hub load relates to cognitive functioning in patients undergoing lesion resection: postoperative cognitive decline can be tracked and even predicted using dynamic hub load, suggesting it may be used as a prognostic marker for tailored treatment planning.

Identifying the epileptogenic zone in interictal resting-state MEG source-space networks.

OBJECTIVE: In one third of patients, seizures remain after epilepsy surgery, meaning that improved preoperative evaluation methods are needed to identify the epileptogenic zone. A potential framework for such a method is network theory, as it can be applied to noninvasive recordings, even in the absence of epileptiform activity. Our aim was to identify the epileptogenic zone on the basis of hub status of local brain areas in interictal magnetoencephalography (MEG) networks. METHODS: Preoperative eyes-closed resting-state MEG recordings were retrospectively analyzed in 22 patients with refractory epilepsy, of whom 14 were seizure-free 1 year after surgery. Beamformer-based time series were reconstructed for 90 cortical and subcortical automated anatomic labeling (AAL) regions of interest (ROIs). Broadband functional connectivity was estimated using the phase lag index in artifact-free epochs without interictal epileptiform abnormalities. A minimum spanning tree was generated to represent the network, and the hub status of each ROI was calculated using betweenness centrality, which indicates the centrality of a node in a network. The correspondence of resection cavity to hub values was evaluated on four levels: resection cavity, lobar, hemisphere, and temporal versus extratemporal areas. RESULTS: Hubs were localized within the resection cavity in 8 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (57% sensitivity, 100% specificity, 73% accuracy). Hubs were localized in the lobe of resection in 9 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (64% sensitivity, 100% specificity, 77% accuracy). For the other two levels, the true negatives are unknown; hence, only sensitivity could be determined: hubs coincided with both the resection hemisphere and the resection location (temporal versus extratemporal) in 11 of 14 seizure-free patients (79% sensitivity). SIGNIFICANCE: Identifying hubs noninvasively before surgery is a valuable approach with the potential of indicating the epileptogenic zone in patients without interictal abnormalities.