Practical measurements distinguishing physiological and pathological stereoelectroencephalography channels based on high-frequency oscillations in the human brain.

OBJECTIVE: The present study aimed to identify various distinguishing features for use in the accurate classification of stereoelectroencephalography (SEEG) channels based on high-frequency oscillations (HFOs) inside and outside the epileptogenic zone (EZ). METHODS: HFOs were detected in patients with focal epilepsy who underwent SEEG. Subsequently, HFOs within the seizure-onset and early spread zones were defined as pathological HFOs, whereas others were defined as physiological. Three features of HFOs were identified at the channel level, namely, morphological repetition, rhythmicity, and phase-amplitude coupling (PAC). A machine-learning (ML) classifier was then built to distinguish two HFO types at the channel level by application of the above-mentioned features, and the contributions were quantified. Further verification of the characteristics and classifier performance was performed in relation to various conscious states, imaging results, EZ location, and surgical outcomes. RESULTS: Thirty-five patients were included in this study, from whom 166 104 pathological HFOs in 255 channels and 53 374 physiological HFOs in 282 channels were entered into the analysis pipeline. The results revealed that the morphological repetitions of pathological HFOs were markedly higher than those of the physiological HFOs; this was also observed for rhythmicity and PAC. The classifier exhibited high accuracy in differentiating between the two forms of HFOs, as indicated by an area under the curve (AUC) of 0.89. Both PAC and rhythmicity contributed significantly to this distinction. The subgroup analyses supported these findings. SIGNIFICANCE: The suggested HFO features can accurately distinguish between pathological and physiological channels substantially improving its usefulness in clinical localization. PLAIN LANGUAGE SUMMARY: In this study, we computed three quantitative features associated with HFOs in each SEEG channel and then constructed a machine learning-based classifier for the classification of pathological and physiological channels. The classifier performed well in distinguishing the two channel types under different levels of consciousness as well as in terms of imaging results, EZ location, and patient surgical outcomes.

The 3-Dimensional Intelligent Structured Light Technique: A New Registration Method in Stereotactic Neurosurgery.

BACKGROUND AND OBJECTIVES: Surface-based facial scanning registration emerged as an essential registration method in the robot-assisted neuronavigation surgery, providing a marker-free way to align a patient's facial surface with the imaging data. The 3-dimensional (3D) structured light was developed as an advanced registration method based on surface-based facial scanning registration. We aspire to introduce the 3D structured light as a new registration method in the procedure of the robot-assisted neurosurgery and assess the accuracy, efficiency, and safety of this method by analyzing the relative operative results. METHODS: We analyzed the results of 47 patients who underwent Ommaya reservoir implantation (n = 17) and stereotactic biopsy (n = 30) assisted by 3D structured light at our hospital from January 2022 to May 2023. The accuracy and additional operative results were analyzed. RESULTS: For the Ommaya reservoir implantation, the target point error was 3.2 ± 2.2 mm and the entry point error was 3.3 ± 2.4 mm, while the operation duration was 35.8 ± 8.3 minutes. For the stereotactic biopsy, the target point error was 2.3 ± 1.3 mm and the entry point error was 2.7 ± 1.2 mm, while the operation duration was 24.5 ± 6.3 minutes. CONCLUSION: The 3D structured light technique reduces the patients' discomfort and offers the advantage of a simpler procedure, which can improve the clinical efficiency with the sufficient accuracy and safety to meet the clinical requirements of the puncture and navigation.

Sensitive and Specific Loop-Mediated Isothermal Amplification Assays for Detection of Salmonella, CTX-M-1 Group Genes, mph(A), and ermB in Stool and Blood Samples Based on Orange to Green Visible Dye.

Salmonella is a globally prevalent foodborne bacterium, and ceftriaxone and azithromycin have been regarded as drugs of choice for treating Salmonella infections, particularly in children. With the growing incidence of ceftriaxone and azithromycin resistance in Salmonella, there is an urgent requirement for a rapid and dependable gene testing approach to enhance the efficacy of treating Salmonella infections. Utilizing the orange to green visible dye approach, this study developed loop-mediated isothermal amplification (LAMP) assays for the sensitive and specific detection of Salmonella, ceftriaxone and azithromycin resistance genes (including CTX-M-1 group, mph(A), and ermB genes) in stool and blood samples. The specificity and sensitivity of primers during the LAMP assays for detection of Salmonella, CTX-M-1 group, mph(A), and ermB genes were determined in this study. The detection threshold for Salmonella was found to be 1.5 × 103 colony-forming units (CFU)/mL, while it was 1.5 × 102 CFU/mL for CTX-M-1 group genes (including blaCTX-M-3, blaCTX-M-15, and blaCTX-M-55), 1.5 × 102 CFU/mL for mph(A), and 1.5 × 102 CFU/mL for ermB, showing 10-103-fold, 103-fold, and 105-fold increased sensitivity compared with the polymerase chain reaction assay, respectively. Results indicated that the LAMP primers designed for Salmonella, CTX-M-1 group, mph(A), and ermB genes possess high specificity (100%) and sensitivity (over 94%). This novel approach advocates its application in detecting Salmonella, CTX-M-1 group, mph(A), and ermB genes.

Deep Learning Approaches for Imaging-Based Automated Segmentation of Tuberous Sclerosis Complex.

The present study presents a novel approach for identifying epileptogenic tubers in patients with tuberous sclerosis complex (TSC) and automating tuber segmentation using a three-dimensional convolutional neural network (3D CNN). The study retrospectively included 31 TSC patients whose lesions were manually annotated from multiparametric neuroimaging data. Epileptogenic tubers were determined via presurgical evaluation and stereoelectroencephalography recording. Neuroimaging metrics were extracted and compared between epileptogenic and non-epileptogenic tubers. Additionally, five datasets with different preprocessing strategies were used to construct and train 3D CNNs for automated tuber segmentation. The normalized positron emission tomography (PET) metabolic value was significantly lower in epileptogenic tubers defined via presurgical evaluation (p = 0.001). The CNNs showed high performance for localizing tubers, with an accuracy between 0.992 and 0.994 across the five datasets. The automated segmentations were highly correlated with clinician-based features. The neuroimaging characteristics for epileptogenic tubers were demonstrated, increasing surgical confidence in clinical practice. The validated deep learning detection algorithm yielded a high performance in determining tubers with an excellent agreement with reference clinician-based segmentation. Collectively, when coupled with our investigation of minimal input requirements, the approach outlined in this study represents a clinically invaluable tool for the management of TSC.

Hypometabolic patterns are related to post-surgical seizure outcomes in focal cortical dysplasia: A semi-quantitative study.

OBJECTIVE: Fluorine-18-fluorodeoxyglucose-positron emission tomography (FDG-PET) is routinely used for presurgical evaluation in many epilepsy centers. Hypometabolic characteristics have been extensively examined in prior studies, but the metabolic patterns associated with specific pathological types of drug-resistant epilepsy remain to be fully defined. This study was developed to explore the relationship between metabolic patterns or characteristics and surgical outcomes in type I and II focal cortical dysplasia (FCD) patients based on results from a large cohort. METHODS: Data from individuals who underwent epilepsy surgery from 2014 to 2019 with a follow-up duration of over 3 years and a pathological classification of type I or II FCD in our hospital were retrospectively analyzed. Hypometabolic patterns were quantitatively identified via statistical parametric mapping (SPM) and qualitatively analyzed via visual examination of PET-MRI co-registration images. Univariate analyses were used to explore the relationship between metabolic patterns and surgical outcomes. RESULTS: In total, this study included data from 210 patients. Following SPM calculations, four hypometabolic patterns were defined including unilobar, multi-lobar, and remote patterns as well as cases where no pattern was evident. In type II FCD patients, the unilobar pattern was associated with the best surgical outcomes (p = 0.014). In visual analysis, single gyrus (p = 0.032) and Clear-cut hypometabolism edge (p = 0.040) patterns exhibited better surgery outcomes in the type II FCD group. CONCLUSIONS: PET metabolic patterns are well-correlated with the prognosis of type II FCD patients. However, similar correlations were not observed in type I FCD, potentially owing to the complex distribution of the epileptogenic region. PLAIN LANGUAGE SUMMARY: In this study, we demonstrated that FDG-PET was a crucial examination for patients with FCD, which was a common cause of epilepsy. We compared the surgical prognosis for patients with different hypometabolism distribution patterns and found that clear and focal abnormal region in PET was correlated with good surgical outcome in type II FCD patients.

Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in focal epilepsy.

OBJECTIVE: We aimed to develop a new approach for identifying the localization of the seizure onset zone (SOZ) based on corticocortical evoked potentials (CCEPs) and to compare the connectivity patterns in patients with different clinical phenotypes. METHODS: Fifty patients who underwent stereoelectroencephalography and CCEP procedures were included. Logistic regression was used in the model, and six CCEP metrics were input as features: root mean square of the first peak (N1RMS) and second peak (N2RMS), peak latency, onset latency, width duration, and area. RESULTS: The area under the curve (AUC) for localizing the SOZ ranged from 0.88 to 0.93. The N1RMS values in the hippocampus sclerosis (HS) group were greater than that of the focal cortical dysplasia (FCD) IIa group (p < 0.001), independent of the distance between the recorded and stimulated sites. The sensitivity of localization was higher in the seizure-free group than in the non-seizure-free group (p = 0.036). CONCLUSIONS: This new method can be used to predict the SOZ localization in various focal epilepsy phenotypes. SIGNIFICANCE: This study proposed a machine-learning approach for localizing the SOZ. Moreover, we examined how clinical phenotypes impact large-scale abnormality of the epileptogenic networks.

Magnetic resonance-guided laser interstitial thermal therapy vs. open surgery for drug-resistant mesial temporal lobe epilepsy: a propensity score matched retrospective cohort study.

BACKGROUND: Magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) and traditional open surgery (OS) are effective and safe options for patients with drug-resistant mesial temporal lobe epilepsy (DR-mTLE). However, their superiority in seizure control and preservation of functional abilities remains unclear. This study aimed to compare the surgical outcomes of MRgLITT and OS. MATERIALS AND METHODS: This multicenter retrospective cohort study included patients with DR-mTLE who underwent MRgLITT or OS at three centres between 2015 and 2023. The data on patient demographics, presurgical non-invasive evaluation, stereoelectroencephalography (SEEG) implantation, memory alteration, and seizure outcomes were collected. Propensity score matching (PSM) analysis was conducted for the comparison of seizure control and functional preservation between two surgical approaches. RESULTS: Of the 244 individuals who met the study criteria, 33 underwent MRgLITT and 211 OS. The median (interquartile range) age at seizure onset was 22.0 (13.0) and 12.3 (10.0) years in the MRgLITT and OS groups, respectively. The first PSM, based on demographic and non-invasive information, resulted in 26 matched pairs for the primary analysis. There were no significant differences in memory preservation ( P = 0.95) or surgical outcomes ( P = 0.96) between the groups. The second PSM, based on demographics and SEEG implantation, yielded 32 matched pairs for the sensitivity analysis, showing similar results. Subset analysis of early and late MRgLITT cases revealed no statistically significant differences in the proportion of patients with memory decline ( P = 0.42) or seizure control ( P = 1.00). Patients who underwent SEEG implantation were 96% less likely to achieve seizure freedom after MRgLITT ( P = 0.02). CONCLUSION: Minimally invasive MRgLITT is associated with memory preservation and seizure control, similar to traditional OS. MRgLITT is effective and safe for DR-mTLE and is relevant for future prospective randomized trials on dominant-side mTLE, providing practical implications for guiding neurosurgeons in the selection of surgical approaches.

Early detection of stroke at the sudden sensorineural hearing loss stage.

Background and purpose: Sudden sensorineural hearing loss (SSNHL) can be a prodromal symptom of ischemic stroke, especially posterior circulation strokes in the anterior inferior cerebellar artery (AICA) area. Early diagnosis and optimal treatment for vascular SSNHL provide an opportunity to prevent more extensive area infarction. The objective of our research was to find clues that suggest stroke at the stage of isolated sudden hearing loss. Methods: We retrospectively investigated the medical records of patients who received an initial diagnosis of sudden sensorineural hearing loss upon admission from January 2017 to December 2022 at Capital Medical University Affiliated Beijing Tiantan Hospital. Among these patients, 30 individuals who developed acute ischemic stroke during their hospital stay were enrolled as the case group. To create a control group, we matched individuals from the nonstroke idiopathic SSNHL patients to the case group in terms of age (±3 years old) at a ratio of 1:4. We collected the clinical characteristics, pure tone hearing threshold test results, and imaging information for all patients included in the study. Results: Three models were constructed to simulate different clinical situations and to identify vascular sudden sensorineural hearing loss (SSNHL). The results revealed that patients with SSNHL who had three or more stroke risk factors, bilateral hearing loss, moderately severe to total hearing loss, and any intracranial large artery stenosis and occlusion (≥50%) were at a higher risk of developing ischemic stroke during hospitalization. Consistent with previous studies, the presence of vertigo at onset also played a significant role in the early detection of upcoming stroke. Conclusion: Clinicians should be alert to SSNHL patients with bilateral hearing loss, moderately severe to total hearing loss and other aforementioned features. Early pure tone audiometric hearing assessment and vascular assessment are necessary for high-risk patients with SSNHL.

Multimodal imaging-based diagnostic approach for MRI-negative posterior cortex epilepsy.

Background: Posterior cortex epilepsy (PCE) primarily comprises seizures originating from the occipital, parietal, and/or posterior edge of the temporal lobe. Electroclinical dissociation and subtle imaging representation render the diagnosis of PCE challenging. Improved methods for accurately identifying patients with PCE are necessary. Objectives: To develop a novel voxel-based image postprocessing method for better visual identification of the neuroimaging abnormalities associated with PCE. Design: Multicenter, retrospective study. Methods: Clinical and imaging features of 165 patients with PCE were retrospectively reviewed and collected from five epilepsy centers. A total of 37 patients (32.4% female, 20.2 ± 8.9 years old) with magnetic resonance imaging (MRI)-negative PCE were finally included for analysis. Image postprocessing features were calculated over a neighborhood for each voxel in the multimodality data. The postprocessed maps comprised structural deformation, hyperintense signal, and hypometabolism. Five raters from three different centers were blinded to the clinical diagnosis and determined the neuroimaging abnormalities in the postprocessed maps. Results: The average accuracy of correct identification was 55.7% (range from 43.2 to 62.2%) and correct lateralization was 74.1% (range from 64.9 to 81.1%). The Cronbach's alpha was 0.766 for the correct identification and 0.683 for the correct lateralization with similar results of the interclass correlation coefficient, thus indicating reliable agreement between the raters. Conclusion: The image postprocessing method developed in this study can potentially improve the visual detection of MRI-negative PCE. The technique could lead to an increase in the number of patients with PCE who could benefit from the surgery.

Neural network mapping of gelastic behavior in children with hypothalamus hamartoma.

BACKGROUND: Hypothalamus hamartomas (HHs) are rare, congenital, tumor-like, and nonprogressive malformations resulting in drug-resistant epilepsy, mainly affecting children. Gelastic seizures (GS) are an early hallmark of epilepsy with HH. The aim of this study was to explore the disease progression and the underlying physiopathological mechanisms of pathological laughter in HH. METHODS: We obtained clinical information and metabolic images of 56 HH patients and utilized ictal semiology evaluation to stratify the specimens into GS-only, GS-plus, and no-GS subgroups and then applied contrasted trajectories inference (cTI) to calculate the pseudotime value and evaluate GS progression. Ordinal logistic regression was performed to identify neuroimaging-clinical predictors of GS, and then voxelwise lesion network-symptom mapping (LNSM) was applied to explore GS-associated brain regions. RESULTS: cTI inferred the specific metabolism trajectories of GS progression and revealed increased complexity from GS to other seizure types. This was further validated via actual disease duration (Pearson R = 0.532, P = 0.028). Male sex [odds ratio (OR) = 2.611, P = 0.013], low age at seizure onset (OR = 0.361, P = 0.005), high normalized HH metabolism (OR = - 1.971, P = 0.037) and severe seizure burden (OR = - 0.006, P = 0.032) were significant neuroimaging clinical predictors. LNSM revealed that the dysfunctional cortico-subcortico-cerebellar network of GS and the somatosensory cortex (S1) represented a negative correlation. CONCLUSIONS: This study sheds light on the clinical characteristics and progression of GS in children with HH. We identified distinct subtypes of GS and demonstrated the involvement of specific brain regions at the cortical-subcortical-cerebellar level. These valuable results contribute to our understanding of the neural correlates of GS.

Altered Metabolic Networks in Mesial Temporal Lobe Epilepsy with Focal to Bilateral Seizures.

This study was designed to identify whether the metabolic network changes in mesial temporal lobe epilepsy (MTLE) patients with focal to bilateral tonic-clonic seizures (FBTCS) differ from changes in patients without FBTCS. This retrospective analysis enrolled 30 healthy controls and 54 total MTLE patients, of whom 27 had FBTCS. Fluorodeoxyglucose positron emission tomography (FDG-PET) data and graph theoretical analyses were used to examine metabolic connectivity. The differences in metabolic networks between the three groups were compared. Significant changes in both local and global network topology were evident in FBTCS+ patients as compared to healthy controls, with a lower assortative coefficient and altered betweenness centrality in 15 brain regions. While global network measures did not differ significantly when comparing FBTCS- patients to healthy controls, alterations in betweenness centrality were evident in 13 brain regions. Significantly altered betweenness centrality was also observed in four brain regions when comparing patients with and without FBTCS. The study revealed greater metabolic network abnormalities in MTLE patients with FBTCS as compared to FBTCS- patients, indicating the existence of distinct epileptogenic networks. These findings can provide insight into the pathophysiological basis of FBTCS.

Associated factors with stimulation induced seizures and the relevance with surgical outcomes.

OBJECTIVE: To analyze the associated factors with stimulation-induced seizures (SIS) and the relevant factors in predicting surgical outcomes. METHODS: We analyzed 80 consecutive epilepsy patients explored by stereo-electroencephalography with routine electrical stimulation mapping (ESM). If seizures induced by ESM, patients were classified as SIS-positive (SIS-P); otherwise, SIS-negative (SIS-N). Patients received radical surgery were further classified as favorable (Engel I) and unfavorable (Engel II-IV) groups. RESULTS: Of the 80 patients included, we identified 44 (55.0%) and 36(45.0%) patients in the SIS-P and SIS-N groups, respectively. Multivariate analysis revealed that the seizure onset pattern (SOP) of preceding repetitive epileptiform discharges following LVFA (PRED→LVFA) (OR 3.319, 95% CI 1.200-9.183, P = 0.021) and pathology of focal cortical dysplasia (FCD) type II (OR 3.943, 95% CI 1.093-14.226, P = 0.036) were independent factors influencing whether the electrical stimulation can induce a seizure. Among the patients received radical surgery, there were 55 and 15 patients in the favorable and unfavorable groups separately. Multivariate analysis revealed that the SOP of PRED→LVFA induced seizures by stimulation (OR 11.409, 95% CI 1.182-110.161, P = 0.035) and bilateral implantation (OR 0.048, 95% CI 0.005-0.497, P = 0.011) were independent factors affecting surgical outcomes. The previous epilepsy surgery had a trend to be a negative factor with SIS (OR 0.156, 95% CI 0.028-0.880, P = 0.035) and surgical outcomes (OR 0.253, 95% CI 0.053-1.219, P = 0.087). CONCLUSION: ESM is a highly valuable method for localizing the seizure onset zone. The SOP of PRED→LVFA and FCD type II were associated with elicitation of SIS by ESM, whereas a previous epilepsy surgery showed a negative association. Furthermore, the SOP of PRED→LVFA together with SIS in the same patient predicted favorable surgical outcomes, whereas bilateral electrode implantation predicted unfavorable outcomes.

Surface-Based Neuroimaging Pattern of Multiple System Atrophy.

RATIONALE AND OBJECTIVES: Overlapping parkinsonism, cerebellar ataxia, and pyramidal signs render challenges in the clinical diagnosis of multiple system atrophy (MSA). The neuroimaging pattern is valuable to understand its pathophysiology and improve its diagnostic effect. MATERIALS AND METHODS: We retrospectively obtained magnetic resonance imaging and susceptibility-weighted imaging in patients with MSA (including parkinsonian type [MSA-P] and cerebellar type [MSA-C]), Parkinson's disease, and normal controls. We quantified neuroimaging features to identify the optimal threshold for diagnosis. Furthermore, we explore neuroimaging patterns of MSA by mapping the subcortical morphological alterations and constructing a diagnostic model. RESULTS: Compared to controls, normalized putaminal volume significantly decreased in patients with MSA-P (P < .001) and normalized pontine volume significantly decreased in patients with MSA-C (P < .001). The Youden index of the threshold-based clinical prediction model was 0.871-0.928 in patients with MSA. The neuroimaging pattern in patients with MSA was jointly located in the lateral putamen, and the neuroimaging pattern prediction model achieved a classification accuracy of 83.9%-100%. CONCLUSION: The quantitative neuroimaging features and surface-based morphologic anomalies represent the markers of MSA and open new avenues for personalized clinical diagnosis.

The altered spontaneous neural activity in patients with Parkinson's disease and its predictive value for the motor improvement of deep brain stimulation.

BACKGROUND: This study aims to investigate the altered spontaneous neural activity in patients with Parkinson's disease (PD) revealed by amplitudes of low-frequency fluctuations (ALFF) of resting-state fMRI, and the feasibility of using ALFF as neuroimaging predictors for motor improvement after bilateral subthalamic nucleus (STN) deep brain stimulation (DBS). METHODS: Fourty-four patients and 44 healthy controls were included in this study. First, the ALFF of patients with PD was compared with that of controls; then significant clusters were correlated with motor improvement after DBS (unified Parkinson's disease rating scale (UPDRS-III)) and other clinical variables. Second, regression and classification of the machine learning models were conducted to predict motor improvement after DBS. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the classification model. RESULTS: Compared with healthy controls, patients with PD showed increased ALFF in the bilateral motor area and decreased ALFF in the bilateral temporal cortex and cerebellum. The Hoehn-Yahr stages correlated with ALFF within the bilateral cerebellum (p = 0.021), and UPDRS-III improvement correlated with ALFF in the left (p < 0.001) and right (p = 0.005) motor areas. The regression model showed a significant correlation between the predicted and observed UPDRS-III changes (R = 0.65, p < 0.001). The ROC analysis revealed an area under the curve (AUC) of 0.94 which differentiated moderate and superior DBS responders. CONCLUSION: The results revealed altered ALFF patterns in patients with PD and their correlations with clinical variables. Both binary and continuous ALFF can potentially serve as predictive biomarkers for DBS response.

Efficient volume-based localization and automatic labeling of intracranial depth electrodes.

Background: The accurate localization and anatomical labeling of intracranial depth electrodes are crucial for stereoelectroencephalography (SEEG) recordings and the interpretation of results in patients with epilepsy. The laborious electrode localization procedure requires an efficient and easy-to-use pipeline. Thus, we developed a useful tool, which we called the depth electrode localizer (DELLO), to automatically identify and label depth electrode contacts with ease. Methods: The DELLO is an open-source package developed in MATLAB (MathWorks). It was specifically fine-tuned to expedite the localization of depth electrodes. The basic procedures include preoperative magnetic resonance imaging (MRI) and postoperative computed tomography coregistration, intensity threshold electrode spatial sampling, the hierarchical clustering of electrode samples, and gray-matter and automatic anatomical labeling (AAL). The DELLO also has a graphical user interface (GUI) that can be used to review the results. The only manual intervention procedures are the identification of the target (tip) and entry point of each electrode and the naming of the clustered electrode contact groups, which generally take ~5 min per case. The coordinates of each contact were recorded in individual spaces and were also transformed in standard space by applying a volume-based deformation field. To validate the performance of the current method, 7 patients with epilepsy were retrospectively included in the analysis. Results: A total of 80 depth electrodes, including 1,030 contacts from the 7 patients with epilepsy, were localized. All the procedures functioned well, and the entire process was robust and intuitive. Among the 1,030 contacts, 746 (72.43%) were labeled as inside the gray matter. The gray-matter and AAL accuracy rates were 95.83% and 90.78%, respectively, over all contacts. Conclusions: The DELLO is an integrated tool that was designed to semi-automatically localize and label intracranial depth electrodes. It is open source and freely available. Given its high accuracy and efficiency, the DELLO could facilitate SEEG interpretation and be used in SEEG-based cognitive neuroscience studies.

Interictal HFO and FDG-PET correlation predicts surgical outcome following SEEG.

OBJECTIVE: This study aimed to investigate the quantitative relationship between interictal 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and interictal high-frequency oscillations (HFOs) from stereo-electroencephalography (SEEG) recordings in patients with refractory epilepsy. METHODS: We retrospectively included 32 patients. FDG-PET data were quantified through statistical parametric mapping (SPM) t test modeling with normal controls. Interictal SEEG segments with four, 10-min segments were selected randomly. HFO detection and classification procedures were automatically performed. Channel-based HFOs separating ripple (80-250 Hz) and fast ripple (FR; 250-500 Hz) counts were correlated with the surrounding metabolism T score at the individual and group level, respectively. The association was further validated across anatomic seizure origins and sleep vs wake states. We built a joint feature FR × T reflecting the FR and hypometabolism concordance to predict surgical outcomes in 28 patients who underwent surgery. RESULTS: We found a negative correlation between interictal FDG-PET and HFOs through the linear mixed-effects model (R2  = .346 and .457 for ripples and FRs, respectively, p < .001); these correlations were generalizable to different epileptogenic-zone lobar localizations and vigilance states. The FR × T inside the resection volume could be used as a predictor for surgical outcomes with an area under the curve of 0.81. SIGNIFICANCE: The degree of hypometabolism is associated with HFO generation rate, especially for FRs. This relationship would be meaningful for selection of SEEG candidates and for optimizing SEEG scheme planning. The concordance between FRs and hypometabolism inside the resection volume could provide prognostic information regarding surgical outcome.

Brain-clinical signatures for vagus nerve stimulation response.

AIM: Vagus nerve stimulation (VNS) is a valuable treatment for drug-resistant epilepsy (DRE) without the indication of surgical resection. The clinical heterogeneity of DRE has limited the optimal indication of choice and diagnosis prediction. The study aimed to explore the correlations of brain-clinical signatures with the clinical phenotype and VNS responsiveness. METHODS: A total of 89 DRE patients, including VNS- (n = 44) and drug-treated (n = 45) patients, were retrospectively recruited. The brain-clinical signature consisted of demographic information and brain structural deformations, which were measured using deformation-based morphometry and presented as Jacobian determinant maps. The efficacy and presurgical differences between these two cohorts were compared. Then, the potential of predicting VNS response using brain-clinical signature was investigated according to the different prognosis evaluation approaches. RESULTS: The seizure reduction was higher in the VNS-treated group (42.50%) as compared to the drug-treated group (12.09%) (p = 0.11). Abnormal imaging representation, showing encephalomalacia (pcorrected  = 0.03), was commonly observed in the VNS-treated group (p = 0.04). In the patients treated with VNS, the mild/subtle brain abnormalities indicated higher seizure frequency (p = 0.03) and worse VNS response (p = 0.04). The partial least square regression analysis showed a moderate prediction potential of brain-clinical signature for VNS response (p < 0.01). The increase in the pre-VNS seizure frequency and structural etiology could indicate a worse prognosis (higher McHugh classification). CONCLUSION: The brain-clinical signature illustrated its clinical potential in predicting the VNS response, which might allow clinicians to personalize treatment decisions for DRE patients.

Magnetic Resonance-Guided Laser Interstitial Thermal Therapy for Hypothalamic Hamartoma: Surgical Approach and Treatment Outcomes.

Hypothalamic hamartoma (HH) is a rare lesion consisting of normal neurons and neuroglia arranged in an abnormal pattern which usually causes gelastic seizures (GS). Magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) has been developed as a minimally invasive approach to treat HH and gradually become a first-line treatment. In total, this study enrolled 47 consecutive HH patients that underwent one round of ablation. Patients were followed for at least one year. Patients' medical records and surgical information were carefully reviewed, and univariate analyses were performed. Of the treated patients, 72.3% remained GS-free in this study, with an overall Engel class I rate of 68.1%. Long-term postoperative complications occurred in six patients. Factors associated with GS prognosis included Delalande classification (p = 0.033), HH volume (p = 0.01), and the ablation rate of the HH body (p = 0.035). The disconnection rate was 0.73 ± 0.14 in the Engel class Ia group as compared to 0.62 ± 0.13 in the Engel Ib-Engel IV group (p = 0.046). MRgLITT represents a safe and effective surgical procedure. Patients with larger or Delalande type IV HH may require multiple rounds of ablation. In addition to assessing the degree of disconnection, ablation volume should also be carefully considered for patients undergoing this procedure.

Is intracranial electroencephalography mandatory for MRI-negative neocortical epilepsy surgery?

OBJECTIVE: MRI-negative focal epilepsy is one of the most challenging cases in surgical epilepsy treatment. Many epilepsy centers recommend intracranial electroencephalography (EEG) for MRI-negative cases, especially neocortical epilepsy. This retrospective study aimed to explore whether intracranial monitoring is mandatory in MRI-negative neocortical epilepsy surgery and the factors that significantly influence the decision on whether to perform intracranial recording. METHODS: In this study, consecutive surgical patients with focal MRI-negative neocortical epilepsy were recruited. All patients underwent routine preoperative evaluation according to the dedicated protocol of the authors' epilepsy center to determine the treatment strategy. Patients were divided into two groups according to the surgical strategy, i.e., a direct group and a stereo-EEG (SEEG)-guided group. History of epilepsy, seizure frequency, interictal and ictal EEG data, PET data, PET/MRI coregistration data, neuropathological findings, and surgical outcomes were compared between the two groups. Multivariate analysis was performed to identify factors influencing the decision to perform SEEG monitoring. RESULTS: Sixty-four patients were included in this study, 19 and 45 of whom underwent direct and SEEG-guided cortical resection, respectively. At an average follow-up of 3.9 years postoperatively, 56 patients (87.5%) had Engel class I results without permanent neurological deficits. Surgical outcomes were not significantly different between the direct and SEEG-guided groups (94.7% vs 84.4%). PET hypometabolic abnormalities were detected in all patients. There were significant differences between the two groups in the extent of hypometabolism (focal vs nonfocal, p < 0.01) and pathological subtype (focal cortical dysplasia type II vs others, p = 0.03). Multivariate analysis revealed that the extent of hypometabolism (OR 0.01, 95% CI 0.00-0.15; p = 0.001) was the only independent factor affecting the treatment strategy. CONCLUSIONS: Careful selection of patients with MRI-negative neocortical epilepsy may yield favorable outcomes after direct cortical resection without intracranial monitoring. PET/MRI coregistration plays an essential role in the preoperative evaluation and subsequent resection of these patients. Intracranial monitoring is not a mandatory requirement for surgery if the focal hypometabolic areas are consistent with the findings of semiology and scalp EEG.

Anatomical features decide the atypical seizure manifestation of parahypothalamic hamartomas.

Background: The intrahypothalamic phenotype of hypothalamic hamartomas (HH) is associated with epilepsy, and the parahypothalamic phenotype usually leads to central precocious puberty but not neurological comorbidities or seizures. No study has confirmed the pathological role of parahypothalamic hamartomas in epileptogenesis, and the underlying mechanism is yet to be elucidated. Objective: We aimed to investigate whether parahypothalamic hamartomas are intrinsically epileptogenic and elucidate the underlying pathway of epileptogenesis. Methods: We reviewed 92 patients with HH-related epilepsy, categorized them by the classification system of Delalande and Fohlen, and further classified Type I (corresponding to parahypothalamic HH) into the following three groups based on the relationship between the lesion and mammillary bodies (MB): entirely invaded (Group 1), partially connected (Group 2), and not connected at all (Group 3). We examined different anatomical features with their relationship to clinical manifestations. Stereoelectroencephalography (SEEG) was implanted in both HH and extra-HH cortices in different groups to identify the epileptogenic zone. Corticocortical evoked potentials (CCEPs) were also used to determine the pathological correlation among different regions to determine the related epileptogenic network. Results: A total of 13 patients presented with parahypothalamic HH and 10 (76.9%) presented with non-GS only, with late-onset age and normal cognitive development, which is different from classical clinical features. SEEG showed that HH is intrinsically epileptogenic in MB-involved parahypothalamic groups. No statistical difference was found in onset age (p = 0.213), and lesions horizontally oriented from the tuber cinereum without connection to MB were not involved in seizure genesis. CCEP indicated a pathological connection among HH, middle cingulate cortex, and insular cortex. Conclusion: The parahypothalamic HH can also cause epilepsy and is different from classic HH-related seizures, by non-GS only with the late-onset age and normal cognitive development. MB is proven to be related to non-GS by the mamillo-cingulate-cortex pathway.