Background: A compilation of high quality systematic reviews (SRs) on lifestyle factors associated with adult glioma and meningioma was developed. Methods and Materials: Phase 1 consisted of a systematic overview of existing SRs. For Phase 2, high quality SRs were incorporated in an update. Moderate or low quality SRs which had not been considered in a high quality review were eligible for a de-novo synthesis. Results: Phase 1 resulted in seven moderate to low quality reviews. From this, in Phase 2, smoking, mobile phone and hair dye use were subjected to de-novo reviews. For smoking, it was suggestive that past smokers had an increased risk. For mobile phone use, there was no overall association, however it was suggestive that ipsilateral and high cumulative call time were associated with slight increased risk. No association was observed for personal hair dye use. Conclusions: Despite these null associations, rigorous SR methods were used providing confidence in conveying these results.

Prognostic factors come in a variety of forms and may be patient, tumour or environmental related. This thesis examines the interaction of prognostic factors for a variety of tumour types. It particularly focuses on single nucleotide polymorphisms (SNPs) of the vascular endothelial growth factor (VEGF) gene. The first section on meningiomas describes the frequency of sex steroid receptors in meningiomas. In this study, absence of progesterone receptors is associated with high tumour grade and male gender. Tumours that are progesterone receptor negative have an odds ratio for recurrence of 5.Choroid plexus carcinomas are aggressive malignant tumours generally occurring in young children. Gross total surgical resection has been shown to be a highly significant factor in tumour recurrence and survival. This study describes a treatment paradigm of neoadjuvant ICE chemotherapy in these children which decreases the vascularity and increase the chance of a complete removal. The operative blood loss with this regimen is reduced to 0.22 blood volumes from 1.11 blood volumes without neoadjuvant chemotherapy. The VEGF gene is highly polymorphic and SNPs of the region have previously been shown to influence VEGF protein expression. This study looks at cohorts of both adult gliomas and a variety of paediatric brain tumours; comparing them to controls. There are several associations described between the development of certain tumours and specific SNP genotypes. In addition to this, certain genotypes and haplotypes have an influence on survival of adult grade 2 astrocytomas and paediatric medulloblastomas and ependymomas. There are consistent themes to the prognostic genotypes throughout both the adult and the paediatric tumours.Prognostic factors come in a variety forms as described in this thesis. It is vital to understand the complex interaction between factors to best utilise them for the benefit of patients.

Philosophiae Doctor - PhD / Glioblastoma multiforme (GBM) remains the most malignant of all primary adult brain tumours. It is a highly invasive and vascularized neoplasm with limited treatment options and very low survival rate. GBM tumours are heterogeneous in nature with cellular hierarchy and at the apex of this hierarchy are the glioblastoma stem cells, known to promote tumourigenesis and resistance to chemotherapeutic agents and tumour recurrence. Currently, the standard care for GBM involves surgical resection, radiation, and chemotherapy treatment with temozolomide. Unfortunately, median survival after treatment is still daunting and tumour relapse is very frequent. Indeed, patients with recurrent glioblastoma have less than a year survival. To address this, novel therapies need to be developed with the early introduction of promising agents into clinical trials and subsequent approval for use. Importantly, for these novel therapies to be approved for GBM, they need to be safe, effective as well as being able to penetrate the blood-brain barrier (BBB). Due to the high cost and process time for the development of new drugs, existing approved drugs are currently being repurposed for new indications and this is gaining significance in clinical pharmacology as it allows rapid delivery of useful drugs from bench to bedside. Drugs of the antipsychotic class are well known to cross the BBB due to their neuroleptic action. To this end, the aim of this study was to identify and characterize the anti-cancer activities of novel phenothiazine-derivatives belonging to the antipsychotic class of drugs in glioblastoma. To achieve this, several novel phenothiazine-derivatives were initially screened for possible anti-cancer activity in the U87 and U251 malignant GBM cells. Two lead compounds, DS00326 and DS00329, were identified and their anti-cancer activities were determined in U87 and U251 cells as well as in primary patient-derived xenograft (PDX) glioblastoma cultures. DS00326 and DS00329 significantly inhibited glioblastoma cell viability, with minimal effects observed in the non-cancerous FG0 fibroblasts. The IC50 values of DS00326 and DS00329 for U251, U87 and PDX cells ranged from 1.61 to 12.53μM. Flow cytometry analyses showed that DS00326 and DS00329 treatment led to an increase in the G1 population of cells. Additionally, DS00326 and DS00329 induced double-strand DNA breaks, which lead to activation of the canonical DNA damage response pathway. Furthermore, DS00326 and DS00329 induced apoptosis as shown by morphological markers, flow cytometry with annexin V-FITC/propidium iodide staining, as well as western blotting with an antibody to detect levels of cleaved PARP. Interestingly, treatment with DS00326 and DS00329 also induced autophagy as evident by the increase of acidic vesicular organelles in cells following staining with acridine orange as well as an increase in levels of the autophagy marker LC3-II. Autophagy was seen as a pro-death pathway in the U87 and U251 cells as inhibition of autophagy led to a reversal of cytotoxicity and consequently increased cell survival. Moreover, it was demonstrated that DS00326 and DS00329 inhibited the PI3/Akt pathway while modulating the mitogen-activated protein kinases p38, ERK1/2 and JNK signalling pathways. Importantly DS00326 and DS00329 displayed anti-cancer stem cell activities by the inhibition of neurosphere formation and regulation of stem cell markers SOX2 and GFAP in PDX cells. Together, the findings from this study suggest that DS00326 and DS00329 may be effective in the treatment of glioblastoma and provide a strong rationale for further clinical studies exploiting phenothiazines and their derivatives as treatments for glioblastoma. / 2021-09-01

This thesis deals with a classification of glioma grade in high and low aggressive tumours and overall survival prediction based on magnetic resonance imaging. Data used in this work is from BRATS challenge 2019 and each set contains information from 4 weighting sequences of MRI. Thesis is implemented in PYTHON programming language and Jupyter Notebooks environment. Software PyRadiomics is used for calculation of image features. Goal of this work is to determine best tumour region and weighting sequence for calculation of image features and consequently select set of features that are the best ones for classification of tumour grade and survival prediction. Part of thesis is dedicated to survival prediction using set of statistical tests, specifically Cox regression

Abstract: The default mode network of the brain is a set of functionally connected regions associated with introspection and daydreaming. Recent fMRI studies have discovered that the default mode network is often perturbed in the diseased brain. For example, the default mode network is known to be modulated in dementia, ADHD, depression, and schizophrenia, among others. This has led many into believing that this network could have a role in the physiopathology of nervous system disease, or could be a useful marker of brain function. However, very few studies have yet been done which investigate how surgical lesions such as brain tumours affect the default mode network. Consequently, the goal of this project was to characterise the effect of brain tumours on the default mode network based on their location, histological type, and other parameters. / Le mode de fonctionement par défaut du cerveau est un réseau cérébral associé à la rêverie et à l’introspection. Des études récentes sur ce réseau ont découvert qu’il est perturbé dans plusieurs pathologies cérébrales. Par example, le mode de fonctionnement par défaut est modulé en démence, TDAH, dépression, schizophrénie et plusieurs autres maladies liés au cerveau. Ceci a mené à l’hypothèse que le mode de fonctionnement par défaut pourrait avoir un rôle dans la physiopathologie des maladies du système nerveux, ou pourrait être un marqueur utile du fonctionnement cérébral. Par contre, très peu d’études ont investigué l’effet de lésions chirurgicaux comme les tumeurs cérébrales sur le mode de fonctionnement par défaut. Par conséquent, le but de ce projet était de caractériser l’importance de l’histologie, de la localisation et de plusieurs autres paramètres de l’effet d’une tumeur cérébrale sur le mode de fonctionnement par défaut.

«Machine Learning» est un champ d'étude de l'intelligence artificielle qui se concentre sur des algorithmes capables d'adapter leur paramètres en se basant sur les données observées par l'optimisation d'une fonction objective ou d'une fonction de cout. Cette discipline a soulevé l'intérêt de la communauté de la recherche biomédicale puisqu'elle permet d'améliorer la sensibilité et la spécificité de la détection et du diagnostic de nombreuses pathologies tout en augmentant l'objectivité dans le processus de prise de décision thérapeutique. L'imagerie biomédicale est devenue indispensable en médecine, puisque plusieurs modalités comme l'imagerie par résonance magnétique (IRM), la tomodensitométrie et la tomographie par émission de positron sont de plus en plus utilisées en recherche et en clinique. L'IRM est la technique d'imagerie non-invasive de référence pour l'étude du cerveau humain puisqu'elle permet dans un temps d'acquisition raisonnable d'obtenir à la fois des cartographies structurelles et fonctionnelles avec une résolution spatiale élevée. Cependant, avec l'augmentation du volume et de la complexité des données IRM, il devient de plus en plus long et difficile pour le clinicien d'intégrer toutes les données afin de prendre des décisions précises. Le but de cette thèse est de développer des méthodes de « machine learning » automatisées pour la détection de tissu cérébral anormal, en particulier dans le cas de suivi de glioblastome multiforme (GBM) et de sclérose en plaques (SEP). Les techniques d'IRM conventionnelles (IRMc) actuelles sont très utiles pour détecter les principales caractéristiques des tumeurs cérébrales et les lésions de SEP, telles que leur localisation et leur taille, mais ne sont pas suffisantes pour spécifier le grade ou prédire l'évolution de la maladie. Ainsi, les techniques d'IRM avancées, telles que l'imagerie de perfusion (PWI), de diffusion (DKI) et la spectroscopie par résonance magnétique (SRM), sont nécessaires pour apporter des informations complémentaires sur les variations du flux sanguin, de l'organisation tissulaire et du métabolisme induits par la maladie. Dans une première étude de suivi de patients GBM, seuls les paramètres d'IRM avancés ont été explorés dans un relativement petit sous-groupe de patients. Les paramètres de PWI moyens, mesurés dans les régions d'intérêts (ROI) délimités manuellement, se sont avérés être d'excellents marqueurs, puisqu'ils permettent de prédire l'évolution du GBM en moyenne un mois plus tôt que le clinicien. Dans une seconde étude, réalisée sur un échantillon plus important que la précédente, la SRM a été remplacée par l'IRMc et la quantification de la PWI et du kurtosis de diffusion (DKI) a été réalisée de manière automatique. L'extraction des paramètres d'imagerie a été effectuée sur des segmentations semi-automatiques des tumeurs, réduisant ainsi le temps nécessaire au clinicien pour la délimitation du ROI de la partie de la lésion rehaussée au produit de contraste (CE-ROI). L'application d'un algorithme modifié de «boosting» sur les paramètres extraits des ROIs a montré une grande précision pour le diagnostic du GBM. Dans une troisième, une version modifiée des cartes paramétriques de réponse (PRM) est proposée pour prendre en compte la région d'infiltration de la tumeur, réduisant toujours plus le temps nécessaire pour la délimitation de la tumeur par le clinicien, puisque toutes les images IRM sont recalées sur la première. Deux façons de générer les RPM ont été comparées, l'une basée sur l'IRMc et l'autre basée sur la PWI, ces deux paramètres étant les meilleurs pour la discrimination de l'évolution du GBM, comme le montrent les deux études précédentes. Les résultats de cette étude montrent que l'emploi de PRM basés sur l'IRMc permet d'obtenir des résultats supérieurs à ceux obtenus avec les PRM basés sur la PWI [etc…] / Machine learning is a subdiscipline in the field of artificial intelligence, which focuses on algorithms capable of adapting their parameters based on a set of observed data, by optimizing an objective or cost function. Machine learning has been the subject of large interest in the biomedical community because it can improve sensitivity and/or specificity of detection and diagnosis of any disease, while increasing the objectivity of the decision-making process. With the late increase in volume and complexity of medical data being collected, there is a clear need for applying machine learning algorithms in multi-parametric analysis for new detection and diagnostic modalities. Biomedical imaging is becoming indispensable for healthcare, as multiple modalities, such as Magnetic Resonance Imaging (MRI), Computed Tomography, and Positron Emission Tomography, are being increasingly used in both research and clinical settings. The non-invasive standard for brain imaging is MRI, as it can provide structural and functional brain maps with high resolution, all within acceptable scanning times. However, with the increase of MRI data volume and complexity, it is becoming more time consuming and difficult for clinicians to integrate all data and make accurate decisions. The aim of this thesis is to develop machine learning methods for automated preprocessing and diagnosis of abnormal brain tissues, in particular for the followup of glioblastoma multiforme (GBM) and multiple sclerosis (MS). Current conventional MRI (cMRI) techniques are very useful in detecting the main features of brain tumours and MS lesions, such as size and location, but are insufficient in specifying the grade or evolution of the disease. Therefore, the acquisition of advanced MRI, such as perfusion weighted imaging (PWI), diffusion kurtosis imaging (DKI), and magnetic resonance spectroscopic imaging (MRSI), is necessary to provide complementary information such as blood flow, tissue organisation, and metabolism, induced by pathological changes. In the GBM experiments our aim is to discriminate and predict the evolution of patients treated with standard radiochemotherapy and immunotherapy based on conventional and advanced MRI data. In the MS experiments our aim is to discriminate between healthy subjects and MS patients, as well as between different MS forms, based only on clinical and MRSI data. As a first experiment in GBM follow-up, only advanced MRI parameters were explored on a relatively small subset of patients. Average PWI parameters computed on manually delineated regions of interest (ROI) were found to be perfect biomarkers for predicting GBM evolution one month prior to the clinicians. In a second experiment in GBM follow-up of a larger subset of patients, MRSI was replaced by cMRI, while PWI and DKI parameter quantification was automated. Feature extraction was done on semi-manual tumour delineations, thereby reducing the time put by the clinician for manual delineating the contrast enhancing (CE) ROI. Learning a modified boosting algorithm on features extracted from semi-manual ROIs was shown to provide very high accuracy results for GBM diagnosis. In a third experiment in GBM follow-up of an extended subset of patients, a modified version of parametric response maps (PRM) was proposed to take into account the most likely infiltration area of the tumour, reducing even further the time a clinician would have to put for manual delineating the tumour, because all subsequent MRI scans were registered to the first one. Two types of computing PRM were compared, one based on cMRI and one based on PWI, as features extracted with these two modalities were the best in discriminating the GBM evolution, according to results from the previous two experiments. Results obtained within this last GBM analysis showed that using PRM based on cMRI is clearly superior to using PRM based on PWI [etc…]

The thesis has been prepared in a paper-based format and includes three papers: Paper 1, a systematic review; Paper 2, an empirical study; and Paper 3, a critical appraisal and reflection on the work. Paper 1 has been prepared for submission to Neuro-Oncology. The paper presents a systematic review of 21 studies concerning the relationships of demographic, clinical and mental health factors on health-related quality of life (HRQoL) and psychological functioning in adults with primary malignant brain tumours. The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) principles. Methodological qualities of studies included were appraised using a checklist based on the Newcastle-Ottawa Scale (Wells et al, n.d.).Findings were synthesised narratively adhering to published guidelines (Popay et al, 2006). The review identified evidence for factors relating to HRQoL and psychological functioning, offered several considerations for clinical practice, and outlined recommendations for improving the methodological rigour of future research. Paper 2 has been prepared for submission to Psycho-Oncology and presents the findings of a qualitative study of patients’ psychological adjustment to glioblastoma, the most aggressive and most common form of brain tumour in adults. Semi-structured interviews were conducted with 10 participants 3.3-5.1 months post-diagnosis. Data were analysed using a constructivist grounded theory methodology (Charmaz, 2014). Analysis yielded three theoretical categories describing processes of maintaining continuity with the past, reframing the present and changing to accommodate an uncertain future. The implications of these findings on current supportive interventions are discussed. Paper 3 is not intended for publication. It offers a critical appraisal of the individual papers and the research process overall, considering their strengths and limitations. The paper also discusses issues of reflexivity encountered during the empirical study, and considers the implications of this research for the author’s professional development as a clinical psychologist.

Proton beam therapy shows dosimetric advantages in terms of sparing healthy tissue compared to conventional photon radiotherapy. Those patients who are supposed to experience the greatest reduction in side effects should preferably be treated with proton beam therapy. One option for this patient selection is the model-based approach. Its feasibility in patients with intracranial tumours is investigated in this thesis. First, normal tissue complication probability models for early and late side effects were developed and validated in external cohorts based on data of patients treated with proton beam therapy. Acute erythema as well as acute and late alopecia were associated with high-dose parameters of the skin. Late mild hearing loss was related to the mean dose of the ipsilateral cochlea. Second, neurocognitive function as a relevant side effect for brain tumour patients was investigated in detail using subjective and objective measures. It remained largely stable during recurrence-free follow-up until two years after proton beam therapy. Finally, potential toxicity differences were evaluated based on an individual proton and photon treatment plan comparison as well as on models predicting various side effects. Although proton beam therapy was able to achieve a high relative reduction of dose exposure in contralateral organs at risk, the associated reduction of side effect probabilities was less pronounced. Using a model-based selection procedure, the majority of the examined patients would have been eligible for proton beam therapy, mainly due to the predictions of a model on neurocognitive function.:1. Introduction 2. Theoretical background 2.1 Treatment strategies for tumours in the brain and skull base 2.1.1 Gliomas 2.1.2 Meningiomas 2.1.3 Pituitary adenomas 2.1.4 Tumours of the skull base 2.1.5 Role of proton beam therapy 2.2 Radiotherapy with photons and protons 2.2.1 Biological effect of radiation 2.2.2 Basic physical principles of radiotherapy 2.2.3 Field formation in radiotherapy 2.2.4 Target definition and delineation of organs at risk 2.2.5 Treatment plan assessment 2.3 Patient outcome 2.3.1 Scoring of side effects 2.3.2 Patient-reported outcome measures – Quality of life 2.3.3 Measures of neurocognitive function 2.4 Normal tissue complication probability models 2.4.1 Types of NTCP models 2.4.2 Endpoint definition and parameter fitting 2.4.3 Assessment of model performance 2.4.4 Model validation 2.5 Model-based approach for patient selection for proton beam therapy 2.5.1 Limits of randomised controlled trials 2.5.2 Principles of the model-based approach 3. Investigated patient cohorts 4. Modelling of side effects following cranial proton beam therapy 4.1 Experimental design for modelling early and late side effects 4.2 Modelling of early side effects 4.2.1 Results 4.2.2 Discussion 4.3 Modelling of late side effects 4.3.1 Results 4.3.2 Discussion 4.4 Interobserver variability of alopecia and erythema assessment 4.4.1 Patient cohort and experimental design 4.4.2 Results 4.4.3 Discussion 4.5 Summary 5. Assessing the neurocognitive function following cranial proton beam therapy 5.1 Patient cohort and experimental design 5.2 Results 5.2.1 Performance at baseline 5.2.2 Correlation between subjective and objective measures 5.2.3 Time-dependent score analyses 5.3 Discussion and conclusion 5.4 Summary 6. Treatment plan and NTCP comparison for patients with intracranial tumours 6.1 Motivation 6.2 Treatment plan comparison of cranial proton and photon radiotherapy 6.2.1 Patient cohort and experimental design 6.2.2 Results 6.2.3 Discussion 6.3 Application of NTCP models 6.3.1 Patient cohort and experimental design 6.3.2 Results 6.3.3 Discussion 6.4 Summary 7. Conclusion and further perspectives 8. Zusammenfassung 9. Summary

Zusammenfassung Einleitung: Die Kraniotomie mit umfassender Tumorresektion bleibt Therapie der Wahl zur Behandlung von Hirntumoren. Bei der geforderten Radikalität des therapeutischen Vorgehens kommt der präoperativen Lokalisationsdiagnostik eloquenter Hirnareale eine besondere Bedeutung zu. In der vorliegenden Arbeit wird ein Verfahren vorgestellt, daß den präzisen Übertrag der Ergebnisse funktioneller MRT-Studien in die Therapie von Hirntumoren ermöglicht. Desweiteren wird das klinische Potential der fMRT untersucht. In einem hierarchischem System erfolgt die Beurteilung der klinischen Wirksamkeit der Methode zur präoperativen Lokalisationsdiagnostik eloquenter Hirnareale bei Patienten mit Hirntumoren. Methode: Bei 40 Patienten mit supratentoriellen Hirntumoren wurden insgesamt 144 präoperative funktionelle MRT-Studien durchgeführt. Die Bewertung der klinischen Wirksamkeit erfolgte in einem hierarchischem Modell unter Betrachtung der aufgeführten Dimensionen: 1. Ebene: Technische Wirksamkeit 2. Ebene: Wirksamkeit in Bezug auf die diagnostische Genauigkeit 3. Ebene: Wirksamkeit in Bezug auf das diagnostische Denken 4. Ebene: Therapeutische Wirksamkeit 5. Ebene: Wirksamkeit in Bezug auf das Patient-Outcome 6. Ebene: Wirksamkeit in Bezug auf die Gesellschaft Die Ergebnisse der funktionellen MRT-Untersuchungen wurden in ein neurochirurgisches Navigationssystem eingebracht. Intraoperativ besteht für den Operateur die Möglichkeit sich die Lagebeziehung zu den gekennzeichneten Arealen in das Okular des Operationsmikroskop einzuspielen. Ergebnisse: Das geschilderte Verfahren ermöglicht die navigierte Operation von Hirntumoren unter besonderer Berücksichtigung eloquenter- das heißt funktionstragender Hirnareale. Die beschriebene Methode zur Integration der fMRT weist ein hohe Praktikabilität auf. Wie diese Arbeit zeigen konnte, erbringt die fMRT als Methode auch bei Patienten mit Hirntumoren für die klinische Anwendung ausreichend zuverlässige Ergebnisse. Schlussfolgerung: 1. Mit dem geschilderten Verfahren gelingt die zuverlässige Integration von fMRT-Daten in die Therapie von Hirntumoren. 2. Die fMRT ist für den klinischen Einsatz zur präoperativen Lokalisation eloquenter Hirntumoren mit Einschränkungen geeignet. / Abstract Introduction: Craniotomy and maximal tumour resection remains the major therapy in patients with brain tumours. Preoperatively it is of great importance to identify eloquent brain areas. In this study we develop a method which allows the precise integration of functional MR-data into the therapy of brain tumours. Additionally we investigated the diagnostic potential of functional MRI in a clinical setting. We were assessing the effectiveness of functional MRI in patients with brain tumours in a hierarchical system. Methods: We performed 122 preoperative, functional MRI studies in 40 patients with supratentorial brain tumours. We evaluated the effectiveness in a hierarchic model. 1. level: technical effectiveness 2. level: diagnostic effectiveness 3. level: diagnostic effectiveness 4. level: therapeutical effectiveness 5. level: patient-outcome 6. level: society The acquired parametric maps were integrated into a neurosurgical navigation system. Intraoperatively the neurosurgeon can have the localisation of functional brain areas displayed within the optical system of the microscope. Conclusion: 1. The described approach allows image guided neurosurgery paying special attention to eloquent brain areas. The method is very practicable and reliable. 2. As we could demonstrate in our work, the functional MRI is sufficiently robust for clinical application

La "chirurgie éveillée du cerveau" consiste à retirer des tumeurs cérébrales infiltrantes (gliomes de bas grade, GIBG) à progression lente chez un patient éveillé. Une cartographie anatomo-fonctionnelle du cerveau est réalisée par stimulation électrique directe (SED) des zones proches de la tumeur afin de discriminer les aires cérébrales fonctionnelles de celles qui ne le sont plus. Les effets inhibiteurs de la stimulation sont mis en évidence par les tests neuropsychologiques réalisés par le patient lors de la chirurgie. Cependant, la SED est paramétrée de manière totalement empirique bien qu’utilisée de façon standardisée. De plus, si ses effets comportementaux sont mis en avant, ses effets électrophysiologiques restent plus méconnus. La conservation de la relation entre électrophysiologie (potentiel évoqué, PE) et comportement (fonction) est cruciale lors de chirurgies des GIBG : l’analyse des PE en temps réel permettrait une identification de ces relations au cours même de la chirurgie.Pour cela, nous avons réalisé des enregistrements peropératoires de l’activité électro-corticographique (ECoG) du cortex (CPP, n° ID-RCB : 2015-A00056-43). L’étude de ces enregistrements a permis de mesurer les effets electrophysiologiques de la SED corticale et sous-corticale, en évaluant la réponse du cerveau à la stimulation au travers des PE. Une chaine d'acquisition spécifique à la mesure de l'ECoG a été développée afin de pouvoir à terme mesurer et visualiser les PE en temps réel. De plus, un algorithme de post-traitement a été implémenté afin de réduire la contamination du signal par l’artefact de stimulation.Mieux comprendre les mécanismes sous-jacents à la SED, notamment au travers de la mesure des réponses électrophysiologiques, doit permettre de proposer des protocoles peropératoires plus objectifs afin d'améliorer la planification chirurgicale et la qualité de vie des patients. / The ‘Awake brain surgery’ consists in removing some slow-growing infiltrative brain tumor (low grade glioma, LGG) in a patient, to delay its development while preserving the functions. An anatomo-functional mapping of the brain is performed by electrically stimulating brain areas near the tumor to discriminate functional versus nonfunctional areas. The inhibitory effects of this direct electrical stimulation (DES) are evidenced by the neuropsychological tests undergone by the patient during the tumor resection. However, the DES parameters are empirically set even though its use is standardised. Moreover, even if its behavioural effects are well known, its electrophysiological effects have been partially depicted.Preserving the relationship between electrophysiology (evoked potential, EP) and behaviour (function) is crucial in LGG surgery.Intra-operative electrocorticographic recordings (ECoG) of the brain activity were thus performed (CPP, n° ID-RCB : 2015-A00056-43). The electrophysiological effects of cortical and subcortical DES on brain activity have been highlighted, by assessing the response of the brain to the stimulation through EP recordings analysis. A new acquisition set-up has also been specifically developed for ECoG recordings in order to measure and eventually visualise the EP in real-time. Furthermore, a post-processing algorithm has been implemented to reduce the signal disturbances induced by the stimulation artefact.A better understanding of the underlying DES mechanisms, in particular through the measurement of electrophysiological responses, should enable designing more perfected protocols in order to improve the surgical planning, and quality of life of the patients.