Neuroscience informatics最新文献

{"title":"Advancing brain tumor diagnostics and therapy (2023–2025): A global bibliometric perspective on innovation and collaboration","authors":"Rosnah Sutan ,&nbsp;Afzal Hussain ,&nbsp;Rizal Abdul Manaf ,&nbsp;Zaleha Md Isa ,&nbsp;Ashfaq Hussain","doi":"10.1016/j.neuri.2025.100250","DOIUrl":"10.1016/j.neuri.2025.100250","url":null,"abstract":"<div><h3>Background</h3><div>Brain tumors present a daunting clinical challenge, necessitating unwavering innovation in diagnostics, imaging, and therapeutics. Emerging advances in artificial intelligence (AI), molecular biomarkers, and neuroimaging have transformed the research landscape.</div></div><div><h3>Objective</h3><div>The current research conducted bibliometric analysis to map global research trends in brain tumor diagnosis, imaging, and treatment strategies between 2023 and 2025, with a particular focus on AI applicability and biomarker-driven precision medicine.</div></div><div><h3>Methods</h3><div>A systematic literature search of the Scopus database was performed in July 2025 for English original research articles between 2023 and 2025. The search keywords included: “brain tumor,” “glioma,” “glioblastoma,” “meningioma,” “astrocytoma,” “diagnosis,” “MRI,” “CT,” “artificial intelligence,” “deep learning,” “machine learning,” “radiotherapy,” “chemotherapy,” “surgery,” “biomarkers,” “prognosis,” “segmentation,” and “classification.” Bibliographic data were analyzed using Biblioshiny to explore publication output, citation impact, prominent authors, institutional productivity, keyword trends, and collaboration networks.</div></div><div><h3>Results</h3><div>The analysis included 23,496 papers from over 93,000 researchers. It indicated a research boom in AI-enhanced diagnostics, radiomics, and individualized treatments. Both China and the U.S. were leading producers, but the U.S. recorded greater international collaboration and citation impact. Glioma classification, MRI-based segmentation, and deep learning applications were the most common topics. Collaboration networks were geographically focused, with a particular concentration in East Asia.</div></div><div><h3>Conclusion</h3><div>Brain tumor research is rapidly moving towards precision and AI-driven strategies. While there is a growing scientific output, more international and intersectoral collaboration is needed to make these advances translate to equitable clinical gain.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100250"},"PeriodicalIF":0.0,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in deep learning for multimodal brain imaging: A comprehensive survey","authors":"Saif M. Balsabti ,&nbsp;Rasool M. Al-Gburi ,&nbsp;Raid gaib ,&nbsp;Ali Mustafa ,&nbsp;Shaimaa Khamees Ahmed ,&nbsp;Ali Mahmoud Issa ,&nbsp;Taha Mahmoud Al-Naimi ,&nbsp;Rawan AlSaad ,&nbsp;Ali M. Elhenidy","doi":"10.1016/j.neuri.2025.100252","DOIUrl":"10.1016/j.neuri.2025.100252","url":null,"abstract":"<div><div>In recent years, the field of medical brain imaging has witnessed remarkable advancements with the integration of artificial intelligence (AI) and deep learning techniques. Traditional unimodal imaging methods, such as MRI and CT, often fall short in providing comprehensive insights into neurological disorders. To address these limitations, multimodal imaging, which combines various imaging modalities like MRI, CT, PET, and SPECT, has emerged as a powerful tool for enhanced diagnosis and treatment planning. This survey presents an in-depth review of the state-of-the-art deep learning models, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), used for brain tumor classification, segmentation, forecasting, and object detection. We also explore the potential of hybrid models that integrate machine learning and deep learning approaches. Furthermore, we highlight the significant developments in multimodal brain imaging techniques from 2019 to 2024 and discuss the future research directions needed to advance this field. By synthesizing the latest findings, this survey aims to provide a comprehensive understanding of the current landscape and future possibilities in multimodal medical brain imaging.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100252"},"PeriodicalIF":0.0,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"15 Years of optimizers in medical deep learning: A systematic review","authors":"Selorm Adablanu ,&nbsp;Utpal Barman ,&nbsp;Dulumani Das","doi":"10.1016/j.neuri.2025.100249","DOIUrl":"10.1016/j.neuri.2025.100249","url":null,"abstract":"<div><div>Optimization algorithms are pivotal in training deep learning (DL) models for medical imaging, determining how efficiently models learn, generalize, and perform across modalities. This systematic review analyzed 69 peer-reviewed studies (2010–2025) on optimizer performance in classification, segmentation, and object detection tasks using MRI, CT, X-ray, ultrasound, histopathology, and ECG data, following PRISMA 2020 guidelines. Adaptive optimizers such as Adam and its variants were most common, offering rapid convergence in CNN-based classification, whereas SGD and momentum-based methods yielded stronger generalization in large-scale segmentation. Emerging techniques—Sharpness-Aware Minimization (SAM), Ranger, and AdamW—improved robustness under domain shift or noisy conditions. Hybrid and metaheuristic optimizers provided marginal accuracy gains but at higher computational cost. Common limitations included inconsistent hyperparameter reporting, limited external validation, and dataset bias toward North American cohorts. Optimizer effectiveness was found to be task- and architecture-dependent: adaptive methods suit small or noisy datasets, while momentum-based and hybrid methods enhance generalization for complex imaging. Future studies should emphasize standardized evaluation, transparent reporting, and diverse data to enable equitable and reproducible deployment of medical AI.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100249"},"PeriodicalIF":0.0,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the effects of wavelet types and windowing on EMG-based IONM through deep learning architectures","authors":"Abdalla Nabil Elsharkawy ,&nbsp;Nourhan Zayed","doi":"10.1016/j.neuri.2025.100253","DOIUrl":"10.1016/j.neuri.2025.100253","url":null,"abstract":"<div><div>Intraoperative neuromonitoring (IONM) plays a critical role in preserving nerve function during high-risk surgeries through real-time monitoring of electromyographic (EMG) activity. Routine EMG analysis, in real-time, is complex and prone to variability. This work presents an end-to-end deep learning-based framework for accurate EMG signal classification of the nerve status using the discrete wavelet transform (DWT) mathematical technique. Four state-of-the-art deep learning architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), a CNN-LSTM ensemble, and a Transformer model, were tested with various Daubechies wavelet families (db1–db6) and window sizes (50–500 samples). The Transformer model performed superiorly in classification, achieving an outstanding accuracy of 98.13 %, an F1-score of 98.14 %, and a recall of 97.50 % using db1 and a 400-sample window. The results summed up that the use of wavelet-based time-frequency decomposition has a significant influence on enhancing classification performance, especially when utilized with deep learning models.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100253"},"PeriodicalIF":0.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the effect of point-sampling on univariate point and interval forecasting of cerebral physiologic signals using ARIMA modeling in acute traumatic neural injury","authors":"Nuray Vakitbilir ,&nbsp;Kevin Y. Stein ,&nbsp;Tobias Bergmann ,&nbsp;Noah Silvaggio ,&nbsp;Amanjyot Singh Sainbhi ,&nbsp;Abrar Islam ,&nbsp;Logan Froese ,&nbsp;Rakibul Hasan ,&nbsp;Mansoor Hayat ,&nbsp;Marcel Aries ,&nbsp;Frederick A. Zeiler","doi":"10.1016/j.neuri.2025.100248","DOIUrl":"10.1016/j.neuri.2025.100248","url":null,"abstract":"<div><div>High-resolution physiological signals, such as intracranial pressure (ICP) and regional cerebral oxygen saturation (rSO₂), are critical for managing traumatic brain injury (TBI) by enabling continuous monitoring of cerebral autoregulation and vascular reactivity. These signals provide essential insights into brain perfusion dynamics, supporting timely clinical interventions. However, the high temporal resolution of these data introduces challenges in real-time use, integration into predictive models, and computational efficiency. Consequently, resolution reduction techniques are essential for simplifying the data while retaining critical features necessary for accurate prediction and modeling. Using the Multi-omic Analytics and Integrative Neuroinformatics in the HUman Brain (MAIN-HUB) Lab database, high-frequency cerebral physiologic dataset, we aimed to evaluate the effects of point-sampling resolution reduction on point and interval predictions using the autoregressive integrated moving average (ARIMA) model for both raw signals and derived indices. Temporal resolution was reduced by selecting the first value within non-overlapping intervals, ranging from 1-min (min) to 12-h windows. A total of 125 patient data was analyzed across various temporal resolutions. The results indicated that ARIMA models performed well at higher resolutions (e.g., 1-min), capturing short-term physiological dynamics with lower errors. However, as resolution decreased, errors and variability increased, particularly for signals like mean arterial pressure (MAP) and cerebral perfusion pressure (CPP), which exhibit rapid or complex physiological changes. The findings underscore the need to carefully balance temporal resolution, model performance, and computational efficiency, especially when dealing with high-frequency physiological data in clinical settings.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100248"},"PeriodicalIF":0.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG-based classification in psychiatry using motif discovery","authors":"Melanija Kraljevska ,&nbsp;Kateřina Hlaváčková-Schindler ,&nbsp;Lukas Miklautz ,&nbsp;Claudia Plant","doi":"10.1016/j.neuri.2025.100242","DOIUrl":"10.1016/j.neuri.2025.100242","url":null,"abstract":"<div><div>In current medical practice, patients undergoing treatment for depression typically must wait four to six weeks before clinicians can assess their response to medication, due to the delayed onset of noticeable effects from antidepressants. Identifying treatment response at an earlier stage is of great importance, as it can reduce both the emotional and economic burden associated with prolonged treatment. We present a novel Motif Discovery Framework (MDF) that extracts dynamic features from EEG time series data to distinguish between treatment responders and non-responders in depression. Our findings show that MDF can predict treatment response with high precision as early as the 7th day of treatment, significantly reducing the waiting time for patients. Furthermore, we demonstrate that MDF generalizes well to classification tasks in other psychiatric conditions, including schizophrenia, Alzheimer’s disease, and dementia. Overall, our experiments show that MDF outperforms relevant benchmarks. The high precision of our classification framework underscores the potential of EEG dynamic properties-represented as motifs-to support clinical decision-making and ultimately enhance patient quality of life.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100242"},"PeriodicalIF":0.0,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attention-Gated CNN and discrete wavelet transform based ensemble framework for brain hemorrhage classification","authors":"Srutanik Bhaduri ,&nbsp;Rasel Mondal ,&nbsp;Prateek Sarangi ,&nbsp;Vinod Kumar Kurmi ,&nbsp;Swati Goyal ,&nbsp;Lovely Kaushal ,&nbsp;Mahek Sodani ,&nbsp;Tanmay Basu","doi":"10.1016/j.neuri.2025.100243","DOIUrl":"10.1016/j.neuri.2025.100243","url":null,"abstract":"<div><div>Brain hemorrhage, or Intracranial Hemorrhage (ICH), is a critical medical condition requiring rapid diagnosis. Existing Convolutional Neural Network (CNN) models often struggle to differentiate similar hemorrhage subtypes like Epidural (EDH) and Subdural (SDH) due to a lack of specific spatial feature identification. This study aims to develop a robust classification framework to address this challenge. We propose an ensemble framework combining two complementary models. The first is an attention-gated 2D CNN designed to highlight subtle hemorrhagic regions. The second is a multi-level Discrete Wavelet Transform (DWT) model that analyzes images in the frequency domain to capture deeper contextual and textural information from the 3D brain volume. The proposed ensemble model was evaluated on the RSNA, CQ500, and a new GMC clinical dataset. The empirical study demonstrates that our model consistently outperforms state-of-the-art methods across standard evaluation metrics, including accuracy, macro-averaged AUC-ROC, specificity, sensitivity, and F1-score. The novel ensembling of an attention-gated CNN and a DWT-based model provides a more comprehensive feature representation, leading to significantly improved accuracy and robustness in ICH classification, particularly in distinguishing challenging subtypes like EDH and SDH.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100243"},"PeriodicalIF":0.0,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145580157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for fetal brain imaging: A systematic review and framework towards privacy-preserving neurodevelopmental informatics","authors":"Sayma Alam Suha,&nbsp;Rifat Shahriyar","doi":"10.1016/j.neuri.2025.100241","DOIUrl":"10.1016/j.neuri.2025.100241","url":null,"abstract":"<div><div>Fetal neurodevelopment is a complex process of neural growth during pregnancy, where early detection of abnormalities is vital, and deep learning offers promising techniques for this purpose. The objective of this systematic review is to investigate deep learning applications in fetal neurodevelopment, aiming to synthesize cutting-edge research, examine methodologies, identify research gaps, and propose a federated learning framework. Following PRISMA 2020 guidelines, 55 peer-reviewed articles were selected from an initial 900 records across major databases and additional sources where each article was examined through six specific data extraction criteria. Peer-reviewed articles from 2005 to 2025, specifically those exploring automated deep learning for fetal neurodevelopment using clinical images were included, while non-deep learning analyses were excluded. Risk of bias was qualitatively assessed based on design, data diversity, validation, and reporting. Key scopes of the studies included brain segmentation and regionalization (50.91%), structural measurement (12.73%), image reconstruction, enhancement and synthesis (21.82%) and predictive modeling and clinical classification (14.55%) which also distinguishes between tasks involving pixel-level analysis and image-level predictions. The 55 included studies used diverse datasets (753 to 433,000 images) as well as synthetic image data in some recent works covering wide-ranging gestational ages, mainly using MRI and ultrasound images. The systematic analysis explicitly categorizes each study by task type, applied methodology (U-Net variants, transformer-based models, CNNs, implicit neural representations), and corresponding evaluation metrics—segmentation (DSC, IoU, HD95), classification (Accuracy, Precision, AUC), regression (MAE, RMSE, R²), and reconstruction (PSNR, SSIM), facilitating standardized performance comparisons and establishing clear benchmarks for future research in automated fetal brain imaging. Significant gaps that were identified include inadequate data diversity, privacy measures, limited clinical interpretability and validity of AI models, and insufficient integration of multimodal data. To address these challenges, a unified framework is proposed that integrates multimodal data fusion, explainable artificial intelligence (XAI) paradigms, and federated learning architectures complemented by synthetic data generation techniques to ensure robust privacy preservation in real-world application. This work was not specifically funded, and the review was not registered.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100241"},"PeriodicalIF":0.0,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting the depth of sedation in the intensive care unit using a 2-channel electroencephalogram: An analysis with 2 machine learning models","authors":"Esteban A. Alarcón-Braga ,&nbsp;Samuel Gruffaz ,&nbsp;Cécile Delagarde ,&nbsp;Axel Roques ,&nbsp;Jean-Clément Riff ,&nbsp;Laurent Oudre ,&nbsp;Clément Dubost","doi":"10.1016/j.neuri.2025.100238","DOIUrl":"10.1016/j.neuri.2025.100238","url":null,"abstract":"<div><div>Existing methods to detect depth of sedation do not fully adjust to the characteristics of the ICU population. The aim of this study is to evaluate the performance of a two-channel EEG in predicting the depth of sedation in ICU patients. The electroencephalographic signal of 21 patients admitted to the ICU were analyzed, and EEG features were calculated. These served as inputs in 2 machine learning models: Random Forest Classifier (RFC) and Support Vector Machine (SVM). The depth of sedation was assessed using the Richmond Agitation-Sedation Scale (RASS). Patients with RASS scores of −4/−5 were classified as “Deeply Sedated”, otherwise they were classified as “Not Deeply Sedated”. In the general models, all EEG features were used, after which sequential feature selection was conducted to improve performance and reduce the number of variables (reduced models). The general models showed a moderate ability to discriminate between sedation categories (RFC: average <span><math><mtext>F1-score</mtext><mo>=</mo><mn>0.60</mn></math></span>, SVM: average <span><math><mtext>F1-score</mtext><mo>=</mo><mn>0.59</mn></math></span>). This ability was improved in the reduced models (RFC: average <span><math><mtext>F1-score</mtext><mo>=</mo><mn>0.65</mn></math></span>, SVM: average <span><math><mtext>F1-score</mtext><mo>=</mo><mn>0.72</mn></math></span>). It was observed that decreasing the number of features in the reduced SVM model from 6 to 3 features could achieve a similar performance while simplifying the model (SVM: average <span><math><mtext>F1-score</mtext><mo>=</mo><mn>0.72</mn></math></span>). An exploratory analysis showed that the individual feature with the best performance was Beta Power–EEG2. Overall, the 2-channel EEG has a moderate power to discriminate between different states of sedation and may not be useful in this purpose if used as a single predictor.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100238"},"PeriodicalIF":0.0,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Power-to-power cross-frequency coupling as a novel approach for temporal lobe seizure detection and analysis","authors":"Bar Lehmann,&nbsp;Andrei V. Medvedev","doi":"10.1016/j.neuri.2025.100240","DOIUrl":"10.1016/j.neuri.2025.100240","url":null,"abstract":"<div><h3>Objective</h3><div>Power-to-power cross-frequency coupling (CFC) is a novel method to index the dynamic spatio-temporal interactions between brain rhythms, including high frequency oscillations (HFOs). This research evaluates this promising method's capacity for seizure detection with intracranial EEG. Seizures can be conceptualized as composites of different electrographic patterns including (1) spike, (2) ripple-on-spike, and (3) ripple-on-oscillation. This study also performs a basic CFC analysis of each of these components which has potential to further the understanding of epileptogenic processes.</div></div><div><h3>Methods</h3><div>In this study, deep learning networks including Stacked Sparse Autoencoder (SSAE) and Long Short Term Memory (LSTM) are trained to detect seizures and help characterize CFC patterns for these three common seizure components. The analysis uses intracranial EEG (iEEG) records from the ieeg.org (Mayo Clinic files) database. Temporal Lobe Epilepsy (TLE) seizures (<span><math><mi>n</mi><mo>=</mo><mn>120</mn></math></span>) from 26 patients were analyzed along with segments of background activity. Power-to-power coupling was calculated between all frequencies 1–250 Hz pairwise using the EEGLAB toolbox. CFC matrices of seizure and background activity were used as training or testing inputs to the autoencoder.</div></div><div><h3>Results</h3><div>The trained network was able to recognize background and seizure segments (not used in training) with a sensitivity of 90.2%, specificity of 96.8% and overall accuracy of 93.4%. The three seizure components (spike, ripple-on-spike, ripple-on-oscillation) were also observed to have unique CFC signatures.</div></div><div><h3>Conclusions</h3><div>The results provide evidence both for (1) the relevance of power-to-power coupling (PPC) for TLE seizure detection in iEEG, as well as (2) there existing unique PPC signatures of three common seizure components.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100240"},"PeriodicalIF":0.0,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}