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Abstract 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.

BackgroundSepsis is a heterogeneous syndrome, and enrollment of more homogeneous patients is essential to improve the efficiency of clinical trials. Artificial intelligence (AI) has facilitated the identification of homogeneous subgroups, but how to estimate the uncertainty of the model outputs when applying AI to clinical decision-making remains unknown. ObjectiveWe aimed to design an AI-based model for purposeful patient enrollment, ensuring that a patient with sepsis recruited into a trial would still be persistently ill by the time the proposed therapy could impact patient outcome. We also expected that the model could provide interpretable factors and estimate the uncertainty of the model outputs at a customized confidence level. MethodsIn this retrospective study, 9135 patients with sepsis requiring vasopressor treatment within 24 hours after sepsis onset were enrolled from Beth Israel Deaconess Medical Center. This cohort was used for model development, and 10-fold cross-validation with 50 repeats was used for internal validation. In total, 3743 patients with sepsis from the eICU Collaborative Research Database were used as the external validation cohort. All included patients with sepsis were stratified based on disease progression trajectories: rapid death, recovery, and persistent ill. A total of 148 variables were selected for predicting the 3 trajectories. Four machine learning algorithms with 3 different setups were used. We estimated the uncertainty of the model outputs using conformal prediction (CP). The Shapley Additive Explanations method was used to explain the model. ResultsThe multiclass gradient boosting machine was identified as the best-performing model with good discrimination and calibration performance in both validation cohorts. The mean area under the receiver operating characteristic curve with SD was 0.906 (0.018) for rapid death, 0.843 (0.008) for recovery, and 0.807 (0.010) for persistent ill in the internal validation cohort. In the external validation cohort, the mean area under the receiver operating characteristic curve (SD) was 0.878 (0.003) for rapid death, 0.764 (0.008) for recovery, and 0.696 (0.007) for persistent ill. The maximum norepinephrine equivalence, total urine output, Acute Physiology Score III, mean systolic blood pressure, and the coefficient of variation of oxygen saturation contributed the most. Compared to the model without CP, using the model with CP at a mixed confidence approach reduced overall prediction errors by 27.6% (n=62) and 30.7% (n=412) in the internal and external validation cohorts, respectively, as well as enabled the identification of more potentially persistent ill patients. ConclusionsThe implementation of our model has the potential to reduce heterogeneity and enroll more homogeneous patients in sepsis clinical trials. The use of CP for estimating the uncertainty of the model outputs allows for a more comprehensive understanding of the model’s reliability and assists in making informed decisions based on the predicted outcomes.

Floods are one of the most destructive disasters that cause loss of life and property worldwide every year. In this study, the aim was to find the best-performing model in flood sensitivity assessment and analyze key characteristic factors, the spatial pattern of flood sensitivity was evaluated using three machine learning (ML) models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). Suqian City in Jiangsu Province was selected as the study area, and a random sample dataset of historical flood points was constructed. Fifteen different meteorological, hydrological, and geographical spatial variables were considered in the flood sensitivity assessment, 12 variables were selected based on the multi-collinearity study. Among the results of comparing the selected ML models, the RF method had the highest AUC value, accuracy, and comprehensive evaluation effect, and is a reliable and effective flood risk assessment model. As the main output of this study, the flood sensitivity map is divided into five categories, ranging from very low to very high sensitivity. Using the RF model (i.e., the highest accuracy of the model), the high-risk area covers about 44% of the study area, mainly concentrated in the central, eastern, and southern parts of the old city area. HIGHLIGHTS Correlation analysis and multi-collinearity analysis were performed to screen flood risk factors by VIF value.; Multi-model evaluation: the RF model is superior to LR and XGBoost models.; Stochastic forest algorithm was used to explore the importance of flood risk factors.; Generation of flood sensitivity maps: providing intuitive, clear information to help decision makers more effectively identify high-risk areas.;

Abstract 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.

Scalp high-frequency oscillations (sHFOs) are a promising non-invasive biomarker of epilepsy. However, the visual marking of sHFOs is a time-consuming and subjective process, existing automatic detectors based on single-dimensional analysis have difficulty with accurately eliminating artifacts and thus do not provide sufficient reliability to meet clinical needs. Therefore, we propose a high-performance sHFOs detector based on a deep learning algorithm. An initial detection module was designed to extract candidate high-frequency oscillations. Then, one-dimensional (1D) and two-dimensional (2D) deep learning models were designed, respectively. Finally, the weighted voting method is used to combine the outputs of the two model. In experiments, the precision, recall, specificity and F1-score were 83.44%, 83.60%, 96.61% and 83.42%, respectively, on average and the kappa coefficient was 80.02%. In addition, the proposed detector showed a stable performance on multi-centre datasets. Our sHFOs detector demonstrated high robustness and generalisation ability, which indicates its potential applicability as a clinical assistance tool. The proposed sHFOs detector achieves an accurate and robust method via deep learning algorithm.

PurposeVector flow mapping and treadmill exercise stress echocardiography were used to evaluate and explore changes in the left ventricular (LV) flow field of patients with nonobstructive coronary artery disease.MethodsOverall, 34 patients with nonobstructive (

BackgroundEarly and reliable identification of patients with sepsis who are at high risk of mortality is important to improve clinical outcomes. However, 3 major barriers to artificial intelligence (AI) models, including the lack of interpretability, the difficulty in generalizability, and the risk of automation bias, hinder the widespread adoption of AI models for use in clinical practice. ObjectiveThis study aimed to develop and validate (internally and externally) a conformal predictor of sepsis mortality risk in patients who are critically ill, leveraging AI-assisted prediction modeling. The proposed approach enables explaining the model output and assessing its confidence level. MethodsWe retrospectively extracted data on adult patients with sepsis from a database collected in a teaching hospital at Beth Israel Deaconess Medical Center for model training and internal validation. A large multicenter critical care database from the Philips eICU Research Institute was used for external validation. A total of 103 clinical features were extracted from the first day after admission. We developed an AI model using gradient-boosting machines to predict the mortality risk of sepsis and used Mondrian conformal prediction to estimate the prediction uncertainty. The Shapley additive explanation method was used to explain the model. ResultsA total of 16,746 (80%) patients from Beth Israel Deaconess Medical Center were used to train the model. When tested on the internal validation population of 4187 (20%) patients, the model achieved an area under the receiver operating characteristic curve of 0.858 (95% CI 0.845-0.871), which was reduced to 0.800 (95% CI 0.789-0.811) when externally validated on 10,362 patients from the Philips eICU database. At a specified confidence level of 90% for the internal validation cohort the percentage of error predictions (n=438) out of all predictions (n=4187) was 10.5%, with 1229 (29.4%) predictions requiring clinician review. In contrast, the AI model without conformal prediction made 1449 (34.6%) errors. When externally validated, more predictions (n=4004, 38.6%) were flagged for clinician review due to interdatabase heterogeneity. Nevertheless, the model still produced significantly lower error rates compared to the point predictions by AI (n=1221, 11.8% vs n=4540, 43.8%). The most important predictors identified in this predictive model were Acute Physiology Score III, age, urine output, vasopressors, and pulmonary infection. Clinically relevant risk factors contributing to a single patient were also examined to show how the risk arose. ConclusionsBy combining model explanation and conformal prediction, AI-based systems can be better translated into medical practice for clinical decision-making.

This paper presents the fabrication of widely-spaced high aspect ratio ring-shape pillars (i.e. hollow pillars). Lateral etching of the pillars during deep reactive ion etching is challenging. To reduce this problem, we proposed adding sacrificial structures surrounding the pillars such that the lateral etching mainly occurs on the sacrificial structures. We designed two different kinds of sacrificial structures, one is circular ring structures surrounding the pillars, the other one is two half circle structures with two small gaps. Both sacrificial structures could help to fabricate pillars with vertical sidewalls. When the width of the sacrificial structures was well designed for a given etching condition, the sacrificial structures could be removed by ultrasonic agitation after the process with clean surface because they had been weakened by the lateral etching. Using this method, 2D widely-spaced ring-shape pillar array with 470 μm high pillars (diameter 200 μm, aspect ratio 2.35) and 370 μm deep holes (diameter 80 μm, aspect ratio 4.63) was fabricated simultaneously.

Abstract Objective To assess seizure semiology and disease evolution in a large number of hypothalamic hamartoma (HH) patients. Methods Seizure semiology and associated medical records for 78 patients with HH‐related epilepsy were retrospectively reviewed. Potential predictors of seizure types were assessed through univariate and binary logistic regression analyses. Results 57 (73.1%) patients presented with gelastic seizures at the onset of epilepsy, of whole 39 (68.4%) experienced additional seizure types with a mean latency interval of 4.59 years. Automatism, version, and sGTCs were increasingly common with disease evolution. The intraventricular size of HH was significantly negatively correlated with the disease evolution interval (r = −0.445, p = 0.009). A significantly higher rate of patients with automatism in the DF‐II group relative to the DF‐III group was found in both χ2 (X = 6.07, p = 0.014) and logistic regression analyses (B = 3.196, p = 0.020). Interpretation Gelastic seizures are the most common initial seizure type in HH patients, but variable semiologies occur with disease evolution. The intraventricular HH lesion size is an important determinant of epilepsy evolution. DF‐II HH lesions contribute to a higher chance of automatism evolution. The present study furthers our understanding of the dynamic organization of the seizure network affected by HH.

Abstract Background Albumin infusion is the primary therapeutic strategy for septic patients with liver cirrhosis. Although recent studies have investigated the efficacy of albumin in the resuscitation stage of septic patients with liver cirrhosis, it remains unclear whether daily albumin administration can improve outcomes. Furthermore, the indications for initiating albumin therapy are not well defined. Methods Septic patients with liver cirrhosis were obtained from the Medical Information Mart for Intensive Care (MIMIC-IV 2.0) database. Marginal structural Cox models were employed to investigate the association between daily albumin infusion and 28-day mortality. We also aimed to explore under what circumstances enrolled patients could benefit most from albumin administration, based on the clinical parameters collected on the day of albumin infusion, including serum albumin concentration, serum lactate concentration, mean arterial pressure (MAP), and vasopressor dosage. Results A total of 2265 patients were included in the final analysis, of whom 1093 (48.3%) had received albumin treatment at least once. The overall 28-day mortality was 29.6%. After marginal structural modeling, daily albumin infusion was associated with a reduced risk of 28-day death (hazard ratio, 0.76; 95% CI 0.61–0.94). We found that patients benefit most from albumin infusion when initiated on the day of serum albumin concentration between 2.5 and 3.0 g/dL, serum lactate concentration greater than or equal to 2 mmol/L, MAP less than 60 mmHg, or vasopressor dosage between 0.2 and 0.3 mcg/kg/min (norepinephrine equivalent, NEE). Conclusions Albumin infusion is associated with a reduction in mortality in septic patients with liver cirrhosis under specific circumstances. Serum albumin concentration, serum lactate, MAP, and vasopressor dosage were found to be modifiers of treatment effectiveness and should be considered when deciding to initial albumin infusion.

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.

ObjectiveTo characterize the PET-MRI co-registration of hypometabolic patterns in focal cortical dysplasia (FCD) types I and II and provide some suggestions in presurgical evaluation of epilepsy surgery.MethodsWe retrospectively analyzed PET-MRI co-registration imaging data from a cohort of 83 epilepsy patients with histologically confirmed FCD types I and II. Hypometabolic patterns were classified into 4 types: bottom of sulcus hypometabolism (BOSH), single island of sulcus hypometabolism (SIOS), single gyrus or sulcus hypometabolism (SGOS), and multiple gyri and sulci hypometabolism (MGOS).ResultsMost of cases that were overlooked by conventional MRI and PET evaluation but positive in PET-MRI co-registration were focalized lesions in dorsolateral frontal lobe (9/15) and FCD type IIa was the most prevalent pathological type (11/15). The FCD histological types (p = 0.027) and locations (p

IntroductionAs a vital component of the ecosystem of the Qinghai-Tibet Plateau, alpine wetlands coexist with their vulnerability, sensitivity, and abundant biodiversity, propelling the material cycle and energy flux of the entire plateau ecosystem. In recent decades, climate change and human activities have significantly altered the regional landscape. Monitoring and assessing changes in the alpine wetlands on the Qinghai-Tibet Plateau requires the efficient and accurate collection of long-term information.MethodsHere, we interpreted the remote sensing data of the first meander of the Yellow River of alpine wetlands from 1990 to 2020 based on Google Earth Engine (GEE) platform, using geographic information system (GIS) and landscape pattern index to analyze the spatial and temporal evolution of wetland landscape patterns, and the primary drivers of changes in wetland area were explored by GeoDetector.ResultsOur result showed that most wetland areas were found in regions with gradients less than 12° and elevations between 3315 and 3600 m. From 1990 to 2010, the area of alpine wetland in the study area decreased by 25.43%. During the period between 2010 and 2020 to the 1990s, the wetland area decreased by 322.9 km2. Conversion to and from grassland was the primary form of wetland transfer out and in, respectively. The overall migration of the wetland centroid in the study area was to the southwest between 1990 and 2010 and to the north between 2010 and 2020. The geometry of the wetland landscape was relatively simple, the landscape was relatively intact, and patches retained a high level of agglomeration and connectivity. However, their level of agglomeration and connectivity was disrupted. A quantitative analysis of the factor detector in GeoDetector revealed that the DEM, slope, and evaporation were the most important driving factors influencing the change of wetland area, with socioeconomic development also influencing changes in the wetland area to a lesser extent.DiscussionUsing interaction detectors, it was discovered that the interaction of various driving factors could better explain the long-term variations in wetland areas, with a greater degree of explanation than that of each driving factor alone.

Background: Understanding the spatiotemporal propagation profiles of seizures is crucial for the preoperative assessment of epilepsy patients. The present study aimed to investigate whether seizures exhibit propagation patterns that align with intrinsic networks (INs). Methods: A quantitative analysis was conducted to examine ictal fast activity (IFA). The Epileptogenicity Index (EI) was employed to assess the epileptogenicity, spectral features, and temporal characteristics of IFA. Intra-network and inter-network comparisons were made regarding the IFA-related metrics. Additionally, the metrics were correlated with Euclidean distance. Network connection maps were generated to visualize seizures originating from different INs, allowing for comparisons between distinct groups. Results: Data for 81 seizures in 43 subjects were captured using stereoelectroencephalography implantation. Three metrics were compared: EI, time involvement (TI), and energy ratio index (ERI). Intra-network channels exhibited higher EI, earlier involvement of IFA, and stronger high-frequency energy. These findings were further validated through subgroup analyses stratified by neuropathology, seizure type, and seizure origination lobe. Correlation analyses revealed a negative association between distance and both EI and ERI, while distance exhibited a positive correlation with TI. Seizures originating from different INs exhibited varying propagation characteristics. Conclusions: The study findings highlight the dominant role of intra-network dynamics over inter-network during seizure propagation. These results contribute to our understanding of seizure dynamics and their relationship with INs.

Highlights 1. Metabolic pattern analysis allowed allocation of the symptomatogenic zone (EZ) and brain network of hand automatisms (HA) in mesial temporal lobe epilepsy (mTLE). 2. The involved network of bilateral HA was larger than the unilateral one, probably due to the occurrence of contralateral dystonic posturing. 3. Increased metabolic synchronization within supplementary motor area (SMA) and middle cingulate cortex (MCC) regions were engaged in the representation and modulation of HA, suggesting these regions as the EZ for HA.

The ecological risks associated with rapid urbanization have seriously undermined the river’s ecological security. Especially for developing countries, the weak ecological foundation and enormous population pressure have increased the difficulty of river ecological protection. It is urgent to propose a bottom-line planning and management approach for river ecological protection in highly urbanized areas of developing countries. We quantified the minimum cumulative resistance of the river protection and construction land expansion processes. Then, based on the difference between these two minimum cumulative resistances, we assessed the river ecological risk in the Beijing plain area and presented a complete method to regional-level planning and management for river ecological protection. The results show that: 1) The spatial distribution of river ecological risks follows a three-tier ring structure, decreasing from the city center to the suburbs and then to the outer suburbs. The number of river segments with ecological risk levels 1, 2, and 3 are 44, 42, and 23, respectively, accounting for 39.2% of the total length of all river sections, which indicates that a larger number of rivers are facing high ecological risk. 2) The study area was divided into three zones according to the suitability of river ecological protection, with 2476.89, 765.51, and 3832.09 km2, respectively. Zone I and Zone II can be developed as protection nodes and riparian protection zones to develop the river ecological network in the central city. 3) We extracted 133 large ecological patches and 219 potential ecological corridors, which should be connected with the river network to enhance the connectivity of the local ecological landscape. The complete method proposed in this study is more focused and implementable to assist administrators in protecting and restoring the ecological security of urban rivers.

Evaluating waterlogging vulnerability and analyzing its characteristics and future trends can provide scientific support for urban disaster prevention and reduction. For this study, taking Fuzhou as an example, an urban waterlogging vulnerability assessment system was constructed from the three dimensions of exposure, sensitivity, and adaptive capacity. The entropy method was used to evaluate urban waterlogging vulnerability in Fuzhou during 2014–2020. The use of CA–Markov to predict waterlogging vulnerability in 2023, 2026, and 2029 in Fuzhou is an important innovation reported in this paper. Study results showed that: (1) Vulnerability to waterlogging in Fuzhou follows a gradually decreasing “center-southeast” distribution pattern, with Level 5 areas mainly located in Cangshan District, Gulou District, and Taijiang District. (2) Changes in waterlogging vulnerability in Fuzhou from 2014 to 2020 can be divided into five change modes, with changing areas, mainly of the late-change type, accounting for 14.13% of the total area. (3) Prediction accuracy verification shows that the CA–Markov model is suitable for predicting waterlogging vulnerability in Fuzhou with high accuracy and a kappa coefficient of 0.9079. (4) From 2020 to 2029, the vulnerability level of the eastern coastal region of Fuzhou is expected to generally increase, and the vulnerability degree will continue to deteriorate. The proportion of Level 5 vulnerable areas will increase by 4.5%, and the growth rate will increase faster and faster with the passage of time.

Due to the increase in frequency and severity, assessing and predicting urban waterlogging risk is critical. The risk assessment framework is based on three factors: hazard, exposure, and vulnerability. The assessment indicators, previously based solely on static indicators, account for the effects of varying temporal and spatial distributions of people and vehicles on the assessment results. Specifically, two dynamic indicators—the population density and the Traffic Performance Index (TPI)—are added to the mix to dynamically assess the risk of waterlogging in the central urban area of Suqian City of Jiangsu Province, China’s central urban area, over various periods. The findings indicate that four–six times more individuals are affected during peak hours than during other periods, and no important roads are within the scope of waterlogging during other periods, while nearly ten important roads will be affected during peak hours. Additionally, the characteristics of the temporal and spatial distribution of waterlogging risk can be more accurately represented by a combination of static and dynamic indicators. The highest risk areas are significantly more prominent during the weekday peak period than during other times; the morning peak is mainly affected by traffic performance indicators, the evening peak is mainly affected by population density, and the main factors affecting the other periods are the same as the other main factors affecting the peak period. The highest risk areas are mainly located in the eastern part of the central urban area of Suqian City, with the lowest risk in the north and south.

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

In order to make the sponge city construction model more consistent with the inherent characteristics of the city, this paper constructs classification of the sponge city construction index system. We carry out multivariate statistical and cluster analysis based on geographic information system (GIS) and classify the different regions of mainland China, putting forward key points of construction for different types of regions. The results show that climatic factor, hydrological factor and soil factor are the main factors affecting sponge city construction, followed by city scale and level of urban economic development; the third is underlying surface type and geomorphological type. The cities are classified into ten clusters, and they present a continuously zonal or flaky distribution from northeast to southwest on the whole; more than 80% of cities obviously present a continuously zonal or flaky distribution. Each of the nine clusters has at least one pilot city, which can be used as a reference for construction. HIGHLIGHTS This paper puts forward the method of cluster analysis to classify Chinese cities.; By using principal component analysis, it is found that the climatic, soil and hydrological factors are the most related to the results of city classification.; Based on the construction experience of existing pilot cities, the construction emphasis of sponge cities in various types of cities is proposed.;

BackgroundThe 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.ObjectiveWe aimed to investigate whether parahypothalamic hamartomas are intrinsically epileptogenic and elucidate the underlying pathway of epileptogenesis.MethodsWe 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.ResultsA 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.ConclusionThe 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.

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.

Abstract 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.

PurposeRegional cortical thickness or volume analyses based upon structural MRI scans have been employed to study the pathophysiology of primary craniocervical dystonia (CCD). In the present study, brain connectivity network analyses based upon morphological distribution similarities among different brain areas were used to study the network disruption in individuals affected by CCD.MethodsThe T1 MRI scans were completed for 37 patients with CCD and 30 healthy controls, with individual brain structural networks being constructed based upon gray matter (GM) similarities in 90 regions within the brain. Area under the curve (AUC) values for each network parameter were determined, and the GRETNA program was used to conduct a graph theory-based measurement of nodal and global network properties. These properties were then compared between healthy controls and those with CCD. In addition, relationships between nodal properties and the severity of clinical dystonia were assessed through Spearman's correlation analyses.ResultsRelative to individuals in the control group, patients with CCD exhibited decreased local nodal properties in the right globus pallidus, right middle frontal gyrus, and right superior temporal pole. The degree of centrality as well as the node efficiency of the right globus pallidus were found to be significantly correlated with ocular dystonic symptom. The node efficiency of right middle frontal gyrus was significantly related to the total motor severity. No nodal properties were significantly correlated with oral dystonic motor scores. Among CCD patients, the right hemisphere exhibited more widespread decreases in connectivity associated with the motor related brain areas, associative cortex, and limbic system, particularly in the middle frontal gyrus, globus pallidus, and cingulate gyrus.ConclusionsThe assessment of morphological correlations between different areas in the brain may represent a sensitive approach for detecting alterations in brain structures and to understand the mechanistic basis for CCD at the network level. Based on the nodal properties identified in this study, the right middle frontal gyrus and globus pallidus were the most severely affected in patients with CCD. The widespread alterations in morphological connectivity, such as the cortico-cortical and cortico-subcortical networks, further support the network mechanism as a basis for CCD.

Objective: The efficacy of globus pallidus internus-deep brain stimulation (GPi-DBS) for the treatment of Huntington’s disease (HD) has not been validated in large-scale studies. We conducted an individual patient analysis to pool outcomes of all of the published HD-GPi-DBS studies. Methods: PubMed, Embase and the Cochrane Library were searched for relevant articles. The Unified Huntington’s Disease Rating Scale (UHDRS)-motor and UHDRS-chorea improvements were analyzed during different follow-up periods. Secondary outcomes, including UHDRS-motor subitem scores and functional assessment results, were also analyzed. Correlation and regression analyses were conducted to find improvement predictors. This study was registered in PROSPERO (CRD42018105995). Results: Eighteen studies including 39 patients with 124 visits were analyzed. GPi-DBS significantly improved the UHDRS-motor score in 30-month follow-up (p = 0.003). Functional assessment was not improved 12 months postoperatively (p = 0.196). The Westphal variant of HD (W-HD) gained no motor benefits 6 months postoperatively (p = 0.178). The Westphal variant was the only risk factor for DBS efficacy (p = 0.044). The rate of stimulation-related adverse events was 87.2%. Conclusions: GPi-DBS has a stable effect on chorea symptoms in HD patients. Chorea-dominant patients may be the best candidates for surgery, while attention should be paid to postoperative stimulation-related complications. Given that GPi-DBS has limited effects on other motor symptoms, W-HD patients are not surgical candidates.

Abstract Objective To report clinical experience with presurgical evaluation in patients with insulo‐opercular epilepsy. Quantitative analysis on PET imaging and stereoelectroencephalography (SEEG) signals was used to summarize their electroclinical features. Methods Twenty‐two patients with focal epilepsy arising from the insular and/or opercular cortex according to SEEG were retrospectively analyzed. Presurgical noninvasive data were analyzed in detail. Interictal PET data of patients were then statistically compared with those of healthy controls to identify the interictal hypometabolic network. The epileptogenicity index (EI) of ictal SEEG signal was computed to identify areas of spread at the beginning of seizure onset. Results Focal tonic seizures of the face and/or neck (16/22, 73%) were the most prevalent early objective signs. Epileptic discharges in the interictal and ictal scalp‐EEG mostly showed an ipsilateral perisylvian distribution. Statistical analysis of interictal PET showed significant hypometabolism in the insular lobe, central operculum, supplementary motor area, middle cingulate cortex, bilateral caudate nuclei, and putamen. According to the EI analysis, insulo‐opercular epilepsy could be classified as insulo‐opercular epilepsy (50%), opercular epilepsy (41%), and insular cortex epilepsy (9%). Significance Clinical diagnosis of insulo‐opercular epilepsy is challenging because of its complex seizure semiology and nonlocalizing discharges on scalp‐EEG. A common hypometabolic network involving the insulo‐opercular cortex, mesial frontal cortex and subcortical nuclei may be involved in the organization of the insulo‐opercular epilepsy network. Furthermore, quantified SEEG analysis suggested that pure insular epilepsy is rare, and the close connection between insular and opercular cortex necessitates SEEG implantation to define the epileptogenic zone.

Objective: Deep brain stimulation (DBS) has shown promising outcomes as new therapeutic opportunities for patients with treatment-resistant depression (TRD) who do not respond adequately to several consecutive treatments. This study aims to systematically review and conduct a meta-analysis on the efficacy and safety of DBS for TRD.Method: The literature was comprehensively reviewed using Medline, Google scholar, Cochrane library, Embase, and World Health Organization International Clinical Trials Registry Platform until January 2019. The studied outcomes included response, remission, recurrence, and adverse events (AEs) rates, and were reported as the rate ratio (RR) or pooled estimate with a 95% confidence interval (95% CI). Heterogeneity was measured by an I-square test and a sensitive analysis.Results: A total of 17 studies involving 7 DBS targets were included. For efficacy, DBS treatment was statistically beneficial for TRD, and the response, remission, and recurrence rates were 56% (ranging from 43 to 69%), 35% (ranging from 27 to 44%), and 14% (ranging from 4 to 25%), respectively. However, only two randomized-controlled trials (RCTs) considered the invalidity of DBS (RR = 1.45, 95% CI = 0.50–4.21). For safety, the AEs rate was 67% (ranging from 54 to 80%). The AEs were common and moderate, but the problems related to suicide and suicidal ideation should not be underestimated.Conclusion: These findings suggest that DBS for TRD is considered promising, which should be confirmed by well-designed and large sample studies. Future basic research and comprehensive clinical trials are needed to reach better understanding on the mechanisms of action and optimal targeted structure.

Introduction: Insular epilepsy is clinically challenging. This study aimed to map cerebral metabolic networks in insular epilepsy and investigate their graph-theoretic properties, with the goal of elucidating altered metabolic network architectures that underlie interictal hypometabolism.Aims: Fluorine-18-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) imaging was performed in 17 individuals with a stereoelectroencephalography (SEEG) confirmed diagnosis of insula epilepsy and 14 age- and sex-matched healthy comparison individuals. Metabolic covariance networks were mapped for each group and graph theoretical analyses of these networks were undertaken. For each pair of regions comprising a whole-brain parcellation, regionally-averaged FDG uptake values were correlated across individuals to estimate connection weights.Results: Correlation in regionally-averaged FDG uptake values in the insular epilepsy group was substantially increased for several pairs of regions compared to the healthy comparison group, particularly for the opercular cortex and subcortical structures. This effect was less prominent in brainstem structures. Metabolic covariance networks in the epilepsy group showed reduced small-worldness as well as altered nodal properties in the ipsilateral hemisphere, compared to the healthy comparison group.Conclusions: Cerebral glucose metabolism in insular epilepsy is marked by a lack of normal regional heterogeneity in metabolic patterns, resulting in metabolic covariance networks that are more tightly coupled between regions than healthy comparison individuals. Metabolic networks in insular epilepsy exhibit altered topological properties and evidence of potentially compensatory formation of aberrant local connections. Taken together, these results demonstrate that insular epilepsy is a systemic neurological disorder with widespread disruption to cerebral metabolic networks.

ObjectiveDuring presurgical evaluation for focal epilepsy patients, the evidence supporting the use of high frequency oscillations (HFOs) for delineating the epileptogenic zone (EZ) increased over the past decade. This study aims to develop and validate an integrated automatic detection, classification and imaging pipeline of HFOs with stereoelectroencephalography (SEEG) to narrow the gap between HFOs quantitative analysis and clinical application.MethodsThe proposed pipeline includes stages of channel inclusion, candidate HFOs detection and automatic labeling with four trained convolutional neural network (CNN) classifiers and HFOs sorting based on occurrence rate and imaging. We first evaluated the initial detector using an open simulated dataset. After that, we validated our full algorithm in a 20-patient cohort against three assumptions based on previous studies. Classified HFOs results were compared with seizure onset zone (SOZ) channels for their concordance. The receiver operating characteristic (ROC) curve and the corresponding area under the curve (AUC) were calculated representing the prediction ability of the labeled HFOs outputs for SOZ.ResultsThe initial detector demonstrated satisfactory performance on the simulated dataset. The four CNN classifiers converged quickly during training, and the accuracies on the validation dataset were above 95%. The localization value of HFOs was significantly improved by HFOs classification. The AUC values of the 20 testing patients increased after HFO classification, indicating a satisfactory prediction value of the proposed algorithm for EZ identification.ConclusionOur detector can provide robust HFOs analysis results revealing EZ at the individual level, which may ultimately push forward the transitioning of HFOs analysis into a meaningful part of the presurgical evaluation and surgical planning.

BackgroundHigh-frequency oscillation (HFO) represents a promising biomarker of epileptogenicity. However, the significant interindividual differences among patients limit its application in clinical practice. Here, we applied and evaluated an individualized, frequency-based approach of HFO analysis in stereoelectroencephalography (SEEG) data for localizing the epileptogenic zones (EZs).MethodsClinical and SEEG data of 19 patients with drug-resistant focal epilepsy were retrospectively analyzed. The individualized spectral power of all signals recorded by electrode array, i.e., the relative strength of HFO, was computed with a wavelet method for each patient. Subsequently, the clinical value of the relative strength of HFO for identifying the EZ was evaluated.ResultsFocal increase in the relative strength of HFO in SEEG recordings were identified in all 19 patients. HFOs identified inside the clinically identified seizure onset zone had more spectral power than those identified outside (p < 0.001), and HFOs in 250–500 Hz band (fast ripples) seemed to be more specific identifying the EZ than in those in 80–250 Hz band (ripples) (p < 0.01). The resection of brain regions generating HFOs resulted in a favorable seizure outcome in 17 patients (17/19; 89.5%), while in the cases of other patients with poor outcomes, the brain regions generating HFOs were not removed completely.ConclusionThe relative strength of HFO, especially fast ripples, is a promising effective biomarker for identifying the EZ and can lead to a favorable seizure outcome if used to guide epilepsy surgery.

PurposeHypermotor seizures (HMS) can be triggered by different epileptogenic foci and thus common symptomatic networks generating HMS may exist among these patients. The goal of the present study was to investigate the specialized networks underlying HMS by analyzing interictal 18FDG-PET imaging and ictal stereo-electroencephalography (SEEG) recordings.MethodsFourteen patients with HMS were retrospectively analyzed. HMS were classified into HMS1 and HMS2 according to the speed and intensity of the motor seizure behavior. Then, the interictal PET data of patients was compared with those of 18 healthy controls using statistical parametric mapping to identify regions with significant hypometabolism. Ictal SEEG recordings were reviewed to identify the spreading areas at the beginning of HMS occurrence.ResultsCompared to controls, patients with HMS presented significant hypometabolism in the bilateral anterosuperior insular lobes, mesial premotor cortex (MPMC), middle cingulate cortex (MCC), as well as in the bilateral caudate nuclei. When comparing patients in the two HMS subgroups with controls, more extensive hypometabolic areas were seen in HMS1 patients than in HMS2 patients, including the orbitofrontal cortex (OFC), the temporal pole, and the anterior cingulate cortex (ACC). OFC and ventromedial prefrontal cortex was also found significantly hypometabolic in patients with HMS1 when compared with HMS2 directly. SEEG recordings further suggested that insula, MCC, and MPMC were commonly recruited at the beginning of HMS.ConclusionWe have identified a specialized interictal hypometabolic pattern in patients with HMS. A network involving the anterosuperior insula, mesiofrontal cortex (MCC-MPMC), and caudate nucleus may contribute to the generation of HMS. ACC, OFC, and temporal pole are possibly associated with the affective components of HMS. Our findings provide further insight into understanding the network basis of HMS semiology.

ObjectiveTo investigate the neuroimaging and clinical features associated with sleep-related epilepsy (SRE) in patients with focal cortical dysplasia (FCD) type II.MethodsPatients with histopathologically proven FCD type II were included from three epilepsy centers. SRE was defined according to the video EEG findings and seizure history. Cortical surface reconstruction and volume calculation were performed using FreeSurfer. The lesions were manually delineated on T1 volumetric MRI using the ITK-SNAP software. The lesion volumes were normalized by the intracranial volume of each patient. The lesions were classified as small or large by placing a threshold based on quantitative (whether the lesion was detected on MRI report) and qualitative (volume) criteria.ResultsA total of 77 consecutive patients were included. Of them, 36 had SRE and 41 had non-SRE. An earlier age of epilepsy onset, high seizure frequency, regional interictal EEG findings, and favorable surgical outcome were characteristic in both groups. Small lesions were defined as those having a volume

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. [ABSTRACT FROM AUTHOR]

Objective Early articular cartilage lesion (CL) is a vital sign in the onset of posttraumatic knee osteoarthritis (PTOA) in patients with anterior cruciate ligament deficiency (ACLD). Researchers have suggested that altered kinematics could accelerate CLs and, therefore, lead to the onset of PTOA. However, little is known about whether specific knee kinematics exist that lead to early CL in chronic ACLD knees. Level walking is the most frequent and relevant in vivo activity, which greatly impacts knee health. We hypothesized that the knee kinematics during level walking in chronic ACLD knees with early tibiofemoral CL would significantly differ from those of chronic ACLD knees without early tibiofemoral CL. Methods Thirty patients with a chronic ACLD history, including 18 subjects with CLs and 12 subjects without CLs, and 35 healthy control subjects were recruited for the study from July 2020 to August 2022. The knee kinematic data during level walking were collected using a three‐dimensional motion analysis system. The kinematic differences between groups were compared using statistical parametric mapping with one dimension for One‐Way ANOVA. The cartilage statuses of the ACLD knees were assessed via MRI examination. The CLs distribution of subjects was evaluated using a modified Noyes scale and analyzed by chi‐square tests. Results ACLD knees with CLs had significantly greater posterior tibial translation (7.7–8.0mm, 12%–18% gait cycle GC, p = 0.014) compared to ACLD knees without CLs during level walking. ACLD knees with CLs had greater posterior tibial translation (4.6–5.5mm, 0%–23% GC, p