Multimodal prognostic features of seizure freedom in epilepsy surgery

Ex-SPECTing success: Predictors of successful SPECT radiotracer injection during presurgical video-EEG admissions

OBJECTIVES

To determine predictors of successful ictal single photon emission computed tomography (SPECT) injections during Epilepsy Monitoring Unit (EMU) admissions for patients undergoing presurgical evaluation for drug-resistant focal epilepsy.

METHODS

In this retrospective study, consecutive EMU admissions were analyzed at a single center between 2019 and 2021. All seizures that occurred during the admission were reviewed. "Injectable seizures" occurred during hours when the radiotracer was available. EMU-level data were analyzed to identify factors predictive of an EMU admission with a successful SPECT injection (successful admission). Seizure-level data were analyzed to identify factors predictive of an injectable seizure receiving a SPECT injection during the ictal phase (successful injection). A multivariate generalized linear model was used to identify predictive variables.

RESULTS

125 EMU admissions involving 103 patients (median 37 years, IQR 27.0-45.5) were analyzed. 38.8% of seizures that were eligible for SPECT (n = 134) were successfully injected; this represented 17.4% of all seizures (n = 298) that occurred during admission. Unsuccessful admissions were most commonly due to a lack of seizures during EMU-SPECT (19.3%) or no injectable seizures (62.3%). Successful EMU-SPECT was associated with baseline seizure frequency >1 per week (95% CI 2.1-3.0, P < 0.001) and focal PET hypometabolism (95% CI 2.0-3.7, P < 0.001). On multivariate analysis, the only factor associated with successful injection was patients being able to indicate they were having a seizure to staff (95% CI 1.0-4.4, P = 0.038).

SIGNIFICANCE

Completing a successful ictal SPECT study remains challenging. A baseline seizure frequency of >1 per week, a PET hypometabolic focus, and a patient's ability to indicate seizure onset were identified as predictors of success. These findings may assist EMUs in optimizing their SPECT protocols, patient selection, and resource allocation.

Automated segmentation of epilepsy surgical resection cavities: Comparison of four methods to manual segmentation

Accurate resection cavity segmentation on MRI is important for neuroimaging research involving epilepsy surgical outcomes. Manual segmentation, the gold standard, is highly labour intensive. Automated pipelines are an efficient potential solution; however, most have been developed for use following temporal epilepsy surgery. Our aim was to compare the accuracy of four automated segmentation pipelines following surgical resection in a mixed cohort of subjects following temporal or extra temporal epilepsy surgery. We identified 4 open-source automated segmentation pipelines. Epic-CHOP and ResectVol utilise SPM-12 within MATLAB, while Resseg and Deep Resection utilise 3D U-net convolutional neural networks. We manually segmented the resection cavity of 50 consecutive subjects who underwent epilepsy surgery (30 temporal, 20 extratemporal). We calculated Dice similarity coefficient (DSC) for each algorithm compared to the manual segmentation. No algorithm identified all resection cavities. ResectVol (n = 44, 88 %) and Epic-CHOP (n = 42, 84 %) were able to detect more resection cavities than Resseg (n = 22, 44 %, P < 0.001) and Deep Resection (n = 23, 46 %, P < 0.001). The SPM-based pipelines (Epic-CHOP and ResectVol) performed better than the deep learning-based pipelines in the overall and extratemporal surgery cohorts. In the temporal cohort, the SPM-based pipelines had higher detection rates, however there was no difference in the accuracy between methods. These pipelines could be applied to machine learning studies of outcome prediction to improve efficiency in pre-processing data, however human quality control is still required.

Genotype-relevant neuroimaging features in low-grade epilepsy-associated tumors

Introduction

Low-grade epilepsy-associated tumors are the second most common histopathological diagnoses in cases of drug-resistant focal epilepsy. However, the connection between neuroimaging features and genetic alterations in these tumors is unclear, prompting an investigation into genotype-relevant neuroimaging characteristics.

Methods

This study retrospectively analyzed neuroimaging and surgical specimens from 46 epilepsy patients with low-grade epilepsy-associated neuroepithelial tumors that had genetic mutations identified through panel sequencing to investigate their relationship to genotypes.

Results

Three distinct neuroimaging groups were established: Group 1 had indistinct borders and iso T1-weighted and slightly high or high T2-weighted signal intensities without a diffuse mass effect, associated with 93.8% sensitivity and 100% specificity to BRAF V600E mutations; Group 2 exhibited sharp borders and very or slightly low T1-weighted and very high T2-weighted signal intensities with a diffuse mass effect and 100% sensitivity and specificity for FGFR1 mutations; and Group 3 displayed various characteristics. Histopathological diagnoses including diffuse low-grade glioma and ganglioglioma showed no clear association with genotypes. Notably, postoperative seizure-free rates were higher in Group 1 tumors (BRAF V600E) than in Group 2 tumors (FGFR1).

Discussion

These findings suggest that tumor genotype may be predicted by neuroimaging before surgery, providing insights for personalized treatment approaches.

A spatial perturbation framework to validate implantation of the epileptogenic zone

Stereo-electroencephalography (SEEG) is the gold standard to delineate surgical targets in focal drug-resistant epilepsy. SEEG uses electrodes placed directly into the brain to identify the seizure-onset zone (SOZ). However, its major constraint is limited brain coverage, potentially leading to misidentification of the 'true' SOZ. Here, we propose a framework to assess adequate SEEG sampling by coupling epileptic biomarkers with their spatial distribution and measuring the system's response to a perturbation of this coupling. We demonstrate that the system's response is strongest in well-sampled patients when virtually removing the measured SOZ. We then introduce the spatial perturbation map, a tool that enables qualitative assessment of the implantation coverage. Probability modelling reveals a higher likelihood of well-implanted SOZs in seizure-free patients or non-seizure free patients with incomplete SOZ resections, compared to non-seizure-free patients with complete resections. This highlights the framework's value in sparing patients from unsuccessful surgeries resulting from poor SEEG coverage.

Artificial intelligence in epilepsy - applications and pathways to the clinic

Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy have increased exponentially over the past decade. Integration of AI into epilepsy management promises to revolutionize the diagnosis and treatment of this complex disorder. However, translation of AI into neurology clinical practice has not yet been successful, emphasizing the need to consider progress to date and assess challenges and limitations of AI. In this Review, we provide an overview of AI applications that have been developed in epilepsy using a variety of data modalities: neuroimaging, electroencephalography, electronic health records, medical devices and multimodal data integration. For each, we consider potential applications, including seizure detection and prediction, seizure lateralization, localization of the seizure-onset zone and assessment for surgical or neurostimulation interventions, and review the performance of AI tools developed to date. We also discuss methodological considerations and challenges that must be addressed to successfully integrate AI into clinical practice. Our goal is to provide an overview of the current state of the field and provide guidance for leveraging AI in future to improve management of epilepsy.

EANM practice guidelines for an appropriate use of PET and SPECT for patients with epilepsy

Epilepsy is one of the most frequent neurological conditions with an estimated prevalence of more than 50 million people worldwide and an annual incidence of two million. Although pharmacotherapy with anti-seizure medication (ASM) is the treatment of choice, ~30% of patients with epilepsy do not respond to ASM and become drug resistant. Focal epilepsy is the most frequent form of epilepsy. In patients with drug-resistant focal epilepsy, epilepsy surgery is a treatment option depending on the localisation of the seizure focus for seizure relief or seizure freedom with consecutive improvement in quality of life. Beside examinations such as scalp video/electroencephalography (EEG) telemetry, structural, and functional magnetic resonance imaging (MRI), which are primary standard tools for the diagnostic work-up and therapy management of epilepsy patients, molecular neuroimaging using different radiopharmaceuticals with single-photon emission computed tomography (SPECT) and positron emission tomography (PET) influences and impacts on therapy decisions. To date, there are no literature-based praxis recommendations for the use of Nuclear Medicine (NM) imaging procedures in epilepsy. The aims of these guidelines are to assist in understanding the role and challenges of radiotracer imaging for epilepsy; to provide practical information for performing different molecular imaging procedures for epilepsy; and to provide an algorithm for selecting the most appropriate imaging procedures in specific clinical situations based on current literature. These guidelines are written and authorized by the European Association of Nuclear Medicine (EANM) to promote optimal epilepsy imaging, especially in the presurgical setting in children, adolescents, and adults with focal epilepsy. They will assist NM healthcare professionals and also specialists such as Neurologists, Neurophysiologists, Neurosurgeons, Psychiatrists, Psychologists, and others involved in epilepsy management in the detection and interpretation of epileptic seizure onset zone (SOZ) for further treatment decision. The information provided should be applied according to local laws and regulations as well as the availability of various radiopharmaceuticals and imaging modalities.

Association of Localizing 18F-FDG-PET Hypometabolism and Outcome Following Epilepsy Surgery: Systematic Review and Meta-Analysis

BACKGROUND AND OBJECTIVES

Although commonly used in the evaluation of patients for epilepsy surgery, the association between the detection of localizing 18fluorine fluorodeoxyglucose PET (18F-FDG-PET) hypometabolism and epilepsy surgery outcome is uncertain. We conducted a systematic review and meta-analysis to determine whether localizing 18F-FDG-PET hypometabolism is associated with favorable outcome after epilepsy surgery.

METHODS

A systematic literature search was undertaken. Eligible publications included evaluation with 18F-FDG-PET before epilepsy surgery, with ≥10 participants, and those that reported surgical outcome at ≥12 months. Random-effects meta-analysis was used to calculate the odds of achieving a favorable outcome, defined as Engel class I, International League Against Epilepsy class 1-2, or seizure-free, with localizing 18F-FDG-PET hypometabolism, defined as concordant with the epilepsy surgery resection zone. Meta-regression was used to characterize sources of heterogeneity.

RESULTS

The database search identified 8,916 studies, of which 98 were included (total patients n = 4,104). Localizing 18F-FDG-PET hypometabolism was associated with favorable outcome after epilepsy surgery for all patients with odds ratio (OR) 2.68 (95% CI 2.08-3.45). Subgroup analysis yielded similar findings for those with (OR 2.64, 95% CI 1.54-4.52) and without epileptogenic lesion detected on MRI (OR 2.49, 95% CI 1.80-3.44). Concordance with EEG (OR 2.34, 95% CI 1.43-3.83), MRI (OR 1.69, 95% CI 1.19-2.40), and triple concordance with both (OR 2.20, 95% CI 1.32-3.64) was associated with higher odds of favorable outcome. By contrast, diffuse 18F-FDG-PET hypometabolism was associated with worse outcomes compared with focal hypometabolism (OR 0.34, 95% CI 0.22-0.54).

DISCUSSION

Localizing 18F-FDG-PET hypometabolism is associated with favorable outcome after epilepsy surgery, irrespective of the presence of an epileptogenic lesion on MRI. The extent of 18F-FDG-PET hypometabolism provides additional information, with diffuse hypometabolism associated with worse surgical outcome than focal 18F-FDG-PET hypometabolism. These findings support the incorporation of 18F-FDG-PET into routine noninvasive investigations for patients being evaluated for epilepsy surgery to improve epileptogenic zone localization and to aid patient selection for surgery.

The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach

Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.

Interictal Electrical Source Imaging

SUMMARY

Interictal electrical source imaging (ESI) determines the neuronal generators of epileptic activity in EEG occurring outside of seizures. It uses computational models to take anatomic and neuronal characteristics of the individual patient into account. The presented article provides an overview of application and clinical value of interictal ESI in patients with pharmacoresistant focal epilepsies undergoing evaluation for surgery. Neurophysiological constraints of interictal data are discussed and technical considerations are summarized. Typical indications are covered as well as issues of integration into clinical routine. Finally, an outlook on novel markers of epilepsy for interictal source analysis is presented. Interictal ESI provides diagnostic performance on par with other established methods, such as MRI, PET, or SPECT. Although its accuracy benefits from high-density recordings, it provides valuable information already when applied to EEG with only a limited number of electrodes with complete coverage. Novel oscillatory markers and the integration of frequency coupling and connectivity may further improve accuracy and efficiency.

Machine learning-based classification of physiological and pathological high-frequency oscillations recorded by stereoelectroencephalography

OBJECTIVE

High-frequency oscillations (HFOs) are an efficient indicator to locate the epileptogenic zone (EZ). However, physiological HFOs produced in the normal brain region may interfere with EZ localization. The present study aimed to build a machine learning-based classifier to distinguish the properties of each HFO event based on features in different domains.

METHODS

HFOs were detected in focal epilepsy patients from two different hospitals who underwent stereoelectroencephalography and subsequent resection surgery. Subsequently, 37 features in four different domains (time, frequency and time-frequency, entropy-based and nonlinear) were extracted for each HFO. After extraction, a fast correlation-based filter (FCBF) algorithm was applied for feature selection. The machine learning classifier was trained on the feature matrix with and without FCBF and then tested on the data set from patients in another hospital.

RESULTS

A dataset was compiled, consisting of 89,844 pathological HFOs and 23,613 physiological HFOs from 17 patients assigned to the training dataset. Additionally, 12,695 pathological HFOs and 5,599 physiological HFOs from 9 patients were assigned to the testing dataset. Four features (ripple band power, arithmetic mean, Petrosian fractal dimension and zero crossings) were obtained for classifier training after FCBF. The classifier showed an area under the curve (AUC) of 0.95/0.98 for FCBF/no FCBF features in the training dataset and AUC of 0.82/0.90 for FCBF/no FCBF features in the testing dataset. Our findings indicated that the classifier utilizing all features demonstrated superior performance compared to the one relying on FCBF-processed features.

CONCLUSION

Our classifier could reliably differentiate pathological HFOs from physiological ones, which could promote the development of HFOs in EZ localization.

New insights for predicting surgery outcome in patients with temporal lobe epilepsy. A systematic review

Resective surgery is the treatment of choice for one-third of adult patients with focal, drug-resistant epilepsy. This procedure is associated with substantial clinical and cognitive risks. In clinical practice, there is no validated model for epilepsy surgery outcome prediction (ESOP). Meta-analyses on ESOP studies assessing prognostic factors report discrepancies in terms of study design. Our review aims to systematically investigate methodological and analytical aspects of studies predicting clinical and cognitive outcomes after temporal lobe epilepsy surgery. A systematic review of ESOP studies published between 2000 and 2022 from three databases (MEDLINE, Web of Science, and PsycINFO) was completed by following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. It yielded 4867 articles. Among them, 21 corresponded to our inclusion criteria and were therefore retained in the final review. The risk of bias was assessed using A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST). Data extracted from the 21 studies were analyzed using narrative synthesis and descriptive statistics. Our findings show an increase in the use of multimodal datasets and machine learning analyses in recent ESOP studies, although regression remained the most frequently used approach. We also identified a more frequent use of network notions in recent ESOP studies. Nevertheless, several methodological issues were noted, such as small sample sizes, lack of information on the follow-up period, variability in seizure outcome, and the definition of neuropsychological postoperative change. Of 21 studies, only one provided a clinical tool to anticipate the cognitive outcome after epilepsy surgery. We conclude that methodological issues should be overcome before we move towards more complete models to better predict clinical and cognitive outcomes after epilepsy surgery. Recommendations for future studies to harness the possibilities of multimodal datasets and data fusion, are provided. A stronger bridge between fundamental and clinical research may result in developing accessible clinical tools.

Value of semiology in predicting epileptogenic zone and surgical outcome following frontal lobe epilepsy surgery

OBJECTIVE

To evaluate the ability of semiology alone in localising the epileptogenic zone (EZ) in people with frontal lobe epilepsy (FLE) who underwent resective surgery.

METHODS

We examined data on all individuals who had FLE surgery at our centre between January 01, 2011 and December 31, 2020. Descriptions of ictal semiology were obtained from video-EEG telemetry reports and presurgical multidisciplinary meeting summaries. The putative EZ was represented by the final site of resection. We assessed how well initial and combined set-of-semiologies correlated anatomically with the EZ, using a semiology visualisation tool to generate probabilistic cortical heatmaps of involvement in seizures.

RESULTS

Sixty-one individuals had FLE surgery over the study period. Twelve months following surgery, 28/61 (46%) were completely seizure-free, with a further eight experiencing only auras. Comparing the semiology database with the putative EZ, combined set-of-semiology correctly lateralised in 77% (95% CI: 69-85%), localised to the frontal lobe in 57% (95% CI: 48-67%), frontal lobe subregions in 52% (95% CI: 43-62%), and frontal gyri in 25% (95% CI: 16-33%). No difference in degree of correlation was seen comparing those with ongoing seizures 12 months after surgery to those seizure free.

SIGNIFICANCE

Semiology alone was able to correctly lateralize the putative EZ in 77%, and localise to a sublobar level in approximately half of individuals who had FLE surgery. Semiology is not adequate alone and must be combined with imaging and EEG data to identify the epileptogenic zone.

18F-FDG-PET hypometabolism as a predictor of favourable outcome in epilepsy surgery: protocol for a systematic review and meta-analysis

INTRODUCTION

A substantial proportion of patients who undergo surgery for drug resistant focal epilepsy do not become seizure free. While some factors, such as the detection of hippocampal sclerosis or a resectable lesion on MRI and electroencephalogram-MRI concordance, can predict favourable outcomes in epilepsy surgery, the prognostic value of the detection of focal hypometabolism with ¹⁸F-fluorodeoxyglucose positive emission tomography (¹⁸F-FDG-PET) hypometabolism is uncertain. We propose a protocol for a systematic review and meta-analysis to examine whether localisation with ¹⁸F-FDG-PET hypometabolism predicts favourable outcomes in epilepsy surgery.

METHODS AND ANALYSIS

A systematic literature search of Medline, Embase and Web of Science will be undertaken. Publications which include evaluation with ¹⁸F-FDG-PET prior to surgery for drug resistant focal epilepsy, and which report ≥12 months of postoperative surgical outcome data will be included. Non-human, non-English language publications, publications with fewer than 10 participants and unpublished data will be excluded. Screening and full-text review of publications for inclusion will be undertaken by two independent investigators, with discrepancies resolved by consensus or a third investigator. Data will be extracted and pooled using random effects meta-analysis, with heterogeneity quantified using the I² analysis.

ETHICS AND DISSEMINATION

Ethics approval is not required. Once complete, the systematic review will be published in a peer-reviewed journal.

PROSPERO REGISTRATION NUMBER

CRD42022324823.

Integrating Network Neuroscience Into Epilepsy Care: Progress, Barriers, and Next Steps

Drug resistant epilepsy is a disorder involving widespread brain network alterations. Recently, many groups have reported neuroimaging and electrophysiology network analysis techniques to aid medical management, support presurgical planning, and understand postsurgical seizure persistence. While these approaches may supplement standard tests to improve care, they are not yet used clinically or influencing medical or surgical decisions. When will this change? Which approaches have shown the most promise? What are the barriers to translating them into clinical use? How do we facilitate this transition? In this review, we will discuss progress, barriers, and next steps regarding the integration of brain network analysis into the medical and presurgical pipeline.

Psychiatric symptoms and comorbidities in patients with drug-resistant epilepsy in presurgical assessment-A prospective explorative single center study

INTRODUCTION

People with epilepsy (PWE) have a higher prevalence of psychiatric disorders. Some individuals with drug-resistant epilepsy might benefit from surgical interventions. The aim of this study was to perform an assessment of psychiatric comorbidities with a follow-up period of 12 months in patients with drug-resistant epilepsy, comparing those who underwent surgery to those who did not.

MATERIAL AND METHODS

We assessed psychiatric comorbidities at baseline, after 4 months and after 12 months. Psychiatric symptoms and diagnoses were assessed using SCID-Interview, Hamilton Rating Scale for Depression, Beck-Depression Inventory, Hamilton Anxiety Rating Scale, Prodromal-Questionnaire and the Global Assessment of Functioning Scale.

RESULTS

Twenty-five patients were included in the study, 12 underwent surgery, 11 were esteemed as being neurologically unqualified for surgery and two refused surgery. Patients in the no-surgery group were significantly older, reported more substance use, had significantly higher levels of anxiety and were more often diagnosed with a personality disorder. Age and levels of anxiety were significant predictors of being in the surgery or the no-surgery group. The described differences between surgery and no-surgery patients did not change significantly over the follow-up period.

DISCUSSION

These data point toward a higher expression of baseline psychiatric symptoms in drug-resistant PWE without surgery. Further studies are warranted to further elucidate these findings and to clarify potential psychotropic effects of epilepsy itself, drug-resistant epilepsy and of epilepsy surgery and their impact on psychopathology. Clinically, it seems highly relevant to include psychiatrists in an interdisciplinary state-of-the-art perioperative management of drug-resistant PWE.