Electrical and Magnetic Neuromodulation Technologies and Brain-Computer Interfaces: Ethical Considerations for Enhancement of Brain Function in Healthy People - A Systematic Scoping Review.

INTRODUCTION: This scoping review aimed to synthesize the fragmented evidence on ethical concerns related to the use of electrical and magnetic neuromodulation technologies, as well as brain-computer interfaces for enhancing brain function in healthy individuals, addressing the gaps in understanding spurred by rapid technological advancements and ongoing ethical debates. METHODS: The following databases and interfaces were queried: MEDLINE (via PubMed), Web of Science, PhilPapers, and Google Scholar. Additional references were identified via bibliographies of included citations. References included experimental studies, reviews, opinion papers, and letters to editors published in peer-reviewed journals that explored the ethical implications of electrical and magnetic neuromodulation technologies and brain-computer interfaces for enhancement of brain function in healthy adult or pediatric populations. RESULTS: A total of 23 articles were included in the review, of which the majority explored expert opinions in the form of qualitative studies or surveys as well as reviews. Two studies explored the view of laypersons on the topic. The majority of evidence pointed to ethical concerns relating to a lack of sufficient efficacy and safety data for these new technologies, with the risks of invasive procedures potentially outweighing the benefits. Additionally, concerns about potential socioeconomic consequences were raised that could further exacerbate existing socioeconomic inequalities, as well as the risk of changes to person and environment. CONCLUSION: This scoping review highlights a critical shortage of ethical research on electrical and magnetic neuromodulation technologies and brain-computer interfaces for enhancement of brain function in healthy individuals, with key concerns regarding the safety, efficacy, and socioeconomic impacts of neuromodulation technologies. It underscores the urgent need for integrating ethical considerations into neuroscientific research to address significant gaps and ensure equitable access and outcomes.

SUPRAMAX-study: supramaximal resection versus maximal resection for glioblastoma patients: study protocol for an international multicentre prospective cohort study (ENCRAM 2201).

INTRODUCTION: A greater extent of resection of the contrast-enhancing (CE) tumour part has been associated with improved outcomes in glioblastoma. Recent results suggest that resection of the non-contrast-enhancing (NCE) part might yield even better survival outcomes (supramaximal resection, SMR). Therefore, this study evaluates the efficacy and safety of SMR with and without mapping techniques in high-grade glioma (HGG) patients in terms of survival, functional, neurological, cognitive and quality of life outcomes. Furthermore, it evaluates which patients benefit the most from SMR, and how they could be identified preoperatively. METHODS AND ANALYSIS: This study is an international, multicentre, prospective, two-arm cohort study of observational nature. Consecutive glioblastoma patients will be operated with SMR or maximal resection at a 1:1 ratio. Primary endpoints are (1) overall survival and (2) proportion of patients with National Institute of Health Stroke Scale deterioration at 6 weeks, 3 months and 6 months postoperatively. Secondary endpoints are (1) residual CE and NCE tumour volume on postoperative T1-contrast and FLAIR (Fluid-attenuated inversion recovery) MRI scans; (2) progression-free survival; (3) receipt of adjuvant therapy with chemotherapy and radiotherapy; and (4) quality of life at 6 weeks, 3 months and 6 months postoperatively. The total duration of the study is 5 years. Patient inclusion is 4 years, follow-up is 1 year. ETHICS AND DISSEMINATION: The study has been approved by the Medical Ethics Committee (METC Zuid-West Holland/Erasmus Medical Center; MEC-2020-0812). The results will be published in peer-reviewed academic journals and disseminated to patient organisations and media.

Safe surgery for glioblastoma: Recent advances and modern challenges.

One of the major challenges during glioblastoma surgery is balancing between maximizing extent of resection and preventing neurological deficits. Several surgical techniques and adjuncts have been developed to help identify eloquent areas both preoperatively (fMRI, nTMS, MEG, DTI) and intraoperatively (imaging (ultrasound, iMRI), electrostimulation (mapping), cerebral perfusion measurements (fUS)), and visualization (5-ALA, fluoresceine)). In this review, we give an update of the state-of-the-art management of both primary and recurrent glioblastomas. We will review the latest surgical advances, challenges, and approaches that define the onco-neurosurgical practice in a contemporary setting and give an overview of the current prospective scientific efforts.

The PROGRAM study: awake mapping versus asleep mapping versus no mapping for high-grade glioma resections: study protocol for an international multicenter prospective three-arm cohort study.

INTRODUCTION: The main surgical dilemma during glioma resections is the surgeon's inability to accurately identify eloquent areas when the patient is under general anaesthesia without mapping techniques. Intraoperative stimulation mapping (ISM) techniques can be used to maximise extent of resection in eloquent areas yet simultaneously minimise the risk of postoperative neurological deficits. ISM has been widely implemented for low-grade glioma resections backed with ample scientific evidence, but this is not yet the case for high-grade glioma (HGG) resections. Therefore, ISM could thus be of important value in HGG surgery to improve both surgical and clinical outcomes. METHODS AND ANALYSIS: This study is an international, multicenter, prospective three-arm cohort study of observational nature. Consecutive HGG patients will be operated with awake mapping, asleep mapping or no mapping with a 1:1:1 ratio. Primary endpoints are: (1) proportion of patients with National Institute of Health Stroke Scale deterioration at 6 weeks, 3 months and 6 months after surgery and (2) residual tumour volume of the contrast-enhancing and non-contrast-enhancing part as assessed by a neuroradiologist on postoperative contrast MRI scans. Secondary endpoints are: (1) overall survival and (2) progression-free survival at 12 months after surgery; (3) oncofunctional outcome and (4) frequency and severity of serious adverse events in each arm. Total duration of the study is 5 years. Patient inclusion is 4 years, follow-up is 1 year. ETHICS AND DISSEMINATION: The study has been approved by the Medical Ethics Committee (METC Zuid-West Holland/Erasmus Medical Center; MEC-2020-0812). The results will be published in peer-reviewed academic journals and disseminated to patient organisations and media. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov ID number NCT04708171 (PROGRAM-study), NCT03861299 (SAFE-trial).

Information-Based Medicine in Glioma Patients: A Clinical Perspective.

Glioma constitutes the most common type of primary brain tumor with a dismal survival, often measured in terms of months or years. The thin line between treatment effectiveness and patient harm underpins the importance of tailoring clinical management to the individual patient. Randomized trials have laid the foundation for many neuro-oncological guidelines. Despite this, their findings focus on group-level estimates. Given our current tools, we are limited in our ability to guide patients on what therapy is best for them as individuals, or even how long they should expect to survive. Machine learning, however, promises to provide the analytical support for personalizing treatment decisions, and deep learning allows clinicians to unlock insight from the vast amount of unstructured data that is collected on glioma patients. Although these novel techniques have achieved astonishing results across a variety of clinical applications, significant hurdles remain associated with the implementation of them in clinical practice. Future challenges include the assembly of well-curated cross-institutional datasets, improvement of the interpretability of machine learning models, and balancing novel evidence-based decision-making with the associated liability of automated inference. Although artificial intelligence already exceeds clinical expertise in a variety of applications, clinicians remain responsible for interpreting the implications of, and acting upon, each prediction.