Frontiers in Computational Neuroscience最新文献

{"title":"BrainNet: an automated approach for brain stress prediction utilizing electrodermal activity signal with XLNet model.","authors":"Liao Xuanzhi, Abeer Hakeem, Linda Mohaisen, Muhammad Umer, Muhammad Attique Khan, Shrooq Alsenan, Shtwai Alsubai, Nisreen Innab","doi":"10.3389/fncom.2024.1482994","DOIUrl":"https://doi.org/10.3389/fncom.2024.1482994","url":null,"abstract":"<p><p>Brain stress monitoring has emerged as a critical research area for understanding and managing stress and neurological health issues. This burgeoning field aims to provide accurate information and prediction about individuals' stress levels by analyzing behavioral data and physiological signals. To address this emerging problem, this research study proposes an innovative approach that uses an attention mechanism-based XLNet model (called BrainNet) for continuous stress monitoring and stress level prediction. The proposed model analyzes streams of brain data, including behavioral and physiological signal patterns using Swell and WESAD datasets. Testing on the Swell multi-class dataset, the model achieves an impressive accuracy of 95.76%. Furthermore, when evaluated on the WESAD dataset, it demonstrates even higher accuracy, reaching 98.32%. When applied to the binary classification of stress and no stress using the Swell dataset, the model achieves an outstanding accuracy of 97.19%. Comparative analysis with other previously published research studies underscores the superior performance of the proposed approach. In addition, cross-validation confirms the significance, efficacy, and robustness of the model in brain stress level prediction and aligns with the goals of smart diagnostics for understanding neurological behaviors.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1482994"},"PeriodicalIF":2.1,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142603906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Latent dynamics of primary sensory cortical population activity structured by fluctuations in the local field potential.","authors":"Audrey Sederberg, Aurélie Pala, Garrett B Stanley","doi":"10.3389/fncom.2024.1445621","DOIUrl":"10.3389/fncom.2024.1445621","url":null,"abstract":"<p><strong>Introduction: </strong>As emerging technologies enable measurement of precise details of the activity within microcircuits at ever-increasing scales, there is a growing need to identify the salient features and patterns within the neural populations that represent physiologically and behaviorally relevant aspects of the network. Accumulating evidence from recordings of large neural populations suggests that neural population activity frequently exhibits relatively low-dimensional structure, with a small number of variables explaining a substantial fraction of the structure of the activity. While such structure has been observed across the brain, it is not known how reduced-dimension representations of neural population activity relate to classical metrics of \"brain state,\" typically described in terms of fluctuations in the local field potential (LFP), single-cell activity, and behavioral metrics.</p><p><strong>Methods: </strong>Hidden state models were fit to spontaneous spiking activity of populations of neurons, recorded in the whisker area of primary somatosensory cortex of awake mice. Classic measures of cortical state in S1, including the LFP and whisking activity, were compared to the dynamics of states inferred from spiking activity.</p><p><strong>Results: </strong>A hidden Markov model fit the population spiking data well with a relatively small number of states, and putative inhibitory neurons played an outsize role in determining the latent state dynamics. Spiking states inferred from the model were more informative of the cortical state than a direct readout of the spiking activity of single neurons or of the population. Further, the spiking states predicted both the trial-by-trial variability in sensory responses and one aspect of behavior, whisking activity.</p><p><strong>Discussion: </strong>Our results show how classical measurements of brain state relate to neural population spiking dynamics at the scale of the microcircuit and provide an approach for quantitative mapping of brain state dynamics across brain areas.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1445621"},"PeriodicalIF":2.1,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11537859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142590127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-stage semi-supervised learning enhances white matter hyperintensity segmentation.","authors":"Kauê T N Duarte, Abhijot S Sidhu, Murilo C Barros, David G Gobbi, Cheryl R McCreary, Feryal Saad, Richard Camicioli, Eric E Smith, Mariana P Bento, Richard Frayne","doi":"10.3389/fncom.2024.1487877","DOIUrl":"10.3389/fncom.2024.1487877","url":null,"abstract":"<p><strong>Introduction: </strong>White matter hyperintensities (WMHs) are frequently observed on magnetic resonance (MR) images in older adults, commonly appearing as areas of high signal intensity on fluid-attenuated inversion recovery (FLAIR) MR scans. Elevated WMH volumes are associated with a greater risk of dementia and stroke, even after accounting for vascular risk factors. Manual segmentation, while considered the ground truth, is both labor-intensive and time-consuming, limiting the generation of annotated WMH datasets. Un-annotated data are relatively available; however, the requirement of annotated data poses a challenge for developing supervised machine learning models.</p><p><strong>Methods: </strong>To address this challenge, we implemented a multi-stage semi-supervised learning (M3SL) approach that first uses un-annotated data segmented by traditional processing methods (\"bronze\" and \"silver\" quality data) and then uses a smaller number of \"gold\"-standard annotations for model refinement. The M3SL approach enabled fine-tuning of the model weights with the gold-standard annotations. This approach was integrated into the training of a U-Net model for WMH segmentation. We used data from three scanner vendors (over more than five scanners) and from both cognitively normal (CN) adult and patients cohorts [with mild cognitive impairment and Alzheimer's disease (AD)].</p><p><strong>Results: </strong>An analysis of WMH segmentation performance across both scanner and clinical stage (CN, MCI, AD) factors was conducted. We compared our results to both conventional and transfer-learning deep learning methods and observed better generalization with M3SL across different datasets. We evaluated several metrics (<i>F</i>-measure, <i>IoU</i>, and Hausdorff distance) and found significant improvements with our method compared to conventional (<i>p</i> < 0.001) and transfer-learning (<i>p</i> < 0.001).</p><p><strong>Discussion: </strong>These findings suggest that automated, non-machine learning, tools have a role in a multi-stage learning framework and can reduce the impact of limited annotated data and, thus, enhance model performance.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1487877"},"PeriodicalIF":2.1,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11534601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142582212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-centric automated approach to predict autism spectrum disorder based on selective features and explainable artificial intelligence.","authors":"Asma Aldrees, Stephen Ojo, James Wanliss, Muhammad Umer, Muhammad Attique Khan, Bayan Alabdullah, Shtwai Alsubai, Nisreen Innab","doi":"10.3389/fncom.2024.1489463","DOIUrl":"10.3389/fncom.2024.1489463","url":null,"abstract":"<p><p>Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by notable challenges in cognitive function, understanding language, recognizing objects, interacting with others, and communicating effectively. Its origins are mainly genetic, and identifying it early and intervening promptly can reduce the necessity for extensive medical treatments and lengthy diagnostic procedures for those impacted by ASD. This research is designed with two types of experimentation for ASD analysis. In the first set of experiments, authors utilized three feature engineering techniques (Chi-square, backward feature elimination, and PCA) with multiple machine learning models for autism presence prediction in toddlers. The proposed XGBoost 2.0 obtained 99% accuracy, F1 score, and recall with 98% precision with chi-square significant features. In the second scenario, main focus shifts to identifying tailored educational methods for children with ASD through the assessment of their behavioral, verbal, and physical responses. Again, the proposed approach performs well with 99% accuracy, F1 score, recall, and precision. In this research, cross-validation technique is also implemented to check the stability of the proposed model along with the comparison of previously published research works to show the significance of the proposed model. This study aims to develop personalized educational strategies for individuals with ASD using machine learning techniques to meet their specific needs better.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1489463"},"PeriodicalIF":2.1,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11532156/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142575728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A combinatorial deep learning method for Alzheimer's disease classification-based merging pretrained networks.","authors":"Houmem Slimi, Ala Balti, Sabeur Abid, Mounir Sayadi","doi":"10.3389/fncom.2024.1444019","DOIUrl":"10.3389/fncom.2024.1444019","url":null,"abstract":"<p><strong>Introduction: </strong>Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired daily functioning. Despite significant research, AD remains incurable, highlighting the critical need for early diagnosis and intervention to improve patient outcomes. Timely detection plays a crucial role in managing the disease more effectively. Pretrained convolutional neural networks (CNNs) trained on large-scale datasets, such as ImageNet, have been employed for AD classification, providing a head start for developing more accurate models.</p><p><strong>Methods: </strong>This paper proposes a novel hybrid deep learning approach that combines the strengths of two specific pretrained architectures. The proposed model enhances the representation of AD-related patterns by leveraging the feature extraction capabilities of both networks. We validated this model using a large dataset of MRI images from AD patients. Performance was evaluated in terms of classification accuracy and robustness against noise, and the results were compared to several commonly used models in AD detection.</p><p><strong>Results: </strong>The proposed hybrid model demonstrated significant performance improvements over individual models, achieving an accuracy classification rate of 99.85%. Comparative analysis with other models further revealed the superiority of the new architecture, particularly in terms of classification rate and resistance to noise interference.</p><p><strong>Discussion: </strong>The high accuracy and robustness of the proposed hybrid model suggest its potential utility in early AD detection. By improving feature representation through the combination of two pretrained networks, this model could provide clinicians with a more reliable tool for early diagnosis and monitoring of AD progression. This approach holds promise for aiding in timely diagnoses and treatment decisions, contributing to better management of Alzheimer's disease.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1444019"},"PeriodicalIF":2.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11525984/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142557513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-scale asynchronous correlation and 2D convolutional autoencoder for adolescent health risk prediction with limited fMRI data.","authors":"Di Gao, Guanghao Yang, Jiarun Shen, Fang Wu, Chao Ji","doi":"10.3389/fncom.2024.1478193","DOIUrl":"https://doi.org/10.3389/fncom.2024.1478193","url":null,"abstract":"<p><strong>Introduction: </strong>Adolescence is a fundamental period of transformation, encompassing extensive physical, psychological, and behavioral changes. Effective health risk assessment during this stage is crucial for timely intervention, yet traditional methodologies often fail to accurately predict mental and behavioral health risks due to the intricacy of neural dynamics and the scarcity of quality-annotated fMRI datasets.</p><p><strong>Methods: </strong>This study introduces an innovative deep learning-based framework for health risk assessment in adolescents by employing a combination of a two-dimensional convolutional autoencoder (2DCNN-AE) with multi-sequence learning and multi-scale asynchronous correlation information extraction techniques. This approach facilitates the intricate analysis of spatial and temporal features within fMRI data, aiming to enhance the accuracy of the risk assessment process.</p><p><strong>Results: </strong>Upon examination using the Adolescent Risk Behavior (AHRB) dataset, which includes fMRI scans from 174 individuals aged 17-22, the proposed methodology exhibited a significant improvement over conventional models. It attained a precision of 83.116%, a recall of 84.784%, and an F1-score of 83.942%, surpassing standard benchmarks in most pertinent evaluative measures.</p><p><strong>Discussion: </strong>The results underscore the superior performance of the deep learning-based approach in understanding and predicting health-related risks in adolescents. It underscores the value of this methodology in advancing the precision of health risk assessments, offering an enhanced tool for early detection and potential intervention strategies in this sensitive developmental stage.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1478193"},"PeriodicalIF":2.1,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11518741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142544667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing extubation success: a comparative analysis of time series algorithms and activation functions.","authors":"Kuo-Yang Huang, Ching-Hsiung Lin, Shu-Hua Chi, Ying-Lin Hsu, Jia-Lang Xu","doi":"10.3389/fncom.2024.1456771","DOIUrl":"10.3389/fncom.2024.1456771","url":null,"abstract":"<p><strong>Background: </strong>The success and failure of extubation of patients with acute respiratory failure is a very important issue for clinicians, and the failure of the ventilator often leads to possible complications, which in turn leads to a lot of doubts about the medical treatment in the minds of the people, so in order to increase the success of extubation success of the doctors to prevent the possible complications, the present study compared different time series algorithms and different activation functions for the training and prediction of extubation success or failure models.</p><p><strong>Methods: </strong>This study compared different time series algorithms and different activation functions for training and predicting the success or failure of the extubation model.</p><p><strong>Results: </strong>The results of this study using four validation methods show that the GRU model and Tanh's model have a better predictive model for predicting the success or failure of the extubation and better predictive result of 94.44% can be obtained using Holdout cross-validation validation method.</p><p><strong>Conclusion: </strong>This study proposes a prediction method using GRU on the topic of extubation, and it can provide the doctors with the clinical application of extubation to give advice for reference.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1456771"},"PeriodicalIF":2.1,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11486667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142461633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding the application of deep learning in neuroscience: a bibliometric analysis.","authors":"Yin Li, Zilong Zhong","doi":"10.3389/fncom.2024.1402689","DOIUrl":"10.3389/fncom.2024.1402689","url":null,"abstract":"<p><p>The application of deep learning in neuroscience holds unprecedented potential for unraveling the complex dynamics of the brain. Our bibliometric analysis, spanning from 2012 to 2023, delves into the integration of deep learning in neuroscience, shedding light on the evolutionary trends and identifying pivotal research hotspots. Through the examination of 421 articles, this study unveils a significant growth in interdisciplinary research, marked by the burgeoning application of deep learning techniques in understanding neural mechanisms and addressing neurological disorders. Central to our findings is the critical role of classification algorithms, models, and neural networks in advancing neuroscience, highlighting their efficacy in interpreting complex neural data, simulating brain functions, and translating theoretical insights into practical diagnostics and therapeutic interventions. Additionally, our analysis delineates a thematic evolution, showcasing a shift from foundational methodologies toward more specialized and nuanced approaches, particularly in areas like EEG analysis and convolutional neural networks. This evolution reflects the field's maturation and its adaptation to technological advancements. The study further emphasizes the importance of interdisciplinary collaborations and the adoption of cutting-edge technologies to foster innovation in decoding the cerebral code. The current study provides a strategic roadmap for future explorations, urging the scientific community toward areas ripe for breakthrough discoveries and practical applications. This analysis not only charts the past and present landscape of deep learning in neuroscience but also illuminates pathways for future research, underscoring the transformative impact of deep learning on our understanding of the brain.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1402689"},"PeriodicalIF":2.1,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11486706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142461622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Understanding and bridging the gap between neuromorphic computing and machine learning, volume II.","authors":"Lei Deng, Huajin Tang, Kaushik Roy","doi":"10.3389/fncom.2024.1455530","DOIUrl":"https://doi.org/10.3389/fncom.2024.1455530","url":null,"abstract":"","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1455530"},"PeriodicalIF":2.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11484035/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142461623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-label remote sensing classification with self-supervised gated multi-modal transformers.","authors":"Na Liu, Ye Yuan, Guodong Wu, Sai Zhang, Jie Leng, Lihong Wan","doi":"10.3389/fncom.2024.1404623","DOIUrl":"https://doi.org/10.3389/fncom.2024.1404623","url":null,"abstract":"<p><strong>Introduction: </strong>With the great success of Transformers in the field of machine learning, it is also gradually attracting widespread interest in the field of remote sensing (RS). However, the research in the field of remote sensing has been hampered by the lack of large labeled data sets and the inconsistency of data modes caused by the diversity of RS platforms. With the rise of self-supervised learning (SSL) algorithms in recent years, RS researchers began to pay attention to the application of \"pre-training and fine-tuning\" paradigm in RS. However, there are few researches on multi-modal data fusion in remote sensing field. Most of them choose to use only one of the modal data or simply splice multiple modal data roughly.</p><p><strong>Method: </strong>In order to study a more efficient multi-modal data fusion scheme, we propose a multi-modal fusion mechanism based on gated unit control (MGSViT). In this paper, we pretrain the ViT model based on BigEarthNet dataset by combining two commonly used SSL algorithms, and propose an intra-modal and inter-modal gated fusion unit for feature learning by combining multispectral (MS) and synthetic aperture radar (SAR). Our method can effectively combine different modal data to extract key feature information.</p><p><strong>Results and discussion: </strong>After fine-tuning and comparison experiments, we outperform the most advanced algorithms in all downstream classification tasks. The validity of our proposed method is verified.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1404623"},"PeriodicalIF":2.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11458396/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142396925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}