Neuroscience informatics最新文献

{"title":"Leveraging transparent ontology learning to refine constructs in neuroscience","authors":"David Moreau, Kristina Wiebels","doi":"10.1016/j.neuri.2025.100199","DOIUrl":"10.1016/j.neuri.2025.100199","url":null,"abstract":"","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100199"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated classification of epileptic seizures using modified one-dimensional convolution neural network based on empirical mode decomposition with high accuracy","authors":"Ibtihal Hassan Elshekhidris ,&nbsp;Magdi B. M. Amien ,&nbsp;Ahmed Fragoon","doi":"10.1016/j.neuri.2025.100188","DOIUrl":"10.1016/j.neuri.2025.100188","url":null,"abstract":"<div><div>Background and objectives: The method of electroencephalography (EEG) is frequently employed to identify epileptic seizures. Visually inspecting nonlinear EEG waves is a very difficult and time-consuming process. Therefore, to help with patients' long-term assessment and treatment, an effective automatic detection system is required. Traditional methods of machine learning require step of feature extraction by manual which leads to time consuming, for it we modified in one-Dimensional convolution neural network architecture for features extraction and features dimension reduction for makes the classification low computational complexity and more accurate.</div><div>Methods: In this study, we did a comparison between three methods for classification: support vector machine, artificial neural network and one-dimensional convolution neural network. We used the stationary wavelet transform with mother function symlet2 for denoising EEG signal and used the empirical mode decomposition for signal decomposition. After that, features extraction step is necessary when used the support vector machine and artificial neural network, but when use the convolution neural network the features are extracted by layers.</div><div>Results: The highest value of a classification accuracy was 100%, and a sensitivity 100%, a specificity 100%, and a precision 100%, which appeared five times when using the one-dimensional convolution neural network after empirical mode decomposition method.</div><div>Conclusions: The efficiency of the three methods has been compared and evaluated by using four metrics: Accuracy, Sensitivity, specificity, and Precision, and the result showed the one-dimensional convolution neural network is the best method for classification with empirical mode decomposition.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100188"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlocking transcranial FUS-EEG feature fusion for non-invasive sleep staging in next-gen clinical applications","authors":"Suneet Gupta ,&nbsp;Praveen Gupta ,&nbsp;Bechoo Lal ,&nbsp;Aniruddha Deka ,&nbsp;Hirakjyoti Sarma ,&nbsp;Sheifali Gupta","doi":"10.1016/j.neuri.2025.100209","DOIUrl":"10.1016/j.neuri.2025.100209","url":null,"abstract":"<div><div>Accurate and non-invasive sleep staging is essential for evaluating sleep quality and diagnosing neurological and sleep disorders. Addressing the variations in electroencephalogram (EEG) and electrooculogram (EOG) signals across different sleep stages, this study introduces a transcranial focused ultrasound (tFUS) based multimodal feature fusion deep learning model (MFDL) for automated sleep staging. The proposed framework integrates two one-dimensional convolutional neural networks (1D-CNNs) to extract sleep-relevant features from EEG and EOG signals, followed by an adaptive feature fusion module that dynamically assigns weights based on feature significance. By enhancing discriminative features and suppressing irrelevant ones, the model generates a robust multimodal representation of sleep information. Furthermore, a bidirectional long short-term memory (Bi-LSTM) network captures temporal dependencies in sleep stage transitions, improving classification accuracy. The effectiveness of MFDL is validated on the publicly available Sleep-EDF dataset, achieving 94.1% accuracy, 88.2% Kappa coefficient, and 81.9% MF1 score. Notably, the recall rates for the challenging N1 and REM sleep stages are significantly enhanced to 64.6% and 93.5%, respectively. These results highlight the potential of MFDL in enhancing tFUS-based neuromodulation by providing precise, data-driven sleep state monitoring, paving the way for advanced non-invasive brain stimulation technologies in next-gen clinical applications.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100209"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069541","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":"A MATLAB-based tool for converting fNIRS time-series data to Homer3-compatible formats","authors":"Chao Wang ,&nbsp;Xiaojun Cheng ,&nbsp;Shichao Liu","doi":"10.1016/j.neuri.2025.100205","DOIUrl":"10.1016/j.neuri.2025.100205","url":null,"abstract":"<div><div>Functional Near-Infrared Spectroscopy (fNIRS) is increasingly used in cognitive neuroscience and clinical research, yet preprocessing raw time-series data remains challenging. We introduce a lightweight MATLAB tool to automate the conversion of fNIRS data into Homer3-compatible “*.nirs” format. Our solution targets non-SNIRF raw data and offers a standardized, user-friendly method to streamline fNIRS data preparation. This Technical Note describes the tool's design, workflow, and potential improvements for future development.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100205"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906484","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":"Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiology","authors":"Senthil Kumar ,&nbsp;J. Ramprasath ,&nbsp;V. Kalpana ,&nbsp;Manikandan Rajagopal ,&nbsp;Maheswaran S ,&nbsp;Rupesh Gupta","doi":"10.1016/j.neuri.2025.100202","DOIUrl":"10.1016/j.neuri.2025.100202","url":null,"abstract":"<div><h3>Introduction</h3><div>Neuroradiology encounters considerable difficulties owing to imaging data's intricacy and high-dimensional characteristics. Conventional diagnostic techniques often encounter challenges regarding precision and scalability, resulting in delays and possible misinterpretations. This paper presents the Big-Data Analytics-based Diagnostics (BDA-D) framework, a revolutionary method using computational models derived from neural architectures and sophisticated analytics to tackle these difficulties.</div></div><div><h3>Methods</h3><div>The BDA-D architecture utilizes data mining, pattern recognition, and machine learning to glean useful neuroanatomical characteristics from massive datasets. By simulating human thought processes, this method speeds up clinical decision-making and improves diagnostic accuracy. To evaluate the effectiveness of the framework, it is put to the test in a clinical environment.</div></div><div><h3>Results and Discussion</h3><div>Diagnostic precision, processing speed, and dependability are all enhanced by experimental validation. By detecting even the most minute neuroanatomical changes, BDA-D allows for more accurate diagnosis than traditional approaches. Based on the results, neuroradiologists may improve their practices by using cutting-edge computational methods to close the gap between data-driven analytics and their practical use in the clinic. BDA-D discovers important patterns from high-dimensional neuroimaging data through biologically inspired neural networks, reaching a remarkable diagnosis accuracy of 97.18%. Its 95.42% increase in processing speed allows rapid study of important disorders such as strokes and neurodegenerative diseases. BDA-D reduces inter-observer variability with a dependable value of 94.96%, increasing clinical confidence in AI-assisted diagnosis.</div></div><div><h3>Conclusion</h3><div>A revolutionary change in neurodiagnostics, the BDA-D framework improves efficiency and reliability. This method has the potential to completely transform neuroradiology by combining big-data analytics with sophisticated computer models. It will allow for more rapid and precise diagnosis.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100202"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942951","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":"Computational intelligence in neuroinformatics: Technologies and data analytics","authors":"Anand Deshpande ,&nbsp;Vania Vieira Estrela ,&nbsp;Anitha Jude ,&nbsp;Jude Hemanth","doi":"10.1016/j.neuri.2025.100187","DOIUrl":"10.1016/j.neuri.2025.100187","url":null,"abstract":"","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 1","pages":"Article 100187"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of a computational intelligence system for detection of multiple sclerosis with visual evoked potentials","authors":"Moussa Mohsenpourian ,&nbsp;Amir Abolfazl Suratgar ,&nbsp;Heidar Ali Talebi ,&nbsp;Mahsa Arzani ,&nbsp;Abdorreza Naser Moghadasi ,&nbsp;Seyed Matin Malakouti ,&nbsp;Mohammad Bagher Menhaj","doi":"10.1016/j.neuri.2024.100177","DOIUrl":"10.1016/j.neuri.2024.100177","url":null,"abstract":"<div><div>In this study, a new approach for modification of membership functions of a fuzzy inference system (FIS) is demonstrated, in order to serve as a pattern recognition tool for classification of patients diagnosed with multiple sclerosis (MS) from healthy controls (HC) using their visually evoked potential (VEP) recordings. The new approach utilizes Krill Herd (KH) optimization algorithm to modify parameters associated with membership functions of both inputs and outputs of an initial Sugeno-type FIS, while making sure that the error corresponding to training of the network is minimized.</div><div>This novel pattern recognition system is applied for classification of VEP signals in 11 MS patients and 11 HC's. A feature extraction routine was performed on the VEP signals, and later substantial features were selected in an optimized feature subset selection scheme employing Ant Colony Optimization (ACO) and Simulated Annealing (SA) algorithms. This alone provided further information regarding clinical value of many previously unused VEP features as an aide for making the diagnosis. The newly designed computational intelligence system is shown to outperform popular classifiers (e.g., multilayer perceptron, support-vector machine, etc.) and was able to distinguish MS patients from HC's with an overall accuracy of 90%.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 1","pages":"Article 100177"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"KL-FedDis: A federated learning approach with distribution information sharing using Kullback-Leibler divergence for non-IID data","authors":"Md. Rahad ,&nbsp;Ruhan Shabab ,&nbsp;Mohd. Sultan Ahammad ,&nbsp;Md. Mahfuz Reza ,&nbsp;Amit Karmaker ,&nbsp;Md. Abir Hossain","doi":"10.1016/j.neuri.2024.100182","DOIUrl":"10.1016/j.neuri.2024.100182","url":null,"abstract":"<div><div>Data Heterogeneity or Non-IID (non-independent and identically distributed) data identification is one of the prominent challenges in Federated Learning (FL). In Non-IID data, clients have their own local data, which may not be independently and identically distributed. This arises because clients involved in federated learning typically have their own unique, local datasets that vary significantly due to factors like geographical location, user behaviors, or specific contexts. Model divergence is another critical challenge where the local models trained on different clients, data may diverge significantly but making it difficult for the global model to converge. To identify the non-IID data, few federated learning models have been introduced as FedDis, FedProx and FedAvg, but their accuracy is too low. To address the clients Non-IID data along with ensuring privacy, federated learning emerged with appropriate distribution mechanism is an effective solution. In this paper, a modified FedDis learning method called KL-FedDis is proposed, which incorporates Kullback-Leibler (KL) divergence as the regularization technique. KL-FedDis improves accuracy and computation time over the FedDis and FedAvg technique by successfully maintaining the distribution information and encouraging improved collaboration among the local models by utilizing KL divergence.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 1","pages":"Article 100182"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG signal based brain stimulation model to detect epileptic neurological disorders","authors":"Haewon Byeon ,&nbsp;Udit Mahajan ,&nbsp;Ashish Kumar ,&nbsp;V. Rama Krishna ,&nbsp;Mukesh Soni ,&nbsp;Monika Bansal","doi":"10.1016/j.neuri.2025.100186","DOIUrl":"10.1016/j.neuri.2025.100186","url":null,"abstract":"<div><div><strong>Background:</strong> Manual visual inspection and analysis of electroencephalogram (EEG) signals of patients are susceptible to the subjective influence of doctors. The introduction of GA-PSO improved the categorization accuracy of both the EP (Evoked potential) and normal groups by automatically screening and optimizing the best feature combination of brain networks. Therefore, selecting effective EEG features for automatic recognition of EP is particularly important for Neuroscience.</div><div><strong>New method:</strong> A phase synchronization index (PSI) brain stimulation is constructed from multi-channel EEG signals, extracting 15 topological features from the perspectives of network nodes and structural functions. In order to optimize and screen feature combinations in both single and cross-frequency bands, the GA-PSO algorithm is utilized as a feature selection tool for choosing epileptic EEG network features.</div><div><strong>Result:</strong> Feature combinations are made both within and between bands, and the optimal feature mix is found using the PSO and GA-PSO algorithms. The study found that the GA-PSO algorithm outperformed the PSO algorithm, achieving a higher EP recognition accuracy of 0.9335 under cross-frequency band conditions.</div><div><strong>Comparison with existing methods:</strong> The results indicate that the introduction of the genetic algorithm enables the GA-PSO algorithm to maintain population diversity and avoid premature convergence to local optima, thereby enhancing the search capabilities of the population.</div><div><strong>Conclusion:</strong> Based on the findings, topological aspects provide a good way to describe the anomalies in the brain networks of epileptic patients and enhance the classification accuracy through combination, which provides help for pathological research and clinical diagnosis of epilepsy.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 1","pages":"Article 100186"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Training size predictably improves machine learning-based epileptic seizure forecasting from wearables","authors":"Mustafa Halimeh ,&nbsp;Michele Jackson ,&nbsp;Tobias Loddenkemper ,&nbsp;Christian Meisel","doi":"10.1016/j.neuri.2024.100184","DOIUrl":"10.1016/j.neuri.2024.100184","url":null,"abstract":"<div><div>Objective: Wrist-worn wearable devices that monitor autonomous nervous system function and movement have shown promise in providing non-invasive, broadly applicable seizure forecasts that increase in accuracy with larger training size. Nevertheless, challenges related to missing validation, small number of enrolled patients, insufficient training data, and lack of patient seizure cycles data hinder its clinical implementation. Here we sought to prospectively validate a previously implemented seizure forecasting algorithm using a larger cohort of pediatric patients with epilepsy (pwe), improve it by including information on seizure cycles, and (3) assess the utility of precise power-laws to predict performance as a function of dataset size.</div><div>Methods: We used video-EEG recordings from 166 pwe as ground-truth for seizures, recorded electrodermal activity (EDA), peripheral body temperature (TEMP), blood volume pulse (BVP), accelerometery (ACC) and applied a deep neural LSTM network model (NN) on these data along with information on 24-hour cycles to forecast seizures in a leave-one-subject-out cross validation. Evaluations were made using improvement over chance (IoC) and the Brier skill score (BSS), which measured the improvement of the NN Brier score compared to the Brier score of a rate-matched random (RMR) forecast.</div><div>Results: Performance quantified by IoC and BSS increased with training data following precise power-law scaling laws, thereby exceeding prior reported performance levels from smaller datasets. Including information on 24-hour seizure cycles further improved performance. For the largest training set we achieved significant IoC in 68% of pwe, an IoC of 27.3% and a BSS of 0.087.</div><div>Interpretation: Our results validate a previous forecast approach and indicate that performance improves predictably as a function of dataset size following power-law scaling.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 1","pages":"Article 100184"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}