Causal Brain Network in Clinically-Annotated Epileptogenic Zone Predicts Surgical Outcomes of Drug-Resistant Epilepsy.

OBJECTIVE: Patients with drug-resistant epilepsy (DRE) are commonly treated using neurosurgery, while its success rate is limited with approximately 50%. Predicting surgical outcomes is currently a prominent topic. The DRE is recognized as a network disorder involving a seizure triggering mechanism within epileptogenic zone (EZ); however, a systematic exploration of the EZ causal network remains lacking. METHODS: This paper will advance DRE study by: (1) developing a novel causal coupling algorithm, "full convergent cross mapping (FCCM)" to improve the quantization performance; (2) characterizing the DRE's multi-frequency epileptogenic network by FCCM calculation of ictal iEEG; (3) predicting surgical outcomes using network features and machine learning. Numerical validations demonstrate the FCCM's superior quantization in terms of nonlinearity, accuracy, and stability. A multicenter cohort containing 22 DRE patients with 81 seizures is included. RESULT: Based on the Mann-Whitney-U-test, coupling strength of the epileptogenic network in successful surgeries is significantly higher than that of the failed group, with the most significant difference observed in α -iEEG network (p = 1.52e - 07 ) Other clinical covariates are also considered and all th α -iEEG networks demonstrate consistent differences comparing successful and failed groups, with p = 0.014 and 9.23e - 06 for lesional and non-lesional DRE, p = 2.32e - 05, 0.0074 and 0.0030 for three clinical centers CHFU, JHU and NIH. Using FCCM features and 10-fold cross validation, the SVM achieves the highest accuracy of 87.65% in predicting surgical outcomes. CONCLUSION: The epileptogenic causal network is a reliable biomarker for estimating DRE's surgical outcomes. SIGNIFICANCE: The proposed approach is promising to facilitate DRE precision medicine.

Computing Smooth and Integrable Cross Fields via Iterative Singularity Adjustment.

We propose a new method for computing smooth and integrable cross fields on 2D and 3D surfaces. We first compute smooth cross fields by minimizing the Dirichlet energy. Unlike the existing optimization based approaches, our method determines the singularity configuration, i.e., the number of singularities, their locations and indices, via iteratively adjusting singularities. The singularities can move, merge and split, as like charges repel and unlike charges attract. Once all singularities stop moving, we obtain a cross field with (locally) lowest Dirichlet energy. In simply connected domains, such a cross field is guaranteed to be integrable. However, this property does not hold in multiply connected domains. To make a smooth cross field integrable, we construct a vector field c, which characterizes how far the cross field is away from a curl-free field. Then we optimize the locations of singularities by moving them along the field lines of c. Our method is fundamentally different from the existing integer programming-based approaches, since it does not require any special numerical solver. It is fully automatic and also has a parameter to control the number of singularities. Our method is well suited for smooth models in which exact boundary alignment and sparse hard directional constraints are desired, and can guide seamless conformal parameterization and T-junction-free quadrangulation. We will make the source code publicly available.

Clinical analysis of developmental and/or epileptic encephalopathy with spike-and-wave activation in sleep: A single tertiary care center experience in China.

PURPOSE: To analyze the electroclinical features of patients with developmental and/or epileptic encephalopathy with spike-and-wave activation in sleep (DEE/EE-SWAS) and study the efficacy of different therapies on seizure control, electroencephalogram (EEG) improvements of electrical status epilepticus during sleep (ESES), and cognition outcomes. METHODS: Patients with DEE/EE-SWAS who underwent at least one follow-up EEG 3 months after therapy were retrospectively enrolled. The demographic and clinical characteristics of the patients were analyzed. Variables that influenced the outcomes were evaluated using logistic regression models. RESULTS: In total, 87 patients (47 males) were included. The median age at ESES recognition was 81.0 months (IQR 64.0, 96.0). Forty-six patients were diagnosed with self-limited focal epilepsies (SeLFEs) before ESES recognition, 24 with developmental and epileptic encephalopathies with spike-and-wave activation in sleep (DEE-SWAS), and 17 with other epilepsies. Steroids, benzodiazepines, and antiseizure medications (ASMs) were the initial treatment options for ESES. Patients with structural etiologies or slow EEG backgrounds at the time of ESES recognition were less likely to respond to treatment than other patients. However, only children with slow EEG backgrounds had lower odds of response in logistic regression models. Children with clinical or EEG response showed improvements in cognition. CONCLUSION: Steroids, benzodiazepines, and ASMs are effective treatments for patients with DEE/EE-SWAS. Children with structural etiologies or slow EEG backgrounds at the time of ESES recognition may have a poor long-term prognosis. The efficacy of seizure reduction and EEG improvement is associated with cognitive improvement.

Design and Optimization of Self-Supporting Surfaces with Arch Beams.

The paper presents a new method for constructing self-supporting surfaces using arch beams that are designed to convert their thrust into supporting force, thereby eliminating shear stress and bending moments. Our method allows for the placement of the arch beams on the boundary or within a surface and partitions the surface into multiple self-supporting parts. The use of arch beams enhances stability and durability, adds aesthetic appeal, and allows for greater flexibility in the design process. We develop an iterative algorithm for designing selfsupporting surfaces with arch beams that enables the user to control the shape of the beams and surface through intuitive parameters and specify the desired location of the arch beams. We verify the physical stability of the structure using finite element analysis. Experimental results show that our method can produce visually pleasing self-supporting surfaces that satisfy the equilibrium equation with high accuracy.

A novel in-frame deletion in KIF5C gene causes infantile onset epilepsy and psychomotor retardation.

Motor proteins, encoded by Kinesin superfamily (KIF) genes, are critical for brain development and plasticity. Increasing studies reported KIF's roles in neurodevelopmental disorders. Here, a 6 years and 3 months-old Chinese boy with markedly symptomatic epilepsy, intellectual disability, brain atrophy, and psychomotor retardation was investigated. His parents and younger sister were phenotypically normal and had no disease-related family history. Whole exome sequencing identified a novel heterozygous in-frame deletion (c.265_267delTCA) in exon 3 of the KIF5C in the proband, resulting in the removal of evolutionarily highly conserved p.Ser90, located in its ATP-binding domain. Sanger sequencing excluded the proband's parents and family members from harboring this variant. The activity of ATP hydrolysis in vitro was significantly reduced as predicted. Immunofluorescence studies showed wild-type KIF5C was widely distributed throughout the cytoplasm, while mutant KIF5C was colocalized with microtubules. The live-cell imaging of the cargo-trafficking assay revealed that mutant KIF5C lost the peroxisome-transporting ability. Drosophila models also confirmed p.Ser90del's essential role in nervous system development. This study emphasized the importance of the KIF5C gene in intracellular cargo-transport as well as germline variants that lead to neurodevelopmental disorders and might enable clinicians for timely and accurate diagnosis and disease management in the future.

Swift-Eye: Towards Anti-blink Pupil Tracking for Precise and Robust High-Frequency Near-Eye Movement Analysis with Event Cameras.

Eye tracking has shown great promise in many scientific fields and daily applications, ranging from the early detection of mental health disorders to foveated rendering in virtual reality (VR). These applications all call for a robust system for high-frequency near-eye movement sensing and analysis in high precision, which cannot be guaranteed by the existing eye tracking solutions with CCD/CMOS cameras. To bridge the gap, in this paper, we propose Swift-Eye, an offline precise and robust pupil estimation and tracking framework to support high-frequency near-eye movement analysis, especially when the pupil region is partially occluded. Swift-Eye is built upon the emerging event cameras to capture the high-speed movement of eyes in high temporal resolution. Then, a series of bespoke components are designed to generate high-quality near-eye movement video at a high frame rate over kilohertz and deal with the occlusion over the pupil caused by involuntary eye blinks. According to our extensive evaluations on EV-Eye, a large-scale public dataset for eye tracking using event cameras, Swift-Eye shows high robustness against significant occlusion. It can improve the IoU and F1-score of the pupil estimation by 20% and 12.5% respectively, compared with the second-best competing approach, when over 80% of the pupil region is occluded by the eyelid. Lastly, it provides continuous and smooth traces of pupils in extremely high temporal resolution and can support high-frequency eye movement analysis and a number of potential applications, such as mental health diagnosis, behaviour-brain association, etc. The implementation details and source codes can be found at https://github.com/ztysdu/Swift-Eye.

Safety, Accuracy, and Efficacy of Robot-Assisted Stereo Electroencephalography in Children of Different Ages.

BACKGROUND AND OBJECTIVES: Aimed to investigate the safety, accuracy, and efficacy of stereo electroencephalography (SEEG) in children of various ages, with particular emphasis on those younger than 3 years. There is limited guidance regarding whether SEEG can conducted on very young children. METHODS: This retrospective study was conducted between July 2018 and August 2022. It involved 88 patients who underwent 99 robot-assisted SEEG procedures at our center. The patients were categorized into 3 groups based on their age at the time of the robot-assisted SEEG procedures: group 1 (3 years and younger, n = 28), group 2 (age 3-6 years, n = 27), and group 3 (older than 6 years, n = 44). Clinical data, SEEG demographics, complications, and seizure outcomes were analyzed. RESULTS: A total of 675 electrodes were implanted, with an average of 6.82 ± 3.47 (2.00-16.00) electrodes per patient (P = .052). The average target point error for the 675 electrodes was 1.93 ± 1.11 mm, and the average entry point error was 1.30 ± 0.97 mm (P = .536 and P = .549, respectively). The overall percentage of complications was 6.06% (P = .879). No severe or long-term neurologic impairment was observed. Of the total 99 procedures included in this study, 78 were admitted for epilepsy surgery for the first time, while 9 patients were treated twice and 1 patient was treated 3 times. There were 21 radiofrequency thermocoagulation and 78 second-stage resective procedures performed after SEEG. There was no statistically significant difference in Engel class I outcomes among the patients who underwent SEEG in the 3 age groups (P = .621). CONCLUSION: Robot-assisted SEEG were demonstrated to be safe, accurate, and efficient across different age groups of children. This technique is suitable for children younger than 3 years who have indications for SEEG placement.

Brain development in newborns and infants after ECMO.

BACKGROUND: Extracorporeal membrane oxygenation (ECMO) not only significantly improves survival rates in severely ill neonates but also is associated with long-term neurodevelopmental issues. To systematically review the available literature on the neurodevelopmental outcomes of neonates and infants who have undergone ECMO treatment, with a focus on motor deficits, cognitive impairments, sensory impairments, and developmental delays. This review aims to understand the incidence, prevalence, and risk factors for these problems and to explore current nursing care and management strategies. DATA SOURCES: A comprehensive literature search was performed across PubMed, EMBASE, and Web of Science using a wide array of keywords and phrases pertaining to ECMO, neonates, infants, and various facets of neurodevelopment. The initial screening involved reviewing titles and abstracts to exclude irrelevant articles, followed by a full-text assessment of potentially relevant literature. The quality of each study was evaluated based on its research methodology and statistical analysis. Moreover, citation searches were conducted to identify potentially overlooked studies. Although the focus was primarily on neonatal ECMO, studies involving children and adults were also included due to the limited availability of neonate-specific literature. RESULTS: About 50% of neonates post-ECMO treatment exhibit varying degrees of brain injury, particularly in the frontal and temporoparietal white matter regions, often accompanied by neurological complications. Seizures occur in 18%-23% of neonates within the first 24 hours, and bleeding events occur in 27%-60% of ECMO procedures, with up to 33% potentially experiencing ischemic strokes. Although some studies suggest that ECMO may negatively impact hearing and visual development, other studies have found no significant differences; hence, the influence of ECMO remains unclear. In terms of cognitive, language, and intellectual development, ECMO treatment may be associated with potential developmental delays, including lower composite scores in cognitive and motor functions, as well as potential language and learning difficulties. These studies emphasize the importance of early detection and intervention of potential developmental issues in ECMO survivors, possibly necessitating the implementation of a multidisciplinary follow-up plan that includes regular neuromotor and psychological evaluations. Overall, further multicenter, large-sample, long-term follow-up studies are needed to determine the impact of ECMO on these developmental aspects. CONCLUSIONS: The impact of ECMO on an infant's nervous system still requires further investigation with larger sample sizes for validation. Fine-tuned management, comprehensive nursing care, appropriate patient selection, proactive monitoring, nutritional support, and early rehabilitation may potentially contribute to improving the long-term outcomes for these infants.

Lipocalin 2 (LCN2) confers acquired resistance to almonertinib in NSCLC through LCN2-MMP-9 signaling pathway.

Almonertinib, a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor, is highly selective for EGFR-activating mutations as well as the EGFR T790M mutation in patients with advanced non-small cell lung cancer (NSCLC). However, the development of resistance inevitably occurs and poses a major obstacle to the clinical efficacy of almonertinib. Therefore, a clear understanding of the mechanism is of great significance to overcome drug resistance to almonertinib in the future. In this study, NCI-H1975 cell lines resistant to almonertinib (NCI-H1975 AR) were developed by concentration-increasing induction and were employed for clarification of underlying mechanisms of acquired resistance. Through RNA-seq analysis, the HIF-1 and TGF-ß signaling pathways were significantly enriched by gene set enrichment analysis. Lipocalin-2 (LCN2), as the core node in these two signaling pathways, were found to be positively correlated to almonertinib-resistance in NSCLC cells. The function of LCN2 in the drug resistance of almonertinib was investigated through knockdown and overexpression assays in vitro and in vivo. Moreover, matrix metalloproteinases-9 (MMP-9) was further identified as a critical downstream effector of LCN2 signaling, which is regulated via the LCN2-MMP-9 axis. Pharmacological inhibition of MMP-9 could overcome resistance to almonertinib, as evidenced in both in vitro and in vivo models. Our findings suggest that LCN2 was a crucial regulator for conferring almonertinib-resistance in NSCLC and demonstrate the potential utility of targeting the LCN2-MMP-9 axis for clinical treatment of almonertinib-resistant lung adenocarcinoma.

Semi-Supervised Medical Image Segmentation Using Cross-Style Consistency With Shape-Aware and Local Context Constraints.

Despite the remarkable progress in semi-supervised medical image segmentation methods based on deep learning, their application to real-life clinical scenarios still faces considerable challenges. For example, insufficient labeled data often makes it difficult for networks to capture the complexity and variability of the anatomical regions to be segmented. To address these problems, we design a new semi-supervised segmentation framework that aspires to produce anatomically plausible predictions. Our framework comprises two parallel networks: shape-agnostic and shape-aware networks. These networks learn from each other, enabling effective utilization of unlabeled data. Our shape-aware network implicitly introduces shape guidance to capture shape fine-grained information. Meanwhile, shape-agnostic networks employ uncertainty estimation to further obtain reliable pseudo-labels for the counterpart. We also employ a cross-style consistency strategy to enhance the network's utilization of unlabeled data. It enriches the dataset to prevent overfitting and further eases the coupling of the two networks that learn from each other. Our proposed architecture also incorporates a novel loss term that facilitates the learning of the local context of segmentation by the network, thereby enhancing the overall accuracy of prediction. Experiments on three different datasets of medical images show that our method outperforms many excellent semi-supervised segmentation methods and outperforms them in perceiving shape. The code can be seen at https://github.com/igip-liu/SLC-Net.

Etiologies and clinical characteristics of acute ataxia in a single national children's medical center.

OBJECTIVE: To analyze etiologic factors of pediatric acute ataxia and to identify the severity of its underlying causes for urgent medical intervention. METHODS: Clinical data of children diagnosed with acute ataxia between December 2015 and December 2021 from one national medical center were analyzed retrospectively. RESULTS: A total of 99 children (59 boys, 40 girls), median age at disease onset 55 (range: 12-168) months, were enrolled. The median follow period was 46 (range 6-78) months. Eighty-six (86.9 %) children were diagnosed with immune-associated acute ataxia, among which acute post-infectious cerebellar ataxia (APCA) was the most common diagnosis (50.5 %), followed by demyelinating diseases of the central nervous system (18.2 %) and Guillain-Barré syndrome (9.1 %). On cerebrospinal fluid (CSF) examination, 35/73 (47.9 %) patients had pleocytosis (>5 cells/mm3), and 18/73 (24.7 %) had elevated protein levels. Thirty-one patients (31.3 %) had an abnormal cerebral MRI. Children with other immune-associated acute cerebellar ataxia had more extracerebellar symptoms, intracranial MRI lesions, abnormal CSF results, longer hospital stay, higher recurrence rates and incidence of neurological sequelae than children with APCA. CONCLUSION: Immune-associated acute ataxia is the main cause of pediatric acute ataxia, among which APCA is the most common phenotype. However, some immune-associated diseases, especially autoantibody-mediated disease, which has a higher recurrence rate and neurological sequelae account for an increasing proportion of pediatric acute ataxia. When children present with extracerebellar symptoms, abnormal cranial MRI or CSF results, and without prodromal infection, prudent differential diagnosis is recommended.

Prognostic Analysis of KCNQ2 Patients via Combining EEG Deep Features and Machine Learning Classifiers.

Pathogenic variants of the KCNQ2 gene often induces neonatal epilepsy in clinical. For better treatment, infants with confirmed KCNQ2 pathogenic variant and epilepsy symptoms need to adjust their treatment plans according to the outcome after taking antiseizure medicines (ASMs). This process is often time-consuming and requires long-term follow-up, which undoubtedly causes unnecessary psychological and economic burdens. In this study, we investigate the feasibility to predict the outcome of KCNQ2 patients via Electroencephalogram (EEG). By using the combination of deep networks and classical classifiers, the abnormal brain pathological activities recorded in EEGs can be encoded into deep features and decoded into specific KCNQ2 outcomes, thus taking the advantage of both powerful feature extraction capability from deep networks and stronger classification ability from classical classifiers. Specifically, we acquire 10-channel EEG signals from 33 infants with KCNQ2 pathogenic variants after taking ASMs. Two well-trained models (Resnet-50 and Resnet-18) are employed to extract deep features from the EEG spectrums. We achieve an accuracy of 78.7% to predict the KCNQ2 outcome of each infant. To our best knowledge, this is the first study to employ potential EEG pathological differences to predict the outcomes of KCNQ2 patients. The investigation of automatic KCNQ2 outcome prediction may contribute to a more convenient diagnosis mechanism for KCNQ2 patients.

Frameless robot-assisted stereoelectroencephalography-guided radiofrequency: methodology, results, complications and stereotactic application accuracy in pediatric hypothalamic hamartomas.

Objective: We aimed to investigate the methodology, results, complications and stereotactic application accuracy of electrode implantation and its explanatory variables in stereoelectroencephalography-guided radiofrequency thermocoagulation (SEEG-RFTC) for pediatric hypothalamic hamartoma. Methods: Children with hypothalamic hamartoma who underwent robot-assisted SEEG-RFTC between December 2017 and November 2021 were retrospectively analyzed. The methodology, seizure outcome, complications, in vivo accuracy of electrode implantation and its explanatory variables were analyzed. Results: A total of 161 electrodes were implanted in 28 patients with 30 surgeries. Nine electrodes not following the planned trajectories due to intraoperative replanning were excluded, and the entry point and target point errors of 152 electrodes were statistically analyzed. The median entry point error was 0.87 mm (interquartile range, 0.50-1.41 mm), and the median target point error was 2.74 mm (interquartile range, 2.01-3.63 mm). Multifactor analysis showed that whether the electrode was bent (b = 2.16, p < 0.001), the length of the intracranial electrode (b = 0.02, p = 0.049), and the entry point error (b = 0.337, p = 0.017) had statistically significant effects on the target error. During follow-up (mean duration 31 months), 27 of 30 (90%) procedures were seizure-free. The implantation-related complication rate was 2.6% (4/152), and the major complication rate in all procedures was 6.7% (2/30). Conclusion: Robot-assisted SEEG-RFTC is a safe, effective and accurate procedure for pediatric hypothalamic hamartoma. Explanatory variables significantly associated with the target point localization error at multivariate analysis include whether the intracranial electrode is bent, the intracranial electrode length and the entry point error.

Telemedicine during the COVID-19 epidemic improves outcomes in children with tuberous sclerosis complex: A 1206 visits retrospective cohort study.

AIMS: To evaluate the benefits of telemedicine in children with tuberous sclerosis complex during the COVID-19 pandemic. METHODS: A retrospective cohort study was conducted, comparing telemedicine and in-person visits within the timeframe spanning from June 1, 2021, to June 1, 2022. Disparities in demographics, emergency visits, hospitalizations, adverse effects (AEs) associated with sirolimus, and the incidence of drug-refractory epilepsy (DRE) between telehealth and in-person care were assessed. Additionally, distinctions between audio and video telehealth, as well as varying frequencies of telehealth encounters, were investigated and reported as odds ratios (ORs). RESULTS: A total of 378 patients with 1206 visits were included, of which 137 were telemedicine patients and 241 were in-person patients. The median age was 5.0 years (IQR 2.8-10.0 years). There were 197 males (52.12%), 691 in-person visits (57.30%), and 515 telemedicine visits (42.70%). Children under 12 years old, those farther away from the center, mothers with more than 12 years of education, and children treated with sirolimus were more likely to visit via telemedicine. Telehealth was associated with significantly fewer emergency visits, hospitalizations, AEs of sirolimus, and DRE. With 10 or more visits, the incidence of emergency visits, hospitalization, and DRE was significantly reduced. CONCLUSION: Telemedicine visits are almost as close in number as in-person visits. Younger patients, patients in remote areas, and mothers with higher education levels are more willing to complete telemedicine visits. Telemedicine visits were associated with a significantly lower number of emergency visits, hospitalizations, and AEs of sirolimus. Patients with more than 10 visits per year seemed to have better clinical outcomes.

iPUNet: Iterative Cross Field Guided Point Cloud Upsampling.

Point clouds acquired by 3D scanning devices are often sparse, noisy, and non-uniform, causing a loss of geometric features. To facilitate the usability of point clouds in downstream applications, given such input, we present a learning-based point upsampling method, i.e., which generates dense and uniform points at arbitrary ratios and better captures sharp features. To generate feature-aware points, we introduce cross fields that are aligned to sharp geometric features by self-supervision to guide point generation. Given cross field defined frames, we enable arbitrary ratio upsampling by learning at each input point a local parameterized surface. The learned surface consumes the neighboring points and 2D tangent plane coordinates as input, and maps onto a continuous surface in 3D where arbitrary ratios of output points can be sampled. To solve the non-uniformity of input points, on top of the cross field guided upsampling, we further introduce an iterative strategy that refines the point distribution by moving sparse points onto the desired continuous 3D surface in each iteration. Within only a few iterations, the sparse points are evenly distributed and their corresponding dense samples are more uniform and better capture geometric features. Through extensive evaluations on diverse scans of objects and scenes, we demonstrate that iPUNet is robust to handle noisy and non-uniformly distributed inputs, and outperforms state-of-the-art point cloud upsampling methods.

Novel Variants of PPP2R1A in Catalytic Subunit Binding Domain and Genotype-Phenotype Analysis in Neurodevelopmentally Delayed Patients.

Neurodevelopmental disorders (NDDs) are a group of high-incidence rare diseases with genetic heterogeneity. PPP2R1A, the regulatory subunit of protein phosphatase 2A, is a recently discovered gene associated with NDDs. Whole/clinical exome sequencing was performed in five patients with a family with NDDs. In vitro experiments were performed to evaluate the mutants' expression and interactions with the complex. The genotype-phenotype correlations of reported cases as well as our patients with PPP2R1A variants were reviewed. We reported five unrelated individuals with PPP2R1A variants, including two novel missense variants and one frameshift variant. The protein expression of the Arg498Leu variant was less than that of the wild-type protein, the frameshift variant Asn282Argfs*14 was not decreased but truncated, and these two variants impaired the interactions with endogenous PPP25RD and PPP2CA. Furthermore, we found that pathogenic variants clustered in HEAT repeats V, VI and VII, and patients with the Met180Val/Thr variants had macrocephaly, severe ID and hypotonia, but no epilepsy, whereas those with Arg258 amino acid changes had microcephaly, while a few had epilepsy or feeding problems. In this study, we reported five NDD patients with PPP2R1A gene variants and expanded PPP2R1A pathogenic variant spectrum. The genotype and phenotype association findings provide reminders regarding the prognostication and evidence for genetic counseling.

The prevalence and risk factors of coagulopathy in pediatric patients undergoing surgery for epilepsy.

OBJECTIVE: Hematological consequences of novel antiseizure medications (ASMs) or combined therapies are rarely reported, especially in pediatric patients undergoing surgery for epilepsy. This study aimed to assess the prevalence and risk factors of coagulation dysfunction in this population and evaluate their relationship with intra- and postoperative bleeding. METHODS: Three hundred ninety children who underwent surgery for epilepsy and 104 children without epilepsy who underwent nonepilepsy surgery at the authors' center were included in the study. The authors retrospectively collected and analyzed the following clinical data: sex, age, weight, course of epilepsy, antiseizure therapy, first laboratory data after admission, and transfusion-related data. RESULTS: ASMs were responsible for the higher incidence of coagulation dysfunction in pediatric epilepsy surgery patients. Low body weight (OR 0.95, 95% CI 0.92-0.98) and valproic acid (VPA) therapy (OR 5.13, 95% CI 3.25-8.22) were the most relevant factors leading to coagulation dysfunction. The most common hematological side effects of VPA were thrombocytopenia and hypofibrinogenemia, whereas low body weight was only associated with hypofibrinogenemia. Both VPA and low body weight increased the need for intra- or postoperative transfusion (p < 0.001). CONCLUSIONS: Pediatric epilepsy surgery patients often take multiple ASMs, resulting in an increased incidence of coagulopathy. VPA levels and low body weight were found to be the main influential factors associated with an increased risk of coagulation dysfunction. Platelet and fibrinogen levels were the main indices that were affected. Both VPA and low body weight were relevant to additional surgery-related transfusion, necessitating the need for increased awareness of preoperative coagulopathy before pediatric epilepsy surgery. Clinical trial registration no.: NCT05675254 (ClinicalTrials.gov).