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":"2025-03-28","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":"Feature fusion based deep learning model for Alzheimer's neurological disorder classification","authors":"Arhath Kumar ,&nbsp;S. Pradeep ,&nbsp;Kumud Arora ,&nbsp;G. Sreeram ,&nbsp;A. Pankajam ,&nbsp;Trupti Patil ,&nbsp;Aradhana Sahu","doi":"10.1016/j.neuri.2025.100196","DOIUrl":"10.1016/j.neuri.2025.100196","url":null,"abstract":"<div><div>Alzheimer's disease (AD) is a severe brain disorder that can cause degradation of brain tissue and memory loss. Owing to Alzheimer's disease's high cost, a number of deep learning-based models have been put out to accurately identify the illness. This study introduces a new way to classify Alzheimer's disease using deep learning and combining different types of features. The 3D lightweight MBANet developed in this research has less parameters and can concentrate on more discriminative deep structures than conventional artificial neural networks like CNN, according to experimental data. We first create a Multi-Branch Attention Network (MBANet) to gather detailed features of the hippocampus from large sets of data. A new method is created to capture texture features in the hippocampus. It uses two techniques: multi-Tree Wavelet Transform (MTWT) and Gray Length Matrix (GLM). This method works in three dimensions and at different scales. Also, standard methods are used to measure the size and shape of the hippocampus. A mixed feature fusion network is created to simplify and combine data from the hippocampus, helping to classify Alzheimer's disease more effectively. Tests on the EADC-ADNI dataset show that the proposed method for classifying Alzheimer's disease achieves an accuracy of 93.39%, a <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-score of 93.10%, and an AUC of 93.21%. The test results show that the proposed method for classifying Alzheimer's disease is effective and better than traditional methods.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100196"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696805","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":"Non-invasive brain stimulation-based sleep stage classification using transcranial infrared based electrocardiogram","authors":"Janjhyam Venkata Naga Ramesh ,&nbsp;Aadam Quraishi ,&nbsp;Yassine Aoudni ,&nbsp;Mustafa Mudhafar ,&nbsp;Divya Nimma ,&nbsp;Monika Bansal","doi":"10.1016/j.neuri.2025.100197","DOIUrl":"10.1016/j.neuri.2025.100197","url":null,"abstract":"<div><div>Non-invasive brain stimulation (NIBS) techniques, such as transcranial infrared (tNIR) stimulation, offer promising advancements in sleep monitoring and regulation. To enhance sleep stage classification without relying on traditional polysomnography (PSG) systems, we propose a novel approach integrating single-channel electrocardiogram (ECG) signals, heart rate variability (HRV) features, and tNIR stimulation. The maximal overlap discrete wavelet transform (MODWT) is applied for multi-resolution analysis of ECG signals, followed by peak information extraction. Based on the first-order deviation of peak positions, multi-dimensional HRV features are extracted. To identify HRV features strongly associated with different sleep stages, we introduce a feature selection method combining the ReliefF algorithm and Gini index. The selected features are then processed using the INFO-ABC Logit Boost method to establish correlations between HRV dynamics and sleep stages. Experimental results on publicly available datasets demonstrate that the proposed model achieves an overall accuracy of 83.67%, a precision of 82.59%, a Kappa coefficient of 77.94%, and an F1-score of 82.97%. Compared with conventional sleep staging methods, our approach enhances sleep quality assessment and facilitates real-time, non-invasive monitoring in home and mobile healthcare settings, leveraging the potential of tNIR-based NIBS for sleep modulation.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100197"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681918","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":"Analysis and development of clinically recorded dysarthric speech corpus for patients affected with various stroke conditions","authors":"Oindrila Banerjee ,&nbsp;K.V.N. Sita Mahalakshmi ,&nbsp;M.V.S. Jyothi ,&nbsp;D. Govind ,&nbsp;U.K. Rakesh ,&nbsp;A. Rajeev ,&nbsp;K. Samudravijaya ,&nbsp;Akhilesh Kumar Dubey ,&nbsp;Suryakanth V. Gangashetty","doi":"10.1016/j.neuri.2025.100198","DOIUrl":"10.1016/j.neuri.2025.100198","url":null,"abstract":"<div><div>The manuscript presents the work related to the development of a dysarthric speech corpus for various types of stroke conditions. The corpus consists of speech recorded from 50 stroke patients and 50 healthy controls in clinical environments. Severity of stroke for each patient has been assessed by the clinician based on the National Institute of Health Stroke Scale. The text read by patients and healthy controls comprises (a) five sustained vowels, (b) three words consisting of the plosive consonant and vowels, and (c) 10 phonetically rich sentences in Telugu language. A discriminative analysis is carried out using conventional Mel Frequency Cepstral Coefficients and Convolutional Neural Networks to quantify the perceptual variations in dysarthric speech of stroke patients and healthy controls. Vowels and word utterances of the speech corpus exhibited better class discrimination characteristics compared to sentences for text dependent and speaker independent scenarios.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100198"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696804","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":"Integration of software-based cognitive approaches and brain-like computer machinery for efficient cognitive computing","authors":"Chitrakant Banchhor ,&nbsp;Manoj Kumar Rawat ,&nbsp;Rahul Joshi ,&nbsp;Dharmesh Dhabliya ,&nbsp;Omkaresh Kulkarni ,&nbsp;Sandeep Dwarkanath Pande ,&nbsp;Umesh Pawar","doi":"10.1016/j.neuri.2025.100194","DOIUrl":"10.1016/j.neuri.2025.100194","url":null,"abstract":"<div><div>The widespread adoption of the Internet has transformed various industries, driving significant systemic reforms across different sectors. This transformation has enhanced the Internet's role in information dissemination, resource sharing, and global connectivity, allowing for more efficient distribution of knowledge and services. The development of the Internet model and its research bring significant benefits from the network, enabling people to use and learn from it. However, the traditional education model provides only limited knowledge, restricting growth and progress. Moreover, there is a vast world of knowledge yet to be explored. Nowadays, with the help of network tools, people can understand the dynamics of the whole world and accept the culture and knowledge of different regions without going out. Throughout the study of English legacy problems in various countries, efficient learning methods and high levels of English skills are the goals pursued, while the traditional English model can't meet the students' learning needs in a short time. The model construction of data mining algorithm based on large open network courses is a model for solving legacy problems adopted both domestically and internationally. According to the survey data of universities in various countries, the use of data mining algorithm can fundamentally meet the student's desire and demand for English knowledge. This research, integrates the mining algorithm into English research, which will essentially improve the English legacy problems.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643546","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":"Bayesian Inference General Procedures for A Single-subject Test study","authors":"Jie Li ,&nbsp;Gary Green ,&nbsp;Sarah J.A. Carr ,&nbsp;Peng Liu ,&nbsp;Jian Zhang","doi":"10.1016/j.neuri.2025.100195","DOIUrl":"10.1016/j.neuri.2025.100195","url":null,"abstract":"<div><div>Abnormality detection in identifying a single-subject which deviates from the majority of a control group dataset is a fundamental problem. Typically, the control group is characterised using standard Normal statistics, and the detection of a single abnormal subject is in that context. However, in many situations, the control group cannot be described by Normal statistics, making standard statistical methods inappropriate. This paper presents a Bayesian Inference General Procedures for A Single-subject Test (BIGPAST) designed to mitigate the effects of skewness under the assumption that the dataset of the control group comes from the skewed Student <em>t</em> distribution. BIGPAST operates under the null hypothesis that the single-subject follows the same distribution as the control group. We assess BIGPAST's performance against other methods through simulation studies. The results demonstrate that BIGPAST is robust against deviations from normality and outperforms the existing approaches in accuracy, nearest to the nominal accuracy 0.95. BIGPAST can reduce model misspecification errors under the skewed Student <em>t</em> assumption by up to 12 times, as demonstrated in Section <span><span>3.3</span></span>. We apply BIGPAST to a Magnetoencephalography (MEG) dataset consisting of an individual with mild traumatic brain injury and an age and gender-matched control group. For example, the previous method failed to detect abnormalities in 8 brain areas, whereas BIGPAST successfully identified them, demonstrating its effectiveness in detecting abnormalities in a single-subject.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100195"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643547","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":"Analyzing infant cry to detect birth asphyxia using a hybrid CNN and feature extraction approach","authors":"Samrat Kumar Dey ,&nbsp;Khandaker Mohammad Mohi Uddin ,&nbsp;Arpita Howlader ,&nbsp;Md. Mahbubur Rahman ,&nbsp;Hafiz Md. Hasan Babu ,&nbsp;Nitish Biswas ,&nbsp;Umme Raihan Siddiqi ,&nbsp;Badhan Mazumder","doi":"10.1016/j.neuri.2025.100193","DOIUrl":"10.1016/j.neuri.2025.100193","url":null,"abstract":"<div><div>Asphyxia, a critical respiratory condition, poses significant risks to newborns and can lead to catastrophic outcomes. Early detection of asphyxia is crucial for reducing infant mortality rates. Traditional medical diagnosis methods can be time-consuming, whereas early detection through artificial intelligence (AI) can expedite the process and improve survival rates. Despite the importance of early asphyxia detection, existing methods are often delayed and not always effective. This research addresses the need for a faster, more accurate approach to detecting infant asphyxia using machine learning (ML) and deep learning (DL) techniques. This study aims to develop a robust AI-driven system to detect asphyxia in newborns using ML and DL models, focusing on improving accuracy and efficiency over traditional diagnostic methods. This study explores feature extraction using Mel-Frequency Cepstral Coefficients (MFCCs), where the features are categorized into time and frequency domains. Data preprocessing techniques, such as noise removal, handling missing values, outliers, and label encoding, are applied to ensure clean data. To address class imbalance, the Random Oversampling (ROS) technique is employed. Hyperparameter optimization is performed using GridSearchCV for various machine-learning models. Deep learning models, including custom artificial neural networks (ANN1) and convolutional neural networks (CNN1, CNN2), are introduced with hidden layers for improved performance. The performance of different ML and DL models is evaluated, with Logistic Regression (LR) achieving an accuracy of 99.16% and a 0.008% error rate. In comparison, ANN1 outperforms other DL models with an accuracy of 98.20% and a 0.018% error rate. The results demonstrate that both ML and DL techniques can significantly enhance early asphyxia detection in newborns. The Logistic Regression model offers the highest accuracy in machine learning, while ANN1 performs optimally in deep learning, suggesting their potential for deployment in clinical settings to improve neonatal care.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100193"},"PeriodicalIF":0.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143478679","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":"Neuroscience-informed nomogram model for early prediction of cognitive impairment in Parkinson's disease","authors":"Sudharshan Putha ,&nbsp;Swaroop Reddy Gayam ,&nbsp;Bhavani Prasad Kasaraneni ,&nbsp;Krishna Kanth Kondapaka ,&nbsp;Sateesh Kumar Nallamala ,&nbsp;Praveen Thuniki","doi":"10.1016/j.neuri.2025.100189","DOIUrl":"10.1016/j.neuri.2025.100189","url":null,"abstract":"<div><div>Cognitive impairment is a common non-motor symptom of Parkinson's disease (PD), significantly affecting patients' quality of life and posing challenges for clinical management. Early prediction of cognitive decline in PD is critical for timely diagnosis and intervention. However, the interplay of multivariate factors such as age, gender, and disease duration complicate early prediction. To address the multifactorial nature of cognitive impairment in PD, this study proposes a neuroscience-informed nomogram model constructed using multivariate logistic regression. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to identify highly correlated clinical variables influencing cognitive function. Subsequently, these variables were integrated into a visualized nomogram model to facilitate early prediction of cognitive impairment (CI) risk. Performance evaluation of the model demonstrated high accuracy, consistency, and clinical applicability, significantly enhancing diagnostic efficiency for neurologists. Furthermore, the model provides visual comparisons of patient distributions across different predictor values, enabling personalized risk assessments. According to experimental analysis and verification, the model demonstrated outstanding prediction with a region under the ROC curve of 0.872 on the original training set and 0.870 on the validation set. Because the anticipated and observed probabilities were so consistent, the model was able to forecast the patient's likelihood of cognitive impairment.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100189"},"PeriodicalIF":0.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474535","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":"2025-02-13","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":"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":"2025-01-15","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}