The Value of Seizure Semiology in Epilepsy Surgery: Epileptogenic-Zone Localisation in Presurgical Patients using Machine Learning and Semiology Visualisation Tool

Background Eight million individuals have focal drug resistant epilepsy worldwide. If their epileptogenic focus is identified and resected, they may become seizure-free and experience significant improvements in quality of life. However, seizure-freedom occurs in less than half of surgical resections. Seizure semiology - the signs and symptoms during a seizure - along with brain imaging and electroencephalography (EEG) are amongst the mainstays of seizure localisation. Although there have been advances in algorithmic identification of abnormalities on EEG and imaging, semiological analysis has remained more subjective. The primary objective of this research was to investigate the localising value of clinician-identified semiology, and secondarily to improve personalised prognostication for epilepsy surgery. Methods I data mined retrospective hospital records to link semiology to outcomes. I trained machine learning models to predict temporal lobe epilepsy (TLE) and determine the value of semiology compared to a benchmark of hippocampal sclerosis (HS). Due to the hospital dataset being relatively small, we also collected data from a systematic review of the literature to curate an open-access Semio2Brain database. We built the Semiology-to-Brain Visualisation Tool (SVT) on this database and retrospectively validated SVT in two separate groups of randomly selected patients and individuals with frontal lobe epilepsy. Separately, a systematic review of multimodal prognostic features of epilepsy surgery was undertaken. The concept of a semiological connectome was devised and compared to structural connectivity to investigate probabilistic propagation and semiology generation. Results Although a (non-chronological) list of patients’ semiologies did not improve localisation beyond the initial semiology, the list of semiology added value when combined with an imaging feature. The absolute added value of semiology in a support vector classifier in diagnosing TLE, compared to HS, was 25%. Semiology was however unable to predict postsurgical outcomes. To help future prognostic models, a list of essential multimodal prognostic features for epilepsy surgery were extracted from meta-analyses and a structural causal model proposed. Semio2Brain consists of over 13000 semiological datapoints from 4643 patients across 309 studies and uniquely enabled a Bayesian approach to localisation to mitigate TLE publication bias. SVT performed well in a retrospective validation, matching the best expert clinician’s localisation scores and exceeding them for lateralisation, and showed modest value in localisation in individuals with frontal lobe epilepsy (FLE). There was a significant correlation between the number of connecting fibres between brain regions and the seizure semiologies that can arise from these regions. Conclusions Semiology is valuable in localisation, but multimodal concordance is more valuable and highly prognostic. SVT could be suitable for use in multimodal models to predict the seizure focus

Apport de l’IRM structurelle multimodale dans la chirurgie d’épilepsie : le cas de l’épilepsie insulaire

L’épilepsie insulaire (ÉI) est une forme rare d’épilepsie focale qui, en raison des défis liés à son diagnostic, est difficilement cernable. De plus, la prise en charge des patients avec ÉI s’avère complexifiée par le fait que cette pathologie est fréquemment résistante aux médicaments anti-crises. Pour ces cas médico-réfractaires, la chirurgie insulaire est une option viable. Cela dit, les patients subissant une telle intervention développent fréquemment des déficits neurologiques postopératoires; heureusement, la grande majorité de ceux-ci récupèrent complètement et rapidement. Or, le mécanisme sous-tendant ce singulier rétablissement fonctionnel demeure à ce jour mal compris. Deux modalités modernes d’IRM structurelle, soit l’analyse d’épaisseur corticale et la tractographie, ont permis, dans les dernières années, de décrire les altérations architecturales caractéristiques et potentiellement diagnostiques de divers types d’épilepsie ainsi que de caractériser les remodelages plastiques qui suivent la chirurgie de l’épilepsie extra-insulaire. Cependant, à ce jour, aucune étude ne s’est encore penchée sur le cas de l’ÉI. De ce fait, les études qui constituent cette thèse exploitent l’IRM structurelle afin, d’une part, de dépeindre les altérations d’épaisseur du cortex et de connectivité de matière blanche associées à l’ÉI et, d’autre part, de définir les réarrangements de connectivité subséquents à la chirurgie insulaire pour contrôle épileptique. Les deux premières études de cette thèse ont révélé que l’ÉI était associée à un pattern majoritairement ipsilatéral d’atrophie corticale et d’hyperconnectivité impliquant principalement des sous-régions insulaires et des régions connectées à l’insula. De manière intéressante, la topologie de ces changements correspondait, au moins en partie, à celle du réseau épileptique de l’ÉI. Ensuite, la troisième étude visait à décrire, par le biais d’une méta-analyse, l’histoire naturelle postopératoire des patients subissant une chirurgie pour ÉI. Cette analyse a, entre autres, confirmé que cette chirurgie était efficace (66.7% de disparition des crises) et qu’elle était fréquemment accompagnée de complications neurologiques (42.5%) qui, dans la plupart des cas, étaient transitoires (78.7% des complications) et récupéraient entièrement dans les trois mois postopératoires (91.6% des complications transitoires). Finalement, la quatrième étude a révélé que la chirurgie pour ÉI était suivie d’altérations de connectivité diffuses et bilatérales. Notamment, les connexions présentant une augmentation de connectivité concernaient particulièrement des régions localisées soit près de la cavité chirurgicale ou dans l’hémisphère controlatéral à l’intervention. De plus, la majorité de ces renforcements structurels se sont produits dans les six premiers mois suivant la chirurgie, un délai comparable à celui durant lequel la majeure partie de la récupération fonctionnelle postopératoire a été observée dans notre méta-analyse. En somme, nos résultats suggèrent que les altérations morphologiques en lien avec l’ÉI peuvent correspondre à son réseau épileptique sous-jacent. La topologie de ces changements pourrait constituer un biomarqueur structurel diagnostique qui aiderait à la reconnaissance de l’ÉI et, concomitamment, favoriserait possiblement un traitement chirurgical plus adapté et plus efficace. De plus, les augmentations de connectivité postopératoires pourraient correspondre à des réponses neuroplastiques permettant de prendre en charge les fonctions altérées par la chirurgie. Nos constats ont ainsi contribué à la caractérisation des mécanismes étayant la singulière récupération fonctionnelle accompagnant la chirurgie pour ÉI. À plus grande échelle, nos travaux offrent un aperçu du potentiel de l’IRM structurelle à assister au diagnostic de l’épilepsie focale ainsi qu’à participer à la description des changements plastiques subséquents à une résection neurochirurgicale.Insular epilepsy (IE) is a rare type of focal epilepsy that is difficult to diagnose. In addition to the challenging nature of IE detection, management of patients with this condition is complicated by the tendency of insular seizures to be resistant to anti-seizure medications. For such medically refractory cases, insular surgery constitutes a viable and long-lasting therapeutic option. That said, patients who undergo an insular resection for seizure control frequently develop postoperative neurological deficits; fortunately, most of these impairments recover fully and rapidly. While this favorable postoperative course contributes to improving the outcome of IE surgery, the mechanism underlying the functional recovery remains unknown. Two contemporary structural MRI modalities, namely cortical thickness analysis and tractography, have recently been used to describe characteristic structural alterations of focal epilepsies and to elucidate the postoperative plastic remodeling associated with surgery for extra-insular epilepsy. While these analyses added to our understanding of several localization-related epilepsies, none specifically studied IE. In this thesis, we exploit structural MRI techniques to, first, depict the alterations of cortical thickness and white matter connectivity in IE and, second, define the progressive rearrangements that follow insular surgery for epilepsy. The first two studies of the current thesis showed that IE is associated with a primarily ipsilateral pattern of cortical thinning and hyperconnectivity that mainly involves insular subregions and insula-connected regions. Interestingly, the topology of these changes corresponded, at least in part, to the epileptic network of IE. Furthermore, the third study aimed to describe, via a meta-analysis, the postoperative outcome of patients undergoing surgery for IE. Among other findings, the analysis revealed that insular surgery was effective (66.7% seizure freedom rate) but was associated with a significant risk of neurological complications (42.5%) which, in most cases, were transient (78.7% of all complications) and recovered fully within three months (91.6% of transient complications). Finally, the fourth study showed that surgery for IE was followed by a diffuse pattern of bilateral structural connectivity changes. Notably, connections exhibiting an increase in connectivity were specifically located near the surgical cavity and in the contralateral healthy hemisphere. In addition, the majority of the structural strengthening occurred in the first six months following surgery, a time course that is consistent with the short delay during which most of the postoperative functional recovery was observed in our meta-analysis. Our results suggest that the morphological alterations in IE may reflect its underlying epileptic network. The topology of these changes may constitute a structural biomarker that could help diagnose IE more readily and, concomitantly, potentially enable a more targeted and more effective surgical treatment. Moreover, the postoperative increases in connectivity may be compatible with compensatory neuroplastic responses, a process that arose to recoup the functions of the injured insular cortex. Our findings have therefore contributed to the characterization of the driving process that supports the striking functional recovery seen following surgery for IE. On a larger scale, our work provides insights into the potential of structural MRI to assist in the diagnosis of focal epilepsy and to describe plastic changes following neurosurgical resections

Methods for noninvasive localization of focal epileptic activity with magnetoencephalography

Magnetoencephalography (MEG) is a noninvasive brain signal acquisition technique that provides excellent temporal resolution and a whole-head coverage allowing the spatial mapping of sources. These characteristics make MEG an appropriate technique to localize the epileptogenic zone (EZ) in the preoperative evaluation of refractory epilepsy. Presurgical evaluation with MEG can guide the placement of intracranial EEG (iEEG), the current gold standard in the clinical practice, and even supply sufficient information for a surgical intervention without invasive recordings, reducing invasiveness, discomfort, and cost of the presurgical epilepsy diagnosis. However, MEG signals have low signal-to-noise ratio compared with iEEG and can sometimes be affected by noise that masks or distorts the brain activity. This may prevent the detection of interictal epileptiform discharges (IEDs) and high-frequency oscillations (HFOs), two important biomarkers used in the preoperative evaluation of epilepsy. In this thesis, the reduction of two kinds of interference is aimed to improve the signal-to-noise ratio of MEG signals: metallic artifacts mask the activity of IEDs; and the high-frequency noise, that masks HFO activity. Considering the large number of MEG channels and the long duration of the recordings, reducing noise and marking events manually is a time-consuming task. The algorithms presented in this thesis provide automatic solutions aimed at the reduction of interferences and the detection of HFOs. Firstly, a novel automatic BSS-based algorithm to reduce metallic interference is presented and validated using simulated and real MEG signals. Three methods are tested: AMUSE, a second-order BSS technique; and INFOMAX and FastICA, based on high-order statistics. The automatic detection algorithm exploits the known characteristics of metallic-related interferences. Results indicate that AMUSE performes better when recovering brain activity and allows an effective removal of artifactual components.Secondly, the influence of metallic artifact filtering using the developed algorithm is evaluated in the source localization of IEDs in patients with refractory focal epilepsy. A comparison between the resulting positions of equivalent current dipoles (ECDs) produced by IEDs is performed: without removing metallic interference, rejecting only channels with large metallic artifacts, and after BSS-based reduction. The results show that a significant reduction on dispersion is achieved using the BSS-based reduction procedure, yielding feasible locations of ECDs in contrast to the other approaches. Finally, an algorithm for the automatic detection of epileptic ripples in MEG using beamformer-based virtual sensors is developed. The automatic detection of ripples is performed using a two-stage approach. In the first step, beamforming is applied to the whole head to determine a region of interest. In the second step, the automatic detection of ripples is performed using the time-frequency characteristics of these oscillations. The performance of the algorithm is evaluated using simultaneous intracranial EEG recordings as gold standard.The novel approaches developed in this thesis allow an improved noninvasive detection and localization of interictal epileptic biomarkers, which can help in the delimitation of the epileptogenic zone and guide the placement of intracranial electrodes, or even to determine these areas without additional invasive recordings. As a consequence of this improved detection, and given that interictal biomarkers are much more frequent and easy to record than ictal episodes, the presurgical evaluation process can be more comfortable for the patient and in a more economic way.La magnetoencefalografía (MEG) es una técnica no invasiva de adquisición de señales cerebrales que proporciona una excelente resolución temporal y una cobertura total de la cabeza, permitiendo el mapeo espacial de las fuentes cerebrales. Estas características hacen del MEG una técnica apropiada para localizar la zona epileptogénica (EZ) en la evaluación preoperatoria de la epilepsia refractaria. La evaluación prequirúrgica con MEG puede orientar la colocación del EEG intracraneal (iEEG), el actual modelo de referencia en la práctica clínica, e incluso suministrar información suficiente para una intervención quirúrgica sin registros invasivos; reduciendo la invasividad, la incomodidad y el costo del diagnóstico de la epilepsia prequirúrgica. Sin embargo, las señales MEG tienen baja relación señal ruido en comparación con el iEEG pudiendo imposibilitar la detección de descargas epileptiformes interictales (IEDs) y oscilaciones de alta frecuencia (HFOs), dos importantes biomarcadores utilizados en la evaluación preoperatoria de la epilepsia.En esta tesis, la reducción de dos tipos de interferencia está dirigida a mejorar la relación señal-ruido de la señal MEG: los artefactos metálicos que enmascaran la actividad de las IEDs; y el ruido de alta frecuencia, que enmascara la actividad de las HFOs. Debido al gran número de canales MEG y la larga duración de los registros, tanto reducir el ruido como seleccionar los biomarcadores manualmente es una tarea que consume mucho tiempo. Los algoritmos presentados en esta tesis aportan soluciones automáticas dirigidas a la reducción de interferencias y la detección de HFOs. En primer lugar, se presenta y valida un nuevo algoritmo automático basado en BSS para reducir interferencias metálicas mediante señales simuladas y reales. Se prueban tres métodos: AMUSE, una técnica BSS de segundo orden; y INFOMAX y FastICA, basados en estadísticos de orden superior. El algoritmo de detección automático utiliza las características conocidas de la señal producida por la interferencia metálica. Los resultados indican que AMUSE recupera mejor la actividad cerebral y permite una eliminación efectiva de componentes artefactuales.Posteriormente, se evalúa la influencia del filtrado de artefactos metálicos en la localización de IEDs en pacientes con epilepsia focal refractaria. Se realiza una comparación entre las posiciones resultantes de dipolos de corriente equivalentes (ECDs) producidos por IEDs: sin eliminar interferencias metálicas, rechazando solamente canales con elevados artefactos metálicos y, por último, después de una reducción utilizando el algoritmo BSS desarrollado. Los resultados muestran que se logra una reducción significativa en la dispersión utilizando el procedimiento de reducción basado en BSS, lo que produce ubicaciones factibles de los dipolos en contraste con los otros enfoques.En segundo lugar, se desarrolla un algoritmo para la detección automática ripples epilépticos en MEG utilizando sensores virtuales basados en la técnica de beamformer. La detección de ripples se realiza mediante un enfoque en dos etapas. Primero, se determina el área de interés usando beamformer. Posteriormente, se realiza la detección automática de ripples utilizando las características en tiempo-frecuencia. El rendimiento del algoritmo se evalúa utilizando registros iEEG simultáneos.Los nuevos enfoques desarrollados en esta tesis permiten una detección no invasiva mejor de los biomarcadores interictales, que pueden ayudar a delimitar la zona epileptogénica y guiar la colocación de electrodos intracraneales, o incluso determinar estas áreas sin este tipo de registros. Como consecuencia de esta mejora en la detección, y dado que los biomarcadores interictales son mucho más frecuentes y fáciles de registrar que los episodios ictales, la evaluación prequirúrgica puede ser más cómoda y menos costosa para el paciente.Postprint (published version

Quantitative Methods For Guiding Epilepsy Surgery From Intracranial Eeg

Despite advances in intracranial EEG (iEEG) technique, technology and neuroimaging, patients today are no more likely to achieve seizure freedom after epilepsy surgery than they were 20 years ago. These poor outcomes are in part due to the difficulty and subjectivity associated with interpreting iEEG recordings, and have led to widespread interest in developing quantitative methods to localize the epileptogenic zone. Approaches to computational iEEG analysis vary widely, spanning studies of both seizures and interictal periods, and encompassing a range of techniques including electrographic signal analysis and graph theory. However, many current methods often fail to generalize to new data and are sensitive to differences in pathology and electrode placement. Indeed, none have completed prospective clinical trials. In this dissertation, I develop and validate tools for guiding epilepsy surgery through the quantitative analysis of intracranial EEG. Specifically, I leverage methods from graph theory for mapping network synchronizability to predict surgical outcome from ictal recordings, and also investigate the effects of sampling bias on network models. Finally, I construct a normative intracranial EEG atlas as a framework for objectively identifying patterns of abnormal neural activity and connectivity. Overall, the methods and results of this dissertation support the implementation of quantitative iEEG analysis in epilepsy surgical evaluation

Quantitative Multimodal Mapping Of Seizure Networks In Drug-Resistant Epilepsy

Over 15 million people worldwide suffer from localization-related drug-resistant epilepsy. These patients are candidates for targeted surgical therapies such as surgical resection, laser thermal ablation, and neurostimulation. While seizure localization is needed prior to surgical intervention, this process is challenging, invasive, and often inconclusive. In this work, I aim to exploit the power of multimodal high-resolution imaging and intracranial electroencephalography (iEEG) data to map seizure networks in drug-resistant epilepsy patients, with a focus on minimizing invasiveness. Given compelling evidence that epilepsy is a disease of distorted brain networks as opposed to well-defined focal lesions, I employ a graph-theoretical approach to map structural and functional brain networks and identify putative targets for removal. The first section focuses on mesial temporal lobe epilepsy (TLE), the most common type of localization-related epilepsy. Using high-resolution structural and functional 7T MRI, I demonstrate that noninvasive neuroimaging-based network properties within the medial temporal lobe can serve as useful biomarkers for TLE cases in which conventional imaging and volumetric analysis are insufficient. The second section expands to all forms of localization-related epilepsy. Using iEEG recordings, I provide a framework for the utility of interictal network synchrony in identifying candidate resection zones, with the goal of reducing the need for prolonged invasive implants. In the third section, I generate a pipeline for integrated analysis of iEEG and MRI networks, paving the way for future large-scale studies that can effectively harness synergy between different modalities. This multimodal approach has the potential to provide fundamental insights into the pathology of an epileptic brain, robustly identify areas of seizure onset and spread, and ultimately inform clinical decision making

Advanced Magnetic Resonance Imaging and Quantitative Analysis Approaches in Patients with Refractory Focal Epilepsy

Background Epilepsy has a high prevalence of 1%, which makes it the most common serious neurological disorder. The most difficult to treat type of epilepsy is temporal lobe epilepsy (TLE) with its most commonly associated lesion being hippocampal sclerosis (HS). About 30-50% of all patients undergoing resective surgery of epileptogenic tissue continue to have seizures postoperatively. Indication for this type of surgery is only given when lesions are clearly visible on magnetic resonance images (MRI). About 30% of all patients with focal epilepsy do not show an underlying structural lesion upon qualitative neuroradiological MRI assessment (MRI-negative). Objectives The work presented in this thesis uses MRI data to quantitatively investigate structural differences between brains of patients with focal epilepsy and healthy controls using automated imaging preprocessing and analysis methods. Methods All patients studied in this thesis had electrophysiological evidence of focal epilepsy, and underwent routine clinical MRI prior to participation in this study. There were two datasets and both included a cohort of age-matched controls: (i) Patients with TLE and associated HS who later underwent selective amygdalahippocampectomy (cohort 1) and (ii) MRI-negative patients with medically refractory focal epilepsy (cohort 2). The participants received high- resolution routine clinical MRI as well as additional sequences for gray and white matter (GM/WM) structural imaging. A neuroradiologist reviewed all images prior to analysis. Hippocampal subfield volume and automated tractography analysis was performed in patients with TLE and HS and related to post-surgical outcomes, while images of MRI- negative patients were analyzed using voxel-based morphometry (VBM) and manual/automated tractography. All studies were designed to detect quantitative differences between patients and controls, except for the hippocampal subfield analysis as control data was not available and comparisons were limited to patients with persistent postoperative seizures and those without. Results 1. Automated hippocampal subfield analysis (cohort 1): The high-resolution hippocampal subfield segmentation technique cannot establish a link between hippocampal subfield volume loss and post-surgical outcome. Ipsilateral and contralateral hippocampal subfield volumes did not correlate with clinical variables such as duration of epilepsy and age of onset of epilepsy. 2. Automated WM diffusivity analysis (cohort 1): Along-the-tract analysis showed that ipsilateral tracts of patients with right/left TLE and HS were more extensively affected than contralateral tracts and the affected regions within tracts could be specified. The extent of hippocampal atrophy (HA) was not related to (i) the diffusion alterations of temporal lobe tracts or (ii) clinical characteristics of patients, whereas diffusion alterations of ipsilateral temporal lobe tracts were significantly related to age at onset of epilepsy, duration of epilepsy and epilepsy burden.Patients without any postoperative seizure symptoms (excellent outcomes) had more ipsilaterally distributed WM tract diffusion alterations than patients with persistent postoperative seizures (poorer outcomes), who were affected bilaterally. 3. Automated epileptogenic lesion detection (cohort 2): Comparison of individual patients against the controls revealed that focal cortical dysplasia (FCD) can be detected automatically using statistical thresholds. All sites of dysplasia reported at the start of the study were detected using this technique. Two additional sites in two different patients, which had previously escaped neuroradiological assessment, could be identified. When taking these statistical results into account during re-assessment of the dedicated epilepsy research MRI, the expert neuroradiologist was able to confirm these as lesions. 4. Manual and automated WM diffusion tensor imaging (DTI) analysis (cohort 2): The analysis of consistency across approaches revealed a moderate to good agreement between extracted tract shape, morphology and space and a strong correlation between diffusion values extracted with both methods. While whole-tract DTI-metrics determined using Automated Fiber Quantification (AFQ) revealed correlations with clinical variables such as age of onset and duration of epilepsy, these correlations were not found using the manual technique. The manual approach revealed more differences than AFQ in group comparisons of whole-tract DTI-metrics. Along-the-tract analysis provided within AFQ gave a more detailed description of localized diffusivity changes along tracts, which correlated with clinical variables such as age of onset and epilepsy duration. Conclusions While hippocampal subfield volume loss in patients with TLE and HS was not related with any clinical variables or to post-surgical outcomes, WM tract diffusion alterations were more bilaterally distributed in patients with persistent postoperative seizures, compared to patients with excellent outcomes. This may indicate that HS as an initial precipitating injury is not affected by clinical features of the disorder and automated hippocampal subfield mapping based on MRI is not sufficient to stratify patients according to outcome. Presence of persisting seizures may depend on other pathological processes such as seizure propagation through WM tracts and WM integrity. Automated and time-efficient three-dimensional voxel-based analysis may complement conventional visual assessments in patients with MRI-negative focal epilepsy and help to identify FCDs escaping routine neuroradiological assessment. Furthermore, automated along-the-tract analysis may identify widespread abnormal diffusivity and correlations between WM integrity loss and clinical variables in patients with MRI-negative epilepsy. However, automated WM tract analysis may differ from results obtained with manual methods and therefore caution should be exercised when using automated techniques

Movie-driven fMRI Reveals Network Asynchrony and Connectivity Alterations in Temporal Lobe Epilepsy

Mesial temporal lobe epilepsy (TLE) is the most common form of focal epilepsy and is often resistant to medication. Recent studies have noted brain-wide disruptions to several neural networks in so-called “focal” epilepsy, notably TLE, leading to it being recognized as a network disease. We aimed to assess the integrity of functional networks while they were simultaneously activated in an ecologically valid manner, using an actively engaging, richly stimulating audio-visual film clip. This stimulus elicits widespread, dynamic patterns of time-locked brain activity, measurable using functional magnetic resonance imaging. Thirteen persons with drug-resistant TLE (persons with epilepsy; PWE) and 10 demographically matched controls were scanned while at rest and while watching a suspenseful movie clip in a 3T MRI system. We observed idiosyncratic activation in several functional networks among PWE during movie-viewing. Activation time courses among PWE synchronized poorly with the highly stereotyped movie-driven BOLD fluctuations exhibited by controls [i.e., high inter-subject correlation (ISC)]. We also examined coupling (functional connectivity) among 10 canonical functional networks during resting-state and movie-viewing conditions. Whereas functional networks in healthy viewers segregate to support movie processing, the auditory and dorsal attention networks among PWE do not segregate as efficiently. Furthermore, we observed a robust pattern of connectivity alterations in temporal and extratemporal regions during movie viewing in PWE compared to controls. Our findings supplement evidence derived from resting-state fMRI and provide novel insight into how the cognitively engaged brain is altered in TLE

Advanced Invasive Neurophysiological Methods to Aid Decision Making in Paediatric Epilepsy Surgery

For patients with drug-resistant focal epilepsy, surgery is the most effective treatment to attain seizure freedom. Intracranial electroencephalogram investigations succeed in defining the seizure onset zone (SOZ) where non-invasive methods fail to identify a single seizure generator. However, resection of the SOZ does not always lead to a surgical benefit and, in addition, eloquent functions like language might be compromised. The aim of this thesis was to use advanced invasive neurophysiological methods to improve pre-surgical planning in two ways. The first aim was to improve delineation of the pathological tissue, the SOZ using novel quantitative neurophysiological biomarkers: high gamma activity (80–150Hz) phase-locked to low frequency iEEG discharges (phase-locked high gamma, PLHG) and high frequency oscillations called fast ripples (FR, 250–500Hz). Resection of contacts containing these markers were recently reported to lead to an improved seizure outcome. The current work shows the first replication of the PLHG metric in a small adult pilot study and a larger paediatric cohort. Furthermore, I tested whether surgical removal of PLHG- and/or FR-generating brain areas resulted in better outcome compared to the current clinical SOZ delineation. The second aim of this work was to aid delineation of eloquent language cortex. Invasive event-related potentials (iERP) and spectral changes in the beta and gamma frequency bands were used to determine cortical dynamics during speech perception and production across widespread brain regions. Furthermore, the relationship between these cortical dynamics and the relationship to electrical stimulation responses was explored. For delineation of pathological tissue, the combination of FRs and SOZ proved to be a promising biomarker. Localising language cortex showed the highest level of activity around the perisylvian brain regions with a significantly higher occurrence rate of iERPs compared to spectral changes. Concerning electrical stimulation mapping beta and high gamma frequency bands represented the most promising markers