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.

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.

Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study.

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.

Atlas of lesion locations and postsurgical seizure freedom in focal cortical dysplasia: A MELD study.

OBJECTIVE: Drug-resistant focal epilepsy is often caused by focal cortical dysplasias (FCDs). The distribution of these lesions across the cerebral cortex and the impact of lesion location on clinical presentation and surgical outcome are largely unknown. We created a neuroimaging cohort of patients with individually mapped FCDs to determine factors associated with lesion location and predictors of postsurgical outcome. METHODS: The MELD (Multi-centre Epilepsy Lesion Detection) project collated a retrospective cohort of 580 patients with epilepsy attributed to FCD from 20 epilepsy centers worldwide. Magnetic resonance imaging-based maps of individual FCDs with accompanying demographic, clinical, and surgical information were collected. We mapped the distribution of FCDs, examined for associations between clinical factors and lesion location, and developed a predictive model of postsurgical seizure freedom. RESULTS: FCDs were nonuniformly distributed, concentrating in the superior frontal sulcus, frontal pole, and temporal pole. Epilepsy onset was typically before the age of 10 years. Earlier epilepsy onset was associated with lesions in primary sensory areas, whereas later epilepsy onset was associated with lesions in association cortices. Lesions in temporal and occipital lobes tended to be larger than frontal lobe lesions. Seizure freedom rates varied with FCD location, from around 30% in visual, motor, and premotor areas to 75% in superior temporal and frontal gyri. The predictive model of postsurgical seizure freedom had a positive predictive value of 70% and negative predictive value of 61%. SIGNIFICANCE: FCD location is an important determinant of its size, the age at epilepsy onset, and the likelihood of seizure freedom postsurgery. Our atlas of lesion locations can be used to guide the radiological search for subtle lesions in individual patients. Our atlas of regional seizure freedom rates and associated predictive model can be used to estimate individual likelihoods of postsurgical seizure freedom. Data-driven atlases and predictive models are essential for evidence-based, precision medicine and risk counseling in epilepsy.

Whole-brain metabolic pattern analysis in patients with anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis.

BACKGROUND AND PURPOSE: Faciobrachial dystonic seizures (FBDS) and hyponatremia are the distinct clinical features of autoimmune encephalitis (AE) caused by antibodies against leucine-rich glioma-inactivated 1 (LGI1). The present study aims to explore the pathophysiological patterns and neural mechanisms underlying these symptoms. METHODS: We included 30 patients with anti-LGI1 AE and 30 controls from a retrospective observational cohort. Whole-brain metabolic pattern analysis was performed to assess the pathological network of anti-LGI1 AE, as well as the symptom networks associated with FBDS. Logistic regression was applied to explore independent predictors of FBDS. Finally, we used a multiple regression model to investigate the hyponatremia-associated brain network and its effect on serum sodium levels. RESULTS: The pathological network of anti-LGI1 AE involved hypermetabolism in the cerebellum, subcortical structures and Rolandic area, as well as hypometabolism in the medial prefrontal cortex. The symptom network of FBDS included hypometabolism in the cerebellum and Rolandic area (pFDR <0.05). Hypometabolism in the cerebellum was an independent predictor of FBDS (p < 0.001). Hyponatremia-associated network highlighted a negative effect on the caudate nucleus, frontal and temporal white matter. The metabolism of the hypothalamus was negatively associated with (Pearson's R = -0.180, p = 0.342), while not the independent predictor for serum sodium level (path c' = -7.238, 95% confidence interval = -30.947 to 16.472). CONCLUSIONS: Our results provide insights into the whole-brain metabolic patterns of patients with anti-LGI1 AE, including the symptom network associated with FBDS and the hyponatremia-associated brain network. The findings help us to understand the neural mechanisms underlying anti-LGI1 AE and to evaluate the progress of this disease.

Neural underpinnings of default mode network on empathy revealed by intracranial stereoelectroencephalography.

BACKGROUND: Empathy is the ability to understand and share the feelings of others. It is fundamental to emotional intelligence and social iterations. Neuroimaging studies have demonstrated that empathy activates brain regions associated with the social cognition network. AIM: To explore the neural underpinnings of empathy revealed by stereoelectroencephalography utilizing recurrence quantification analysis (RQA). METHODS: This retrospective cohort included 38 epilepsy patients with stereoelectroencephalography implantation. RQA metrics were applied to parameterize the network organization of default mode network (DMN) brain regions. The relationships between DMN, seizure burden activity, and empathy, as measured using the Interpersonal Reactivity Index, were examined using partial least-square regression and mediation analysis. RESULTS: RQA metrics with DMN (R2  = 0.75, PBonferroni  < 0.001) and its subsystems (medial temporal subsystem: R2  = 0.53, PBonferroni  < 0.001; core subsystem: R2  = 0.70, PBonferroni  < 0.001; dorsal medial subsystem: R2  = 0.48, PBonferroni  < 0.001) were positively correlated with empathy scores. Of 13 RQA metrics, the mean diagonal line length, entropy of the diagonal line lengths, trapping time, maximal vertical line length, and recurrence time of second type were found to be statistically higher in patient cohorts with reportedly high empathy. Furthermore, DMN characteristics (b path: F = 3.69, P = 0.04), rather than seizure burdens (direct effect: t = 0.33, P = 0.74, c' = - 0.007), mediated empathy status. CONCLUSION: The present study used various RQA metrics to parameterize the network organization of DMN and determine the neural underpinning of DMN for empathy modulation.

Automatic analysis of integrated magnetic resonance and positron emission tomography images improves the accuracy of detection of focal cortical dysplasia type IIb lesions.

We aimed to develop an efficient and objective pre-evaluation method to identify the precise location of a focal cortical dysplasia lesion before surgical resection to reduce medication use and decrease the post-operative frequency of seizure attacks. We developed a novel machine learning-based approach using cortical surface-based features by integrating MRI and metabolic PET to identify focal cortical dysplasia lesions. Significant surface-based features of 22 patients with histopathologically proven FCD IIb lesions were extracted from PET and MRI images using FreeSurfer. We modified significant parameters, trained and tested the XGBoost model using these surface-based features, and made predictions. We detected lesions in all 20 patients using the XGBoost model, with an accuracy of 91%. We used one-way chi-squared test to test the null hypothesis that the population proportion was 50% (p = 0.0001), indicating that our classification of the algorithm was statistically significant. The sensitivity, specificity, and false-positive rates were 93%, 91%, and 9%, respectively. We developed an objective, quantitative XGBoost classifier that combined MRI and PET imaging features to locate focal cortical dysplasia. This automated method yielded better outcomes than conventional visual analysis and single modality quantitative analysis for surgical pre-evaluation, especially in subtle or visually unidentifiable FCD lesions. This time-efficient method would also help doctors identify otherwise overlooked details.

Neuroimaging Phenotyping and Assessment of Structural-Metabolic-Electrophysiological Alterations in the Temporal Neocortex of Focal Cortical Dysplasia IIIa.

BACKGROUND: Focal cortical dysplasia IIIa (FCD IIIa) is a common histopathological finding in temporal lobe epilepsy. However, subtle alterations in the temporal neocortex of FCD IIIa renders presurgical diagnosis and definition of the resective range challenging. PURPOSE: To explore neuroimaging phenotyping and structural-metabolic-electrophysiological alterations in FCD IIIa. STUDY TYPE: Retrospective. SUBJECTS: One hundred and sixty-seven subjects aged 4-39 years, including 64 FCD IIIa patients, 89 healthy controls and 14 FCD I patients as disease controls. FIELD STRENGTH/SEQUENCE: 3 T, fast-spin-echo T2 -weighted fluid-attenuated inversion recovery (FLAIR), synthetic T1 -weighted magnetization prepared rapid acquisition gradient echo (MPRAGE). ASSESSMENT: Surface-based linear model was applied to reveal neuroimaging phenotyping in FCD IIIa and assess its relationship with clinical variables. Logistic regression was implemented to identify FCD IIIa patients. Epileptogenicity mapping (EM) was conducted to explore the structural-metabolic-electrophysiological alterations in temporal neocortex of FCD IIIa. STATISTICAL TESTS: Student's t-test was applied to determine the significance of paired differences. Calibration curves were plotted to assess the goodness-of-fit (GOF) of the models, combined with the Hosmer-Lemeshow test. RESULTS: FCD IIIa exhibited widespread hyperintensities in temporal neocortex, and these alterations correlated with disease duration (Puncorrected < 0.01). Machine learning model accurately identified 84.4% of FCD IIIa patients, 92.1% of healthy controls and 92.9% of FCD I patients. Cross-modality analysis showed a significant negative correlation between FLAIR hyperintensity and positron emission tomography hypometabolism P < 0.01). Furthermore, epileptogenic cortices were located predominantly in brain regions with FLAIR hyperintensity and hypometabolism. DATA CONCLUSION: FCD IIIa exhibited widespread temporal neocortex FLAIR hyperintensity. Automated machine learning of neuroimaging patterns is conducive for accurate identification of FCD IIIa. The degree and distribution of morphological alterations related to the extent of metabolic and epileptogenic abnormalities, lending support to its potential value for reduction of the radiative and invasive approaches during presurgical workup. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Effective connectivity among the hippocampus, amygdala, and temporal neocortex in epilepsy patients: A cortico-cortical evoked potential study.

OBJECTIVE: Mesial temporal lobe epilepsy (MTLE) is one of the most common types of intractable epilepsy. The hippocampus and amygdala are two crucial structures of the mesial temporal lobe and play important roles in the epileptogenic network of MTLE. This study aimed to explore the effective connectivity among the hippocampus, amygdala, and temporal neocortex and to determine whether differences in effective connectivity exist between MTLE patients and non-MTLE patients. METHODS: This study recruited 20 patients from a large cohort of drug-resistant epilepsy patients, of whom 14 were MTLE patients. Single-pulse electrical stimulation (SPES) was performed to acquire cortico-cortical evoked potentials (CCEPs). The root mean square (RMS) was used as the metric of the magnitude of CCEP to represent the effective connectivity. We then conducted paired and independent sample t-tests to assess the directionality of the effective connectivity. RESULTS: In both MTLE patients and non-MTLE patients, the directional connectivity from the amygdala to the hippocampus was stronger than that from the hippocampus to the amygdala (P < 0.01); the outward connectivity from the amygdala to the cortex was stronger than the inward connectivity from the cortex to the amygdala (P < 0.01); the amygdala had stronger connectivity to the neocortex than the hippocampus (P < 0.01). In MTLE patients, the neocortex had stronger connectivity to the hippocampus than to the amygdala (P < 0.01). No significant differences in directional connectivity were noted between the two groups. CONCLUSIONS: A unique effective connectivity pattern among the hippocampus, amygdala, and temporal neocortex was identified through CCEPs analysis. This study may aid in our understanding of physiological and pathological networks in the brain and inspire neurostimulation protocols for neurological and psychiatric disorders.

Neural networks underlying hyperkinetic seizures: A quantitative PET and SEEG study.

OBJECTIVE: Hyperkinetic seizures (HKS) are characterized by complex movements that commonly occur during seizures arising from diverse cortical structures. A common semiology network may exist and analyzing the anatomo-electrical mechanisms would facilitate presurgical evaluation. Here, quantitative positron emission tomography (PET) and stereoelectroencephalography (SEEG) analysis was used to explore the underlying mechanism of HKS. METHODS: We retrospectively collected patients with epilepsy with HKS between 2014 and 2019. The interictal PET data of patients with epilepsy with HKS were compared with those of 25 healthy subjects using statistical parametric mapping to identify regions with significant hypometabolism. Then, regions of interest (ROI) for SEEG analysis were identified based on the results of PET analysis. Patients in which the ROIs were covered by intracerebral electrodes were selected for further analysis. Stereoelectroencephalography -clinical correlations with latency measurements were analyzed, and we also performed coherence analysis among ROIs both before and during HKS. RESULTS: Based on the inclusion criteria, 27 patients were analyzed. In the PET analysis, significant hypometabolism was observed in the ipsilateral dorsoanterior insular lobe, bilateral mesial frontal lobes (supplementary motor area/middle cingulate cortex, SMA/MCC), and the bilateral heads of the caudate nuclei in patients with HKS compared with the control group (p < 0.001). We selected dorsoanterior insula and SMA/MCC as ROIs for SEEG analysis. Eight patients with 23 HKS events were selected for further analysis. There was a linear correlation between the ictal involvement of both the dorsoanterior insula and SMA/MCC with the onset of HKS. Stereoelectroencephalography analysis indicated alpha range activity seemed more often associated with dorsoanterior insula and SMA/MCC involvement during HKS. CONCLUSIONS: The dorsoanterior insular lobe, mesial frontal lobes (SMA/MCC), and the bilateral heads of the caudate nuclei were probably involved in the generation of HKS. The SEEG analysis further indicated that the occurrence of HKS might be partly associated with synchronized rhythmical alpha activity between dorsoanterior insula and SMA/MCC.

Whole-brain morphological alterations associated with trigeminal neuralgia.

BACKGROUND: Novel neuroimaging strategies have the potential to offer new insights into the mechanistic basis for trigeminal neuralgia (TN). The present study aims to conduct whole-brain morphometry analyses of TN patients and to assess the value of group-level neocortical and subcortical structural patterns as tools for diagnostic biomarker exploration. METHODS: Cortical thickness, surface area, and myelin levels in the neocortex were measured via magnetic resonance imaging (MRI). The radial distance and the Jacobian determinant of the subcortex in 43 TN patients and 43 matched controls were compared. Pattern learning algorithms were employed to establish the utility of group-level MRI findings as tools for predicting TN. An additional 40 control patients with hemifacial spasms were then evaluated to assess algorithm sensitivity and specificity. RESULTS: TN patients exhibited reductions in cortical indices in the anterior cingulate cortex (ACC), the midcingulate cortex (MCC), and the posterior cingulate cortex (PCC) relative to controls. They further presented with widespread subcortical volume reduction that was most evident in the putamen, the thalamus, the accumbens, the pallidum, and the hippocampus. Whole brain-level morphological alterations successfully enable automated TN diagnosis with high specificity (TN: 95.35 %; disease controls: 46.51 %). CONCLUSIONS: TN is associated with a distinctive whole-brain structural neuroimaging pattern, underscoring the value of machine learning as an approach to differentiating between morphological phenotypes, ultimately revealing the full spectrum of this disease and highlighting relevant diagnostic biomarkers.

Symptomatogenic zone and network of oroalimentary automatisms in mesial temporal lobe epilepsy.

OBJECTIVE: Oroalimentary automatisms (OAAs) are common clinical manifestations of medial temporal lobe epilepsy. Nevertheless, the location of the symptomatogenic zone of OAAs remains unclear. The generation mechanism of OAAs also has not been clarified. We attempt to explain these problems by analyzing interictal [18 F]-fluorodeoxyglucose positron emission tomography (18 FDG-PET) imaging and ictal stereo-electroencephalography (SEEG) recordings in patients with medial temporal lobe epilepsy. METHODS: Fifty-seven patients with mesial temporal lobe epilepsy were analyzed retrospectively. All underwent anterior temporal lobectomy (ATL) and were seizure-free. The patients were divided into OAA (+) and OAA (-) groups according to the occurrence of consistent stereotyped OAAs. The interictal PET data were compared with those of 18 healthy controls and were then compared between groups using statistical parametric mapping (SPM). Functional connectivity using linear regression analysis was performed between the target brain regions. To clarify the network of OAAs, ictal epileptogenicity index (EI) values, and the nonlinear correlation method h2 were performed with SEEG on patients. RESULTS: Compared to OAAs (-), the rolandic operculum was the only area with significant differences. Hippocampus and rolandic operculum showed significant correlations in the OAA (+) group (y = 0.758x+0.470, R2  = 0.456, P = 0.000). No correlation was found in the OAA (-) group (P = 0.486). The EI values of the OAA (+) group (median 0.20) were significantly higher (P < 0.0001) than those of the OAA (-) group (median 0). The h2 in the OAA (+) group (h2  = 0.23 ± 0.13) showed stronger functional connectivity (t = 6.166, P < 0.0001) than that of the OAA (-) group (h2  = 0.08 ± 0.05). SIGNIFICANCE: The rolandic operculum is most likely to be the symptomatogenic zone of OAAs. In medial temporal lobe epilepsy, unilateral functional connection from the hippocampus to the rolandic operculum during seizure onset is the basis for the generation of OAAs.

Automated detection of hippocampal sclerosis using clinically empirical and radiomics features.

OBJECTIVE: Temporal lobe epilepsy is a common form of epilepsy that might be amenable to surgery. However, magnetic resonance imaging (MRI)-negative hippocampal sclerosis (HS) can hamper early diagnosis and surgical intervention for patients in clinical practice, resulting in disease progression. Our aim was to automatically detect and evaluate the structural alterations of HS. METHODS: Eighty patients with pharmacoresistant epilepsy and histologically proven HS and 80 healthy controls were included in the study. Two automated classifiers relying on clinically empirical and radiomics features were developed to detect HS. Cross-validation was implemented on all participants, and specificity was assessed in the 80 controls. The performance, robustness, and clinical utility of the model were also evaluated. Structural analysis was performed to investigate the morphological abnormalities of HS. RESULTS: The computational model based on clinical empirical features showed excellent performance, with an area under the curve (AUC) of 0.981 in the primary cohort and 0.993 in the validation cohort. One of the features, gray-white matter boundary blurring in the temporal pole, exhibited the highest weight in model performance. Another model based on radiomics features also showed satisfactory performance, with AUC of 0.997 in the primary cohort and 0.978 in the validation cohort. In particular, the model improved the detection rate of MRI-negative HS to 96.0%. The novel feature of cortical folding complexity of the temporal pole not only played a crucial role in the classifier but also had significant correlation with disease duration. SIGNIFICANCE: Machine learning with quantitative clinical and radiomics features is shown to improve HS detection. HS-related structural alterations were similar in the MRI-positive and MRI-negative HS patient groups, indicating that misdiagnosis originates mainly from empirical interpretation. The cortical folding complexity of the temporal pole is a potentially valuable feature for exploring the nature of HS.

Motor cortex stimulation: a systematic literature-based analysis of effectiveness and case series experience.

BACKGROUND: Aim to quantitatively analyze the clinical effectiveness for motor cortex stimulation (MCS) to refractory pain. METHODS: The literatures were systematically searched in database of Cocharane library, Embase and PubMed, using relevant strategies. Data were extracted from eligible articles and pooled as mean with standard deviation (SD). Comparative analysis was measured by non-parametric t test and linear regression model. RESULTS: The pooled effect estimate from 12 trials (n = 198) elucidated that MCS shown the positive effect on refractory pain, and the total percentage improvement was 35.2% in post-stroke pain and 46.5% in trigeminal neuropathic pain. There is no statistical differences between stroke involved thalamus or non-thalamus. The improvement of plexus avulsion (29.8%) and phantom pain (34.1%) was similar. The highest improvement rate was seen in post-radicular plexopathy (65.1%) and MCS may aggravate the pain induced by spinal cord injury, confirmed by small sample size. Concurrently, Both the duration of disease (r = 0.233, p = 0.019*) and the time of follow-up (r = 0.196, p = 0.016*) had small predicative value, while age (p = 0.125) had no correlation to post-operative pain relief. CONCLUSIONS: MCS is conducive to the patients with refractory pain. The duration of disease and the time of follow-up can be regarded as predictive factor. Meanwhile, further studies are needed to reveal the mechanism of MCS and to reevaluate the cost-benefit aspect with better-designed clinical trials.

Value of stereo-electroencephalogram in reoperation of patients with pharmacoresistant epilepsy: a single center, retrospective study.

PURPOSE: To systematically elucidate the value of stereo-electroencephalogram (SEEG) in the reoperation of patients with pharmacoresistant epilepsy. METHODS: Epilepsy patients who had previously undergone a failed operation and agreed to a reoperation were included in this study. The single center retrospective study evaluated the value of SEEG in epileptogenic zones (EZ) lateralization and localization as well as the complications related to the implantation. RESULTS: In total, fourteen patients met the inclusion criteria and received reoperation after implantation of SEEG. The average number of electrodes in each patient is 7.9. At the last available follow-up, nine (64.3%) patients were completely seizure-free according to the International League Against Epilepsy (ILAE) criteria. No significant complications were found in the cohort, two patients' electrodes were loosened and removed because of the seizure. No significant predictors of seizure-free status were identified in the present study, including the result of presurgical MRI, pathology and surgical strategy. CONCLUSIONS: Based on the comprehensive presurgical assessment data and careful discussion of multidisciplinary team, failed epileptic surgery deserves a second chance. Moreover, SEEG is an effective and safe methodology to determine the location of the EZ with the goal of performing reoperation.

The effectiveness of creatine treatment for Parkinson's disease: an updated meta-analysis of randomized controlled trials.

BACKGROUND: The effectiveness of creatine in treating Parkinson's disease (PD) has not been conclusively determined. Therefore, we performed a meta-analysis to address this issue. METHODS: The Cochrane Central Register of Controlled Trials, PUBMED, EMBASE, and other databases were searched, and outcomes measured by the Total Unified Parkinson's Disease Rating Scale (UPDRS) and the Schwab & England Scale were analyzed. RESULTS: Five randomized controlled trials (RCTs) were selected, and 1339 participants were included in the analysis. There were no significant differences between the control and treatment groups in the total, mental, activities of daily living (ADL), or motor UPDRS scores, but an improvement in Schwab & England Scale scores was observed. CONCLUSIONS: Creatine has no observed benefit in PD patients, although more correlated studies are still needed.

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.

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.

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.

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.