Search Results (43)

Drug-resistant epilepsy affects nearly one-third of individuals with epilepsy and remains a major cause of neurological morbidity worldwide. Surgical intervention offers a potential cure, but its success critically depends on the precise identification of the epileptogenic zone and the preservation of eloquent cortical and subcortical regions. This review aims to provide a comprehensive synthesis of current evidence on the role of multimodal neuroimaging in the personalized presurgical evaluation and planning of epilepsy surgery. We analyze how structural, functional, metabolic, and electro-physiological imaging modalities contribute synergistically to improving localization accuracy and surgical outcomes. Structural MRI remains the cornerstone of presurgical assessment, with advanced sequences, post-processing techniques, and ultra-high-field (7 T) MRI enhancing lesion detection in previously MRI-negative cases. Functional and metabolic imaging, including FDG-PET, ictal/interictal SPECT, and arterial spin labeling MRI, offer complementary insights by revealing regions of altered metabolism or perfusion associated with seizure onset. Functional MRI enables non-invasive mapping of language, memory, and motor networks, while diffusion tensor imaging and tractography delineate critical white-matter pathways to minimize postoperative deficits. Electrophysiological integration through EEG source imaging and magnetoencephalography refines localization when combined with MRI and PET data, forming the basis of multimodal image integration platforms used for surgical navigation. Our review also briefly explores emerging intraoperative applications such as augmented and virtual reality, intraoperative MRI, and laser interstitial thermal therapy, as well as advances driven by artificial intelligence, such as automated lesion detection and predictive modeling of surgical outcomes. By consolidating recent developments and clinical evidence, this review underscores how multimodal imaging transforms epilepsy surgery from a lesion-centered to a patient-centered discipline. The purpose is to highlight best practices, identify evidence gaps, and outline future directions toward precision-guided, minimally invasive, and function-preserving neurosurgical strategies for patients with drug-resistant focal epilepsy. Full article

EEG remains the primary diagnostic tool for evaluating seizures in children, with interictal epileptiform discharges (IEDs) serving as key biomarkers of epileptogenic activity. However, not all IEDs have the same prognostic significance. Variations in IED topography, morphology, frequency, and timing influence outcomes in pediatric epilepsy. The developing brain’s maturation affects IED location and features, creating age-specific patterns with distinct implications. For example, occipital and midline IEDs are common in young children, with midline IEDs strongly linked to increased seizures and developmental delay than control patients. Morphological features provide additional prognostic stratification. While centrotemporal IEDs with tangential dipoles are well-established as favorable prognostic markers, IEDs exhibiting tangential dipoles in any brain region are associated with more benign clinical courses than control patients. Conversely, positive sharp waves persisting beyond the neonatal period signal less favorable prognosis, including developmental delay, abnormal neurological examination, and structural brain abnormalities. Additionally, IEDs occurring on ripples have been shown to serve as more reliable interictal biomarkers of the epileptogenic zone than IEDs or ripples alone. Topography, frequency and sleep-state dependence also carry clinical significance, as frequent IEDs during slow-wave sleep may impact cognition. Furthermore, the temporal context of IED occurrence during seizure onset, treatment, activation procedures, medication withdrawal, or after epilepsy surgery provides valuable prognostic information. Recognition of these nuanced electrophysiological distinctions enhances clinicians’ ability to predict clinical trajectories and optimize long-term management strategies. Full article

Brain tumor-related epilepsy (BTRE) is a common and debilitating symptom of central nervous system (CNS) tumors. The epileptogenic zone, defined as cortex responsible for seizure generation, is located at the peritumoral region for most tumors, and lower-grade intrinsic brain tumors have the highest seizure incidence. Surgery is often the most effective treatment for the reduction in seizures in BTRE. However, surgical decisions have historically often been made exclusively for oncologic reasons, with less emphasis on seizure control. Surgical approaches for all tumor types are reviewed, highlighting relevant risk factors. Adjunctive tools during surgery, such as intraoperative electrocorticography (ECoG), may help identify and remove surrounding brain areas which are epileptogenic. Minimally invasive surgery is also gaining traction, given its utility in treating seizures deep-seated tumors. This review explores epileptogenic brain tumors, surgery for BTRE, and emerging strategies to better achieve seizure control. Full article

Background/Objectives: Epilepsy is a neurological disorder that severely impacts patients’ quality of life. In clinical practice, specific pharmacological and surgical interventions are tailored to distinct seizure types. The identification of the epileptogenic zone enables the implementation of surgical procedures and neuromodulation therapies. Consequently, accurate classification of seizure types and precise determination of focal epileptic signals are critical to provide clinicians with essential diagnostic insights for optimizing therapeutic strategies. Traditional machine learning approaches are constrained in their efficacy due to limited capability in autonomously extracting features. Methods: This study proposes a novel deep learning framework integrating temporal and spatial information extraction to address this limitation. Multivariate variational mode decomposition (MVMD) is employed to maintain inter-channel mode alignment during the decomposition of multi-channel epileptic signals, ensuring the synchronization of time–frequency characteristics across channels and effectively mitigating mode mixing and mode mismatch issues. Results: The Bern–Barcelona database is employed to classify focal epileptic signals, with the proposed framework achieving an accuracy of 98.85%, a sensitivity of 98.75%, and a specificity of 98.95%. For multi-class seizure type classification, the TUSZ database is utilized. Subject-dependent experiments yield an accuracy of 96.17% with a weighted F1-score of 0.962. Meanwhile, subject-independent experiments attain an accuracy of 87.97% and a weighted F1-score of 0.884. Conclusions: The proposed framework effectively integrates temporal and spatial domain information derived from multi-channel epileptic signals, thereby significantly enhancing the algorithm’s classification performance. The performance on unseen patients demonstrates robust generalization capability, indicating the potential clinical applicability in assisting neurologists with epileptic signal classification. Full article

Background: Despite its central role in pediatric pre-surgical evaluation of drug-resistant focal epilepsy, conventional analog ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/CT (aPET) systems often yield modest epileptogenic zone (EZ) detection rates (~50–60%). Silicon photomultiplier–based digital PET/CT (dPET) promises enhanced image quality, but its performance in pediatric epilepsy remains untested. Methods: We retrospectively analyzed 22 children (mean age 11.5 ± 2.6 years) who underwent interictal brain ¹⁸F-FDG PET/CT: 11 on an analog system (Discovery ST, 2018–2019) and 11 on a digital system (Biograph Vision 450, 2020–2021). Three blinded nuclear medicine physicians independently scored EZ localization and image quality (4-point scale); post-surgical histology and ≥1-year clinical follow-up served as reference. Results: The EZ was correctly identified in 8/11 analog scans (72.7%) versus 10/11 digital scans (90.9%). Average image quality was significantly higher with dPET (3.0 ± 0.9 vs. 2.1 ± 0.9; p < 0.05), and inter-reader agreement improved from good (ICC = 0.63) to excellent (ICC = 0.91). Conclusions: Our preliminary findings suggest that dPET enhances image clarity and reader consistency, potentially improving localization accuracy in pediatric epilepsy presurgical workups. Full article

Precision neurosurgery is rapidly evolving as a medical specialty by merging genomic medicine, multi-omics technologies, and artificial intelligence (AI) technology, while at the same time, society is shifting away from the traditional, anatomic model of care to consider a more precise, molecular model of care. The general purpose of this review is to contemporaneously reflect on how these advances will impact neurosurgical care by providing us with more precise diagnostic and treatment pathways. We hope to provide a relevant review of the recent advances in genomics and multi-omics in the context of clinical practice and highlight their transformational opportunities in the existing models of care, where improved molecular insights can support improvements in clinical care. More specifically, we will highlight how genomic profiling, CRISPR-Cas9, and multi-omics platforms (genomics, transcriptomics, proteomics, and metabolomics) are increasing our understanding of central nervous system (CNS) disorders. Achievements obtained with transformational technologies such as single-cell RNA sequencing and intraoperative mass spectrometry are exemplary of the molecular diagnostic possibilities in real-time molecular diagnostics to enable a more directed approach in surgical options. We will also explore how identifying specific biomarkers (e.g., IDH mutations and MGMT promoter methylation) became a tipping point in the care of glioblastoma and allowed for the establishment of a new taxonomy of tumors that became applicable for surgeons, where a change in practice enjoined a different surgical resection approach and subsequently stratified the adjuvant therapies undertaken after surgery. Furthermore, we reflect on how the novel genomic characterization of mutations like DEPDC5 and SCN1A transformed the pre-surgery selection of surgical candidates for refractory epilepsy when conventional imaging did not define an epileptogenic zone, thus reducing resective surgery occurring in clinical practice. While we are atop the crest of an exciting wave of advances, we recognize that we also must be diligent about the challenges we must navigate to implement genomic medicine in neurosurgery—including ethical and technical challenges that could arise when genomic mutation-based therapies require the concurrent application of multi-omics data collection to be realized in practice for the benefit of patients, as well as the constraints from the blood–brain barrier. The primary challenges also relate to the possible gene privacy implications around genomic medicine and equitable access to technology-based alternative practice disrupting interventions. We hope the contribution from this review will not just be situational consolidation and integration of knowledge but also a stimulus for new lines of research and clinical practice. We also hope to stimulate mindful discussions about future possibilities for conscientious and sustainable progress in our evolution toward a genomic model of precision neurosurgery. In the spirit of providing a critical perspective, we hope that we are also adding to the larger opportunity to embed molecular precision into neuroscience care, striving to promote better practice and better outcomes for patients in a global sense. Full article

Background/Objectives: Intracranial macroelectrode implantation is a pivotal clinical tool in the evaluation of drug-resistant epilepsy, allowing further insights into the localization of the epileptogenic zone and the delineation of eloquent cortical regions through cortical stimulation. Additionally, it provides an avenue to study brain functions by analyzing cerebral responses during neuropsychological paradigms. By combining macroelectrodes with microelectrodes, which allow recording the activity of individual neurons or smaller neural clusters, recordings could provide deeper insights into neuronal microcircuits and the brain’s transitions in epilepsy and contribute to a better understanding of neuropsychological functions. In this study, one or two hybrid macro-micro electrodes were implanted in the anterior-inferior insular region in patients with refractory epilepsy. We report our experience and share some preliminary results; we also provide some recommendations regarding the implantation procedure for hybrid electrodes in the insular cortex. Methods: Stereoelectroencephalography was performed in 13 patients, with one or two hybrid macro-microelectrodes positioned in the insular region in each patient. Research neuropsychological paradigms could not be implemented in two patients for clinical reasons. In total, 23 hybrid macro-microelectrodes with eight microcontacts each were implanted, of which 20 were recorded. Spiking activity was detected and assessed using WaveClus3. Results: No spiking neural activity was detected in the microcontacts of the first seven patients. After iterative refinement during this process, successful recordings were obtained from 13 microcontacts in the anterior-inferior insula in the last four patients (13/64, 20.3%). Hybrid electrode implantation was uneventful with no complications. Obstacles included the absence of spiking activity signals, unsuccessful microwire dispersion, and the interference of environmental electrical noise in recordings. Conclusions: Human microelectrode recording presents a complex array of challenges; however, it holds the potential to facilitate a more comprehensive understanding of individual neuronal attributes and their specific stimulus responses. Full article

The mainstream method for treating drug-resistant epilepsy (DRE) is surgical resection of the epileptogenic zone. Non-invasive automatic localization of epileptogenic zone can be used to guide electrode implantation and improve the effectiveness and safety of neurosurgical treatments. Previous researchers have proposed a range of methods for this purpose, but these suffer from limits such as unclear post-operative outcomes, invasiveness, limited data volume, and single DRE type. This study constructed a non-invasive epilepsy localization method, integrating sLORETA source imaging, time–frequency analysis, and Visual Geometry Group (VGG-16) deep learning. Firstly, 16-channel scalp electroencephalogram (EEG) from 25 successfully operated DRE patients were included. Secondly, time–frequency features by short-time Fourier transform (STFT), continuous wavelet transform (CWT), and superlets algorithm were extracted. Finally, the VGG-16 network was applied to automatically locate the epileptogenic zone. All three feature extraction methods achieved significant accuracy on the dataset. Using STFT for processing and combining it with VGG-16 for image classification achieved an average classification accuracy of 80.2% and a channel identification rate of 80.7% for epileptogenic zones. After processing with CWT, the accuracy increased to 81.7% and the epileptogenic zone channel recognition rate increased to 81.4%. After processing with the superlets method, the classification accuracy was further improved to 83.1%, and the epileptogenic zone channel recognition rate was increased to 83.3%. This marks the pioneering proposal of a systematic framework for non-invasive localization to the epileptogenic zone. Full article

Background: Pharmacoresistant epilepsy affects approximately one-third of all epilepsy patients, and resective surgery may offer favorable outcomes for carefully selected patients with focal epilepsy. The accurate identification of the epileptogenic zone (EZ) is essential for successful surgery, particularly in cases where non-invasive diagnostics are inconclusive. Invasive diagnostics with stereoelectroencephalography (SEEG) offer a reliable approach to localizing the EZ, especially in MRI-negative cases. Methods: This retrospective study analyzed the data of 22 patients with pharmacoresistant epilepsy who underwent frameless stereotactic SEEG electrode implantation with automated CT-based registration between September 2016 and November 2024. For measuring accuracy, Euclidean distance, radial deviation, angular deviation, and depth deviation were calculated for each electrode. Results: A total of 153 depth electrodes were implanted, targeting various cortical regions. The median Euclidean distance at the entry point was 1.54 mm (IQR 1.31), with a radial deviation of 1.33 mm (IQR 1.32). At the target level, the median Euclidean distance was 2.61 mm (IQR 1.53), with a radial deviation of 1.67 mm (IQR 1.54) and depth deviation of 0.95 mm (IQR 2.43). Accuracy was not significantly affected by electrode order, anatomical location, skull thickness, or intracranial length. Conclusions: These findings demonstrate that frameless stereotactic SEEG electrode implantation is safe and feasible for identifying the EZ. The integration of automatic intraoperative CT-based registration ensures precision. While maintaining workflow efficiency, it achieves accuracy comparable to frame-based methods. Further studies with larger cohorts are warranted to validate these results and assess their impact on surgical outcomes. Full article

Background: The pre-surgical evaluation for drug-resistant epilepsy achieves seizure freedom in only 50–60% of patients. Efforts to identify quantitative intracranial EEG (qEEG) biomarkers of epileptogenicity are needed. This review summarizes and evaluates the design of qEEG studies, discusses barriers to biomarker adoption, and proposes refinements of qEEG study protocols. Methods: We included exploratory and prediction prognostic studies from MEDLINE and Scopus published between 2017 and 2023 that investigated qEEG markers for identifying the epileptogenic network as the surgical target. Cohort parameters, ground truth references, and analytical approaches were extracted. Results: Out of 1789 search results, 128 studies were included. The study designs were highly heterogeneous. Half of the studies included a non-consecutive cohort, with sample sizes ranging from 2 to 166 patients (median of 16). The most common minimum follow-up was one year, and the seizure onset zone was the most common ground truth. Prediction studies were heterogeneous in their analytical approaches, and only 25 studies validated the marker through post-surgical outcome prediction. Outcome prediction performance decreased in larger cohorts. Conversely, longer follow-up periods correlated with higher prediction accuracy, and connectivity-based approaches yielded better predictions. The data and code were available in only 9% of studies. Conclusions: To enhance the validation qEEG markers, we propose standardizing study designs to resemble clinical trials. This includes using a consecutive cohort with long-term follow-up, validating against surgical resection as ground truth, and evaluating markers through post-surgical outcome prediction. These considerations would improve the reliability and clinical adoption of qEEG markers. Full article

Background: Epilepsy surgery for extratemporal lobe epilepsy (ETLE) is challenging, particularly when MRI findings are non-lesional and seizure patterns are complex. Invasive diagnostic techniques are crucial for accurately identifying the epileptogenic zone and its relationship with surrounding functional tissue. Microscope-based augmented reality (AR) support, combined with navigation, may enhance intraoperative orientation, particularly in cases involving subtle or indistinct lesions, thereby improving patient outcomes and safety (e.g., seizure freedom and preservation of neuronal integrity). Therefore, this study was conducted to prove the clinical advantages of microscope-based AR support in ETLE surgery. Methods: We retrospectively analyzed data from ten patients with pharmacoresistant ETLE who underwent invasive diagnostics with depth and/or subdural grid electrodes, followed by resective surgery. AR support was provided via the head-up displays of the operative microscope, with navigation based on automatic intraoperative computed tomography (iCT)-based registration. The surgical plan included the suspected epileptogenic lesion, electrode positions, and relevant surrounding functional structures, all of which were visualized intraoperatively. Results: Six patients reported complete seizure freedom following surgery (ILAE 1), one patient was seizure-free at the 2-year follow-up, and one patient experienced only auras (ILAE 2). Two patients developed transient neurological deficits that resolved shortly after surgery. Conclusions: Microscope-based AR support enhanced intraoperative orientation in all cases, contributing to improved patient outcomes and safety. It was highly valued by experienced surgeons and as a training tool for less experienced practitioners. Full article

Hyperventilation (HV) is an activation technique performed during clinical practices to trigger epileptiform activities, supporting the neurophysiological evaluation of patients with epilepsy. Although the role of HV has often been questioned, especially in the case of focal epilepsy, no studies have ever assessed how cortical structures respond to such a maneuver via intracranial EEG recordings. This work aims to fill this gap by evaluating the HV effects on the Stereo-EEG (SEEG) signals from a cohort of 10 patients with drug-resistant focal epilepsy. We extracted multiple quantitative metrics from the SEEG signals and compared the results obtained during HV, awake status, non-REM sleep, and seizure onset. Our findings show that the cortical connectivity, estimated via the phase transfer entropy (PTE) algorithm, strongly increases during the HV maneuver, similar to non-REM sleep. The opposite effect is observed during seizure onset, as ictal transitions involve the desynchronization of the brain structures within the epileptogenic zone. We conclude that HV promotes a conductive environment that may facilitate the propagation of epileptiform activities but is not sufficient to trigger seizures in focal epilepsy. Full article

Around 70 million people worldwide are affected by epilepsy, a neurological disorder characterized by non-induced seizures that occur at irregular and unpredictable intervals. During an epileptic seizure, transient symptoms emerge as a result of extreme abnormal neural activity. Epilepsy imposes limitations on individuals and has a significant impact on the lives of their families. Therefore, the development of reliable diagnostic tools for the early detection of this condition is considered beneficial to alleviate the social and emotional distress experienced by patients. While the Bonn University dataset contains five collections of EEG data, not many studies specifically focus on subsets D and E. These subsets correspond to EEG recordings from the epileptogenic zone during ictal and interictal events. In this work, the parallel ictal-net (PIN) neural network architecture is introduced, which utilizes scalograms obtained through a continuous wavelet transform to achieve the high-accuracy classification of EEG signals into ictal or interictal states. The results obtained demonstrate the effectiveness of the proposed PIN model in distinguishing between ictal and interictal events with a high degree of confidence. This is validated by the computing accuracy, precision, recall, and F1 scores, all of which consistently achieve around 99% confidence, surpassing previous approaches in the related literature. Full article

Epilepsy is a chronic neurological disorder characterized by recurrent seizures resulting from abnormal neuronal hyperexcitability. In the case of pharmacoresistant epilepsy requiring resection surgery, the identification of the Epileptogenic Zone (EZ) is critical. Fast Ripples (FRs; 200–600 Hz) are one of the promising biomarkers that can aid in EZ delineation. However, recording FRs requires physically small electrodes. These microelectrodes suffer from high impedance, which significantly impacts FRs’ observability and detection. In this study, we investigated the potential of a conductive polymer coating to enhance FR observability. We employed biophysical modeling to compare two types of microelectrodes: Gold (Au) and Au coated with the conductive polymer poly(3,4-ethylenedioxythiophene)-poly(styrene sulfonate) (Au/PEDOT:PSS). These electrodes were then implanted into the CA1 hippocampal neural network of epileptic mice to record FRs during epileptogenesis. The results showed that the polymer-coated electrodes had a two-order lower impedance as well as a higher transfer function amplitude and cut-off frequency. Consequently, FRs recorded with the PEDOT:PSS-coated microelectrode yielded significantly higher signal energy compared to the uncoated one. The PEDOT:PSS coating improved the observability of the recorded FRs and thus their detection. This work paves the way for the development of signal-specific microelectrode designs that allow for better targeting of pathological biomarkers. Full article

In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signals is often needed to localize the epileptogenic zone (EZ) and guide neurosurgery. The visual assessment of iEEG time-frequency (TF) images is an alternative to signal inspection, but subtle variations may escape the human eye. Here, we propose a deep learning-based metric of visual complexity to interpret TF images extracted from iEEG data and aim to assess its ability to identify the EZ in the brain. We analyzed interictal iEEG data from 1928 contacts recorded from 20 children with drug-resistant epilepsy who became seizure-free after neurosurgery. We localized each iEEG contact in the MRI, created TF images (1–70 Hz) for each contact, and used a pre-trained VGG16 network to measure their visual complexity by extracting unsupervised activation energy (UAE) from 13 convolutional layers. We identified points of interest in the brain using the UAE values via patient- and layer-specific thresholds (based on extreme value distribution) and using a support vector machine classifier. Results show that contacts inside the seizure onset zone exhibit lower UAE than outside, with larger differences in deep layers (L10, L12, and L13: p < 0.001). Furthermore, the points of interest identified using the support vector machine, localized the EZ with 7 mm accuracy. In conclusion, we presented a pre-surgical computerized tool that facilitates the EZ localization in the patient’s MRI without requiring long-term iEEG inspection. Full article