Neuroscience informatics最新文献

Computational intelligence in neuroinformatics: Technologies and data analytics

Neuroscience informatics Pub Date : 2025-01-15 DOI: 10.1016/j.neuri.2025.100187

Anand Deshpande , Vania Vieira Estrela , Anitha Jude , Jude Hemanth

引用次数: 0

EEG signal based brain stimulation model to detect epileptic neurological disorders

Neuroscience informatics Pub Date : 2025-01-14 DOI: 10.1016/j.neuri.2025.100186

Haewon Byeon , Udit Mahajan , Ashish Kumar , V. Rama Krishna , Mukesh Soni , Monika Bansal

{"title":"EEG signal based brain stimulation model to detect epileptic neurological disorders","authors":"Haewon Byeon , Udit Mahajan , Ashish Kumar , V. Rama Krishna , Mukesh Soni , 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":"2025-01-14","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}

引用次数: 0

Advances in neurosurgical procedures: Quantum computing and its applications in MRI-guided laser interstitial thermal therapy

Neuroscience informatics Pub Date : 2025-01-03 DOI: 10.1016/j.neuri.2024.100185

Afzal Hussain , Ashfaq Hussain , Mohammad Rashid

引用次数: 0

Training size predictably improves machine learning-based epileptic seizure forecasting from wearables

Neuroscience informatics Pub Date : 2024-12-09 DOI: 10.1016/j.neuri.2024.100184

Mustafa Halimeh , Michele Jackson , Tobias Loddenkemper , Christian Meisel

{"title":"Training size predictably improves machine learning-based epileptic seizure forecasting from wearables","authors":"Mustafa Halimeh , Michele Jackson , Tobias Loddenkemper , 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":"2024-12-09","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}

引用次数: 0

Understanding risk factors of post-stroke mortality

Neuroscience informatics Pub Date : 2024-11-29 DOI: 10.1016/j.neuri.2024.100181

David Castro , Nuno Antonio , Ana Marreiros , Hipólito Nzwalo

{"title":"Understanding risk factors of post-stroke mortality","authors":"David Castro , Nuno Antonio , Ana Marreiros , Hipólito Nzwalo","doi":"10.1016/j.neuri.2024.100181","DOIUrl":"10.1016/j.neuri.2024.100181","url":null,"abstract":"<div><div>Stroke is one of the leading causes of death worldwide. Understanding the risk factors for post-stroke mortality is crucial for improving patient outcomes. This study analyzes and predicts post-stroke mortality using the modified Rankin Scale (mRS), a functional neurological evaluation scale. Several Machine Learning models were developed and assessed using a dataset of 332 stroke patients from Hospital de Faro, Portugal, from 2016 to 2018. The Random Forest model outperformed others, achieving an accuracy of 98.5% and a recall of 91.3. Twenty-four risk factors were identified, with stroke severity as the most critical. These findings provide healthcare professionals with valuable tools for early identification and intervention for high-risk stroke patients, enabling informed decision-making and customized treatment plans. This research advances healthcare predictive analytics, offering a precise mortality prediction model and a comprehensive analysis of risk factors, potentially improving clinical outcomes and reducing mortality rates. Future applications could extend to patient monitoring and management across various medical conditions.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 1","pages":"Article 100181"},"PeriodicalIF":0.0,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160181","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}

引用次数: 0

KL-FedDis: A federated learning approach with distribution information sharing using Kullback-Leibler divergence for non-IID data

Neuroscience informatics Pub Date : 2024-11-28 DOI: 10.1016/j.neuri.2024.100182

Md. Rahad , Ruhan Shabab , Mohd. Sultan Ahammad , Md. Mahfuz Reza , Amit Karmaker , Md. Abir Hossain

{"title":"KL-FedDis: A federated learning approach with distribution information sharing using Kullback-Leibler divergence for non-IID data","authors":"Md. Rahad , Ruhan Shabab , Mohd. Sultan Ahammad , Md. Mahfuz Reza , Amit Karmaker , 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":"2024-11-28","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}

引用次数: 0

Deep learning-based edge detection for random natural images

Neuroscience informatics Pub Date : 2024-11-28 DOI: 10.1016/j.neuri.2024.100183

Kanija Muntarina , Rafid Mostafiz , Sumaita Binte Shorif , Mohammad Shorif Uddin

{"title":"Deep learning-based edge detection for random natural images","authors":"Kanija Muntarina , Rafid Mostafiz , Sumaita Binte Shorif , Mohammad Shorif Uddin","doi":"10.1016/j.neuri.2024.100183","DOIUrl":"10.1016/j.neuri.2024.100183","url":null,"abstract":"<div><div>Edge detection plays a critical role in computer vision, particularly in the analysis of random natural images. It serves as a fundamental step in tasks such as image segmentation, shape extraction, pattern recognition, auto-navigation, and motion analysis, with applications spanning various domains including radar and sonar image processing. The edge detection model attempts to identify points in digital images where significant intensity changes occur, known as edges or region boundaries. Traditionally, edge detection relied on gradient-based operators, which often produced jagged edges and were susceptible to image noise. In recent years, the emergence of deep learning technology has revolutionized this field by utilizing its ability to automatically learn complex features from natural images. Deep learning approaches offer significant advantages in capturing high-level representations, thereby improving the accuracy and robustness of edge detection algorithms. Moreover, the effectiveness of edge detection techniques varies depending on the content and classification of images, such as natural scenes, medical images, or underwater environments. This study aims to evaluate and compare the performance of five widely used deep learning-based edge detection methods to identify the most effective approach specifically tailored for natural images. Through comprehensive experimentation and analysis, this research contributes to advancing the state-of-the-art in edge detection for random natural images, providing insights into the optimal application of deep learning techniques in this domain.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 1","pages":"Article 100183"},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160179","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}

引用次数: 0

Design of a computational intelligence system for detection of multiple sclerosis with visual evoked potentials

Neuroscience informatics Pub Date : 2024-10-30 DOI: 10.1016/j.neuri.2024.100177

Moussa Mohsenpourian , Amir Abolfazl Suratgar , Heidar Ali Talebi , Mahsa Arzani , Abdorreza Naser Moghadasi , Seyed Matin Malakouti , Mohammad Bagher Menhaj

{"title":"Design of a computational intelligence system for detection of multiple sclerosis with visual evoked potentials","authors":"Moussa Mohsenpourian , Amir Abolfazl Suratgar , Heidar Ali Talebi , Mahsa Arzani , Abdorreza Naser Moghadasi , Seyed Matin Malakouti , 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":"2024-10-30","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}

引用次数: 0

Integrated analysis of lncRNA-miRNA-mRNA ceRNA network in neurodegenerative diseases 综合分析神经退行性疾病中的 lncRNA-miRNA-mRNA ceRNA 网络

Neuroscience informatics Pub Date : 2024-09-30 DOI: 10.1016/j.neuri.2024.100176

Mehran Asadi Peighan , Negar Sadat Soleimani Zakeri , Seyed Mehdi Jazayeri , Sajjad Nematzadeh , Habib MotieGhader

{"title":"Integrated analysis of lncRNA-miRNA-mRNA ceRNA network in neurodegenerative diseases","authors":"Mehran Asadi Peighan , Negar Sadat Soleimani Zakeri , Seyed Mehdi Jazayeri , Sajjad Nematzadeh , Habib MotieGhader","doi":"10.1016/j.neuri.2024.100176","DOIUrl":"10.1016/j.neuri.2024.100176","url":null,"abstract":"<div><h3>Background</h3><div>Neurodegenerative diseases are one of the main causes of physical or behavioral complications which is considered as one of the main health concerns of the elderly population. However, treatment options for neurological diseases are still limited. Recent advances in bioinformatics studies provide an opportunity to understand the mechanisms of these diseases to identify therapeutic targets. In this research, the mRNAs involved in Alzheimer's, Multiple sclerosis, Parkinson's, and Huntington's neurological diseases, which are regulated by upstream factors, have been investigated. Only 2% of all transcripts of a gene are translated into protein and the rest are converted into miRNAs, lncRNAs or circRNAs. miRNAs have crucial role in regulating mRNAs and in a similar sequence lncRNAs or circRNAs are crucial in regulating miRNAs, which disrupts gene expression.</div></div><div><h3>Results</h3><div>To discover above relations in neurodegenerative disease, miRNA-mRNA and lncRNA-miRNA bipartite networks were constructed and then were integrated to construct lncRNA-miRNA-mRNA tripartite networks. Constructing these networks leads to understand deeply about the structure of this mechanism and introducing new biomarkers for the studied diseases. In the next step, enrichment analysis was performed to recognize the genes involved in crucial pathways. Finally, the obtained biomarkers were investigated over the previous studies to prove the accuracy of proposed method.</div></div><div><h3>Conclusions</h3><div>In conclusion, for all four diseases, several numbers of mRNAs, miRNAs and lncRNAs were identified, which are introduced as biomarkers extracted by this study.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"4 4","pages":"Article 100176"},"PeriodicalIF":0.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142416917","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}

引用次数: 0

Topic modeling of neuropsychiatric diseases related to gut microbiota and gut brain axis using artificial intelligence based BERTopic model on PubMed abstracts 利用基于人工智能的 BERTopic 模型对 PubMed 摘要中与肠道微生物群和肠道脑轴相关的神经精神疾病进行主题建模

Neuroscience informatics Pub Date : 2024-09-10 DOI: 10.1016/j.neuri.2024.100175

Ashok Kumar , Avi Karamchandani , Sourabh Singh

{"title":"Topic modeling of neuropsychiatric diseases related to gut microbiota and gut brain axis using artificial intelligence based BERTopic model on PubMed abstracts","authors":"Ashok Kumar , Avi Karamchandani , Sourabh Singh","doi":"10.1016/j.neuri.2024.100175","DOIUrl":"10.1016/j.neuri.2024.100175","url":null,"abstract":"<div><p>Gut microbiota play a crucial role in complex interactions of the gut brain axis between the gastrointestinal system and the central nervous system. The intricate network of bidirectional communication between the gut and brain, mediated through neural, hormonal, and immunological pathways, known as the gut-brain axis, has been implicated in the pathophysiology of several mental, neurological and behavioral disorders. Alterations in the gut microbiota composition, or dysbiosis, have been associated with disorders like Alzheimer's disease, Parkinson's disease, Multiple Sclerosis, Autism Spectrum Disorder, Ischemic Stroke, Eating Disorders, depression, anxiety, stress and addiction. In this study, a Python package BERTopic, based on Artificial Intelligence based Natural Language Processing using Transformer model BERT, specializing in topic modeling, was applied to abstracts of 3,482 PubMed articles published from year 2014 until May 2024, to explore the mental, neurological, and behavioral diseases influenced by the gut microbiota. There were some variations in individual runs of BERTopic due to stochastic nature of one of its components, but overall the discovered topics corresponded to major neuropsychiatric diseases. To understand the impact of the variability in outcomes ten repeated runs of BERTopic were performed with keeping identical parameters. The major topics that were found consistently in all the ten repeated runs of BERTopic were Depression, Alzheimer Disease, Autism Spectrum Disorder, Parkinson's Disease, Multiple Sclerosis, Ischemic Stroke, Anorexia Nervosa and Schizophrenia.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"4 4","pages":"Article 100175"},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528624000207/pdfft?md5=6ac1f58830303096fa9a0b1ce22216a5&pid=1-s2.0-S2772528624000207-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169044","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}

引用次数: 0