Neuroscience informatics最新文献

{"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":"2025-04-28","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":"AI-driven EEG neuroscientific analysis for evaluating the influence of emotions on false memory","authors":"V. Mahalakshmi","doi":"10.1016/j.neuri.2025.100201","DOIUrl":"10.1016/j.neuri.2025.100201","url":null,"abstract":"<div><div>Investigating the brain mechanisms behind memory processing depends on an awareness of how emotions influence false memory. This study used AI-driven EEG microstate analysis to investigate how emotions affect the generation of false memories from both a temporal and a geographic perspective. Within emotional groups, AI-augmented computational models showed distinct brain processing patterns, particularly during the recall processing stage. By altering cognitive processing dynamics, these results support the hypothesis that AI-enhanced brain activity analysis can effectively mimic the influence of emotional states on the formation of false memories. This work explores emotional implications on false memory by combining artificial intelligence (AI) with EEG-based microstate analysis, therefore offering greater understanding of brain dynamics at several cognitive phases. EEG data collected under various emotional states were analyzed using AI-powered techniques to enable exact extraction of microstate templates (Microstates 1–5) for every emotional group. Phase-locked value (AI-PLV) brain functional networks were built inside microstates displaying notable temporal coverage variations. Driven by artificial intelligence, temporal and geographical analysis of EEG signals revealed different brain processing mechanisms among emotional groupings. The group with pleasant emotions showed continuous activity in prefrontal Microstates 3 and 5, therefore suggesting improved cognitive processing. Reflecting a concentration on information integration, the neutral group showed extended involvement in central-active Microstates 3 and 4. These results emphasize how artificial intelligence is helping neuroscientific research to progress by offering a strong framework for comprehending AI-driven emotional-based aberrations in memory recall.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100201"},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843617","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":"Enhanced detection of headache presentation in unruptured brain arteriovenous malformation through combined radiologic features: A cross-sectional study","authors":"Chia-Yu Liu ,&nbsp;Chia-Feng Lu ,&nbsp;Jr-Wei Wu ,&nbsp;Yong-Sin Hu ,&nbsp;Jih-Yuan Lin ,&nbsp;Huai-Che Yang ,&nbsp;Jing-Kai Loo ,&nbsp;Feng-Chi Chang ,&nbsp;Kang-Du Liu ,&nbsp;Chung-Jung Lin","doi":"10.1016/j.neuri.2025.100200","DOIUrl":"10.1016/j.neuri.2025.100200","url":null,"abstract":"<div><h3>Background</h3><div>Although determining angioarchitecture provide qualitative insights into headache-susceptible brain arteriovenous malformation (BAVM), the potential of quantitative radiomics to detect headache in unruptured BAVM remains unclear. We developed classification models that integrate radiomic features and angioarchitecture to assist unruptured BAVM headache treatment decision-making.</div></div><div><h3>Methods</h3><div>We considered patients with unruptured BAVM who underwent magnetic resonance imaging between 2010 and 2023. 146 radiomic features were assessed. Radiomic features were delineated, and angioarchitecture was analyzed. Statistical analyses, including least absolute shrinkage and selection operator regression and logistic regression, were used to select features and develop models. Receiver operating characteristic and decision curve analyses were performed to evaluate performance.</div></div><div><h3>Results</h3><div>The clinical model based on age, sex, and parieto-occipital lesion location achieved an area under the curve (AUC) of 0.741. Adding two significant radiomic features and one angioarchitecture feature enhanced the models. The radiomic and angioarchitecture models achieved an AUC of 0.763. The combined model, with an AUC of 0.799, significantly outperformed the clinical model (<span><math><mi>P</mi><mo>=</mo><mn>0.046</mn></math></span>). Decision curve analysis indicated that the combined model performed best at threshold probabilities between 15% and 40%.</div></div><div><h3>Conclusion</h3><div>Integrating radiomic features and angioarchitecture enhances the identification of unruptured BAVM headache.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100200"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807506","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":"Leveraging transparent ontology learning to refine constructs in neuroscience","authors":"David Moreau,&nbsp;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}